[V0_FILE]typescriptreact:file="app/settings/page.tsx" isMerged="true" "use client" import { useState } from "react" import { NavBar } from "@/components/nav-bar" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Button } from "@/components/ui/button" import { Switch } from "@/components/ui/switch" import { Label } from "@/components/ui/label" export default function SettingsPage() { const [emailAlerts, setEmailAlerts] = useState(true) const [riskWarnings, setRiskWarnings] = useState(true) const [oddsFormat, setOddsFormat] = useState("american") return (

Settings

{/* Profile */} Profile

Name

{/* Preferences */} Preferences

Time Zone

Odds Format

Email alerts for new events

Get notified when new UFC events are available for analysis

Variance warnings

Alert when simulations show high outcome uncertainty

{/* Legal Section */} Legal

Analytics Disclaimer

MAFS does not facilitate or enable gambling. The app provides analytical simulations and statistical insights intended for educational and entertainment purposes only. Users are solely responsible for any decisions they make outside the app.

All simulations are hypothetical and based on historical data. Past performance does not guarantee future results.

) } [V0_FILE]typescriptreact:file="components/ui/switch.tsx" isMerged="true" 'use client' import * as React from 'react' import * as SwitchPrimitive from '@radix-ui/react-switch' import { cn } from '@/lib/utils' function Switch({ className, ...props }: React.ComponentProps) { return ( ) } export { Switch } [V0_FILE]typescriptreact:file="components/ui/label.tsx" isMerged="true" 'use client' import * as React from 'react' import * as LabelPrimitive from '@radix-ui/react-label' import { cn } from '@/lib/utils' function Label({ className, ...props }: React.ComponentProps) { return ( ) } export { Label } [V0_FILE]typescriptreact:file="components/ui/select.tsx" isMerged="true" 'use client' import * as React from 'react' import * as SelectPrimitive from '@radix-ui/react-select' import { CheckIcon, ChevronDownIcon, ChevronUpIcon } from 'lucide-react' import { cn } from '@/lib/utils' function Select({ ...props }: React.ComponentProps) { return } function SelectGroup({ ...props }: React.ComponentProps) { return } function SelectValue({ ...props }: React.ComponentProps) { return } function SelectTrigger({ className, size = 'default', children, ...props }: React.ComponentProps & { size?: 'sm' | 'default' }) { return ( {children} ) } function SelectContent({ className, children, position = 'popper', ...props }: React.ComponentProps) { return ( {children} ) } function SelectLabel({ className, ...props }: React.ComponentProps) { return ( ) } function SelectItem({ className, children, ...props }: React.ComponentProps) { return ( {children} ) } function SelectSeparator({ className, ...props }: React.ComponentProps) { return ( ) } function SelectScrollUpButton({ className, ...props }: React.ComponentProps) { return ( ) } function SelectScrollDownButton({ className, ...props }: React.ComponentProps) { return ( ) } export { Select, SelectContent, SelectGroup, SelectItem, SelectLabel, SelectScrollDownButton, SelectScrollUpButton, SelectSeparator, SelectTrigger, SelectValue, } [V0_FILE]json:file="package.json" isMerged="true" { "name": "my-v0-project", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "eslint ." }, "dependencies": { "@hookform/resolvers": "^3.10.0", "@radix-ui/react-accordion": "1.2.2", "@radix-ui/react-alert-dialog": "1.1.4", "@radix-ui/react-aspect-ratio": "1.1.1", "@radix-ui/react-avatar": "1.1.2", "@radix-ui/react-checkbox": "1.1.3", "@radix-ui/react-collapsible": "1.1.2", "@radix-ui/react-context-menu": "2.2.4", "@radix-ui/react-dialog": "1.1.4", "@radix-ui/react-dropdown-menu": "2.1.4", "@radix-ui/react-hover-card": "1.1.4", "@radix-ui/react-label": "2.1.1", "@radix-ui/react-menubar": "1.1.4", "@radix-ui/react-navigation-menu": "1.2.3", "@radix-ui/react-popover": "1.1.4", "@radix-ui/react-progress": "1.1.1", "@radix-ui/react-radio-group": "1.2.2", "@radix-ui/react-scroll-area": "1.2.2", "@radix-ui/react-select": "2.1.4", "@radix-ui/react-separator": "1.1.1", "@radix-ui/react-slider": "1.2.2", "@radix-ui/react-slot": "1.1.1", "@radix-ui/react-switch": "1.1.2", "@radix-ui/react-tabs": "1.1.2", "@radix-ui/react-toast": "1.2.4", "@radix-ui/react-toggle": "1.1.1", "@radix-ui/react-toggle-group": "1.1.1", "@radix-ui/react-tooltip": "1.1.6", "@vercel/analytics": "1.3.1", "autoprefixer": "^10.4.20", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "cmdk": "1.0.4", "date-fns": "4.1.0", "embla-carousel-react": "8.5.1", "input-otp": "1.4.1", "lucide-react": "^0.454.0", "next": "16.0.10", "next-themes": "^0.4.6", "react": "19.2.0", "react-day-picker": "9.8.0", "react-dom": "19.2.0", "react-hook-form": "^7.60.0", "react-resizable-panels": "^2.1.7", "recharts": "2.15.4", "sonner": "^1.7.4", "tailwind-merge": "^3.3.1", "tailwindcss-animate": "^1.0.7", "vaul": "^1.1.2", "zod": "3.25.76" }, "devDependencies": { "@tailwindcss/postcss": "^4.1.9", "@types/node": "^22", "@types/react": "^19", "@types/react-dom": "^19", "postcss": "^8.5", "tailwindcss": "^4.1.9", "tw-animate-css": "1.3.3", "typescript": "^5" } } [V0_FILE]typescriptreact:file="hooks/use-count-up.ts" isMerged="true" "use client" import { useEffect, useState } from "react" export function useCountUp(end: number, duration = 1000, decimals = 0) { const [count, setCount] = useState(0) useEffect(() => { let startTime: number | null = null let animationFrame: number const animate = (currentTime: number) => { if (!startTime) startTime = currentTime const progress = Math.min((currentTime - startTime) / duration, 1) // Ease-out function const easeOut = 1 - Math.pow(1 - progress, 3) setCount(Number((end * easeOut).toFixed(decimals))) if (progress < 1) { animationFrame = requestAnimationFrame(animate) } } animationFrame = requestAnimationFrame(animate) return () => cancelAnimationFrame(animationFrame) }, [end, duration, decimals]) return count } [V0_FILE]typescriptreact:file="components/why-mafs-likes-this.tsx" isMerged="true" "use client" import { useState } from "react" import { ChevronDown, CheckCircle2, AlertTriangle, TrendingUp, Target } from "lucide-react" interface BetReasoning { marketInefficiency: string[] matchupDrivers: string[] dataSignals: string[] riskFactors: string[] whyThisLine: string[] confidenceSummary: string } interface WhyMAFSLikesThisProps { reasoning: BetReasoning } export function WhyMAFSLikesThis({ reasoning }: WhyMAFSLikesThisProps) { const [isExpanded, setIsExpanded] = useState(false) return (

{isExpanded && (

{/* Market Inefficiency Detected */}

Market Inefficiency Detected

{point}

{/* Matchup Drivers */}

Matchup Drivers

{point}

{/* Data Signals Aligned */}

Data Signals Aligned

{reasoning.dataSignals.map((signal, i) => (

{signal}

))}

{/* Risk Factors */}

Risk Factors

{risk}

{/* Why This Line */}

Why This Line (Not Others)

{point}

{/* Confidence Summary */}

{reasoning.confidenceSummary}

)}

) } [V0_FILE]typescriptreact:file="app/billing/page.tsx" isEdit="true" isQuickEdit="true" isMerged="true" import { NavBar } from "@/components/nav-bar" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Button } from "@/components/ui/button" import { Check } from "lucide-react" export default function BillingPage() { return (

Billing

{/* Current Plan */} Current Plan

Free Plan

$0 / month • 1 fight per day

{/* Plans */}

Free

per month

1 fight analysis per day
Basic EV calculations
Limited market edges
No full card analysis
No play tracking

MAFS Intelligence Engine

Multi-agent simulation in progress

{/* Progress bar */}

Processing {progress}%

{/* Steps */}

{steps.map((step, idx) => (

{step.status === "complete" ? ( ) : step.status === "active" ? (

) : (

)}

{step.label} {step.status === "active" && (

)}

))}

{/* Agent status */}

{agents.map((agent, idx) => (

{agent.name} {agent.status === "complete" ? ( {agent.signal} ) : (

)}

{agent.status === "complete" && agent.confidence && (

)}

))}

{/* Model convergence */}

Model Convergence

{[18, 22, 20, 24, 21].map((h, i) => (

))}

{modelConfidence}%

) } [V0_FILE]typescriptreact:file="components/premium-metrics.tsx" isMerged="true" "use client" import { useEffect, useState } from "react" // Animated confidence ring - single focal point for probability export function ConfidenceRing({ value, size = 64, label }: { value: number size?: number label?: string }) { const [animatedValue, setAnimatedValue] = useState(0) const strokeWidth = size >= 64 ? 6 : size >= 56 ? 5 : 4 const radius = (size - strokeWidth) / 2 const circumference = 2 * Math.PI * radius const strokeDashoffset = circumference - (animatedValue / 100) * circumference useEffect(() => { const timer = setTimeout(() => setAnimatedValue(value), 100) return () => clearTimeout(timer) }, [value]) // Determine text size based on ring size - larger for better readability const textSize = size >= 64 ? "text-xl font-bold" : size >= 56 ? "text-lg font-bold" : "text-sm font-semibold" return (

{Math.round(animatedValue)}%

{label && ( {label} )}

) } // Animated edge meter with visual scale // 0–2% = Market Efficient (gray) // 3–5% = Small Edge (gray) // 6–9% = Strong Edge (green) // 10%+ = Elite Edge (bright emerald) export function EdgeMeter({ value, label, showPulse = true, showHelper = true }: { value: number label?: string showPulse?: boolean showHelper?: boolean }) { const [animatedValue, setAnimatedValue] = useState(0) const isPositive = value > 0 const absValue = Math.abs(value) useEffect(() => { const timer = setTimeout(() => setAnimatedValue(absValue), 100) return () => clearTimeout(timer) }, [absValue]) // Determine edge strength label with updated scale const getEdgeLabel = () => { if (absValue >= 10) return { text: "Elite Edge", color: "text-emerald-400", barColor: "bg-gradient-to-r from-emerald-500/60 to-emerald-400" } if (absValue >= 6) return { text: "Strong Edge", color: "text-green-400", barColor: "bg-gradient-to-r from-green-500/60 to-green-400" } if (absValue >= 3) return { text: "Moderate Edge", color: "text-primary", barColor: "bg-gradient-to-r from-primary/60 to-primary" } return { text: "Market Efficient", color: "text-muted-foreground", barColor: "bg-gradient-to-r from-muted-foreground/40 to-muted-foreground/60" } } const edgeInfo = getEdgeLabel() return (

{label && (

{label}

)} {/* Large percentage display */}

{isPositive ? '+' : ''}{value.toFixed(1)}%

{/* Edge strength label */} {showHelper && (

{edgeInfo.text}

)} {/* Progress bar with visual scale */}

{showPulse && isPositive && absValue >= 10 && (

)}

{/* Helper text */}

Advantage vs. market odds

) } // Animated stat counter export function AnimatedStat({ value, suffix = '', prefix = '', label, size = 'default' }: { value: number suffix?: string prefix?: string label?: string size?: 'small' | 'default' | 'large' }) { const [displayValue, setDisplayValue] = useState(0) useEffect(() => { const duration = 1000 const startTime = Date.now() const startValue = 0 const animate = () => { const elapsed = Date.now() - startTime const progress = Math.min(elapsed / duration, 1) const eased = 1 - Math.pow(1 - progress, 3) // easeOutCubic setDisplayValue(startValue + (value - startValue) * eased) if (progress < 1) { requestAnimationFrame(animate) } } requestAnimationFrame(animate) }, [value]) const sizeClasses = { small: 'text-xl', default: 'text-3xl', large: 'text-5xl' } return (

{prefix}{displayValue.toFixed(value % 1 === 0 ? 0 : 1)}{suffix}

{label && (

{label}

)}

) } // Model agreement visualization export function ModelAgreement({ agents }: { agents: Array<{ name: string; signal: 'pass' | 'neutral' | 'fail' }> }) { const passCount = agents.filter(a => a.signal === 'pass').length const total = agents.length const percentage = (passCount / total) * 100 return (

Model Agreement {passCount}/{total} agents

{agents.map((agent, i) => (

{agent.signal === 'pass' ? 'P' : agent.signal === 'fail' ? 'F' : 'N'}

))}

{percentage >= 75 ? 'Strong consensus' : percentage >= 50 ? 'Moderate agreement' : 'Low agreement'}

) } // Variance indicator export function VarianceIndicator({ level }: { level: 'low' | 'medium' | 'high' }) { const bars = level === 'low' ? 1 : level === 'medium' ? 2 : 3 const color = level === 'low' ? 'bg-green-400' : level === 'medium' ? 'bg-yellow-400' : 'bg-red-400' return (

{[1, 2, 3].map((i) => (

))}

) } // Probability comparison bar export function ProbabilityCompare({ simulated, implied, label }: { simulated: number implied: number label?: string }) { const edge = ((simulated - implied) / implied) * 100 return (

{label && {label}}

{/* Implied probability line */}

{/* Simulated probability bar */}

Implied: {(implied * 100).toFixed(0)}% Simulated: {(simulated * 100).toFixed(0)}%

) } [V0_FILE]typescript:file="lib/betting/types.ts" isMerged="true" // MAFS Betting Data Types // Comprehensive type definitions for sportsbook odds, markets, and edge calculations // ============================================ // SPORTSBOOK & ODDS TYPES // ============================================ export type Sportsbook = | "DraftKings" | "FanDuel" | "BetMGM" | "Caesars" | "PointsBet" | "BetRivers" | "Barstool" | "Unibet" export type MarketType = | "moneyline" | "method_of_victory" | "ko_tko" | "submission" | "decision" | "over_under_rounds" | "round_props" | "fight_goes_distance" | "round_betting" | "fight_to_go_distance" export interface SportsbookOdds { sportsbook: Sportsbook odds: number // American odds format impliedProbability: number lastUpdated: Date isStale: boolean // true if > 10 minutes old } export interface HistoricalOddsPoint { sportsbook: Sportsbook market: MarketType timestamp: Date odds: number impliedProbability: number } export interface BettingMarket { marketType: MarketType selection: string // e.g., "Fighter A by KO/TKO" sportsbookOdds: SportsbookOdds[] historicalOdds: HistoricalOddsPoint[] // Calculated fields bestOdds: SportsbookOdds | null consensusImpliedProbability: number openingOdds: number | null currentOdds: number | null lineMovement: number // percentage change from opening volatility: number // standard deviation of odds changes } // ============================================ // EDGE CALCULATION TYPES // ============================================ export interface MarketEdge { market: MarketType selection: string sportsbook: Sportsbook odds: number impliedProbability: number modelProbability: number edgePercent: number isValueBet: boolean // edge >= 5% and valid liquidity confidenceScore: number lastCalculated: Date } export interface EdgeCalculationInput { modelProbability: number americanOdds: number } export interface EdgeCalculationResult { impliedProbability: number edge: number expectedValue: number kellyFraction: number isPositiveEV: boolean } // ============================================ // PROFITABILITY & TRACKING TYPES // ============================================ export interface BetHistory { id: string fightId: string fightName: string market: MarketType selection: string sportsbook: Sportsbook odds: number modelProbability: number impliedProbability: number edgeAtPlacement: number stake: number result: "win" | "loss" | "push" | "pending" payout: number unitsWon: number closingOdds: number | null closingLineValue: number | null placedAt: Date settledAt: Date | null } export interface ProfitabilityMetrics { totalBets: number wins: number losses: number pushes: number winRate: number roi: number totalStaked: number totalReturned: number profit: number averageEdge: number averageClosingLineValue: number byMarket: Record bySportsbook: Record } // ============================================ // CONFIDENCE & RANKING TYPES // ============================================ export interface ConfidenceFactors { modelCertainty: number // 0-100 historicalMarketAccuracy: number // 0-100 lineStability: number // 0-100, higher = more stable sampleSize: number recentPerformance: number // 0-100 } export interface ConfidenceScore { overall: number factors: ConfidenceFactors breakdown: { modelWeight: number historicalWeight: number stabilityWeight: number } } export interface RankedBet { rank: number market: MarketType selection: string sportsbook: Sportsbook odds: number modelProbability: number edgePercent: number confidenceScore: number lineValue: number composite: number // weighted score for ranking recommendation: "strong" | "moderate" | "speculative" | "pass" } // ============================================ // FIGHT DATA EXTENSIONS // ============================================ export interface FightOddsData { fightId: string fighters: [string, string] date: Date sportsbookOdds: { fighter1: SportsbookOdds[] fighter2: SportsbookOdds[] } markets: BettingMarket[] historicalOdds: HistoricalOddsPoint[] // Calculated marketEdges: MarketEdge[] rankedBets: RankedBet[] lastRefreshed: Date } // ============================================ // DATA REFRESH TYPES // ============================================ export interface RefreshConfig { intervalMs: number // 2-5 minutes = 120000-300000ms staleThresholdMs: number // 10 minutes = 600000ms maxRetries: number backoffMultiplier: number } export interface RefreshStatus { lastRefresh: Date nextRefresh: Date isRefreshing: boolean errorCount: number lastError: string | null } [V0_FILE]typescript:file="lib/betting/edge-calculator.ts" isMerged="true" // MAFS Edge Calculation Engine // Core algorithms for calculating betting edges, expected value, and profitability import type { EdgeCalculationInput, EdgeCalculationResult, MarketEdge, ConfidenceFactors, ConfidenceScore, SportsbookOdds, HistoricalOddsPoint, BetHistory, ProfitabilityMetrics, MarketType, Sportsbook, } from "./types" // ============================================ // ODDS CONVERSION UTILITIES // ============================================ /** * Convert American odds to implied probability * Positive odds: 100 / (odds + 100) * Negative odds: |odds| / (|odds| + 100) */ export function americanToImpliedProbability(odds: number): number { if (odds > 0) { return 100 / (odds + 100) } else { return Math.abs(odds) / (Math.abs(odds) + 100) } } /** * Convert implied probability to American odds */ export function impliedProbabilityToAmerican(probability: number): number { if (probability <= 0 || probability >= 1) { throw new Error("Probability must be between 0 and 1") } if (probability >= 0.5) { return Math.round(-100 * probability / (1 - probability)) } else { return Math.round(100 * (1 - probability) / probability) } } /** * Convert American odds to decimal odds */ export function americanToDecimal(odds: number): number { if (odds > 0) { return (odds / 100) + 1 } else { return (100 / Math.abs(odds)) + 1 } } /** * Format American odds as string with +/- prefix */ export function formatAmericanOdds(odds: number): string { if (odds > 0) { return `+${odds}` } return odds.toString() } // ============================================ // EDGE CALCULATION // ============================================ /** * Calculate edge between model probability and market implied probability */ export function calculateEdge(input: EdgeCalculationInput): EdgeCalculationResult { const { modelProbability, americanOdds } = input const impliedProbability = americanToImpliedProbability(americanOdds) const edge = modelProbability - impliedProbability // Expected value calculation const decimalOdds = americanToDecimal(americanOdds) const expectedValue = (modelProbability * (decimalOdds - 1)) - (1 - modelProbability) // Kelly Criterion for optimal bet sizing // f* = (bp - q) / b where b = decimal odds - 1, p = probability, q = 1 - p const b = decimalOdds - 1 const kellyFraction = Math.max(0, (b * modelProbability - (1 - modelProbability)) / b) return { impliedProbability, edge, expectedValue, kellyFraction, isPositiveEV: expectedValue > 0, } } /** * Calculate market edge with full context */ export function calculateMarketEdge( market: MarketType, selection: string, sportsbook: Sportsbook, odds: number, modelProbability: number, oddsTimestamp: Date ): MarketEdge { const edgeResult = calculateEdge({ modelProbability, americanOdds: odds }) const isStale = Date.now() - oddsTimestamp.getTime() > 10 * 60 * 1000 // 10 minutes // Value bet criteria: edge >= 5%, valid liquidity, fresh odds const isValueBet = edgeResult.edge >= 0.05 && !isStale // Calculate confidence score (simplified version) const confidenceScore = calculateSimpleConfidence(modelProbability, edgeResult.edge) return { market, selection, sportsbook, odds, impliedProbability: edgeResult.impliedProbability, modelProbability, edgePercent: edgeResult.edge * 100, isValueBet, confidenceScore, lastCalculated: new Date(), } } // ============================================ // CONFIDENCE SCORING // ============================================ /** * Simple confidence score based on model probability and edge */ function calculateSimpleConfidence(modelProbability: number, edge: number): number { // Higher confidence when model probability is more certain (closer to 0 or 1) const certaintySCore = Math.abs(modelProbability - 0.5) * 2 // 0-1 scale // Higher confidence when edge is larger const edgeScore = Math.min(1, Math.abs(edge) / 0.15) // Cap at 15% edge return (certaintySCore * 0.6 + edgeScore * 0.4) * 100 } /** * Calculate comprehensive confidence score with all factors */ export function calculateConfidenceScore(factors: ConfidenceFactors): ConfidenceScore { const weights = { modelWeight: 0.45, historicalWeight: 0.30, stabilityWeight: 0.25, } // Adjust weights based on sample size const sampleSizeMultiplier = Math.min(1, factors.sampleSize / 50) // Full weight at 50+ samples const adjustedHistoricalWeight = weights.historicalWeight * sampleSizeMultiplier const adjustedModelWeight = weights.modelWeight + (weights.historicalWeight - adjustedHistoricalWeight) const overall = factors.modelCertainty * adjustedModelWeight + factors.historicalMarketAccuracy * adjustedHistoricalWeight + factors.lineStability * weights.stabilityWeight return { overall, factors, breakdown: { modelWeight: adjustedModelWeight, historicalWeight: adjustedHistoricalWeight, stabilityWeight: weights.stabilityWeight, }, } } // ============================================ // LINE MOVEMENT ANALYSIS // ============================================ /** * Calculate line movement from historical odds */ export function calculateLineMovement(historicalOdds: HistoricalOddsPoint[]): { openingOdds: number | null currentOdds: number | null movement: number direction: "steam" | "reverse" | "stable" volatility: number } { if (historicalOdds.length === 0) { return { openingOdds: null, currentOdds: null, movement: 0, direction: "stable", volatility: 0, } } const sorted = [...historicalOdds].sort((a, b) => a.timestamp.getTime() - b.timestamp.getTime()) const openingOdds = sorted[0].odds const currentOdds = sorted[sorted.length - 1].odds // Calculate movement as change in implied probability const openingProb = americanToImpliedProbability(openingOdds) const currentProb = americanToImpliedProbability(currentOdds) const movement = (currentProb - openingProb) * 100 // Determine direction let direction: "steam" | "reverse" | "stable" = "stable" if (Math.abs(movement) > 3) { direction = movement > 0 ? "steam" : "reverse" } // Calculate volatility (standard deviation of implied probabilities) const probs = sorted.map(o => americanToImpliedProbability(o.odds)) const mean = probs.reduce((a, b) => a + b, 0) / probs.length const variance = probs.reduce((sum, p) => sum + Math.pow(p - mean, 2), 0) / probs.length const volatility = Math.sqrt(variance) * 100 return { openingOdds, currentOdds, movement, direction, volatility, } } /** * Calculate closing line value (CLV) * Positive CLV indicates the bet was placed at better odds than closing */ export function calculateClosingLineValue( placementOdds: number, closingOdds: number ): number { const placementProb = americanToImpliedProbability(placementOdds) const closingProb = americanToImpliedProbability(closingOdds) return (closingProb - placementProb) * 100 } // ============================================ // PROFITABILITY TRACKING // ============================================ /** * Calculate profitability metrics from bet history */ export function calculateProfitabilityMetrics(history: BetHistory[]): ProfitabilityMetrics { const settled = history.filter(b => b.result !== "pending") const wins = settled.filter(b => b.result === "win").length const losses = settled.filter(b => b.result === "loss").length const pushes = settled.filter(b => b.result === "push").length const totalStaked = settled.reduce((sum, b) => sum + b.stake, 0) const totalReturned = settled.reduce((sum, b) => sum + b.payout, 0) const profit = totalReturned - totalStaked const averageEdge = settled.length > 0 ? settled.reduce((sum, b) => sum + b.edgeAtPlacement, 0) / settled.length : 0 const betsWithCLV = settled.filter(b => b.closingLineValue !== null) const averageClosingLineValue = betsWithCLV.length > 0 ? betsWithCLV.reduce((sum, b) => sum + (b.closingLineValue || 0), 0) / betsWithCLV.length : 0 // Calculate by market const byMarket: ProfitabilityMetrics["byMarket"] = {} as ProfitabilityMetrics["byMarket"] const markets = [...new Set(settled.map(b => b.market))] for (const market of markets) { const marketBets = settled.filter(b => b.market === market) const marketWins = marketBets.filter(b => b.result === "win").length const marketStaked = marketBets.reduce((sum, b) => sum + b.stake, 0) const marketReturned = marketBets.reduce((sum, b) => sum + b.payout, 0) byMarket[market] = { bets: marketBets.length, winRate: marketBets.length > 0 ? marketWins / marketBets.length : 0, roi: marketStaked > 0 ? ((marketReturned - marketStaked) / marketStaked) * 100 : 0, averageEdge: marketBets.length > 0 ? marketBets.reduce((sum, b) => sum + b.edgeAtPlacement, 0) / marketBets.length : 0, } } // Calculate by sportsbook const bySportsbook: ProfitabilityMetrics["bySportsbook"] = {} as ProfitabilityMetrics["bySportsbook"] const sportsbooks = [...new Set(settled.map(b => b.sportsbook))] for (const book of sportsbooks) { const bookBets = settled.filter(b => b.sportsbook === book) const bookWins = bookBets.filter(b => b.result === "win").length const bookStaked = bookBets.reduce((sum, b) => sum + b.stake, 0) const bookReturned = bookBets.reduce((sum, b) => sum + b.payout, 0) bySportsbook[book] = { bets: bookBets.length, winRate: bookBets.length > 0 ? bookWins / bookBets.length : 0, roi: bookStaked > 0 ? ((bookReturned - bookStaked) / bookStaked) * 100 : 0, } } return { totalBets: settled.length, wins, losses, pushes, winRate: settled.length > 0 ? wins / (settled.length - pushes) : 0, roi: totalStaked > 0 ? (profit / totalStaked) * 100 : 0, totalStaked, totalReturned, profit, averageEdge, averageClosingLineValue, byMarket, bySportsbook, } } // ============================================ // BET RANKING // ============================================ /** * Rank bets by composite score */ export function rankBets(edges: MarketEdge[]): MarketEdge[] { return [...edges] .filter(e => e.isValueBet) .sort((a, b) => { // Primary: highest edge const edgeDiff = b.edgePercent - a.edgePercent if (Math.abs(edgeDiff) > 1) return edgeDiff // Secondary: highest confidence const confDiff = b.confidenceScore - a.confidenceScore if (Math.abs(confDiff) > 5) return confDiff // Tertiary: best odds (higher American odds = better for positive, lower for negative) return b.odds - a.odds }) } /** * Get recommendation level based on edge and confidence */ export function getRecommendation( edgePercent: number, confidenceScore: number ): "strong" | "moderate" | "speculative" | "pass" { if (edgePercent >= 8 && confidenceScore >= 70) return "strong" if (edgePercent >= 5 && confidenceScore >= 50) return "moderate" if (edgePercent >= 3 && confidenceScore >= 30) return "speculative" return "pass" } [V0_FILE]typescript:file="lib/betting/odds-service.ts" isMerged="true" // MAFS Odds Data Service // Manages sportsbook odds data, refresh logic, and real-time updates import type { Sportsbook, MarketType, SportsbookOdds, HistoricalOddsPoint, BettingMarket, FightOddsData, RefreshConfig, RefreshStatus, MarketEdge, RankedBet, } from "./types" import { calculateMarketEdge, calculateLineMovement, americanToImpliedProbability, rankBets, getRecommendation, } from "./edge-calculator" // ============================================ // CONFIGURATION // ============================================ const DEFAULT_REFRESH_CONFIG: RefreshConfig = { intervalMs: 3 * 60 * 1000, // 3 minutes staleThresholdMs: 10 * 60 * 1000, // 10 minutes maxRetries: 3, backoffMultiplier: 2, } // ============================================ // MOCK DATA GENERATORS (Replace with real API calls) // ============================================ /** * Generate mock sportsbook odds for a selection * In production, this would fetch from a real odds API */ function generateMockOdds( baseOdds: number, variance: number = 10 ): SportsbookOdds[] { const sportsbooks: Sportsbook[] = [ "DraftKings", "FanDuel", "BetMGM", "Caesars", "PointsBet", ] return sportsbooks.map(sportsbook => { // Add some variance to odds across books const adjustment = Math.floor(Math.random() * variance * 2) - variance const odds = baseOdds + adjustment return { sportsbook, odds, impliedProbability: americanToImpliedProbability(odds), lastUpdated: new Date(Date.now() - Math.random() * 5 * 60 * 1000), // 0-5 mins ago isStale: false, } }) } /** * Generate mock historical odds */ function generateMockHistoricalOdds( currentOdds: number, market: MarketType, sportsbook: Sportsbook, daysBack: number = 7 ): HistoricalOddsPoint[] { const points: HistoricalOddsPoint[] = [] const now = Date.now() const msPerDay = 24 * 60 * 60 * 1000 // Start with opening odds (usually more extreme) let odds = currentOdds + (Math.random() > 0.5 ? 15 : -15) for (let i = daysBack; i >= 0; i--) { // 4 data points per day for (let j = 0; j < 4; j++) { const timestamp = new Date(now - i * msPerDay + j * 6 * 60 * 60 * 1000) // Gradually move toward current odds with some noise const progress = 1 - (i * 4 + (4 - j)) / (daysBack * 4) odds = odds + (currentOdds - odds) * 0.1 + (Math.random() - 0.5) * 5 points.push({ sportsbook, market, timestamp, odds: Math.round(odds), impliedProbability: americanToImpliedProbability(odds), }) } } return points } // ============================================ // ODDS SERVICE CLASS // ============================================ export class OddsService { private config: RefreshConfig private refreshStatus: RefreshStatus private fightsData: Map = new Map() private refreshInterval: ReturnType | null = null constructor(config: Partial = {}) { this.config = { ...DEFAULT_REFRESH_CONFIG, ...config } this.refreshStatus = { lastRefresh: new Date(0), nextRefresh: new Date(), isRefreshing: false, errorCount: 0, lastError: null, } } /** * Start automatic odds refresh */ startAutoRefresh(): void { if (this.refreshInterval) return this.refreshInterval = setInterval(() => { this.refreshAllOdds() }, this.config.intervalMs) // Initial refresh this.refreshAllOdds() } /** * Stop automatic refresh */ stopAutoRefresh(): void { if (this.refreshInterval) { clearInterval(this.refreshInterval) this.refreshInterval = null } } /** * Get refresh status */ getRefreshStatus(): RefreshStatus { return { ...this.refreshStatus } } /** * Refresh odds for all tracked fights */ async refreshAllOdds(): Promise { if (this.refreshStatus.isRefreshing) return this.refreshStatus.isRefreshing = true try { for (const [fightId, fightData] of this.fightsData) { await this.refreshFightOdds(fightId) } this.refreshStatus.lastRefresh = new Date() this.refreshStatus.nextRefresh = new Date(Date.now() + this.config.intervalMs) this.refreshStatus.errorCount = 0 this.refreshStatus.lastError = null } catch (error) { this.refreshStatus.errorCount++ this.refreshStatus.lastError = error instanceof Error ? error.message : "Unknown error" } finally { this.refreshStatus.isRefreshing = false } } /** * Refresh odds for a specific fight */ async refreshFightOdds(fightId: string): Promise { const existingData = this.fightsData.get(fightId) if (!existingData) return null // In production, this would fetch from real APIs // For now, we'll update timestamps and add slight variance const updatedMarkets = existingData.markets.map(market => { const updatedOdds = market.sportsbookOdds.map(odds => ({ ...odds, lastUpdated: new Date(), isStale: false, // Add small random movement odds: odds.odds + (Math.random() > 0.7 ? (Math.random() > 0.5 ? 5 : -5) : 0), })) // Recalculate implied probabilities updatedOdds.forEach(o => { o.impliedProbability = americanToImpliedProbability(o.odds) }) return { ...market, sportsbookOdds: updatedOdds, bestOdds: this.findBestOdds(updatedOdds), consensusImpliedProbability: this.calculateConsensusProb(updatedOdds), } }) const updatedData: FightOddsData = { ...existingData, markets: updatedMarkets, lastRefreshed: new Date(), } // Recalculate edges updatedData.marketEdges = this.calculateAllEdges(updatedData) updatedData.rankedBets = this.rankAllBets(updatedData) this.fightsData.set(fightId, updatedData) return updatedData } /** * Initialize fight odds data */ initializeFightOdds( fightId: string, fighters: [string, string], date: Date, modelProbabilities: Record // selection -> model probability ): FightOddsData { // Generate mock data for all standard markets const markets: BettingMarket[] = [] // Moneyline market const fighter1MlOdds = this.modelProbToOdds(modelProbabilities[`${fighters[0]} ML`] || 0.5) const fighter2MlOdds = this.modelProbToOdds(modelProbabilities[`${fighters[1]} ML`] || 0.5) markets.push( this.createMarket("moneyline", `${fighters[0]} ML`, fighter1MlOdds), this.createMarket("moneyline", `${fighters[1]} ML`, fighter2MlOdds) ) // Method of victory markets const methods = ["KO/TKO", "Submission", "Decision"] for (const fighter of fighters) { for (const method of methods) { const selection = `${fighter} by ${method}` const baseProb = modelProbabilities[selection] || 0.15 const odds = this.modelProbToOdds(baseProb) markets.push(this.createMarket("method_of_victory", selection, odds)) } } // Over/Under rounds const rounds = [1.5, 2.5, 3.5, 4.5] for (const round of rounds) { markets.push( this.createMarket("over_under_rounds", `Over ${round}`, -120 + Math.random() * 40 - 20), this.createMarket("over_under_rounds", `Under ${round}`, -110 + Math.random() * 40 - 20) ) } // Fight goes distance markets.push( this.createMarket("fight_goes_distance", "Yes", 200 + Math.random() * 50), this.createMarket("fight_goes_distance", "No", -250 + Math.random() * 50) ) const fightData: FightOddsData = { fightId, fighters, date, sportsbookOdds: { fighter1: generateMockOdds(fighter1MlOdds), fighter2: generateMockOdds(fighter2MlOdds), }, markets, historicalOdds: [], marketEdges: [], rankedBets: [], lastRefreshed: new Date(), } // Calculate initial edges with mock model probabilities fightData.marketEdges = this.calculateAllEdges(fightData, modelProbabilities) fightData.rankedBets = this.rankAllBets(fightData) this.fightsData.set(fightId, fightData) return fightData } /** * Get fight odds data */ getFightOdds(fightId: string): FightOddsData | null { return this.fightsData.get(fightId) || null } /** * Get all value bets across all tracked fights */ getAllValueBets(): MarketEdge[] { const allEdges: MarketEdge[] = [] for (const fightData of this.fightsData.values()) { allEdges.push(...fightData.marketEdges.filter(e => e.isValueBet)) } return rankBets(allEdges) } // ============================================ // PRIVATE HELPERS // ============================================ private createMarket( marketType: MarketType, selection: string, baseOdds: number ): BettingMarket { const sportsbookOdds = generateMockOdds(baseOdds) const historicalOdds = generateMockHistoricalOdds( baseOdds, marketType, "DraftKings" ) const lineMovement = calculateLineMovement(historicalOdds) return { marketType, selection, sportsbookOdds, historicalOdds, bestOdds: this.findBestOdds(sportsbookOdds), consensusImpliedProbability: this.calculateConsensusProb(sportsbookOdds), openingOdds: lineMovement.openingOdds, currentOdds: lineMovement.currentOdds, lineMovement: lineMovement.movement, volatility: lineMovement.volatility, } } private findBestOdds(odds: SportsbookOdds[]): SportsbookOdds | null { if (odds.length === 0) return null // Best odds = highest for positive, lowest absolute for negative return odds.reduce((best, current) => { if (current.odds > 0 && best.odds > 0) { return current.odds > best.odds ? current : best } if (current.odds < 0 && best.odds < 0) { return current.odds > best.odds ? current : best // Less negative is better } // Mixed: positive is always better than negative for the bettor return current.odds > best.odds ? current : best }) } private calculateConsensusProb(odds: SportsbookOdds[]): number { if (odds.length === 0) return 0 return odds.reduce((sum, o) => sum + o.impliedProbability, 0) / odds.length } private modelProbToOdds(prob: number): number { if (prob <= 0.01) prob = 0.01 if (prob >= 0.99) prob = 0.99 if (prob >= 0.5) { return Math.round(-100 * prob / (1 - prob)) } else { return Math.round(100 * (1 - prob) / prob) } } private calculateAllEdges( fightData: FightOddsData, modelProbabilities?: Record ): MarketEdge[] { const edges: MarketEdge[] = [] for (const market of fightData.markets) { const bestOdds = market.bestOdds if (!bestOdds) continue // Get model probability for this selection const modelProb = modelProbabilities?.[market.selection] || market.consensusImpliedProbability * (1 + (Math.random() * 0.2 - 0.1)) // Add some edge variation const edge = calculateMarketEdge( market.marketType, market.selection, bestOdds.sportsbook, bestOdds.odds, modelProb, bestOdds.lastUpdated ) edges.push(edge) } return edges } private rankAllBets(fightData: FightOddsData): RankedBet[] { const rankedEdges = rankBets(fightData.marketEdges) return rankedEdges.map((edge, index) => ({ rank: index + 1, market: edge.market, selection: edge.selection, sportsbook: edge.sportsbook, odds: edge.odds, modelProbability: edge.modelProbability, edgePercent: edge.edgePercent, confidenceScore: edge.confidenceScore, lineValue: 0, // Would come from historical comparison composite: edge.edgePercent * 0.5 + edge.confidenceScore * 0.5, recommendation: getRecommendation(edge.edgePercent, edge.confidenceScore), })) } } // ============================================ // SINGLETON INSTANCE // ============================================ let oddsServiceInstance: OddsService | null = null export function getOddsService(): OddsService { if (!oddsServiceInstance) { oddsServiceInstance = new OddsService() } return oddsServiceInstance } [V0_FILE]typescript:file="hooks/use-betting-data.ts" isMerged="true" "use client" import { useState, useEffect, useCallback } from "react" import { getOddsService, type FightOddsData, type MarketEdge, type RefreshStatus, type RankedBet, } from "@/lib/betting" /** * Hook to access and manage betting odds data */ export function useBettingData(fightId: string | null) { const [fightOdds, setFightOdds] = useState(null) const [isLoading, setIsLoading] = useState(false) const [refreshStatus, setRefreshStatus] = useState(null) const oddsService = getOddsService() // Load fight odds useEffect(() => { if (!fightId) { setFightOdds(null) return } const data = oddsService.getFightOdds(fightId) setFightOdds(data) }, [fightId, oddsService]) // Update refresh status periodically useEffect(() => { const updateStatus = () => { setRefreshStatus(oddsService.getRefreshStatus()) } updateStatus() const interval = setInterval(updateStatus, 5000) return () => clearInterval(interval) }, [oddsService]) // Manual refresh const refresh = useCallback(async () => { if (!fightId) return setIsLoading(true) try { const updated = await oddsService.refreshFightOdds(fightId) setFightOdds(updated) } finally { setIsLoading(false) } }, [fightId, oddsService]) // Initialize fight data const initializeFight = useCallback(( id: string, fighters: [string, string], date: Date, modelProbabilities: Record ) => { const data = oddsService.initializeFightOdds(id, fighters, date, modelProbabilities) if (id === fightId) { setFightOdds(data) } return data }, [fightId, oddsService]) return { fightOdds, isLoading, refreshStatus, refresh, initializeFight, } } /** * Hook to get all value bets across fights */ export function useValueBets() { const [valueBets, setValueBets] = useState([]) const [isLoading, setIsLoading] = useState(true) const oddsService = getOddsService() useEffect(() => { const updateBets = () => { const bets = oddsService.getAllValueBets() setValueBets(bets) setIsLoading(false) } updateBets() const interval = setInterval(updateBets, 10000) // Update every 10 seconds return () => clearInterval(interval) }, [oddsService]) return { valueBets, isLoading } } /** * Hook to get best ranked bets for a fight */ export function useRankedBets(fightId: string | null) { const { fightOdds } = useBettingData(fightId) const rankedBets: RankedBet[] = fightOdds?.rankedBets || [] const topBets = rankedBets.filter(b => b.recommendation !== "pass").slice(0, 5) const strongBets = rankedBets.filter(b => b.recommendation === "strong") return { rankedBets, topBets, strongBets, hasBets: rankedBets.length > 0, } } /** * Hook to start/stop auto refresh */ export function useOddsAutoRefresh(enabled: boolean = false) { const oddsService = getOddsService() useEffect(() => { if (enabled) { oddsService.startAutoRefresh() } else { oddsService.stopAutoRefresh() } return () => { oddsService.stopAutoRefresh() } }, [enabled, oddsService]) } [V0_FILE]typescript:file="lib/betting/profitability-tracker.ts" isMerged="true" // MAFS Profitability Tracker // Simulates historical performance and tracks ROI import type { BetHistory, ProfitabilityMetrics, MarketType, Sportsbook, } from "./types" import { calculateProfitabilityMetrics, calculateClosingLineValue, } from "./edge-calculator" // ============================================ // SIMULATED HISTORICAL DATA // ============================================ /** * Generate simulated historical bet performance * Based on expected outcomes from edge betting */ export function generateSimulatedHistory(count: number = 250): BetHistory[] { const history: BetHistory[] = [] const markets: MarketType[] = [ "moneyline", "method_of_victory", "ko_tko", "over_under_rounds", "fight_goes_distance", ] const sportsbooks: Sportsbook[] = [ "DraftKings", "FanDuel", "BetMGM", "Caesars", ] // Realistic edge betting win rate: ~54-58% at average odds const baseWinRate = 0.545 for (let i = 0; i < count; i++) { const market = markets[Math.floor(Math.random() * markets.length)] const sportsbook = sportsbooks[Math.floor(Math.random() * sportsbooks.length)] // Generate realistic odds distribution const isUnderdog = Math.random() > 0.4 const odds = isUnderdog ? Math.round(100 + Math.random() * 200) // +100 to +300 : Math.round(-110 - Math.random() * 200) // -110 to -310 // Model probability with edge const impliedProb = odds > 0 ? 100 / (odds + 100) : Math.abs(odds) / (Math.abs(odds) + 100) const edge = 0.03 + Math.random() * 0.12 // 3-15% edge const modelProb = Math.min(0.95, impliedProb + edge) // Simulate result based on model probability // Better models win more often when they have edge const adjustedWinRate = baseWinRate + (edge * 0.5) // Higher edge = higher win rate const won = Math.random() < adjustedWinRate const stake = 1 // 1 unit per bet const decimalOdds = odds > 0 ? (odds / 100) + 1 : (100 / Math.abs(odds)) + 1 const payout = won ? stake * decimalOdds : 0 // Generate closing odds (usually move toward model prediction) const closingOddsDrift = won ? -5 - Math.random() * 10 : 5 + Math.random() * 5 const closingOdds = odds + (Math.random() > 0.3 ? closingOddsDrift : 0) const clv = calculateClosingLineValue(odds, closingOdds) const daysAgo = Math.floor(Math.random() * 365) const placedAt = new Date(Date.now() - daysAgo * 24 * 60 * 60 * 1000) const settledAt = new Date(placedAt.getTime() + (1 + Math.random() * 3) * 24 * 60 * 60 * 1000) history.push({ id: `bet-${i + 1}`, fightId: `fight-${Math.floor(i / 5) + 1}`, fightName: `UFC Event ${Math.floor(i / 5) + 1}`, market, selection: `Fighter ${i % 2 === 0 ? "A" : "B"} ${market === "moneyline" ? "ML" : market}`, sportsbook, odds, modelProbability: modelProb, impliedProbability: impliedProb, edgeAtPlacement: edge * 100, stake, result: won ? "win" : "loss", payout, unitsWon: payout - stake, closingOdds, closingLineValue: clv, placedAt, settledAt, }) } // Sort by date return history.sort((a, b) => b.placedAt.getTime() - a.placedAt.getTime()) } // ============================================ // PROFITABILITY TRACKER CLASS // ============================================ export class ProfitabilityTracker { private history: BetHistory[] = [] private metrics: ProfitabilityMetrics | null = null constructor(initialHistory?: BetHistory[]) { if (initialHistory) { this.history = initialHistory this.recalculateMetrics() } } /** * Load simulated historical data */ loadSimulatedHistory(count: number = 250): void { this.history = generateSimulatedHistory(count) this.recalculateMetrics() } /** * Add a new bet to history */ addBet(bet: BetHistory): void { this.history.unshift(bet) this.recalculateMetrics() } /** * Update bet result */ settleBet(betId: string, result: "win" | "loss" | "push", payout: number): void { const bet = this.history.find(b => b.id === betId) if (bet) { bet.result = result bet.payout = payout bet.unitsWon = payout - bet.stake bet.settledAt = new Date() this.recalculateMetrics() } } /** * Get current profitability metrics */ getMetrics(): ProfitabilityMetrics | null { return this.metrics } /** * Get full bet history */ getHistory(): BetHistory[] { return [...this.history] } /** * Get recent bets */ getRecentBets(count: number = 10): BetHistory[] { return this.history.slice(0, count) } /** * Get performance by time period */ getPerformanceByPeriod(days: number): ProfitabilityMetrics { const cutoff = Date.now() - days * 24 * 60 * 60 * 1000 const periodBets = this.history.filter(b => b.placedAt.getTime() >= cutoff) return calculateProfitabilityMetrics(periodBets) } /** * Get streak information */ getStreakInfo(): { currentStreak: number; type: "win" | "loss" | "none"; longestWinStreak: number; longestLossStreak: number } { const settled = this.history.filter(b => b.result !== "pending" && b.result !== "push") if (settled.length === 0) { return { currentStreak: 0, type: "none", longestWinStreak: 0, longestLossStreak: 0 } } let currentStreak = 0 let currentType: "win" | "loss" = settled[0].result as "win" | "loss" let longestWinStreak = 0 let longestLossStreak = 0 let tempStreak = 0 let tempType: "win" | "loss" | null = null for (const bet of settled) { if (bet.result === tempType) { tempStreak++ } else { if (tempType === "win") longestWinStreak = Math.max(longestWinStreak, tempStreak) if (tempType === "loss") longestLossStreak = Math.max(longestLossStreak, tempStreak) tempType = bet.result as "win" | "loss" tempStreak = 1 } } // Final streak if (tempType === "win") longestWinStreak = Math.max(longestWinStreak, tempStreak) if (tempType === "loss") longestLossStreak = Math.max(longestLossStreak, tempStreak) // Count current streak for (const bet of settled) { if (bet.result === currentType) { currentStreak++ } else { break } } return { currentStreak, type: currentType, longestWinStreak, longestLossStreak, } } /** * Recalculate all metrics */ private recalculateMetrics(): void { this.metrics = calculateProfitabilityMetrics(this.history) } } // ============================================ // SINGLETON INSTANCE // ============================================ let trackerInstance: ProfitabilityTracker | null = null export function getProfitabilityTracker(): ProfitabilityTracker { if (!trackerInstance) { trackerInstance = new ProfitabilityTracker() trackerInstance.loadSimulatedHistory(247) // Match the sample count shown in UI } return trackerInstance } [V0_FILE]typescript:file="lib/betting/index.ts" isMerged="true" // MAFS Betting Module // Central export for all betting-related functionality // Types export * from "./types" // Edge Calculator export { americanToImpliedProbability, impliedProbabilityToAmerican, americanToDecimal, formatAmericanOdds, calculateEdge, calculateMarketEdge, calculateConfidenceScore, calculateLineMovement, calculateClosingLineValue, calculateProfitabilityMetrics, rankBets, getRecommendation, } from "./edge-calculator" // Odds Service export { OddsService, getOddsService } from "./odds-service" // Profitability Tracker export { ProfitabilityTracker, getProfitabilityTracker, generateSimulatedHistory, } from "./profitability-tracker" // CLV Types export * from "./clv-types" // CLV Calculator export { calculateCLV, createSettlement, calculateAggregateCLV, calculateCLVWindow, getLineMovementDisplay, formatCLV, formatOddsMovement, } from "./clv-calculator" // Market Efficiency export { calculateMarketFamilyStats, createMarketEfficiencyModel, adjustConfidenceForMarketQuality, getMarketQualityMultiplier, getMarketFamilyDisplayName, } from "./market-efficiency" // Portfolio Optimizer export { getCorrelationLevel, analyzeCorrelations, optimizePortfolio, getFilteredPicks, getRelatedProps, } from "./portfolio-optimizer" // True Line Calculator export { probToFairDecimal, probToTrueLineAmerican, americanToImpliedProb, americanToDecimal as trueLineAmericanToDecimal, calculateEV, calculateMispricingProb, formatAmericanOdds as formatTrueLineOdds, generateMarketPricing, checkPricingThresholds, formatFilterReason, calculateRankingScore, rankByScore, getRelatedPropsByFamily, formatEV, formatMispricing, formatOddsAge, getOddsAgeColor, PRICING_THRESHOLDS, } from "./true-line-calculator" export type { MarketPricing, FilterReason } from "./true-line-calculator" // Market Scanner export { MARKET_TYPES, MARKET_FAMILIES, calculateModelProbability, calculateTrueLine, calculateImpliedProbability, calculateExpectedValue, calculateMispricing, calculateLineDiff, getMarketFamily, calculateRankingScore as scannerRankingScore, getVarianceLevel, filterSelection, scanFightMarkets, selectTopInefficiencies, getRelatedProps as getScannerRelatedProps, generateMockSelections, formatOdds as formatScannerOdds, formatEV as formatScannerEV, formatProb, scannerResultToSimulationBet, } from "./market-scanner" export type { MarketType, MarketFamily, ScannerResult, MarketSelection, } from "./market-scanner" [V0_FILE]typescript:file="lib/betting/clv-types.ts" isMerged="true" // MAFS CLV & Market Efficiency Types // Canonical market definitions, CLV tracking, and portfolio optimization // ============================================ // CANONICAL MARKET KEYS // ============================================ export type MarketFamily = | "moneyline" | "fight_distance" | "totals_rounds" | "method_victory" | "fighter_round_props" | "fighter_method_props" export interface CanonicalMarketKey { market_key: string // e.g., "ml_fighter1", "ov_2.5_rounds" selection_key: string // e.g., "pereira_ml", "over_2.5" market_family: MarketFamily fighter_id?: string // For fighter-specific props round_number?: number // For round props method?: string // For method props } // ============================================ // ODDS SNAPSHOTS // ============================================ export interface OddsSnapshot { snapshot_id: string fight_id: string market_key: string selection_key: string timestamp: Date american_odds: number implied_prob_raw: number implied_prob_novig: number data_age_seconds: number } export interface ClosingOddsSnapshot extends OddsSnapshot { closing_snapshot_id: string is_final: boolean } // ============================================ // BET SIGNAL RECORD // ============================================ export interface BetSignal { signal_id: string created_at: Date fight_id: string fight_name: string market_key: string selection_key: string selection_label: string // Human readable market_family: MarketFamily odds_at_signal: number implied_prob_at_signal: number model_prob: number edge: number // model_prob - implied_prob_at_signal confidence_score: number variance_level: "low" | "medium" | "high" sim_run_id: string notes_summary: string // Opening odds for CLV tracking opening_odds?: number opening_implied_prob?: number } // ============================================ // SETTLEMENT RECORD // ============================================ export type BetResult = "win" | "loss" | "push" | "void" | "pending" export interface SettlementRecord { signal_id: string settled_at: Date result: BetResult units_risked: number units_won: number closing_odds: number implied_prob_closing: number // CLV metrics clv_odds: number // closing_odds - odds_at_signal (directionally normalized) clv_prob: number // implied_prob_closing - implied_prob_at_signal (primary metric) closing_edge: number // model_prob - implied_prob_closing } // ============================================ // CLV METRICS // ============================================ export interface CLVMetrics { // Per-signal clv_prob: number clv_odds: number beat_close: boolean // Did we get better odds than closing? // Aggregate (for windows) avg_clv_prob: number clv_positive_rate: number // % of signals with positive CLV sample_size: number } export interface CLVWindow { window_type: "30_days" | "100_signals" | "all_time" start_date: Date end_date: Date signals_count: number metrics: CLVMetrics roi: number win_rate: number avg_edge_at_signal: number } // ============================================ // MARKET EFFICIENCY SCORING // ============================================ export interface MarketFamilyStats { market_family: MarketFamily sample_size: number roi: number win_rate: number avg_edge: number avg_clv: number volatility: number // std dev of units // Calculated quality score market_quality_score: number } export interface MarketEfficiencyModel { stats_by_family: Record last_updated: Date total_signals: number // Weights for confidence adjustment confidence_boost_threshold: number // Quality score above this boosts confidence confidence_penalty_threshold: number // Quality score below this reduces confidence } // ============================================ // PORTFOLIO / CORRELATION // ============================================ export type CorrelationLevel = "none" | "low" | "medium" | "high" | "invalid" export interface CorrelationPair { signal_a: string signal_b: string correlation_level: CorrelationLevel reason: string same_fighter: boolean same_outcome_family: boolean } export interface PortfolioAnalysis { signals: BetSignal[] correlations: CorrelationPair[] total_correlation_risk: number // 0-100 max_correlated_exposure: number filtered_signals: BetSignal[] kept_signals: BetSignal[] filter_reasons: Record // signal_id -> reason } // ============================================ // FILTERED PICKS // ============================================ export interface FilteredPick { signal: BetSignal filter_reason: string filter_type: "correlation" | "low_edge" | "high_variance" | "market_quality" original_rank: number } [V0_FILE]typescript:file="lib/betting/clv-calculator.ts" isMerged="true" // MAFS CLV Calculator // Closing Line Value computation and tracking import type { BetSignal, SettlementRecord, CLVMetrics, CLVWindow, BetResult, } from "./clv-types" import { americanToImpliedProbability } from "./edge-calculator" // ============================================ // CLV CALCULATION // ============================================ /** * Calculate CLV for a single settled bet * CLV = Did we beat the closing line? */ export function calculateCLV( oddsAtSignal: number, closingOdds: number ): { clv_odds: number; clv_prob: number; beat_close: boolean } { const impliedAtSignal = americanToImpliedProbability(oddsAtSignal) const impliedAtClose = americanToImpliedProbability(closingOdds) // CLV in probability terms (primary metric) // Positive CLV = we got better odds than closing // For a bet we made: if closing implied is HIGHER, we beat the close const clv_prob = impliedAtClose - impliedAtSignal // CLV in odds terms (directionally normalized) // For positive odds: higher is better // For negative odds: less negative is better let clv_odds: number if (oddsAtSignal > 0 && closingOdds > 0) { clv_odds = closingOdds - oddsAtSignal } else if (oddsAtSignal < 0 && closingOdds < 0) { clv_odds = closingOdds - oddsAtSignal // Less negative = positive CLV } else { // Mixed signs - convert to implied prob difference scaled to odds clv_odds = clv_prob * 100 } // We beat the close if we got better odds // Better odds = lower implied probability for our side const beat_close = clv_prob > 0 return { clv_odds, clv_prob, beat_close } } /** * Create a settlement record from a signal and result */ export function createSettlement( signal: BetSignal, result: BetResult, closingOdds: number, unitsRisked: number = 1 ): SettlementRecord { const impliedProbClosing = americanToImpliedProbability(closingOdds) const { clv_odds, clv_prob } = calculateCLV(signal.odds_at_signal, closingOdds) // Calculate units won based on result let units_won = 0 if (result === "win") { if (signal.odds_at_signal > 0) { units_won = unitsRisked * (signal.odds_at_signal / 100) } else { units_won = unitsRisked * (100 / Math.abs(signal.odds_at_signal)) } } else if (result === "loss") { units_won = -unitsRisked } // push/void = 0 return { signal_id: signal.signal_id, settled_at: new Date(), result, units_risked: unitsRisked, units_won, closing_odds: closingOdds, implied_prob_closing: impliedProbClosing, clv_odds, clv_prob, closing_edge: signal.model_prob - impliedProbClosing, } } // ============================================ // AGGREGATE CLV METRICS // ============================================ /** * Calculate aggregate CLV metrics from settlements */ export function calculateAggregateCLV( settlements: SettlementRecord[] ): CLVMetrics { if (settlements.length === 0) { return { clv_prob: 0, clv_odds: 0, beat_close: false, avg_clv_prob: 0, clv_positive_rate: 0, sample_size: 0, } } const validSettlements = settlements.filter( s => s.result !== "void" && s.result !== "pending" ) const totalCLVProb = validSettlements.reduce((sum, s) => sum + s.clv_prob, 0) const totalCLVOdds = validSettlements.reduce((sum, s) => sum + s.clv_odds, 0) const positiveCLVCount = validSettlements.filter(s => s.clv_prob > 0).length return { clv_prob: totalCLVProb, clv_odds: totalCLVOdds, beat_close: totalCLVProb > 0, avg_clv_prob: totalCLVProb / validSettlements.length, clv_positive_rate: positiveCLVCount / validSettlements.length, sample_size: validSettlements.length, } } /** * Calculate CLV metrics for a specific time/signal window */ export function calculateCLVWindow( signals: BetSignal[], settlements: SettlementRecord[], windowType: "30_days" | "100_signals" | "all_time" ): CLVWindow { const now = new Date() let filteredSignals: BetSignal[] let startDate: Date switch (windowType) { case "30_days": startDate = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000) filteredSignals = signals.filter(s => s.created_at >= startDate) break case "100_signals": filteredSignals = signals.slice(-100) startDate = filteredSignals[0]?.created_at || now break case "all_time": filteredSignals = signals startDate = signals[0]?.created_at || now break } const signalIds = new Set(filteredSignals.map(s => s.signal_id)) const relevantSettlements = settlements.filter(s => signalIds.has(s.signal_id)) const clvMetrics = calculateAggregateCLV(relevantSettlements) // Calculate ROI and win rate const completedSettlements = relevantSettlements.filter( s => s.result === "win" || s.result === "loss" ) const totalUnitsRisked = completedSettlements.reduce((sum, s) => sum + s.units_risked, 0) const totalUnitsWon = completedSettlements.reduce((sum, s) => sum + s.units_won, 0) const wins = completedSettlements.filter(s => s.result === "win").length const roi = totalUnitsRisked > 0 ? (totalUnitsWon / totalUnitsRisked) * 100 : 0 const winRate = completedSettlements.length > 0 ? wins / completedSettlements.length : 0 // Average edge at signal time const avgEdge = filteredSignals.length > 0 ? filteredSignals.reduce((sum, s) => sum + s.edge, 0) / filteredSignals.length : 0 return { window_type: windowType, start_date: startDate, end_date: now, signals_count: filteredSignals.length, metrics: clvMetrics, roi, win_rate: winRate, avg_edge_at_signal: avgEdge, } } // ============================================ // LINE MOVEMENT TRACKING // ============================================ export interface LineMovementPoint { timestamp: Date odds: number implied_prob: number label: string // "Opening", "Now", "Close" } /** * Generate line movement display data */ export function getLineMovementDisplay( openingOdds: number | undefined, currentOdds: number, closingOdds?: number ): LineMovementPoint[] { const points: LineMovementPoint[] = [] if (openingOdds !== undefined) { points.push({ timestamp: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000), odds: openingOdds, implied_prob: americanToImpliedProbability(openingOdds), label: "Opening", }) } points.push({ timestamp: new Date(), odds: currentOdds, implied_prob: americanToImpliedProbability(currentOdds), label: "Now", }) if (closingOdds !== undefined) { points.push({ timestamp: new Date(), odds: closingOdds, implied_prob: americanToImpliedProbability(closingOdds), label: "Close", }) } return points } /** * Format CLV for display */ export function formatCLV(clvProb: number): string { const sign = clvProb >= 0 ? "+" : "" return `${sign}${(clvProb * 100).toFixed(1)}%` } /** * Format odds movement for display */ export function formatOddsMovement( openingOdds: number | undefined, currentOdds: number ): string { if (openingOdds === undefined) return `Now: ${formatOdds(currentOdds)}` return `Opening: ${formatOdds(openingOdds)} | Now: ${formatOdds(currentOdds)}` } function formatOdds(odds: number): string { return odds > 0 ? `+${odds}` : `${odds}` } [V0_FILE]typescript:file="lib/betting/market-efficiency.ts" isMerged="true" // MAFS Market Efficiency Learner // Tracks profitability by market family and adjusts confidence scores import type { MarketFamily, MarketFamilyStats, MarketEfficiencyModel, BetSignal, SettlementRecord, } from "./clv-types" // ============================================ // CONSTANTS // ============================================ const MARKET_FAMILIES: MarketFamily[] = [ "moneyline", "fight_distance", "totals_rounds", "method_victory", "fighter_round_props", "fighter_method_props", ] const DEFAULT_QUALITY_SCORE = 50 // Neutral starting point const MIN_SAMPLE_SIZE = 10 // Minimum for reliable stats const SAMPLE_SIZE_PENALTY_THRESHOLD = 30 // Below this, apply penalty // ============================================ // MARKET EFFICIENCY MODEL // ============================================ /** * Calculate market family statistics from settled signals */ export function calculateMarketFamilyStats( signals: BetSignal[], settlements: SettlementRecord[] ): Record { const stats: Record = {} as Record // Initialize all families for (const family of MARKET_FAMILIES) { stats[family] = { market_family: family, sample_size: 0, roi: 0, win_rate: 0, avg_edge: 0, avg_clv: 0, volatility: 0, market_quality_score: DEFAULT_QUALITY_SCORE, } } // Group signals by market family const signalsByFamily = new Map() for (const signal of signals) { const existing = signalsByFamily.get(signal.market_family) || [] existing.push(signal) signalsByFamily.set(signal.market_family, existing) } // Create settlement lookup const settlementMap = new Map() for (const settlement of settlements) { settlementMap.set(settlement.signal_id, settlement) } // Calculate stats for each family for (const [family, familySignals] of signalsByFamily) { const familySettlements = familySignals .map(s => settlementMap.get(s.signal_id)) .filter((s): s is SettlementRecord => s !== undefined && s.result !== "void" && s.result !== "pending") if (familySettlements.length === 0) continue // Sample size const sampleSize = familySettlements.length // ROI const totalUnitsRisked = familySettlements.reduce((sum, s) => sum + s.units_risked, 0) const totalUnitsWon = familySettlements.reduce((sum, s) => sum + s.units_won, 0) const roi = totalUnitsRisked > 0 ? (totalUnitsWon / totalUnitsRisked) * 100 : 0 // Win rate const wins = familySettlements.filter(s => s.result === "win").length const winRate = wins / familySettlements.length // Average edge (from signals) const avgEdge = familySignals.reduce((sum, s) => sum + s.edge, 0) / familySignals.length // Average CLV const avgClv = familySettlements.reduce((sum, s) => sum + s.clv_prob, 0) / familySettlements.length // Volatility (standard deviation of units won) const mean = totalUnitsWon / familySettlements.length const squaredDiffs = familySettlements.map(s => Math.pow(s.units_won - mean, 2)) const volatility = Math.sqrt(squaredDiffs.reduce((sum, d) => sum + d, 0) / familySettlements.length) // Calculate quality score const qualityScore = calculateQualityScore(roi, avgClv, sampleSize, volatility) stats[family] = { market_family: family, sample_size: sampleSize, roi, win_rate: winRate, avg_edge: avgEdge, avg_clv: avgClv, volatility, market_quality_score: qualityScore, } } return stats } /** * Calculate quality score for a market family * Weighted combination of ROI, CLV, sample size, and volatility */ function calculateQualityScore( roi: number, avgClv: number, sampleSize: number, volatility: number ): number { // Base score from ROI (primary factor) // ROI of 10% = 70 score, ROI of -10% = 30 score const roiScore = Math.max(0, Math.min(100, 50 + roi * 2)) // CLV score (secondary factor) // Positive CLV is good, negative is bad const clvScore = Math.max(0, Math.min(100, 50 + avgClv * 100 * 10)) // Sample size penalty let samplePenalty = 0 if (sampleSize < MIN_SAMPLE_SIZE) { samplePenalty = 20 // Heavy penalty for very small samples } else if (sampleSize < SAMPLE_SIZE_PENALTY_THRESHOLD) { samplePenalty = 10 * (1 - sampleSize / SAMPLE_SIZE_PENALTY_THRESHOLD) } // Volatility penalty (high volatility = less reliable) const volatilityPenalty = Math.min(15, volatility * 5) // Weighted combination const rawScore = roiScore * 0.5 + clvScore * 0.3 + (50 - samplePenalty) * 0.2 const finalScore = Math.max(0, Math.min(100, rawScore - volatilityPenalty)) return finalScore } /** * Create or update the market efficiency model */ export function createMarketEfficiencyModel( signals: BetSignal[], settlements: SettlementRecord[] ): MarketEfficiencyModel { const stats = calculateMarketFamilyStats(signals, settlements) return { stats_by_family: stats, last_updated: new Date(), total_signals: signals.length, confidence_boost_threshold: 65, // Quality score above this boosts confidence confidence_penalty_threshold: 35, // Quality score below this reduces confidence } } /** * Adjust confidence score based on market quality */ export function adjustConfidenceForMarketQuality( baseConfidence: number, marketFamily: MarketFamily, model: MarketEfficiencyModel ): number { const familyStats = model.stats_by_family[marketFamily] if (!familyStats || familyStats.sample_size < MIN_SAMPLE_SIZE) { return baseConfidence // No adjustment for insufficient data } const qualityScore = familyStats.market_quality_score if (qualityScore >= model.confidence_boost_threshold) { // Boost confidence for high-quality markets const boostFactor = (qualityScore - model.confidence_boost_threshold) / 35 // Max 1.0 return Math.min(100, baseConfidence * (1 + boostFactor * 0.15)) } else if (qualityScore <= model.confidence_penalty_threshold) { // Reduce confidence for low-quality markets const penaltyFactor = (model.confidence_penalty_threshold - qualityScore) / 35 // Max 1.0 return Math.max(0, baseConfidence * (1 - penaltyFactor * 0.2)) } return baseConfidence } /** * Rank bets considering market quality * Returns a multiplier for the composite ranking score */ export function getMarketQualityMultiplier( marketFamily: MarketFamily, model: MarketEfficiencyModel ): number { const familyStats = model.stats_by_family[marketFamily] if (!familyStats || familyStats.sample_size < MIN_SAMPLE_SIZE) { return 1.0 // Neutral for insufficient data } // Quality score of 50 = 1.0 multiplier // Quality score of 80 = 1.15 multiplier // Quality score of 20 = 0.85 multiplier return 0.7 + (familyStats.market_quality_score / 100) * 0.6 } /** * Get market family display name */ export function getMarketFamilyDisplayName(family: MarketFamily): string { const names: Record = { moneyline: "Moneyline", fight_distance: "Fight Distance", totals_rounds: "Totals (Rounds)", method_victory: "Method of Victory", fighter_round_props: "Round Props", fighter_method_props: "Fighter Method Props", } return names[family] || family } [V0_FILE]typescript:file="lib/betting/portfolio-optimizer.ts" isMerged="true" // MAFS Portfolio Optimizer // Detects correlated picks and filters for optimal portfolio import type { BetSignal, MarketFamily, CorrelationLevel, CorrelationPair, PortfolioAnalysis, FilteredPick, MarketEfficiencyModel, } from "./clv-types" import { getMarketQualityMultiplier } from "./market-efficiency" // ============================================ // CORRELATION DETECTION // ============================================ /** * Define correlation rules between market families */ const CORRELATION_RULES: Array<{ families: [MarketFamily, MarketFamily] level: CorrelationLevel reason: string }> = [ // High correlation - same fighter outcome types { families: ["method_victory", "fighter_method_props"], level: "high", reason: "Same fighter method exposure", }, { families: ["fighter_round_props", "fighter_method_props"], level: "high", reason: "Overlapping fighter finish exposure", }, { families: ["moneyline", "method_victory"], level: "medium", reason: "Winner correlation with method", }, // Medium correlation - related outcomes { families: ["totals_rounds", "fight_distance"], level: "medium", reason: "Fight duration correlation", }, { families: ["fighter_round_props", "totals_rounds"], level: "medium", reason: "Round-based outcome overlap", }, // Low correlation { families: ["moneyline", "totals_rounds"], level: "low", reason: "Partial outcome dependency", }, ] /** * Check if two signals are on opposing sides of the same market */ function areOpposingSides(signalA: BetSignal, signalB: BetSignal): boolean { // Same market key but different selection if (signalA.market_key === signalB.market_key && signalA.selection_key !== signalB.selection_key) { return true } // Over/Under on same round total if (signalA.market_family === "totals_rounds" && signalB.market_family === "totals_rounds") { const aIsOver = signalA.selection_label.toLowerCase().includes("over") const bIsOver = signalB.selection_label.toLowerCase().includes("over") if (aIsOver !== bIsOver) return true } return false } /** * Check if two signals share the same fighter */ function shareSameFighter(signalA: BetSignal, signalB: BetSignal): boolean { // Extract fighter names from selection labels const fighterA = signalA.selection_label.split(" ")[0].toLowerCase() const fighterB = signalB.selection_label.split(" ")[0].toLowerCase() return fighterA === fighterB } /** * Determine correlation level between two signals */ export function getCorrelationLevel( signalA: BetSignal, signalB: BetSignal ): { level: CorrelationLevel; reason: string } { // Same fight check if (signalA.fight_id !== signalB.fight_id) { return { level: "none", reason: "Different fights" } } // Check for opposing sides (invalid) if (areOpposingSides(signalA, signalB)) { return { level: "invalid", reason: "Opposing sides of same market" } } // Same market family and same fighter = high correlation if (signalA.market_family === signalB.market_family && shareSameFighter(signalA, signalB)) { return { level: "high", reason: "Same market family and fighter" } } // Check correlation rules for (const rule of CORRELATION_RULES) { if ((signalA.market_family === rule.families[0] && signalB.market_family === rule.families[1]) || (signalA.market_family === rule.families[1] && signalB.market_family === rule.families[0])) { // Upgrade to high if same fighter if (rule.level !== "high" && shareSameFighter(signalA, signalB)) { return { level: "high", reason: `${rule.reason} (same fighter)` } } return { level: rule.level, reason: rule.reason } } } // Default: no correlation return { level: "none", reason: "No correlation detected" } } /** * Analyze all correlations in a set of signals */ export function analyzeCorrelations(signals: BetSignal[]): CorrelationPair[] { const pairs: CorrelationPair[] = [] for (let i = 0; i < signals.length; i++) { for (let j = i + 1; j < signals.length; j++) { const { level, reason } = getCorrelationLevel(signals[i], signals[j]) if (level !== "none") { pairs.push({ signal_a: signals[i].signal_id, signal_b: signals[j].signal_id, correlation_level: level, reason, same_fighter: shareSameFighter(signals[i], signals[j]), same_outcome_family: signals[i].market_family === signals[j].market_family, }) } } } return pairs } // ============================================ // PORTFOLIO OPTIMIZATION // ============================================ /** * Calculate expected value score for a signal */ function calculateEVScore( signal: BetSignal, model?: MarketEfficiencyModel ): number { const baseScore = signal.edge * signal.confidence_score if (model) { const qualityMultiplier = getMarketQualityMultiplier(signal.market_family, model) return baseScore * qualityMultiplier } return baseScore } /** * Optimize portfolio by filtering correlated picks */ export function optimizePortfolio( signals: BetSignal[], model?: MarketEfficiencyModel, options: { maxHighCorrelationPerFight?: number allowMediumCorrelation?: boolean } = {} ): PortfolioAnalysis { const { maxHighCorrelationPerFight = 1, allowMediumCorrelation = true, } = options const correlations = analyzeCorrelations(signals) const kept: BetSignal[] = [] const filtered: BetSignal[] = [] const filterReasons: Record = {} // Sort signals by EV score (highest first) const sortedSignals = [...signals].sort( (a, b) => calculateEVScore(b, model) - calculateEVScore(a, model) ) // Track high correlation counts per fight const highCorrelationCounts = new Map() for (const signal of sortedSignals) { const fightId = signal.fight_id // Check for invalid correlations (opposing sides) const hasInvalidCorrelation = correlations.some( c => c.correlation_level === "invalid" && (c.signal_a === signal.signal_id || c.signal_b === signal.signal_id) && kept.some(k => k.signal_id === c.signal_a || k.signal_id === c.signal_b) ) if (hasInvalidCorrelation) { filtered.push(signal) filterReasons[signal.signal_id] = "Invalid: opposing sides of same market" continue } // Check for high correlations with already-kept signals const highCorrelationsWithKept = correlations.filter( c => c.correlation_level === "high" && (c.signal_a === signal.signal_id || c.signal_b === signal.signal_id) && kept.some(k => k.signal_id === c.signal_a || k.signal_id === c.signal_b) ) if (highCorrelationsWithKept.length > 0) { const currentCount = highCorrelationCounts.get(fightId) || 0 if (currentCount >= maxHighCorrelationPerFight) { filtered.push(signal) filterReasons[signal.signal_id] = "Filtered: correlation risk (overlapping exposure)" continue } highCorrelationCounts.set(fightId, currentCount + 1) } // Check medium correlations if not allowed if (!allowMediumCorrelation) { const hasMediumCorrelation = correlations.some( c => c.correlation_level === "medium" && (c.signal_a === signal.signal_id || c.signal_b === signal.signal_id) && kept.some(k => k.signal_id === c.signal_a || k.signal_id === c.signal_b) ) if (hasMediumCorrelation) { filtered.push(signal) filterReasons[signal.signal_id] = "Filtered: medium correlation with existing pick" continue } } // Signal passes all checks kept.push(signal) } // Calculate total correlation risk const keptIds = new Set(kept.map(s => s.signal_id)) const activeCorrelations = correlations.filter( c => keptIds.has(c.signal_a) && keptIds.has(c.signal_b) ) const highCount = activeCorrelations.filter(c => c.correlation_level === "high").length const mediumCount = activeCorrelations.filter(c => c.correlation_level === "medium").length const totalCorrelationRisk = Math.min(100, highCount * 30 + mediumCount * 15) return { signals, correlations, total_correlation_risk: totalCorrelationRisk, max_correlated_exposure: highCount, filtered_signals: filtered, kept_signals: kept, filter_reasons: filterReasons, } } /** * Get filtered picks with reasons for display */ export function getFilteredPicks( analysis: PortfolioAnalysis, originalRanks: Map ): FilteredPick[] { return analysis.filtered_signals.map(signal => { const reason = analysis.filter_reasons[signal.signal_id] || "Filtered by simulation" let filterType: FilteredPick["filter_type"] = "correlation" if (reason.includes("edge")) filterType = "low_edge" if (reason.includes("variance")) filterType = "high_variance" if (reason.includes("quality")) filterType = "market_quality" return { signal, filter_reason: reason, filter_type: filterType, original_rank: originalRanks.get(signal.signal_id) || 0, } }) } // ============================================ // RELATED PROPS FILTERING // ============================================ /** * Get related props for a primary pick based on market family */ export function getRelatedProps( primarySignal: BetSignal, allSignals: BetSignal[] ): BetSignal[] { // Define which market families are related const relatedFamilies: Record = { moneyline: ["method_victory"], method_victory: ["fighter_method_props", "moneyline"], fighter_method_props: ["method_victory", "fighter_round_props"], fighter_round_props: ["fighter_method_props", "totals_rounds"], totals_rounds: ["fight_distance", "fighter_round_props"], fight_distance: ["totals_rounds"], } const allowedFamilies = new Set([ primarySignal.market_family, ...(relatedFamilies[primarySignal.market_family] || []), ]) return allSignals.filter(signal => { // Must be same fight if (signal.fight_id !== primarySignal.fight_id) return false // Must not be the primary signal if (signal.signal_id === primarySignal.signal_id) return false // Must be in allowed market families if (!allowedFamilies.has(signal.market_family)) return false // Prefer same fighter for fighter-specific props if (primarySignal.market_family.includes("fighter") && signal.market_family.includes("fighter")) { return shareSameFighter(primarySignal, signal) } return true }) } [V0_FILE]typescript:file="lib/betting/true-line-calculator.ts" isMerged="true" /** * MAFS True Line Calculator * Computes fair odds, EV, and mispricing for all market selections */ // =================================================================== // CORE CONVERSION FUNCTIONS // =================================================================== /** * Convert model probability to fair decimal odds */ export function probToFairDecimal(modelProb: number): number { if (modelProb <= 0 || modelProb >= 1) return 0 return 1 / modelProb } /** * Convert model probability to fair American odds (True Line) */ export function probToTrueLineAmerican(modelProb: number): number { if (modelProb <= 0 || modelProb >= 1) return 0 if (modelProb >= 0.5) { // Favorite: negative odds return -Math.round((modelProb / (1 - modelProb)) * 100) } else { // Underdog: positive odds return Math.round(((1 - modelProb) / modelProb) * 100) } } /** * Convert American odds to implied probability */ export function americanToImpliedProb(odds: number): number { if (odds > 0) { return 100 / (odds + 100) } else { return (-odds) / ((-odds) + 100) } } /** * Convert American odds to decimal odds */ export function americanToDecimal(odds: number): number { if (odds > 0) { return 1 + (odds / 100) } else { return 1 + (100 / -odds) } } /** * Calculate Expected Value per 1 unit stake * EV = (model_prob * (decimal - 1)) - ((1 - model_prob) * 1) */ export function calculateEV(modelProb: number, marketOddsAmerican: number): number { const decimalOdds = americanToDecimal(marketOddsAmerican) return (modelProb * (decimalOdds - 1)) - ((1 - modelProb) * 1) } /** * Calculate mispricing (probability difference) */ export function calculateMispricingProb(modelProb: number, marketOddsAmerican: number): number { const impliedProb = americanToImpliedProb(marketOddsAmerican) return modelProb - impliedProb } /** * Format American odds for display */ export function formatAmericanOdds(odds: number): string { if (odds > 0) return `+${odds}` return `${odds}` } // =================================================================== // PRICING OBJECT GENERATOR // =================================================================== export type MarketFamily = | "moneyline" | "fight_distance" | "totals_rounds" | "method_victory" | "fighter_round_props" | "fighter_method_props" export interface MarketPricing { // Canonical keys fight_id: string market_key: string selection_key: string market_family: MarketFamily // Model outputs model_prob: number true_line_american: number true_line_decimal: number // Market data market_odds_american: number market_implied_prob: number // Derived metrics mispricing_prob: number ev_per_unit: number // Quality signals confidence_score: number variance_level: "low" | "medium" | "high" // Freshness odds_updated_at: Date odds_age_seconds: number sim_run_id?: string } /** * Generate complete pricing object for a market selection */ export function generateMarketPricing(params: { fight_id: string market_key: string selection_key: string market_family: MarketFamily model_prob: number market_odds_american: number confidence_score: number variance_level: "low" | "medium" | "high" odds_updated_at?: Date sim_run_id?: string }): MarketPricing { const { fight_id, market_key, selection_key, market_family, model_prob, market_odds_american, confidence_score, variance_level, odds_updated_at = new Date(), sim_run_id, } = params const true_line_american = probToTrueLineAmerican(model_prob) const true_line_decimal = probToFairDecimal(model_prob) const market_implied_prob = americanToImpliedProb(market_odds_american) const mispricing_prob = calculateMispricingProb(model_prob, market_odds_american) const ev_per_unit = calculateEV(model_prob, market_odds_american) const odds_age_seconds = Math.floor((Date.now() - odds_updated_at.getTime()) / 1000) return { fight_id, market_key, selection_key, market_family, model_prob, true_line_american, true_line_decimal, market_odds_american, market_implied_prob, mispricing_prob, ev_per_unit, confidence_score, variance_level, odds_updated_at, odds_age_seconds, sim_run_id, } } // =================================================================== // RANKING & FILTERING // =================================================================== // Configurable thresholds export const PRICING_THRESHOLDS = { min_mispricing_prob: 0.05, // 5% min_confidence_score: 0.55, max_odds_age_seconds: 300, // 5 minutes min_ev_per_unit: 0, // Must be positive EV } export type FilterReason = | "negative_ev" | "insufficient_mispricing" | "low_confidence" | "stale_odds" | "correlation_risk" /** * Check if a pricing object passes all thresholds */ export function checkPricingThresholds(pricing: MarketPricing): { passes: boolean reasons: FilterReason[] } { const reasons: FilterReason[] = [] if (pricing.ev_per_unit <= PRICING_THRESHOLDS.min_ev_per_unit) { reasons.push("negative_ev") } if (pricing.mispricing_prob < PRICING_THRESHOLDS.min_mispricing_prob) { reasons.push("insufficient_mispricing") } if (pricing.confidence_score < PRICING_THRESHOLDS.min_confidence_score) { reasons.push("low_confidence") } if (pricing.odds_age_seconds > PRICING_THRESHOLDS.max_odds_age_seconds) { reasons.push("stale_odds") } return { passes: reasons.length === 0, reasons, } } /** * Format filter reason for display */ export function formatFilterReason(reason: FilterReason): string { switch (reason) { case "negative_ev": return "Filtered: negative EV" case "insufficient_mispricing": return "Filtered: insufficient mispricing" case "low_confidence": return "Filtered: low confidence score" case "stale_odds": return "Filtered: stale odds data" case "correlation_risk": return "Filtered: correlation risk (overlapping exposure)" default: return "Filtered" } } /** * Calculate ranking score for best bets * score = ev_per_unit * confidence_weighted * market_quality (if available) */ export function calculateRankingScore( pricing: MarketPricing, marketQualityScore?: number ): number { const confidenceWeight = pricing.confidence_score const evComponent = Math.max(0, pricing.ev_per_unit) let score = evComponent * confidenceWeight if (marketQualityScore !== undefined && marketQualityScore > 0) { score *= marketQualityScore } return score } /** * Rank multiple pricing objects by score */ export function rankByScore(pricings: MarketPricing[]): MarketPricing[] { return [...pricings].sort((a, b) => { const scoreA = calculateRankingScore(a) const scoreB = calculateRankingScore(b) return scoreB - scoreA }) } /** * Get related props by market family, ranked by EV */ export function getRelatedPropsByFamily( pricings: MarketPricing[], primaryMarketFamily: MarketFamily, excludeMarketKey?: string ): MarketPricing[] { return pricings .filter(p => p.market_family === primaryMarketFamily) .filter(p => p.market_key !== excludeMarketKey) .sort((a, b) => { // Primary sort: EV if (b.ev_per_unit !== a.ev_per_unit) { return b.ev_per_unit - a.ev_per_unit } // Secondary sort: confidence return b.confidence_score - a.confidence_score }) } // =================================================================== // DISPLAY HELPERS // =================================================================== /** * Format EV for display */ export function formatEV(ev: number): string { const sign = ev >= 0 ? "+" : "" return `${sign}${(ev * 100).toFixed(1)}%` } /** * Format mispricing for display */ export function formatMispricing(mispricing: number): string { const sign = mispricing >= 0 ? "+" : "" return `${sign}${(mispricing * 100).toFixed(1)}%` } /** * Format odds age for display */ export function formatOddsAge(seconds: number): string { if (seconds < 60) return `${seconds}s ago` if (seconds < 3600) return `${Math.floor(seconds / 60)}m ago` return `${Math.floor(seconds / 3600)}h ago` } /** * Get freshness indicator color class */ export function getOddsAgeColor(seconds: number): string { if (seconds <= 60) return "text-green-400" if (seconds <= 180) return "text-yellow-400" return "text-red-400" } [V0_FILE]typescript:file="lib/betting/market-scanner.ts" isMerged="true" /** * MAFS Prop Market Auto-Scanner * Scans ALL available betting markets for each fight and identifies * the highest-value betting opportunities based on model probability * vs consensus market odds. */ // Market types to scan export const MARKET_TYPES = [ "moneyline", "fight_goes_distance", "round_totals", "fighter_finish", "fighter_itd", "method_of_victory", "round_finish_props", "submission_props", "ko_props", "decision_props", ] as const export type MarketType = (typeof MARKET_TYPES)[number] // Market family groupings for correlation filtering export const MARKET_FAMILIES = { moneyline: ["moneyline"], finish_props: ["fighter_finish", "fighter_itd", "ko_props", "submission_props"], round_props: ["round_finish_props"], totals: ["round_totals", "fight_goes_distance"], method: ["method_of_victory", "decision_props"], } as const export type MarketFamily = keyof typeof MARKET_FAMILIES // Scanner result for each selection export interface ScannerResult { fight_id: string market_key: string selection_key: string selection_name: string market_type: MarketType market_family: MarketFamily // Model calculations model_prob: number true_line: number // Market data market_odds: number market_implied_prob: number // Value calculations mispricing_prob: number ev_per_unit: number line_diff: number // Quality metrics confidence_score: number variance_level: "low" | "medium" | "high" | "extreme" // Ranking ranking_score: number // Metadata odds_last_updated: Date // Filter status is_qualified: boolean filter_reasons: string[] } // Filter thresholds const FILTER_THRESHOLDS = { min_ev: 0, min_model_prob: 0.25, // Lowered from 0.52 to allow underdog props min_confidence: 0.5, blocked_variance: ["extreme"] as const, } // STEP 1: Calculate model probability from simulation frequency export function calculateModelProbability( outcomeWins: number, totalSimulations: number ): number { if (totalSimulations === 0) return 0 return outcomeWins / totalSimulations } // STEP 2: Calculate true line from model probability export function calculateTrueLine(modelProb: number): number { if (modelProb <= 0) return 9999 if (modelProb >= 1) return -9999 if (modelProb >= 0.5) { return Math.round(-((modelProb / (1 - modelProb)) * 100)) } else { return Math.round(((1 - modelProb) / modelProb) * 100) } } // STEP 3: Calculate market implied probability from consensus odds export function calculateImpliedProbability(odds: number): number { if (odds > 0) { return 100 / (odds + 100) } else { return -odds / (-odds + 100) } } // STEP 4: Calculate expected value export function calculateExpectedValue( modelProb: number, marketOdds: number ): number { // Convert to decimal odds const decimalOdds = marketOdds > 0 ? 1 + marketOdds / 100 : 1 + 100 / -marketOdds // EV = (model_prob * profit) - ((1 - model_prob) * stake) return modelProb * (decimalOdds - 1) - (1 - modelProb) * 1 } // STEP 5: Calculate mispricing export function calculateMispricing( modelProb: number, impliedProb: number ): number { return modelProb - impliedProb } // Calculate line difference export function calculateLineDiff( marketOdds: number, trueLine: number ): number { return marketOdds - trueLine } // Get market family for a market type export function getMarketFamily(marketType: MarketType): MarketFamily { for (const [family, types] of Object.entries(MARKET_FAMILIES)) { if ((types as readonly string[]).includes(marketType)) { return family as MarketFamily } } return "method" // default } // Calculate ranking score export function calculateRankingScore( ev: number, confidence: number, variance: "low" | "medium" | "high" | "extreme" ): number { const variancePenalty = { low: 0, medium: 0.02, high: 0.05, extreme: 0.15, } return ev + confidence * 0.1 - variancePenalty[variance] } // Determine variance level from stability score export function getVarianceLevel( stabilityScore: number ): "low" | "medium" | "high" | "extreme" { if (stabilityScore >= 0.85) return "low" if (stabilityScore >= 0.7) return "medium" if (stabilityScore >= 0.5) return "high" return "extreme" } // STEP 7: Filter low quality markets export function filterSelection(result: ScannerResult): { qualified: boolean reasons: string[] } { const reasons: string[] = [] if (result.ev_per_unit <= FILTER_THRESHOLDS.min_ev) { reasons.push(`Negative EV (${(result.ev_per_unit * 100).toFixed(1)}%)`) } if (result.model_prob < FILTER_THRESHOLDS.min_model_prob) { reasons.push( `Low model probability (${(result.model_prob * 100).toFixed(0)}%)` ) } if (result.confidence_score < FILTER_THRESHOLDS.min_confidence) { reasons.push( `Low confidence (${(result.confidence_score * 100).toFixed(0)}%)` ) } if ( (FILTER_THRESHOLDS.blocked_variance as readonly string[]).includes( result.variance_level ) ) { reasons.push(`Extreme variance blocked`) } return { qualified: reasons.length === 0, reasons, } } // Market selection definition export interface MarketSelection { market_type: MarketType selection_key: string selection_name: string consensus_odds: number model_prob: number confidence_score: number stability_score: number } // STEP 6: Market Scanner Engine - scan all markets for a fight export function scanFightMarkets( fightId: string, selections: MarketSelection[] ): ScannerResult[] { const results: ScannerResult[] = [] for (const selection of selections) { const trueLine = calculateTrueLine(selection.model_prob) const impliedProb = calculateImpliedProbability(selection.consensus_odds) const ev = calculateExpectedValue(selection.model_prob, selection.consensus_odds) const mispricing = calculateMispricing(selection.model_prob, impliedProb) const lineDiff = calculateLineDiff(selection.consensus_odds, trueLine) const variance = getVarianceLevel(selection.stability_score) const rankingScore = calculateRankingScore( ev, selection.confidence_score, variance ) const marketFamily = getMarketFamily(selection.market_type) const result: ScannerResult = { fight_id: fightId, market_key: `${fightId}_${selection.market_type}`, selection_key: selection.selection_key, selection_name: selection.selection_name, market_type: selection.market_type, market_family: marketFamily, model_prob: selection.model_prob, true_line: trueLine, market_odds: selection.consensus_odds, market_implied_prob: impliedProb, mispricing_prob: mispricing, ev_per_unit: ev, line_diff: lineDiff, confidence_score: selection.confidence_score, variance_level: variance, ranking_score: rankingScore, odds_last_updated: new Date(), is_qualified: false, filter_reasons: [], } // Apply filters const filterResult = filterSelection(result) result.is_qualified = filterResult.qualified result.filter_reasons = filterResult.reasons results.push(result) } // Sort by ranking score (highest first) return results.sort((a, b) => b.ranking_score - a.ranking_score) } // STEP 8: Select top inefficiencies with correlation filtering export function selectTopInefficiencies( results: ScannerResult[], maxPicks: number = 3 ): { qualified: ScannerResult[]; filtered: ScannerResult[] } { const qualified = results.filter((r) => r.is_qualified) const filtered = results.filter((r) => !r.is_qualified) const selectedPicks: ScannerResult[] = [] const usedFamilies = new Set() for (const result of qualified) { // Skip if we already have a pick from this market family if (usedFamilies.has(result.market_family)) { // Move to filtered with correlation reason filtered.push({ ...result, is_qualified: false, filter_reasons: [ ...result.filter_reasons, `Correlation filter: already have ${result.market_family} pick`, ], }) continue } selectedPicks.push(result) usedFamilies.add(result.market_family) if (selectedPicks.length >= maxPicks) break } return { qualified: selectedPicks, filtered } } // STEP 10: Get related props from same market family export function getRelatedProps( allResults: ScannerResult[], primaryPick: ScannerResult, maxRelated: number = 3 ): ScannerResult[] { return allResults .filter( (r) => r.selection_key !== primaryPick.selection_key && r.market_family === primaryPick.market_family && r.ev_per_unit > 0 ) .sort((a, b) => b.ev_per_unit - a.ev_per_unit) .slice(0, maxRelated) } // Generate mock market selections for a fight (simulates simulation engine output) export function generateMockSelections( fightId: string, fighter1: string, fighter2: string ): MarketSelection[] { const selections: MarketSelection[] = [] // Helper to generate realistic odds and probabilities const genSelection = ( marketType: MarketType, name: string, baseProb: number, baseOdds: number ): MarketSelection => { const variance = (Math.random() - 0.5) * 0.08 const modelProb = Math.min(0.95, Math.max(0.05, baseProb + variance)) const oddsVariance = Math.floor((Math.random() - 0.5) * 30) const odds = baseOdds + oddsVariance return { market_type: marketType, selection_key: `${fightId}_${marketType}_${name.toLowerCase().replace(/\s+/g, "_")}`, selection_name: name, consensus_odds: odds, model_prob: modelProb, confidence_score: 0.6 + Math.random() * 0.3, stability_score: 0.5 + Math.random() * 0.4, } } // Moneyline selections.push(genSelection("moneyline", `${fighter1} ML`, 0.55, -140)) selections.push(genSelection("moneyline", `${fighter2} ML`, 0.45, +120)) // Fight goes distance selections.push(genSelection("fight_goes_distance", "Fight Goes Distance", 0.35, +180)) selections.push(genSelection("fight_goes_distance", "Fight Doesn't Go Distance", 0.65, -220)) // Round totals selections.push(genSelection("round_totals", "Over 1.5 Rounds", 0.72, -180)) selections.push(genSelection("round_totals", "Under 1.5 Rounds", 0.28, +150)) selections.push(genSelection("round_totals", "Over 2.5 Rounds", 0.45, +110)) selections.push(genSelection("round_totals", "Under 2.5 Rounds", 0.55, -130)) // Fighter ITD selections.push(genSelection("fighter_itd", `${fighter1} ITD`, 0.42, +130)) selections.push(genSelection("fighter_itd", `${fighter2} ITD`, 0.35, +180)) // Fighter finish selections.push(genSelection("fighter_finish", `${fighter1} by Finish`, 0.38, +160)) selections.push(genSelection("fighter_finish", `${fighter2} by Finish`, 0.30, +220)) // Method of victory selections.push(genSelection("method_of_victory", `${fighter1} by KO/TKO`, 0.28, +200)) selections.push(genSelection("method_of_victory", `${fighter1} by Submission`, 0.10, +550)) selections.push(genSelection("method_of_victory", `${fighter1} by Decision`, 0.17, +350)) selections.push(genSelection("method_of_victory", `${fighter2} by KO/TKO`, 0.22, +280)) selections.push(genSelection("method_of_victory", `${fighter2} by Submission`, 0.08, +700)) selections.push(genSelection("method_of_victory", `${fighter2} by Decision`, 0.15, +400)) // KO props selections.push(genSelection("ko_props", `${fighter1} KO/TKO R1`, 0.12, +450)) selections.push(genSelection("ko_props", `${fighter1} KO/TKO R2`, 0.10, +550)) selections.push(genSelection("ko_props", `${fighter2} KO/TKO R1`, 0.09, +600)) selections.push(genSelection("ko_props", `${fighter2} KO/TKO R2`, 0.08, +700)) // Submission props selections.push(genSelection("submission_props", `${fighter1} by Sub R1-2`, 0.06, +900)) selections.push(genSelection("submission_props", `${fighter2} by Sub R1-2`, 0.05, +1100)) // Decision props selections.push(genSelection("decision_props", "Fight to Decision", 0.35, +180)) selections.push(genSelection("decision_props", `${fighter1} UD`, 0.12, +500)) selections.push(genSelection("decision_props", `${fighter2} UD`, 0.10, +600)) return selections } // Format odds for display export function formatOdds(odds: number): string { return odds > 0 ? `+${odds}` : `${odds}` } // Format EV for display export function formatEV(ev: number): string { const pct = ev * 100 return pct >= 0 ? `+${pct.toFixed(1)}%` : `${pct.toFixed(1)}%` } // Format probability for display export function formatProb(prob: number): string { return `${(prob * 100).toFixed(0)}%` } // Convert scanner result to SimulationBet format for UI compatibility export function scannerResultToSimulationBet( result: ScannerResult, index: number ): Record { return { id: result.selection_key, bet_type: result.market_type, label: result.selection_name, odds_american: formatOdds(result.market_odds), P_sim: result.model_prob, P_imp: result.market_implied_prob, edge_pct: result.mispricing_prob * 100, stability_score: result.variance_level === "low" ? 0.9 : result.variance_level === "medium" ? 0.75 : result.variance_level === "high" ? 0.6 : 0.4, confidence_label: result.confidence_score >= 0.8 ? "High" : result.confidence_score >= 0.6 ? "Medium" : "Low", confidencePct: Math.round(result.confidence_score * 100), varianceTag: result.variance_level === "extreme" ? "high" : result.variance_level, reason: `Model identifies ${(result.mispricing_prob * 100).toFixed(1)}% mispricing with ${formatEV(result.ev_per_unit)} expected value`, detailedReason: { marketInefficiency: `Consensus market prices this at ${formatOdds(result.market_odds)} (${formatProb(result.market_implied_prob)} implied). Model calculates true probability at ${formatProb(result.model_prob)}, creating a ${(result.mispricing_prob * 100).toFixed(1)}% edge.`, keyDrivers: [ `True line: ${formatOdds(result.true_line)} vs market ${formatOdds(result.market_odds)}`, `EV per unit: ${formatEV(result.ev_per_unit)}`, `Confidence: ${formatProb(result.confidence_score)}`, ], riskFactors: result.variance_level === "high" || result.variance_level === "extreme" ? ["Elevated variance - consider reduced stake"] : [], }, agentSignals: [ { name: "Edge Scanner", signal: result.ev_per_unit > 0.05 ? "pass" : result.ev_per_unit > 0 ? "neutral" : "fail", desc: `EV: ${formatEV(result.ev_per_unit)}`, }, { name: "Mispricing", signal: result.mispricing_prob > 0.05 ? "pass" : result.mispricing_prob > 0 ? "neutral" : "fail", desc: `${(result.mispricing_prob * 100).toFixed(1)}% edge`, }, { name: "Confidence", signal: result.confidence_score >= 0.7 ? "pass" : result.confidence_score >= 0.5 ? "neutral" : "fail", desc: `${formatProb(result.confidence_score)} model confidence`, }, { name: "Variance", signal: result.variance_level === "low" ? "pass" : result.variance_level === "medium" ? "neutral" : "fail", desc: `${result.variance_level} variance`, }, ], // Market scanner specific fields market_key: result.market_key, selection_key: result.selection_key, market_family: result.market_family, true_line_american: result.true_line, market_implied_prob: result.market_implied_prob, mispricing_prob: result.mispricing_prob, ev_per_unit: result.ev_per_unit, // Computed status status: result.is_qualified ? "qualified" : "filtered", rejectReasons: result.filter_reasons, } } [V0_FILE]typescript:file="hooks/use-market-scanner.ts" isMerged="true" "use client" import { useState, useEffect, useCallback, useMemo } from "react" import { scanFightMarkets, selectTopInefficiencies, getScannerRelatedProps, generateMockSelections, scannerResultToSimulationBet, type ScannerResult, type MarketSelection, } from "@/lib/betting" // Scanner refresh interval (30 seconds max) const REFRESH_INTERVAL = 30000 export interface UseScannerOptions { fightId: string fighter1: string fighter2: string maxPicks?: number autoRefresh?: boolean } export interface ScannerState { isScanning: boolean lastScan: Date | null allResults: ScannerResult[] qualifiedPicks: ScannerResult[] filteredPicks: ScannerResult[] error: Error | null } export function useMarketScanner({ fightId, fighter1, fighter2, maxPicks = 3, autoRefresh = true, }: UseScannerOptions) { const [state, setState] = useState({ isScanning: false, lastScan: null, allResults: [], qualifiedPicks: [], filteredPicks: [], error: null, }) // Run the scanner const runScan = useCallback(async () => { if (!fightId || !fighter1 || !fighter2) return setState((prev) => ({ ...prev, isScanning: true, error: null })) try { // Generate market selections (in production, this would come from the simulation engine) const selections = generateMockSelections(fightId, fighter1, fighter2) // Scan all markets const allResults = scanFightMarkets(fightId, selections) // Select top inefficiencies with correlation filtering const { qualified, filtered } = selectTopInefficiencies(allResults, maxPicks) setState({ isScanning: false, lastScan: new Date(), allResults, qualifiedPicks: qualified, filteredPicks: filtered, error: null, }) } catch (error) { setState((prev) => ({ ...prev, isScanning: false, error: error instanceof Error ? error : new Error("Scanner failed"), })) } }, [fightId, fighter1, fighter2, maxPicks]) // Auto-refresh effect useEffect(() => { // Initial scan runScan() // Set up auto-refresh if enabled if (autoRefresh) { const interval = setInterval(runScan, REFRESH_INTERVAL) return () => clearInterval(interval) } }, [runScan, autoRefresh]) // Get related props for a specific pick const getRelatedForPick = useCallback( (pick: ScannerResult, maxRelated: number = 3) => { return getScannerRelatedProps(state.allResults, pick, maxRelated) }, [state.allResults] ) // Convert results to SimulationBet format for UI compatibility const qualifiedBets = useMemo(() => { return state.qualifiedPicks.map((result, index) => scannerResultToSimulationBet(result, index) ) }, [state.qualifiedPicks]) const filteredBets = useMemo(() => { return state.filteredPicks.map((result, index) => scannerResultToSimulationBet(result, index) ) }, [state.filteredPicks]) // Time since last scan const timeSinceLastScan = useMemo(() => { if (!state.lastScan) return null return Math.floor((Date.now() - state.lastScan.getTime()) / 1000) }, [state.lastScan]) return { ...state, runScan, getRelatedForPick, qualifiedBets, filteredBets, timeSinceLastScan, } } // Hook for scanning multiple fights export function useMultiFightScanner( fights: Array<{ id: string; fighter1: string; fighter2: string }> ) { const [results, setResults] = useState>(new Map()) const [isScanning, setIsScanning] = useState(false) const scanAllFights = useCallback(async () => { setIsScanning(true) const newResults = new Map() for (const fight of fights) { try { const selections = generateMockSelections( fight.id, fight.fighter1, fight.fighter2 ) const allResults = scanFightMarkets(fight.id, selections) const { qualified, filtered } = selectTopInefficiencies(allResults, 3) newResults.set(fight.id, { isScanning: false, lastScan: new Date(), allResults, qualifiedPicks: qualified, filteredPicks: filtered, error: null, }) } catch (error) { newResults.set(fight.id, { isScanning: false, lastScan: new Date(), allResults: [], qualifiedPicks: [], filteredPicks: [], error: error instanceof Error ? error : new Error("Scan failed"), }) } } setResults(newResults) setIsScanning(false) }, [fights]) useEffect(() => { scanAllFights() const interval = setInterval(scanAllFights, REFRESH_INTERVAL) return () => clearInterval(interval) }, [scanAllFights]) return { results, isScanning, scanAllFights, } } [V0_FILE]typescriptreact:file="app/layout.tsx" isMerged="true" import type React from "react" import type { Metadata } from "next" import { Geist, Geist_Mono } from "next/font/google" import { Analytics } from "@vercel/analytics/next" import "./globals.css" const _geist = Geist({ subsets: ["latin"] }) const _geistMono = Geist_Mono({ subsets: ["latin"] }) export const metadata: Metadata = { title: "MAFS - Multi-Agent Fight Simulator", description: "Find the truth behind the odds. AI-powered UFC betting analysis.", generator: "v0.app", icons: { icon: [ { url: "/icon-light-32x32.png", media: "(prefers-color-scheme: light)", }, { url: "/icon-dark-32x32.png", media: "(prefers-color-scheme: dark)", }, { url: "/icon.svg", type: "image/svg+xml", }, ], apple: "/apple-icon.png", }, } export default function RootLayout({ children, }: Readonly<{ children: React.ReactNode }>) { return ( {children} ) } [V0_FILE]typescriptreact:file="components/nav-bar.tsx" isMerged="true" "use client" import Link from "next/link" import { usePathname } from "next/navigation" import { Avatar, AvatarFallback } from "@/components/ui/avatar" import { DropdownMenu, DropdownMenuContent, DropdownMenuItem, DropdownMenuTrigger } from "@/components/ui/dropdown-menu" import { cn } from "@/lib/utils" const navItems = [ { name: "Dashboard", href: "/" }, { name: "Billing", href: "/billing" }, { name: "Settings", href: "/settings" }, ] export function NavBar() { const pathname = usePathname() return (

{/* Logo */}

MAFS Multi-Agent Fight Simulator {/* Center Nav */}

{navItems.map((item) => ( {item.name} ))}

{/* Right Side */}

Free Plan

U Settings Logout

) } [V0_FILE]css:file="app/globals.css" isMerged="true" @import "tailwindcss"; @import "tw-animate-css"; @custom-variant dark (&:is(.dark *)); /* Enhanced MAFS premium theme with light/dark mode support */ :root { /* Light mode - Premium slate/teal aesthetic */ --background: oklch(0.985 0.005 260); --foreground: oklch(0.15 0.02 260); --card: oklch(0.99 0.003 260); --card-foreground: oklch(0.15 0.02 260); --popover: oklch(0.99 0.003 260); --popover-foreground: oklch(0.15 0.02 260); --primary: oklch(0.45 0.18 168); --primary-foreground: oklch(0.99 0.005 260); --secondary: oklch(0.95 0.01 260); --secondary-foreground: oklch(0.15 0.02 260); --muted: oklch(0.96 0.008 260); --muted-foreground: oklch(0.45 0.02 260); --accent: oklch(0.45 0.18 168); --accent-foreground: oklch(0.99 0.005 260); --destructive: oklch(0.55 0.22 25); --destructive-foreground: oklch(0.99 0.005 260); --border: oklch(0.88 0.01 260); --input: oklch(0.96 0.008 260); --ring: oklch(0.45 0.18 168); --chart-1: oklch(0.45 0.18 168); --chart-2: oklch(0.50 0.15 150); --chart-3: oklch(0.55 0.12 200); --chart-4: oklch(0.40 0.18 160); --chart-5: oklch(0.60 0.1 190); --radius: 0.625rem; --neon-teal: oklch(0.45 0.18 168); --neon-green: oklch(0.50 0.18 155); --navy-dark: oklch(0.15 0.02 260); --navy-light: oklch(0.25 0.015 260); } .dark { --background: oklch(0.08 0.015 260); --foreground: oklch(0.96 0.01 180); --card: oklch(0.11 0.015 260); --card-foreground: oklch(0.96 0.01 180); --popover: oklch(0.11 0.015 260); --popover-foreground: oklch(0.96 0.01 180); --primary: oklch(0.72 0.18 168); --primary-foreground: oklch(0.08 0.015 260); --secondary: oklch(0.16 0.015 260); --secondary-foreground: oklch(0.96 0.01 180); --muted: oklch(0.18 0.015 260); --muted-foreground: oklch(0.55 0.02 180); --accent: oklch(0.72 0.18 168); --accent-foreground: oklch(0.08 0.015 260); --destructive: oklch(0.55 0.22 25); --destructive-foreground: oklch(0.96 0.01 180); --border: oklch(0.22 0.015 260); --input: oklch(0.16 0.015 260); --ring: oklch(0.72 0.18 168); --sidebar: oklch(0.1 0.015 260); --sidebar-foreground: oklch(0.96 0.01 180); --sidebar-primary: oklch(0.72 0.18 168); --sidebar-primary-foreground: oklch(0.96 0.01 180); --sidebar-accent: oklch(0.16 0.015 260); --sidebar-accent-foreground: oklch(0.96 0.01 180); --sidebar-border: oklch(0.22 0.015 260); --sidebar-ring: oklch(0.72 0.18 168); } @theme inline { --font-sans: "Inter", "Geist", "Geist Fallback", system-ui, sans-serif; --font-mono: "JetBrains Mono", "Geist Mono", "Geist Mono Fallback", monospace; --color-background: var(--background); --color-foreground: var(--foreground); --color-card: var(--card); --color-card-foreground: var(--card-foreground); --color-popover: var(--popover); --color-popover-foreground: var(--popover-foreground); --color-primary: var(--primary); --color-primary-foreground: var(--primary-foreground); --color-secondary: var(--secondary); --color-secondary-foreground: var(--secondary-foreground); --color-muted: var(--muted); --color-muted-foreground: var(--muted-foreground); --color-accent: var(--accent); --color-accent-foreground: var(--accent-foreground); --color-destructive: var(--destructive); --color-destructive-foreground: var(--destructive-foreground); --color-border: var(--border); --color-input: var(--input); --color-ring: var(--ring); --color-chart-1: var(--chart-1); --color-chart-2: var(--chart-2); --color-chart-3: var(--chart-3); --color-chart-4: var(--chart-4); --color-chart-5: var(--chart-5); --radius-sm: calc(var(--radius) - 4px); --radius-md: calc(var(--radius) - 2px); --radius-lg: var(--radius); --radius-xl: calc(var(--radius) + 4px); --color-sidebar: var(--sidebar); --color-sidebar-foreground: var(--sidebar-foreground); --color-sidebar-primary: var(--sidebar-primary); --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); --color-sidebar-accent: var(--sidebar-accent); --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); --color-sidebar-border: var(--sidebar-border); --color-sidebar-ring: var(--sidebar-ring); } @layer base { * { @apply border-border outline-ring/50; } body { @apply bg-background text-foreground antialiased; } /* Premium background with subtle grid pattern */ .premium-bg { position: relative; background: linear-gradient(180deg, var(--background) 0%, oklch(0.97 0.005 260) 100%); } .dark .premium-bg { background: linear-gradient(180deg, oklch(0.08 0.015 260) 0%, oklch(0.06 0.012 260) 100%); } .premium-bg::before { content: ""; position: absolute; inset: 0; background-image: linear-gradient(rgba(0, 0, 0, 0.03) 1px, transparent 1px), linear-gradient(90deg, rgba(0, 0, 0, 0.03) 1px, transparent 1px); background-size: 60px 60px; pointer-events: none; mask-image: linear-gradient(180deg, transparent 0%, black 10%, black 90%, transparent 100%); } .dark .premium-bg::before { background-image: linear-gradient(rgba(255, 255, 255, 0.02) 1px, transparent 1px), linear-gradient(90deg, rgba(255, 255, 255, 0.02) 1px, transparent 1px); } /* Animated gradient orb for hero */ .hero-orb { position: absolute; top: -200px; left: 50%; transform: translateX(-50%); width: 800px; height: 800px; background: radial-gradient( ellipse at center, rgba(0, 180, 140, 0.08) 0%, rgba(0, 140, 180, 0.04) 30%, transparent 70% ); filter: blur(80px); pointer-events: none; animation: orb-pulse 8s ease-in-out infinite; } .dark .hero-orb { background: radial-gradient( ellipse at center, rgba(0, 255, 200, 0.12) 0%, rgba(0, 200, 255, 0.06) 30%, transparent 70% ); } @keyframes orb-pulse { 0%, 100% { opacity: 0.6; transform: translateX(-50%) scale(1); } 50% { opacity: 0.8; transform: translateX(-50%) scale(1.1); } } /* Secondary orb for depth */ .hero-orb-secondary { position: absolute; top: 100px; left: 30%; width: 400px; height: 400px; background: radial-gradient(circle at center, rgba(100, 255, 218, 0.08) 0%, transparent 70%); filter: blur(60px); pointer-events: none; animation: orb-float 12s ease-in-out infinite; } @keyframes orb-float { 0%, 100% { transform: translate(0, 0); } 50% { transform: translate(30px, -20px); } } /* Enhanced hero text gradient */ .hero-title { background: linear-gradient(135deg, oklch(0.15 0.02 260) 0%, oklch(0.15 0.02 260) 40%, oklch(0.40 0.18 168) 100%); background-clip: text; -webkit-background-clip: text; } .dark .hero-title { background: linear-gradient(135deg, #ffffff 0%, #ffffff 40%, rgba(100, 255, 218, 0.9) 100%); background-clip: text; -webkit-background-clip: text; -webkit-text-fill-color: transparent; text-shadow: none; line-height: 1.2; padding-bottom: 0.1em; } /* Subtle text shimmer animation */ .text-shimmer { background: linear-gradient( 90deg, rgba(255, 255, 255, 0.8) 0%, rgba(100, 255, 218, 0.9) 50%, rgba(255, 255, 255, 0.8) 100% ); background-size: 200% 100%; background-clip: text; -webkit-background-clip: text; -webkit-text-fill-color: transparent; animation: text-shimmer 6s ease-in-out infinite; } @keyframes text-shimmer { 0%, 100% { background-position: 0% 50%; } 50% { background-position: 100% 50%; } } /* Premium card with glass morphism effect - Light mode */ .glass-card { background: rgba(255, 255, 255, 0.9); backdrop-filter: blur(20px) saturate(1.4); -webkit-backdrop-filter: blur(20px) saturate(1.4); border: 1px solid rgba(0, 0, 0, 0.08); box-shadow: 0 10px 30px rgba(0, 0, 0, 0.08); border-radius: 10px; transition: all 0.2s ease; } .glass-card:hover { transform: translateY(-2px); background: rgba(255, 255, 255, 0.95); border-color: rgba(0, 0, 0, 0.12); box-shadow: 0 14px 34px rgba(0, 0, 0, 0.12); } /* Dark mode glass-card */ .dark .glass-card { background: #0F1116; border: 1px solid #1B1F2A; box-shadow: 0 10px 30px rgba(0, 0, 0, 0.35); } .dark .glass-card:hover { background: #0F1116; border-color: #2A3140; box-shadow: 0 14px 34px rgba(0, 0, 0, 0.4); } /* Glass card intense - more visible glass effect - Light mode */ .glass-card-intense { background: rgba(255, 255, 255, 0.95); backdrop-filter: blur(24px) saturate(1.6); -webkit-backdrop-filter: blur(24px) saturate(1.6); border: 1px solid rgba(0, 0, 0, 0.1); box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1); border-radius: 10px; transition: all 0.2s ease; } .glass-card-intense:hover { transform: translateY(-2px); background: rgba(255, 255, 255, 0.98); border-color: rgba(0, 0, 0, 0.15); box-shadow: 0 14px 34px rgba(0, 0, 0, 0.14); } /* Dark mode glass-card-intense - enhanced shadows */ .dark .glass-card-intense { background: #0F1116; border: 1px solid rgba(255, 255, 255, 0.06); box-shadow: 0 10px 40px rgba(0, 0, 0, 0.5), 0 2px 8px rgba(0, 0, 0, 0.3), inset 0 1px 0 rgba(255, 255, 255, 0.04); } .dark .glass-card-intense:hover { background: #101318; border-color: rgba(255, 255, 255, 0.1); box-shadow: 0 16px 50px rgba(0, 0, 0, 0.6), 0 4px 12px rgba(0, 0, 0, 0.35), inset 0 1px 0 rgba(255, 255, 255, 0.06); transform: translateY(-2px); } /* Glass panel - for larger sections */ .glass-panel { background: linear-gradient( 135deg, rgba(255, 255, 255, 0.8) 0%, rgba(255, 255, 255, 0.6) 100% ); backdrop-filter: blur(30px) saturate(1.5); -webkit-backdrop-filter: blur(30px) saturate(1.5); border: 1px solid rgba(0, 0, 0, 0.06); box-shadow: 0 8px 32px rgba(0, 0, 0, 0.08), inset 0 1px 0 rgba(255, 255, 255, 0.9); } .dark .glass-panel { background: linear-gradient( 135deg, rgba(255, 255, 255, 0.04) 0%, rgba(255, 255, 255, 0.02) 100% ); border: 1px solid rgba(255, 255, 255, 0.08); box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3), inset 0 1px 0 rgba(255, 255, 255, 0.08); } /* Glass button */ .glass-button { background: rgba(0, 0, 0, 0.03); backdrop-filter: blur(12px); -webkit-backdrop-filter: blur(12px); border: 1px solid rgba(0, 0, 0, 0.08); transition: all 0.2s ease; } .dark .glass-button { background: rgba(255, 255, 255, 0.06); border: 1px solid rgba(255, 255, 255, 0.1); } .glass-button:hover { background: rgba(0, 0, 0, 0.06); border-color: rgba(0, 180, 140, 0.4); box-shadow: 0 4px 20px rgba(0, 180, 140, 0.1); } .dark .glass-button:hover { background: rgba(255, 255, 255, 0.1); border-color: rgba(100, 255, 218, 0.3); box-shadow: 0 4px 20px rgba(100, 255, 218, 0.15); } /* Glass input */ .glass-input { background: rgba(0, 0, 0, 0.3); backdrop-filter: blur(12px); -webkit-backdrop-filter: blur(12px); border: 1px solid rgba(255, 255, 255, 0.08); } .glass-input:focus { border-color: rgba(100, 255, 218, 0.4); box-shadow: 0 0 0 2px rgba(100, 255, 218, 0.1); } /* Frosted glass overlay */ .glass-frosted { background: rgba(8, 10, 15, 0.7); backdrop-filter: blur(40px) saturate(1.8); -webkit-backdrop-filter: blur(40px) saturate(1.8); } /* Glass divider */ .glass-divider { height: 1px; background: linear-gradient( 90deg, transparent, rgba(255, 255, 255, 0.1) 20%, rgba(255, 255, 255, 0.1) 80%, transparent ); } /* Glass shimmer effect */ .glass-shimmer { position: relative; overflow: hidden; } .glass-shimmer::after { content: ""; position: absolute; inset: 0; background: linear-gradient( 105deg, transparent 20%, rgba(255, 255, 255, 0.03) 35%, rgba(255, 255, 255, 0.05) 50%, rgba(255, 255, 255, 0.03) 65%, transparent 80% ); transform: translateX(-100%); animation: glass-shimmer-sweep 8s ease-in-out infinite; } @keyframes glass-shimmer-sweep { 0%, 100% { transform: translateX(-100%); } 50% { transform: translateX(100%); } } /* Glass border glow */ .glass-glow { position: relative; } .glass-glow::before { content: ""; position: absolute; inset: -1px; background: linear-gradient( 135deg, rgba(100, 255, 218, 0.2) 0%, transparent 30%, transparent 70%, rgba(100, 255, 218, 0.1) 100% ); border-radius: inherit; z-index: -1; opacity: 0; transition: opacity 0.3s ease; } .glass-glow:hover::before { opacity: 1; } /* Stats strip styling */ .stats-strip { display: flex; justify-content: center; gap: 1px; background: rgba(255, 255, 255, 0.03); border-top: 1px solid rgba(255, 255, 255, 0.06); border-bottom: 1px solid rgba(255, 255, 255, 0.06); } .stat-item { flex: 1; max-width: 200px; padding: 24px 32px; text-align: center; position: relative; } .stat-item:not(:last-child)::after { content: ""; position: absolute; right: 0; top: 50%; transform: translateY(-50%); height: 40px; width: 1px; background: rgba(255, 255, 255, 0.08); } .stat-value { font-size: 1.75rem; font-weight: 600; color: white; letter-spacing: -0.02em; } .stat-label { font-size: 0.75rem; color: rgba(255, 255, 255, 0.5); text-transform: uppercase; letter-spacing: 0.1em; margin-top: 4px; } /* Premium button with glow effect */ .premium-button { position: relative; background: linear-gradient(135deg, oklch(0.72 0.18 168) 0%, oklch(0.65 0.16 175) 100%); color: oklch(0.08 0.015 260); font-weight: 600; padding: 14px 28px; border-radius: 8px; border: none; cursor: pointer; transition: all 0.2s ease; overflow: hidden; box-shadow: 0 0 12px rgba(0, 255, 180, 0.25), 0 0 28px rgba(0, 255, 180, 0.15); } .premium-button::before { content: ""; position: absolute; inset: 0; background: linear-gradient(135deg, transparent 0%, rgba(255, 255, 255, 0.2) 50%, transparent 100%); opacity: 0; transition: opacity 0.2s ease; } .premium-button:hover { transform: translateY(-2px); box-shadow: 0 0 16px rgba(0, 255, 180, 0.35), 0 0 36px rgba(0, 255, 180, 0.2), 0 8px 24px rgba(100, 255, 218, 0.3); } .premium-button:hover::before { opacity: 1; } .premium-button:active { transform: translateY(0); } /* Floating particles animation (desktop) */ .particle { position: absolute; width: 2px; height: 2px; background: rgba(100, 255, 218, 0.4); border-radius: 50%; pointer-events: none; animation: particle-float 15s linear infinite; } @keyframes particle-float { 0% { transform: translateY(100vh) translateX(0); opacity: 0; } 10% { opacity: 1; } 90% { opacity: 1; } 100% { transform: translateY(-100vh) translateX(50px); opacity: 0; } } /* Scanline overlay for terminal feel */ .scanlines { position: absolute; inset: 0; pointer-events: none; background: repeating-linear-gradient( 0deg, transparent, transparent 2px, rgba(0, 0, 0, 0.03) 2px, rgba(0, 0, 0, 0.03) 4px ); opacity: 0.5; } /* Enhanced event selector card */ .event-selector-card { background: rgba(0, 0, 0, 0.3); border: 1px solid rgba(255, 255, 255, 0.06); border-radius: 16px; padding: 32px; position: relative; overflow: hidden; } .event-selector-card::before { content: ""; position: absolute; top: 0; left: 0; right: 0; height: 1px; background: linear-gradient(90deg, transparent, rgba(100, 255, 218, 0.3), transparent); } /* Existing styles below */ .neon-glow { text-shadow: none; } .dark .neon-glow { text-shadow: 0 0 10px currentColor, 0 0 20px currentColor; } .card-glow { box-shadow: 0 0 20px rgba(0, 140, 110, 0.08); } .dark .card-glow { box-shadow: 0 0 20px rgba(100, 255, 218, 0.1); } .bg-atmosphere { background: linear-gradient(to bottom, var(--background) 0%, oklch(0.97 0.005 260) 100%); } .dark .bg-atmosphere { background: linear-gradient(to bottom, oklch(0.08 0.015 260) 0%, oklch(0.06 0.012 260) 100%); } .hero-glow { position: relative; } .hero-glow::before { content: ""; position: absolute; top: -100px; left: 50%; transform: translateX(-50%); width: 600px; height: 600px; background: radial-gradient(circle, rgba(100, 255, 218, 0.08) 0%, transparent 70%); pointer-events: none; z-index: -1; } .card-elevation { box-shadow: 0px 2px 6px rgba(0, 0, 0, 0.3); transition: all 0.25s ease; } .card-hover:hover { box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.4); filter: brightness(1.05); } .ev-glow { text-shadow: 0 0 3px rgba(100, 255, 218, 0.6), 0 0 6px rgba(100, 255, 218, 0.4); filter: drop-shadow(0 0 2px rgba(100, 255, 218, 0.1)); } .ev-pulse { animation: ev-pulse 4.5s ease-in-out infinite; } @keyframes ev-pulse { 0%, 100% { opacity: 1; filter: brightness(1) drop-shadow(0 0 2px rgba(100, 255, 218, 0.1)); } 50% { opacity: 0.95; filter: brightness(1.08) drop-shadow(0 0 4px rgba(100, 255, 218, 0.2)); } } .ev-glow:hover, .ev-pulse:hover { filter: brightness(1.15) drop-shadow(0 0 6px rgba(100, 255, 218, 0.3)); } .text-neon-green { color: oklch(0.75 0.18 155); } .value-badge { transition: transform 0.15s ease-out, box-shadow 0.15s ease-out; cursor: default; } .value-badge:hover { transform: scale(1.03); } .value-badge-plus { background: linear-gradient( 135deg, rgba(100, 255, 218, 0.3) 0%, rgba(100, 255, 218, 0.2) 50%, rgba(100, 255, 218, 0.3) 100% ); background-size: 200% 200%; animation: badge-sheen 8s ease infinite; border: 1px solid rgba(100, 255, 218, 0.3); } .value-badge-plus:hover { box-shadow: 0 0 12px rgba(100, 255, 218, 0.5); } .value-badge-a { background: rgba(100, 255, 218, 0.22); border: 1px solid rgba(100, 255, 218, 0.2); } .value-badge-a:hover { box-shadow: 0 0 8px rgba(100, 255, 218, 0.35); } .value-badge-b { background: rgba(100, 255, 218, 0.15); border: 1px solid rgba(100, 255, 218, 0.12); } .value-badge-b:hover { box-shadow: 0 0 6px rgba(100, 255, 218, 0.25); } @keyframes badge-sheen { 0%, 100% { background-position: 0% 50%; } 50% { background-position: 100% 50%; } } .button-scan-hover { position: relative; overflow: hidden; transition: all 0.12s cubic-bezier(0.4, 0, 0.2, 1); } .button-scan-hover:hover:not(:disabled) { transform: scale(1.03); box-shadow: 0 4px 14px rgba(100, 255, 218, 0.35); } .button-scan-hover:hover:not(:disabled)::before { content: ""; position: absolute; inset: 0; background: linear-gradient(90deg, transparent 0%, rgba(255, 255, 255, 0.15) 50%, transparent 100%); animation: button-shimmer 1.2s ease-out; } .button-scan-hover:active:not(:disabled) { transform: scale(0.96); transition: all 0.06s cubic-bezier(0.4, 0, 0.2, 1); } @keyframes button-shimmer { 0% { transform: translateX(-100%); } 100% { transform: translateX(200%); } } .scan-complete { box-shadow: 0 0 20px rgba(34, 197, 94, 0.6); } @keyframes scan { 0% { transform: translateX(-100%); } 100% { transform: translateX(200%); } } .animate-scan { animation: scan 1.8s ease-in-out; } .best-bet-entrance { animation: slide-up-fade 0.25s ease-out forwards; } @keyframes slide-up-fade { from { opacity: 0; transform: translateY(16px); } to { opacity: 1; transform: translateY(0); } } .top-ev-spotlight { position: relative; border: 1px solid rgba(100, 255, 218, 0.35); box-shadow: 0 0 20px rgba(100, 255, 218, 0.08), 0 0 40px rgba(100, 255, 218, 0.04), inset 0 1px 0 rgba(100, 255, 218, 0.1); } .top-ev-spotlight::before { content: ""; position: absolute; inset: -2px; background: radial-gradient(circle at 50% 0%, rgba(100, 255, 218, 0.2), transparent 60%); border-radius: inherit; z-index: -1; pointer-events: none; animation: spotlight-pulse 4s ease-in-out infinite; } @keyframes spotlight-pulse { 0%, 100% { opacity: 0.6; } 50% { opacity: 1; } } .shimmer-text { background: linear-gradient(90deg, oklch(0.72 0.18 168) 0%, oklch(0.75 0.18 155) 50%, oklch(0.72 0.18 168) 100%); background-size: 200% 100%; background-clip: text; -webkit-background-clip: text; -webkit-text-fill-color: transparent; animation: shimmer 8s linear infinite; } @keyframes shimmer { 0% { background-position: 0% 50%; } 100% { background-position: 200% 50%; } } .market-edge-row { transition: all 0.2s ease; } .market-edge-row:hover { transform: translateY(-2px); background: oklch(0.14 0.015 260) !important; border-color: rgba(100, 255, 218, 0.3); box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3); } .hover-slide-in { transition: transform 0.2s ease; } tr:hover .hover-slide-in, .market-edge-row:hover .hover-slide-in { transform: translateX(4px); } .animate-fade-in { animation: fade-in 0.3s ease-out; } @keyframes fade-in { from { opacity: 0; } to { opacity: 1; } } .stat-number-enhanced { filter: brightness(1.15) contrast(1.1); text-shadow: 0 0 8px rgba(100, 255, 218, 0.06); } .stats-card-glow { box-shadow: 0px 2px 6px rgba(0, 0, 0, 0.3), 0 0 30px rgba(100, 255, 218, 0.04); } .best-bet-card-enhanced { box-shadow: 0px 2px 6px rgba(0, 0, 0, 0.3), 0 0 40px rgba(100, 255, 218, 0.07); transition: all 0.14s cubic-bezier(0.4, 0, 0.2, 1); } .best-bet-card-enhanced:hover { transform: scale(1.02) translateY(-3px); box-shadow: 0px 6px 16px rgba(0, 0, 0, 0.4), 0 0 50px rgba(100, 255, 218, 0.12); filter: brightness(1.06); } .ev-glow-enhanced { text-shadow: 0 0 4px rgba(100, 255, 218, 0.7), 0 0 8px rgba(100, 255, 218, 0.5); filter: drop-shadow(0 0 3px rgba(100, 255, 218, 0.15)); } .ev-pulse-enhanced { animation: ev-pulse-enhanced 4.5s ease-in-out infinite; } @keyframes ev-pulse-enhanced { 0%, 100% { opacity: 1; filter: brightness(1) drop-shadow(0 0 3px rgba(100, 255, 218, 0.15)); } 50% { opacity: 0.95; filter: brightness(1.12) drop-shadow(0 0 6px rgba(100, 255, 218, 0.25)); } } .ev-glow-enhanced:hover, .ev-pulse-enhanced:hover { filter: brightness(1.18) drop-shadow(0 0 8px rgba(100, 255, 218, 0.35)); } .fight-row-selected { background: rgba(100, 255, 218, 0.065) !important; } .market-edge-row-enhanced { transition: all 0.2s ease; border-bottom: 1px solid rgba(100, 255, 218, 0.07); } .market-edge-row-enhanced:last-child { border-bottom: none; } .market-edge-row-enhanced:hover { transform: translateY(-2px); background: oklch(0.14 0.015 260) !important; border-color: rgba(100, 255, 218, 0.3); box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3); } .best-bet-card-polish { background: rgba(0, 0, 0, 0.18) !important; border: 1px solid rgba(0, 255, 190, 0.07) !important; box-shadow: 0 0 22px rgba(0, 255, 190, 0.06); transition: all 0.14s ease-out; } .best-bet-card-polish:hover { transform: scale(1.025) translateY(-3px); box-shadow: 0 0 26px rgba(0, 255, 190, 0.14); border-color: rgba(0, 255, 190, 0.18); } .best-bet-entrance-polish { animation: best-bet-entrance-polish 280ms cubic-bezier(0.22, 1, 0.36, 1) forwards; } @keyframes best-bet-entrance-polish { from { opacity: 0; transform: translateY(14px) scale(0.96); } to { opacity: 1; transform: translateY(0) scale(1); } } .ev-text-polish { color: #00f3a8; filter: drop-shadow(0 0 6px rgba(0, 255, 180, 0.35)); transition: filter 0.14s ease-out; } .best-bet-card-polish:hover .ev-text-polish { filter: drop-shadow(0 0 8px rgba(0, 255, 170, 0.35)); } .confidence-text-polish { color: rgba(255, 255, 255, 0.72); } .value-badge-polish { box-shadow: 0 0 12px rgba(0, 255, 190, 0.18); } .value-badge-polish.value-badge-plus { background: linear-gradient( 135deg, rgba(100, 255, 218, 0.38) 0%, rgba(100, 255, 218, 0.28) 50%, rgba(100, 255, 218, 0.38) 100% ) !important; } .value-badge-polish.value-badge-a { background: rgba(100, 255, 218, 0.3) !important; } .value-badge-polish.value-badge-b { background: rgba(100, 255, 218, 0.23) !important; } .fight-table-row-hover { transition: all 0.12s ease-out; } .fight-table-row-hover:hover { transform: scale(1.012); background: rgba(0, 255, 190, 0.06) !important; box-shadow: 0 0 18px rgba(0, 255, 190, 0.08); } .fight-table-row-hover:hover .ev-cell { filter: drop-shadow(0 0 6px rgba(0, 255, 170, 0.25)); } .fight-table-row-hover:hover .value-badge { box-shadow: 0 0 10px rgba(0, 255, 190, 0.18); } /* ======================================== BILLION DOLLAR PREMIUM ANIMATIONS ======================================== */ /* Neural network animated background */ .neural-bg { position: relative; overflow: hidden; } .neural-bg::before { content: ""; position: absolute; inset: 0; background: radial-gradient(circle at 20% 80%, rgba(100, 255, 218, 0.03) 0%, transparent 50%), radial-gradient(circle at 80% 20%, rgba(0, 200, 255, 0.03) 0%, transparent 50%), radial-gradient(circle at 40% 40%, rgba(100, 255, 218, 0.02) 0%, transparent 30%); animation: neural-pulse 20s ease-in-out infinite; } @keyframes neural-pulse { 0%, 100% { opacity: 0.5; } 50% { opacity: 1; } } /* Premium number counter animation */ .number-roll { display: inline-block; animation: number-roll-in 0.6s cubic-bezier(0.16, 1, 0.3, 1) forwards; } @keyframes number-roll-in { from { opacity: 0; transform: translateY(100%) rotateX(-90deg); filter: blur(4px); } to { opacity: 1; transform: translateY(0) rotateX(0); filter: blur(0); } } /* Staggered card entrance */ .stagger-entrance { opacity: 0; animation: stagger-slide-up 0.5s cubic-bezier(0.16, 1, 0.3, 1) forwards; } .stagger-entrance:nth-child(1) { animation-delay: 0ms; } .stagger-entrance:nth-child(2) { animation-delay: 80ms; } .stagger-entrance:nth-child(3) { animation-delay: 160ms; } .stagger-entrance:nth-child(4) { animation-delay: 240ms; } .stagger-entrance:nth-child(5) { animation-delay: 320ms; } @keyframes stagger-slide-up { from { opacity: 0; transform: translateY(30px) scale(0.95); } to { opacity: 1; transform: translateY(0) scale(1); } } /* AI Thinking/Processing animation */ .ai-thinking { position: relative; } .ai-thinking::after { content: ""; position: absolute; bottom: 0; left: 0; height: 2px; background: linear-gradient(90deg, transparent, rgba(100, 255, 218, 0.8), transparent); animation: ai-scan 1.5s ease-in-out infinite; } @keyframes ai-scan { 0% { width: 0%; left: 0%; } 50% { width: 100%; left: 0%; } 100% { width: 0%; left: 100%; } } /* Confidence meter animation */ .confidence-meter { position: relative; height: 4px; background: rgba(255, 255, 255, 0.1); border-radius: 2px; overflow: hidden; } .confidence-meter-fill { height: 100%; background: linear-gradient(90deg, rgba(100, 255, 218, 0.6), rgba(100, 255, 218, 1)); border-radius: 2px; transform-origin: left; animation: meter-fill 1s cubic-bezier(0.16, 1, 0.3, 1) forwards; } @keyframes meter-fill { from { transform: scaleX(0); } to { transform: scaleX(1); } } /* Edge indicator pulse */ .edge-pulse { position: relative; } .edge-pulse::before { content: ""; position: absolute; inset: -4px; border-radius: inherit; background: rgba(100, 255, 218, 0.3); animation: edge-ring 2s ease-out infinite; z-index: -1; } @keyframes edge-ring { 0% { transform: scale(0.8); opacity: 1; } 100% { transform: scale(1.2); opacity: 0; } } /* Premium data grid */ .data-grid { display: grid; gap: 1px; background: rgba(255, 255, 255, 0.05); border-radius: 12px; overflow: hidden; } .data-cell { background: rgba(0, 0, 0, 0.4); padding: 16px; transition: all 0.2s ease; } .data-cell:hover { background: rgba(100, 255, 218, 0.05); } /* Live indicator */ .live-indicator { display: flex; align-items: center; gap: 6px; } .live-dot { width: 6px; height: 6px; background: #22c55e; border-radius: 50%; animation: live-pulse 2s ease-in-out infinite; } @keyframes live-pulse { 0%, 100% { opacity: 1; box-shadow: 0 0 0 0 rgba(34, 197, 94, 0.4); } 50% { opacity: 0.8; box-shadow: 0 0 0 4px rgba(34, 197, 94, 0); } } /* =========================================== PREMIUM ANALYTICS POLISH SYSTEM =========================================== */ /* 1. Card Depth - Premium Separation */ .premium-card { background: #0F1116; border: 1px solid #1B1F2A; box-shadow: 0 10px 30px rgba(0,0,0,0.35); border-radius: 10px; transition: all 0.2s ease; } .premium-card-interactive { background: #0F1116; border: 1px solid #1B1F2A; box-shadow: 0 10px 30px rgba(0,0,0,0.35); border-radius: 10px; transition: all 0.2s ease; cursor: pointer; } .premium-card-interactive:hover { transform: translateY(-2px); box-shadow: 0 14px 34px rgba(0,0,0,0.4); border-color: #2A3140; } /* 2. Typography Hierarchy */ .section-title { font-size: 18px; font-weight: 600; letter-spacing: -0.01em; } .card-title { font-size: 16px; font-weight: 600; letter-spacing: -0.01em; } .metric-label { font-size: 13px; font-weight: 500; color: rgba(255, 255, 255, 0.5); } .description-text { font-size: 13px; font-weight: 400; line-height: 1.5; } .meta-label { font-size: 12px; font-weight: 500; text-transform: uppercase; letter-spacing: 0.05em; color: rgba(255, 255, 255, 0.4); } /* 3. Metric Emphasis - Numbers are the Hero */ .metric-value { font-weight: 600; font-size: 16px; font-family: var(--font-mono); letter-spacing: -0.01em; } .metric-value-lg { font-weight: 700; font-size: 22px; font-family: var(--font-mono); letter-spacing: -0.02em; } .metric-value-xl { font-weight: 800; font-size: 32px; font-family: var(--font-mono); letter-spacing: -0.03em; } /* Extra large metrics for hero numbers */ .metric-value-hero { font-weight: 800; font-size: 40px; font-family: var(--font-mono); letter-spacing: -0.03em; line-height: 1; } /* 4. Accent Color Consistency */ .text-positive { color: #00E0A4; } .text-neutral-info { color: #5AC8FA; } .text-risk { color: #FF9F43; } .bg-positive { background-color: rgba(0, 224, 164, 0.1); } .bg-neutral-info { background-color: rgba(90, 200, 250, 0.1); } .bg-risk { background-color: rgba(255, 159, 67, 0.1); } .border-positive { border-color: rgba(0, 224, 164, 0.3); } .border-neutral-info { border-color: rgba(90, 200, 250, 0.3); } .border-risk { border-color: rgba(255, 159, 67, 0.3); } /* 5. Spacing Rhythm Utilities */ .section-spacing { margin-bottom: 32px; } .card-spacing { margin-bottom: 20px; } .metric-row-spacing { margin-bottom: 12px; } .bullet-spacing { margin-bottom: 8px; } /* Premium stat card - Light mode */ .stat-card-premium { position: relative; background: rgba(255, 255, 255, 0.9); border: 1px solid rgba(0, 0, 0, 0.08); box-shadow: 0 10px 30px rgba(0,0,0,0.06); border-radius: 10px; padding: 24px; overflow: hidden; transition: all 0.2s ease; } .stat-card-premium:hover { transform: translateY(-2px); box-shadow: 0 14px 34px rgba(0,0,0,0.1); border-color: rgba(0, 0, 0, 0.12); } /* Dark mode stat-card-premium - enhanced shadows */ .dark .stat-card-premium { background: #0F1116; border: 1px solid rgba(255, 255, 255, 0.06); box-shadow: 0 10px 40px rgba(0, 0, 0, 0.45), 0 2px 8px rgba(0, 0, 0, 0.25), inset 0 1px 0 rgba(255, 255, 255, 0.04); } .dark .stat-card-premium:hover { transform: translateY(-2px); box-shadow: 0 16px 50px rgba(0, 0, 0, 0.55), 0 4px 12px rgba(0, 0, 0, 0.3), inset 0 1px 0 rgba(255, 255, 255, 0.06); border-color: rgba(100, 255, 218, 0.15); } .stat-card-premium::before { content: ""; position: absolute; top: 0; left: 0; right: 0; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 180, 140, 0.3), transparent); } .dark .stat-card-premium::before { background: linear-gradient(90deg, transparent, rgba(100, 255, 218, 0.3), transparent); } .stat-card-premium::after { content: ""; position: absolute; top: -50%; left: -50%; width: 200%; height: 200%; background: radial-gradient(circle at center, rgba(0, 180, 140, 0.03) 0%, transparent 50%); opacity: 0; transition: opacity 0.3s ease; } .dark .stat-card-premium::after { background: radial-gradient(circle at center, rgba(100, 255, 218, 0.03) 0%, transparent 50%); } .stat-card-premium:hover::after { opacity: 1; } /* Morphing border effect */ .morph-border { position: relative; } .morph-border::before { content: ""; position: absolute; inset: -2px; background: linear-gradient(45deg, rgba(100, 255, 218, 0.5), transparent, rgba(100, 255, 218, 0.3)); border-radius: inherit; z-index: -1; opacity: 0; transition: opacity 0.3s ease; animation: morph-rotate 4s linear infinite; } .morph-border:hover::before { opacity: 1; } @keyframes morph-rotate { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } /* Typewriter text effect */ .typewriter { overflow: hidden; white-space: nowrap; animation: typewriter 2s steps(40, end) forwards; } @keyframes typewriter { from { width: 0; } to { width: 100%; } } /* Premium loading skeleton */ .skeleton-premium { background: linear-gradient( 90deg, rgba(255, 255, 255, 0.03) 0%, rgba(255, 255, 255, 0.06) 50%, rgba(255, 255, 255, 0.03) 100% ); background-size: 200% 100%; animation: skeleton-sweep 1.5s ease-in-out infinite; border-radius: 4px; } @keyframes skeleton-sweep { 0% { background-position: 200% 0; } 100% { background-position: -200% 0; } } /* Floating action button */ .fab-premium { position: fixed; bottom: 24px; right: 24px; width: 56px; height: 56px; border-radius: 16px; background: linear-gradient(135deg, rgba(100, 255, 218, 0.9), rgba(100, 255, 218, 0.7)); color: #0a0f14; display: flex; align-items: center; justify-content: center; cursor: pointer; transition: all 0.3s cubic-bezier(0.16, 1, 0.3, 1); box-shadow: 0 8px 32px rgba(100, 255, 218, 0.3); } .fab-premium:hover { transform: translateY(-4px) scale(1.05); box-shadow: 0 12px 40px rgba(100, 255, 218, 0.4); } /* Model convergence visualization */ .model-convergence { display: flex; gap: 2px; align-items: flex-end; height: 24px; } .model-bar { width: 3px; background: rgba(100, 255, 218, 0.6); border-radius: 1px; animation: bar-grow 0.8s cubic-bezier(0.16, 1, 0.3, 1) forwards; } .model-bar:nth-child(1) { animation-delay: 0ms; } .model-bar:nth-child(2) { animation-delay: 50ms; } .model-bar:nth-child(3) { animation-delay: 100ms; } .model-bar:nth-child(4) { animation-delay: 150ms; } .model-bar:nth-child(5) { animation-delay: 200ms; } @keyframes bar-grow { from { height: 0; } } /* Glowing text effect */ .glow-text { color: oklch(0.40 0.18 168); text-shadow: none; } .dark .glow-text { color: #64ffda; text-shadow: 0 0 4px rgba(100, 255, 218, 0.5), 0 0 8px rgba(100, 255, 218, 0.3), 0 0 16px rgba(100, 255, 218, 0.2); } /* Premium True Line glow - matches neutral-info accent */ .true-line-glow { color: oklch(0.50 0.15 220); text-shadow: none; } .dark .true-line-glow { color: #5AC8FA; text-shadow: 0 0 4px rgba(90, 200, 250, 0.6), 0 0 10px rgba(90, 200, 250, 0.4), 0 0 20px rgba(90, 200, 250, 0.2); filter: drop-shadow(0 0 3px rgba(90, 200, 250, 0.15)); } /* Line Movement sparkline glow */ .sparkline-glow { filter: drop-shadow(0 0 4px rgba(100, 255, 218, 0.5)); } /* =========================================== MOBILE RESPONSIVENESS =========================================== */ /* Hero headline with directional stain highlight */ .hero-headline { font-size: clamp(28px, 6vw, 40px); font-weight: 700; letter-spacing: -0.02em; line-height: 1.1; /* Base white with subtle right-side green stain */ background: linear-gradient( 90deg, #FFFFFF 0%, #FFFFFF 50%, rgba(126, 247, 201, 0.85) 80%, rgba(0, 224, 164, 0.7) 100% ); background-clip: text; -webkit-background-clip: text; -webkit-text-fill-color: transparent; /* Soft directional shadow - right-side bias, low opacity */ filter: drop-shadow(4px 2px 6px rgba(0, 224, 164, 0.12)); } @media (min-width: 768px) { .hero-headline { font-size: clamp(36px, 5vw, 48px); /* Slightly stronger stain on desktop */ filter: drop-shadow(6px 3px 10px rgba(0, 224, 164, 0.18)); } } /* Reduced glow on mobile for crisp text */ @media (max-width: 640px) { .hero-headline { filter: drop-shadow(3px 1px 4px rgba(0, 224, 164, 0.08)); } } /* Mobile touch targets - ensure 44px minimum */ @media (max-width: 640px) { /* Reduce heavy visual effects on mobile for performance */ .hero-orb, .hero-orb-secondary { opacity: 0.4; filter: blur(80px); } .scanlines { opacity: 0.02; } /* Reduce glow intensity on mobile */ .glow-text, .true-line-glow { text-shadow: 0 0 4px rgba(100, 255, 218, 0.4), 0 0 8px rgba(100, 255, 218, 0.2); filter: none; } /* Card touch targets */ .glass-card, .premium-card-interactive { min-height: 44px; } /* Button touch targets */ button, [role="button"] { min-height: 44px; } /* Dropdown items touch targets */ [role="option"], [role="menuitem"] { min-height: 44px; } } /* Ring progress indicator */ .ring-progress { position: relative; width: 48px; height: 48px; } .ring-progress svg { transform: rotate(-90deg); } .ring-progress-bg { fill: none; stroke: rgba(255, 255, 255, 0.1); stroke-width: 3; } .ring-progress-fill { fill: none; stroke: #64ffda; stroke-width: 3; stroke-linecap: round; stroke-dasharray: 126; stroke-dashoffset: 126; animation: ring-fill 1s cubic-bezier(0.16, 1, 0.3, 1) forwards; } @keyframes ring-fill { to { stroke-dashoffset: var(--progress, 0); } } /* Premium tooltip */ .tooltip-premium { position: relative; } .tooltip-premium::after { content: attr(data-tooltip); position: absolute; bottom: 100%; left: 50%; transform: translateX(-50%) translateY(-8px); padding: 8px 12px; background: rgba(0, 0, 0, 0.9); border: 1px solid rgba(255, 255, 255, 0.1); border-radius: 6px; font-size: 12px; white-space: nowrap; opacity: 0; pointer-events: none; transition: all 0.2s ease; } .tooltip-premium:hover::after { opacity: 1; transform: translateX(-50%) translateY(-12px); } /* Animated gradient border */ .gradient-border { position: relative; background: #0a0f14; border-radius: 12px; } .gradient-border::before { content: ""; position: absolute; inset: 0; padding: 1px; border-radius: inherit; background: linear-gradient(135deg, rgba(100, 255, 218, 0.5), transparent 50%, rgba(100, 255, 218, 0.3)); -webkit-mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0); mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0); -webkit-mask-composite: xor; mask-composite: exclude; } /* Spotlight effect on hover - enhanced glow */ .spotlight-hover { position: relative; overflow: hidden; transition: all 0.25s cubic-bezier(0.4, 0, 0.2, 1); } .spotlight-hover:hover { transform: translateY(-2px); box-shadow: 0 20px 50px rgba(0, 0, 0, 0.5), 0 0 30px rgba(100, 255, 218, 0.05); } .spotlight-hover::before { content: ""; position: absolute; width: 250px; height: 250px; background: radial-gradient(circle, rgba(100, 255, 218, 0.12) 0%, transparent 60%); opacity: 0; pointer-events: none; transition: opacity 0.4s ease; transform: translate(-50%, -50%); z-index: 1; } .spotlight-hover:hover::before { opacity: 1; } /* Blur reveal animation */ .blur-reveal { animation: blur-reveal 0.6s cubic-bezier(0.16, 1, 0.3, 1) forwards; } @keyframes blur-reveal { from { opacity: 0; filter: blur(10px); transform: scale(0.95); } to { opacity: 1; filter: blur(0); transform: scale(1); } } /* Intelligence score visualization */ .intel-score { position: relative; display: inline-flex; align-items: center; justify-content: center; width: 64px; height: 64px; border-radius: 50%; background: conic-gradient( from 0deg, rgba(100, 255, 218, 0.8) var(--score, 0%), rgba(255, 255, 255, 0.1) var(--score, 0%) ); } .intel-score::before { content: ""; position: absolute; inset: 4px; border-radius: 50%; background: #0a0f14; } .intel-score-value { position: relative; font-size: 18px; font-weight: 700; color: #64ffda; } } [V0_FILE]typescriptreact:file="components/mobile-splash.tsx" isMerged="true" "use client" import { useState, useEffect } from "react" import { cn } from "@/lib/utils" interface MobileSplashProps { onComplete: () => void duration?: number } export function MobileSplash({ onComplete, duration = 2400 }: MobileSplashProps) { const [phase, setPhase] = useState(0) useEffect(() => { // Quick smooth sequence const t1 = setTimeout(() => setPhase(1), 50) const t2 = setTimeout(() => setPhase(2), 400) const t3 = setTimeout(() => setPhase(3), 900) const t4 = setTimeout(() => setPhase(4), duration - 500) const t5 = setTimeout(onComplete, duration) return () => { clearTimeout(t1) clearTimeout(t2) clearTimeout(t3) clearTimeout(t4) clearTimeout(t5) } }, [duration, onComplete]) const nodes = [ { x: 50, y: 8 }, { x: 80, y: 20 }, { x: 92, y: 50 }, { x: 80, y: 80 }, { x: 50, y: 92 }, { x: 20, y: 80 }, { x: 8, y: 50 }, { x: 20, y: 20 }, ] return (

{/* Particles */}

{[...Array(30)].map((_, i) => (

= 1 ? 0.2 + Math.random() * 0.3 : 0, animation: phase >= 1 ? `float-up ${3 + Math.random() * 4}s linear ${Math.random() * 2}s infinite` : "none", transition: "opacity 0.3s ease", }} /> ))}

{/* Center glow */}

= 2 ? 1 : 0, transform: `scale(${phase >= 2 ? 1 : 0.5})`, transition: "all 0.6s cubic-bezier(0.4, 0, 0.2, 1)", }} /> {/* Logo */}

= 2 ? 1 : 0, transform: `scale(${phase >= 2 ? 1 : 0.85})`, filter: phase >= 2 ? "drop-shadow(0 0 25px rgba(100, 255, 218, 0.35))" : "none", transition: "all 0.5s cubic-bezier(0.4, 0, 0.2, 1)", }} >

{/* MAFS */}

= 3 ? 1 : 0, transform: `translateY(${phase >= 3 ? 0 : 12}px)`, textShadow: "0 0 30px rgba(100, 255, 218, 0.4)", transition: "all 0.4s cubic-bezier(0.4, 0, 0.2, 1)", }} > MAFS

{/* Tagline */}

= 3 ? 1 : 0, transform: `translateY(${phase >= 3 ? 0 : 8}px)`, transition: "all 0.4s cubic-bezier(0.4, 0, 0.2, 1) 0.1s", }} > Multi-Agent Fight Simulator

) } [V0_FILE]typescriptreact:file="app/page.tsx" "use client" import { useState, useEffect } from "react" import { Button } from "@/components/ui/button" import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select" import { NavBar } from "@/components/nav-bar" import { Shield, ChevronDown, ChevronUp, Lock, AlertTriangle, CheckCircle2, Minus, Info, Sparkles, Target, TrendingUp, Zap } from "lucide-react" import { AISimulationOverlay } from "@/components/ai-simulation-overlay" import { ConfidenceRing, EdgeMeter, AnimatedStat, VarianceIndicator } from "@/components/premium-metrics" import { MobileSplash } from "@/components/mobile-splash" import { scanFightMarkets, selectTopInefficiencies, generateMockSelections, formatScannerOdds, formatScannerEV, type ScannerResult, } from "@/lib/betting" const MIN_MAF_PROB = 0.55 const MIN_AGENT_CONSENSUS_PASS_RATE = 0.7 const MIN_EDGE_PCT = 0.5 const BLOCK_HIGH_VARIANCE_IF_CONFIDENCE_BELOW = 0.55 // Placeholder for useCountUp hook const useCountUp = (initialValue: number, duration: number, decimals: number) => { const [count, setCount] = useState(initialValue) useEffect(() => { const start = 0 const startTime = Date.now() const frame = () => { const elapsed = Date.now() - startTime if (elapsed >= duration) { setCount(initialValue) return } const progress = elapsed / duration setCount(initialValue * progress) // Simplified for placeholder requestAnimationFrame(frame) } requestAnimationFrame(frame) }, [initialValue, duration, decimals]) return count.toFixed(decimals) } type MarketFamily = "moneyline" | "fight_distance" | "totals_rounds" | "method_victory" | "fighter_round_props" | "fighter_method_props" type SimulationBet = { id: string bet_type: string label: string odds_american: string P_sim: number P_imp: number edge_pct: number stability_score: number confidence_label: "Low" | "Medium" | "High" confidencePct: number varianceTag: "low" | "medium" | "high" reason: string detailedReason: { marketInefficiency: string keyDrivers: string[] riskFactors: string[] } agentSignals: Array<{ name: string; signal: "pass" | "neutral" | "fail"; desc: string }> // Intelligence breakdown fields patternInsight?: string patternMechanics?: string[] marketThesis?: string // Computed fields status?: "qualified" | "filtered" rejectReasons?: string[] // Canonical market keys market_key?: string selection_key?: string market_family?: MarketFamily // CLV tracking opening_odds?: number closing_odds?: number clv_prob?: number // True Line pricing fields true_line_american?: number market_implied_prob?: number mispricing_prob?: number ev_per_unit?: number odds_updated_at?: Date // Additional display fields verdict?: string edgeSource?: string historicalValidation?: string executiveSummary?: string // Line movement tracking lineMovement?: { opening: number current: number history: number[] // sparkline data points sharpAction?: "toward" | "away" | "neutral" } } export default function DashboardPage() { const [selectedEvent, setSelectedEvent] = useState("ufc-305") const [showResults, setShowResults] = useState(false) const [selectedFight, setSelectedFight] = useState(null) const [isScanning, setIsScanning] = useState(false) const [scanComplete, setScanComplete] = useState(false) const [isGeneratingBets, setIsGeneratingBets] = useState(false) const [showHowWePick, setShowHowWePick] = useState(false) const [betSeed, setBetSeed] = useState(0) const [expandedBetIdx, setExpandedBetIdx] = useState(null) const [showFilteredBets, setShowFilteredBets] = useState(false) const [showSimulation, setShowSimulation] = useState(false) const [liveFeedEntries, setLiveFeedEntries] = useState>([]) const [feedIdCounter, setFeedIdCounter] = useState(0) const [showSplash, setShowSplash] = useState(true) const [isMobile, setIsMobile] = useState(false) // Detect mobile on mount useEffect(() => { const checkMobile = () => setIsMobile(window.innerWidth < 768) checkMobile() window.addEventListener("resize", checkMobile) return () => window.removeEventListener("resize", checkMobile) }, []) const roiCount = useCountUp(42.8, 1000, 1) const winRateCount = useCountUp(68.2, 1200, 1) const sampleCount = useCountUp(247, 800, 0) const handleRunCard = () => { setShowSimulation(true) setIsScanning(true) setShowResults(false) } const handleSimulationComplete = () => { setShowSimulation(false) setIsScanning(false) setShowResults(true) setScanComplete(true) setTimeout(() => setScanComplete(false), 600) } // Track scanner refresh trigger const [scannerRefreshTrigger, setScannerRefreshTrigger] = useState(0) const handleRegenerateBets = () => { setIsGeneratingBets(true) setBetSeed((prev) => prev + 1) setScannerRefreshTrigger((prev) => prev + 1) // Trigger scanner rescan setTimeout(() => { setIsGeneratingBets(false) }, 1000) } const getEvColorClass = (ev: string) => { const evNum = Number.parseFloat(ev.replace(/[+%]/g, "")) if (evNum >= 10) return "text-positive" // #00E0A4 return "text-positive" } const shouldGlow = (ev: string) => { const evNum = Number.parseFloat(ev.replace(/[+%]/g, "")) return evNum >= 20 } const eventData: Record< string, { name: string date: string location: string fights: Array<{ id: string; matchup: string; odds: string }> simulationBets: SimulationBet[] bestBets: Array<{ rank: string bet: string ev: string confidence: string tier: string isTopCard: boolean reasoning: { marketInefficiency: string[] matchupDrivers: string[] dataSignals: string[] riskFactors: string[] whyThisLine: string[] confidenceSummary: string } }> systemParlays: Array<{ legs: number confidence: string correlation: string expectedReturn: string worstCase: string hitProbability: string bets: Array<{ market: string fight: string tag: string }> explanation: string }> } > = { "ufc-300": { name: "UFC 300: Pereira vs Hill", date: "April 13, 2024", location: "Las Vegas, NV", fights: [ { id: "fight-1", matchup: "Pereira vs Hill", odds: "-400 / +310" }, { id: "fight-2", matchup: "Zhang vs Yan", odds: "-185 / +155" }, { id: "fight-3", matchup: "Oliveira vs Tsarukyan", odds: "+145 / -170" }, { id: "fight-4", matchup: "Sterling vs Kattar", odds: "-220 / +180" }, { id: "fight-5", matchup: "Figueiredo vs Garbrandt", odds: "-165 / +140" }, ], simulationBets: [ { id: "ufc300-1", bet_type: "ITD", label: "Pereira ITD", odds_american: "-180", P_sim: 0.72, P_imp: 0.643, edge_pct: 7.7, stability_score: 0.85, confidence_label: "High", confidencePct: 85, varianceTag: "low", reason: "Pereira's knockout power against Hill's aggressive style creates high finish probability.", detailedReason: { marketInefficiency: "Market undervalues Pereira's power advantage at light heavyweight. Hill's forward pressure plays directly into Pereira's counter-striking strengths.", keyDrivers: [ "Pereira lands 4.2 significant strikes per minute with 89% KO rate when he hurts opponents", "Hill absorbed 5.8 sig strikes per minute in last 3 fights — vulnerability window", "10,000 sims show 72% finish rate with R2-R3 as peak danger zone", ], riskFactors: [ "Hill's chin has never been cracked at LHW — sample size risk", "If Hill survives R3, cardio advantage shifts fight dynamics", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "exceeds 70% confidence threshold" }, { name: "Market Efficiency", signal: "pass", desc: "line below model fair value" }, { name: "Matchup Fit", signal: "pass", desc: "stylistic advantage confirmed" }, { name: "Risk Filters", signal: "neutral", desc: "heavy favorite variance applied" }, ], verdict: "Playable — ITD up to -200. Consider half-unit given heavy favorite volatility.", edgeSource: "Hill's aggressive forward pressure creates predictable timing windows for Pereira's counter-striking. LHW markets overprice chin durability when facing elite power.", executiveSummary: "Pressure fighters historically struggle against counter-strikers with KO power. Market underweights finish probability against durable but hittable opponents.", // Canonical market keys market_key: "itd_pereira", selection_key: "pereira_itd", market_family: "fighter_method_props", // CLV tracking opening_odds: -165, // Line movement lineMovement: { opening: -165, current: -180, history: [-165, -168, -172, -175, -178, -180], sharpAction: "toward", }, }, { id: "ufc300-2", bet_type: "Over", label: "Zhang vs Yan Over 2.5", odds_american: "-130", P_sim: 0.68, P_imp: 0.565, edge_pct: 11.5, stability_score: 0.82, confidence_label: "High", confidencePct: 82, varianceTag: "low", reason: "Both fighters' durability and technical striking make early finish unlikely.", detailedReason: { marketInefficiency: "Line implies only 56.5% chance of going distance, but both fighters have gone to decision in 70%+ of recent bouts.", keyDrivers: [ "Zhang: 4 of last 5 fights went to decision — elite durability", "Yan's power output dropped 18% since bantamweight — less finish threat", "Combined absorption rate suggests neither has KO power to stop the other", ], riskFactors: [ "Zhang's improved power could change dynamic at 135", "Early knockdown could shift fight trajectory", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "68% sim probability vs 56.5% implied" }, { name: "Market Efficiency", signal: "pass", desc: "11.5% edge detected" }, { name: "Matchup Fit", signal: "pass", desc: "durability profiles align" }, { name: "Risk Filters", signal: "pass", desc: "low variance outcome" }, ], verdict: "Strong play — Over 2.5 up to -145. Full unit recommended.", edgeSource: "Both fighters absorb damage well and favor technical exchanges over power shots. Yan's output has declined since bantamweight, reducing finish threat.", executiveSummary: "Durable fighters with declining power output produce longer fights. Markets anchor to Zhang's KO reputation while ignoring recent technical evolution.", // Canonical market keys market_key: "total_rounds_zhang_yan", selection_key: "over_2.5", market_family: "totals_rounds", // CLV tracking opening_odds: -115, // Line movement lineMovement: { opening: -115, current: -130, history: [-115, -118, -122, -125, -128, -130], sharpAction: "toward", }, }, { id: "ufc300-3", bet_type: "ML", label: "Tsarukyan ML", odds_american: "-170", P_sim: 0.65, P_imp: 0.63, edge_pct: 2.0, stability_score: 0.72, confidence_label: "Medium", confidencePct: 72, varianceTag: "medium", reason: "Tsarukyan's wrestling pressure and cardio advantage controls pace against Oliveira's fading output in later rounds.", detailedReason: { marketInefficiency: "Tsarukyan's chain wrestling neutralizes Oliveira's submission game while cardio differential widens in championship rounds. Market overweights Oliveira's finishing reputation.", keyDrivers: [ "Tsarukyan: 90% takedown defense, 4.1 takedowns per 15 min", "Oliveira lost 40% of scramble exchanges vs elite grapplers recently", "Cardio differential: Tsarukyan maintains output while Oliveira fades R3+", ], riskFactors: [ "Oliveira's submission threat from bottom is always live", "One moment of carelessness can end fight — Oliveira has 22 submissions", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "65% win probability confirmed" }, { name: "Market Efficiency", signal: "neutral", desc: "slim 2% edge — borderline" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling advantage verified" }, { name: "Risk Filters", signal: "neutral", desc: "submission threat flagged" }, ], // Line movement lineMovement: { opening: -155, current: -170, history: [-155, -158, -162, -165, -168, -170], sharpAction: "toward", }, }, { id: "ufc300-4", bet_type: "ML", label: "Sterling ML", odds_american: "-220", P_sim: 0.52, P_imp: 0.688, edge_pct: -16.8, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Sterling's wrestling advantage diminishes against larger featherweights. Kattar's boxing range and power create problems Sterling hasn't faced.", detailedReason: { marketInefficiency: "Sterling's bantamweight dominance doesn't translate to 145 where his size advantage disappears. Kattar's improved boxing punishes smaller opponents.", keyDrivers: ["Size disadvantage at featherweight", "Kattar's boxing improved significantly"], riskFactors: ["Sterling's cardio untested at 145", "Power differential favors Kattar"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "52% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative edge detected" }, { name: "Matchup Fit", signal: "fail", desc: "size disadvantage at 145" }, { name: "Risk Filters", signal: "neutral", desc: "cardio untested at weight" }, ], // Line movement lineMovement: { opening: -240, current: -220, history: [-240, -235, -230, -225, -222, -220], sharpAction: "away", }, }, { id: "ufc300-5", bet_type: "Under", label: "Figueiredo vs Garbrandt Under 1.5", odds_american: "+180", P_sim: 0.38, P_imp: 0.357, edge_pct: 2.3, stability_score: 0.55, confidence_label: "Low", confidencePct: 48, varianceTag: "high", reason: "Garbrandt's compromised chin meets Figueiredo's flyweight power, but early finish outcomes remain volatile with limited sample.", detailedReason: { marketInefficiency: "Garbrandt has been stopped in 4 of last 5 losses, all via strikes. Figueiredo's power translated from flyweight creates danger, but bantamweight sample is limited.", keyDrivers: ["Garbrandt's chin compromised", "Figueiredo's flyweight power translates"], riskFactors: ["Sample size concerns at bantamweight", "Garbrandt's speed could counter"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "38% below minimum threshold" }, { name: "Market Efficiency", signal: "neutral", desc: "marginal 2.3% edge" }, { name: "Matchup Fit", signal: "pass", desc: "power matchup confirmed" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], // Line movement lineMovement: { opening: +165, current: +180, history: [165, 170, 172, 175, 178, 180], sharpAction: "neutral", }, }, ], bestBets: [], systemParlays: [], }, "ufc-301": { name: "UFC 301: Pantoja vs Erceg", date: "May 4, 2024", location: "Rio de Janeiro, Brazil", fights: [ { id: "fight-1", matchup: "Pantoja vs Erceg", odds: "-310 / +250" }, { id: "fight-2", matchup: "Aldo vs Martinez", odds: "-175 / +145" }, { id: "fight-3", matchup: "Chiasson vs Bueno Silva", odds: "-140 / +120" }, { id: "fight-4", matchup: "Caio Borralho vs Pyfer", odds: "-195 / +165" }, { id: "fight-5", matchup: "Lemos vs Jandiroba", odds: "+155 / -185" }, ], simulationBets: [ { id: "ufc301-1", bet_type: "Decision", label: "Pantoja Dec", odds_american: "-120", P_sim: 0.61, P_imp: 0.545, edge_pct: 6.5, stability_score: 0.78, confidence_label: "High", confidencePct: 78, varianceTag: "medium", reason: "Pantoja's control-oriented grappling against Erceg's durability points to scorecards. Flyweight title fights historically go the distance.", detailedReason: { marketInefficiency: "Pantoja has shifted to point-fighting in title defenses while Erceg has never been finished. Flyweight markets overprice finishes when both fighters prioritize control.", keyDrivers: [ "Pantoja: 3 of last 4 championship fights went to decision", "Erceg has never been finished in professional MMA — durability confirmed", "Grappling exchanges favor Pantoja but not finish positions", ], riskFactors: [ "Erceg is relatively unknown quantity — model uncertainty higher", "Pantoja's submission game could find opening if Erceg fatigues", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "61% exceeds threshold" }, { name: "Market Efficiency", signal: "pass", desc: "6.5% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "grappling styles favor distance" }, { name: "Risk Filters", signal: "pass", desc: "low finish probability" }, ], // Line movement lineMovement: { opening: -110, current: -120, history: [-110, -112, -115, -117, -118, -120], sharpAction: "toward", }, }, { id: "ufc301-2", bet_type: "ML", label: "Aldo ML", odds_american: "-175", P_sim: 0.68, P_imp: 0.636, edge_pct: 4.4, stability_score: 0.76, confidence_label: "High", confidencePct: 76, varianceTag: "medium", reason: "Aldo's counter-striking and leg kicks neutralize Martinez's pressure-heavy style. Home crowd in Rio adds intangible edge.", detailedReason: { marketInefficiency: "Martinez relies on closing distance against counter-strikers who fade, but Aldo maintains output. Markets underweight experience advantage in technical matchups.", keyDrivers: [ "Aldo: 78% win rate when fight stays on feet for 2+ rounds", "Martinez absorbed 4.7 leg kicks per fight — vulnerability to Aldo's signature weapon", "Home crowd advantage in Rio historically adds 3-5% to Brazilian fighters", ], riskFactors: [ "Aldo is 37 — age decline is real factor", "Martinez's pressure could be problematic if Aldo slows", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "68% win probability" }, { name: "Market Efficiency", signal: "pass", desc: "4.4% edge vs implied odds" }, { name: "Matchup Fit", signal: "pass", desc: "counter-striking advantage" }, { name: "Risk Filters", signal: "neutral", desc: "age factor flagged" }, ], // Line movement lineMovement: { opening: -160, current: -175, history: [-160, -163, -167, -170, -173, -175], sharpAction: "toward", }, }, { id: "ufc301-3", bet_type: "ITD", label: "Borralho ITD", odds_american: "+140", P_sim: 0.45, P_imp: 0.417, edge_pct: 3.3, stability_score: 0.69, confidence_label: "Medium", confidencePct: 69, varianceTag: "high", reason: "Borralho's chain wrestling exploits Pyfer's porous takedown defense. Prolonged top control leads to late submissions.", detailedReason: { marketInefficiency: "Pyfer gave up back control against lower-level grapplers. Borralho's top pressure accumulates damage until submission openings appear in later rounds.", keyDrivers: [ "Borralho: 67% finish rate when he secures top position for 2+ min", "Pyfer gave up back twice vs lower-level grapplers", "Simulations show R2-R3 ground control leads to submission in 45% of wins", ], riskFactors: [ "Pyfer's power can end fight early if Borralho is reckless", "Borralho's stand-up is limited — needs takedowns to win", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "45% ITD probability" }, { name: "Market Efficiency", signal: "neutral", desc: "3.3% edge — borderline" }, { name: "Matchup Fit", signal: "pass", desc: "grappling pressure exploits weakness" }, { name: "Risk Filters", signal: "neutral", desc: "power threat flagged" }, ], // Line movement lineMovement: { opening: 155, current: 140, history: [155, 150, 148, 145, 142, 140], sharpAction: "toward", }, }, { id: "ufc301-4", bet_type: "ML", label: "Jandiroba ML", odds_american: "-185", P_sim: 0.62, P_imp: 0.652, edge_pct: -3.2, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Jandiroba's submission threat is elite but Lemos's power creates knockout risk that limits grappling entries.", detailedReason: { marketInefficiency: "Lemos has been submitted three times but improved takedown defense makes Jandiroba's path to victory narrower than market suggests.", keyDrivers: ["Jandiroba's submission game is top tier", "Lemos has been submitted 3 times"], riskFactors: ["Lemos's power is significant", "Jandiroba's striking is limited"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "62% win probability" }, { name: "Market Efficiency", signal: "fail", desc: "negative edge -3.2%" }, { name: "Matchup Fit", signal: "pass", desc: "grappling advantage" }, { name: "Risk Filters", signal: "neutral", desc: "power differential flagged" }, ], }, { id: "ufc301-5", bet_type: "Under", label: "Chiasson vs Bueno Silva Under 2.5", odds_american: "+110", P_sim: 0.48, P_imp: 0.476, edge_pct: 0.4, stability_score: 0.45, confidence_label: "Low", confidencePct: 48, varianceTag: "high", reason: "Low edge, high variance fight. Below qualification threshold.", detailedReason: { marketInefficiency: "No significant edge detected.", keyDrivers: ["Both fighters can be wild", "Early finish possible"], riskFactors: ["Neither fighter has elite defense", "High volatility expected"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "48% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "no significant edge" }, { name: "Matchup Fit", signal: "neutral", desc: "styles unpredictable" }, { name: "Risk Filters", signal: "fail", desc: "high volatility blocked" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-302": { name: "UFC 302: Makhachev vs Poirier", date: "June 1, 2024", location: "Newark, NJ", fights: [ { id: "fight-1", matchup: "Makhachev vs Poirier", odds: "-450 / +350" }, { id: "fight-2", matchup: "Nzechukwu vs Barnett", odds: "-170 / +145" }, { id: "fight-3", matchup: "Holland vs Everse", odds: "-240 / +195" }, { id: "fight-4", matchup: "Nickal vs Brundage", odds: "-1200 / +700" }, { id: "fight-5", matchup: "Morales vs Grant", odds: "-135 +115" }, ], simulationBets: [ { id: "ufc302-1", bet_type: "ITD", label: "Islam ITD", odds_american: "-140", P_sim: 0.62, P_imp: 0.583, edge_pct: 3.7, stability_score: 0.79, confidence_label: "Medium", confidencePct: 79, varianceTag: "medium", reason: "Grappling dominance creates finish windows — 62% ITD vs implied 58.3%.", detailedReason: { marketInefficiency: "Market prices Islam ML heavily but undervaluess his finish rate. Once Islam secures top position, his ground-and-pound and submissions are elite.", keyDrivers: [ "Islam: 83% finish rate in championship rounds when fight hits the mat", "Poirier's takedown defense dropped to 64% vs elite wrestlers", "Simulations show R3-R4 fatigue window opens submission opportunities", ], riskFactors: [ "Poirier's boxing in R1-R2 is dangerous — Islam must survive early", "Islam's chin hasn't been tested by Poirier-level power", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "62% ITD probability confirmed" }, { name: "Market Efficiency", signal: "pass", desc: "3.7% edge vs market" }, { name: "Matchup Fit", signal: "pass", desc: "grappling dominance verified" }, { name: "Risk Filters", signal: "neutral", desc: "early boxing threat flagged" }, ], // Line movement lineMovement: { opening: -125, current: -140, history: [-125, -128, -132, -135, -138, -140], sharpAction: "toward", }, }, { id: "ufc302-2", bet_type: "Under", label: "Islam vs Poirier Under 3.5", odds_american: "+120", P_sim: 0.48, P_imp: 0.455, edge_pct: 2.5, stability_score: 0.65, confidence_label: "Low", confidencePct: 65, varianceTag: "medium", reason: "Islam's finishing ability against fatiguing opponents creates under value.", detailedReason: { marketInefficiency: "Line implies only 45.5% early finish, but Islam's recent fights show increasing aggression to close fights once he establishes control.", keyDrivers: [ "Islam's last 3 finishes came in R2-R3 once control was established", "Poirier has been finished in 4 of last 7 losses", "Cumulative damage from wrestling drains Poirier faster than pure strikers", ], riskFactors: [ "If Islam plays it safe, fight goes distance", "Poirier's durability is underrated — has heart to survive", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "72% decision probability" }, { name: "Market Efficiency", signal: "pass", desc: "8.5% edge detected" }, { name: "Matchup Fit", signal: "pass", desc: "durability profiles align" }, { name: "Risk Filters", signal: "pass", desc: "low variance outcome" }, ], }, { id: "ufc302-3", bet_type: "ML", label: "Nickal -1200", odds_american: "-1200", P_sim: 0.95, P_imp: 0.923, edge_pct: 2.7, stability_score: 0.92, confidence_label: "High", confidencePct: 92, varianceTag: "low", reason: "Massive skill gap makes this a near-certain outcome despite heavy juice.", detailedReason: { marketInefficiency: "Even at -1200, there's edge here. Nickal is an elite wrestler facing a journeyman. The only risk is a puncher's chance.", keyDrivers: [ "Nickal: NCAA Division I champion with 100% finish rate in MMA", "Brundage has been submitted 3 times — worst possible matchup", "Skill differential is generational — model gives 95% win probability", ], riskFactors: [ "Heavy favorite risks always exist — one punch can change everything", "Opportunity cost of tying up capital at -1200", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "90%+ win probability" }, { name: "Market Efficiency", signal: "pass", desc: "skill gap underpriced" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling dominance" }, { name: "Risk Filters", signal: "neutral", desc: "heavy favorite variance" }, ], }, { id: "ufc302-4", bet_type: "ML", label: "Holland ML", odds_american: "-240", P_sim: 0.71, P_imp: 0.706, edge_pct: 0.4, stability_score: 0.55, confidence_label: "Low", confidencePct: 55, varianceTag: "high", reason: "Holland's size advantage and striking should be enough.", detailedReason: { marketInefficiency: "Line fairly reflects a close fight, slight edge to Holland.", keyDrivers: ["Holland's size at 185 is significant", "Costa's cardio is a major concern"], riskFactors: ["Costa's power is always a threat", "Holland has shown susceptibility to pressure"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "71% win probability" }, { name: "Market Efficiency", signal: "fail", desc: "minimal 0.4% edge" }, { name: "Matchup Fit", signal: "pass", desc: "size advantage confirmed" }, { name: "Risk Filters", signal: "neutral", desc: "power variance flagged" }, ], }, { id: "ufc302-5", bet_type: "ITD", label: "Nzechukwu ITD", odds_american: "+145", P_sim: 0.42, P_imp: 0.417, edge_pct: 0.3, stability_score: 0.55, confidence_label: "Low", confidencePct: 55, varianceTag: "high", reason: "Low edge, high variance fight. Below qualification threshold.", detailedReason: { marketInefficiency: "No significant edge detected.", keyDrivers: ["Both fighters have power", "Nzechukwu has shown finishing ability"], riskFactors: ["Barnett's durability is questionable", "Barnett has submission threat"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "42% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "0.3% edge — insufficient" }, { name: "Matchup Fit", signal: "neutral", desc: "power vs durability unclear" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-303": { name: "UFC 303: Pereira vs Prochazka 2", date: "June 29, 2024", location: "Las Vegas, NV", fights: [ { id: "fight-1", matchup: "Pereira vs Prochazka", odds: "-185 / +155" }, { id: "fight-2", matchup: "Garry vs Page", odds: "-280 / +225" }, { id: "fight-3", matchup: "Cerrone vs Miller", odds: "+135 / -160" }, { id: "fight-4", matchup: "Ribas vs Namajunas", odds: "+195 / -240" }, { id: "fight-5", matchup: "Turner vs Mendes", odds: "-145 / +125" }, ], simulationBets: [ { id: "ufc303-1", bet_type: "ITD", label: "Pereira ITD", odds_american: "-160", P_sim: 0.68, P_imp: 0.615, edge_pct: 6.5, stability_score: 0.82, confidence_label: "High", confidencePct: 82, varianceTag: "low", reason: "Power advantage creates knockout paths in 68% of simulations vs implied 61.5%.", detailedReason: { marketInefficiency: "Market respects Prochazka's durability but underweights Pereira's precision counter-striking. First fight KO wasn't a fluke — it's a stylistic nightmare for Prochazka.", keyDrivers: [ "Pereira: 91% KO rate when opponent overcommits — Prochazka's style does exactly that", "Prochazka's head movement is erratic, creating clean counter opportunities", "First fight data: Pereira landed 78% of counters vs Prochazka's entries", ], riskFactors: [ "Prochazka's volume could overwhelm if Pereira doesn't find timing", "Championship rounds favor Prochazka's cardio if fight goes late", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "68% ITD probability" }, { name: "Market Efficiency", signal: "pass", desc: "6.5% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "counter-striker vs pressure" }, { name: "Risk Filters", signal: "pass", desc: "first fight data validates" }, ], // Line movement lineMovement: { opening: -145, current: -160, history: [-145, -148, -152, -155, -158, -160], sharpAction: "toward", }, }, { id: "ufc303-2", bet_type: "ML", label: "Garry ML", odds_american: "-280", P_sim: 0.76, P_imp: 0.737, edge_pct: 2.3, stability_score: 0.74, confidence_label: "Medium", confidencePct: 74, varianceTag: "medium", reason: "Garry's technical advantage should handle Page's unorthodox style.", detailedReason: { marketInefficiency: "Page's unorthodox movement creates highlight reel moments but poor defensive metrics. Garry's jab and patient gameplan should dominate.", keyDrivers: [ "Garry: 72% strike accuracy with jab — Page's stance is vulnerable to this", "Page absorbed 6.2 sig strikes per minute against UFC-level competition", "Simulations show Garry wins 76% via decision or late TKO", ], riskFactors: [ "Page's unpredictability could catch Garry clean once", "If Garry chases, he plays into Page's game", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "76% win probability" }, { name: "Market Efficiency", signal: "neutral", desc: "2.3% edge — borderline" }, { name: "Matchup Fit", signal: "pass", desc: "technical advantage verified" }, { name: "Risk Filters", signal: "neutral", desc: "unorthodox style flagged" }, ], }, { id: "ufc303-3", bet_type: "Over", label: "Ribas vs Namajunas Over 2.5", odds_american: "-140", P_sim: 0.72, P_imp: 0.583, edge_pct: 13.7, stability_score: 0.81, confidence_label: "High", confidencePct: 81, varianceTag: "low", reason: "Technical strikers with good defense should go the distance.", detailedReason: { marketInefficiency: "Line implies only 58.3% chance of going distance, but both fighters are point-strikers with excellent takedown defense. Neither has KO power at 125.", keyDrivers: [ "Namajunas: 5 of last 6 fights went to decision", "Ribas lacks finishing power — 0 KOs in last 4 years", "Combined 82% takedown defense means fight stays on feet", ], riskFactors: [ "Namajunas has been finished twice — not completely durable", "Ribas's aggression could create openings if she overcommits", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "72% over probability" }, { name: "Market Efficiency", signal: "pass", desc: "13.7% edge detected" }, { name: "Matchup Fit", signal: "pass", desc: "point-strikers with TDD" }, { name: "Risk Filters", signal: "pass", desc: "low variance confirmed" }, ], }, { id: "ufc303-4", bet_type: "ML", label: "Turner ML", odds_american: "-145", P_sim: 0.58, P_imp: 0.595, edge_pct: -1.5, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Turner is durable but Mendes's wrestling should control the fight.", detailedReason: { marketInefficiency: "Line fairly reflects a close fight, slight edge to Mendes.", keyDrivers: ["Mendes's wrestling advantage is significant", "Turner has shown vulnerability to grapplers"], riskFactors: ["Turner's power could land early", "Mendes has shown passivity"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "58% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -1.5% edge" }, { name: "Matchup Fit", signal: "neutral", desc: "wrestling vs power unclear" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, { id: "ufc303-5", bet_type: "Under", label: "Cerrone vs Miller Under 1.5", odds_american: "+150", P_sim: 0.42, P_imp: 0.4, edge_pct: 2.0, stability_score: 0.55, confidence_label: "Low", confidencePct: 55, varianceTag: "high", reason: "Veteran matchup, but both have faded. Fight could end early or go to decision.", detailedReason: { marketInefficiency: "Slight edge on early finish given recent performances.", keyDrivers: ["Both fighters have power", "Durability has declined"], riskFactors: ["Fight IQ dictates pace", "Either could win a decision"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "42% below threshold" }, { name: "Market Efficiency", signal: "neutral", desc: "2% edge — marginal" }, { name: "Matchup Fit", signal: "neutral", desc: "veteran pace uncertain" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-304": { name: "UFC 304: Edwards vs Muhammad 2", date: "July 27, 2024", location: "Manchester, England", fights: [ { id: "fight-1", matchup: "Edwards vs Muhammad", odds: "-225 / +185" }, { id: "fight-2", matchup: "Aspinall vs Blaydes", odds: "-340 / +265" }, { id: "fight-3", matchup: "Allen vs Holloway", odds: "+155 / -185" }, { id: "fight-4", matchup: "Pimblett vs Green", odds: "-180 / +150" }, { id: "fight-5", matchup: "Shore vs Umar", odds: "+185 / -225" }, ], simulationBets: [ { id: "ufc304-1", bet_type: "ITD", label: "Edwards ITD", odds_american: "+165", P_sim: 0.42, P_imp: 0.377, edge_pct: 4.3, stability_score: 0.71, confidence_label: "Medium", confidencePct: 71, varianceTag: "medium", reason: "Head kick KO threat persists — 42% finish rate vs implied 37.7%.", detailedReason: { marketInefficiency: "Market focuses on Leon's decision wins, but his KO of Usman proves the finish threat is real. Muhammad's style opens him up to high kicks.", keyDrivers: [ "Edwards: 3 KO/TKOs in last 5 fights — finishing rate improving", "Muhammad ducks into strikes when pressured — head kick vulnerability", "Home crowd advantage in Manchester adds finishing urgency", ], riskFactors: [ "Muhammad's wrestling could grind out decision if he commits", "Edwards historically coasts in fights he's winning", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "42% ITD probability" }, { name: "Market Efficiency", signal: "pass", desc: "4.3% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "head kick threat validated" }, { name: "Risk Filters", signal: "neutral", desc: "wrestling threat flagged" }, ], patternInsight: "Champions defending at home with a head kick KO in their last 3 fights finish opponents who duck into strikes at a 58% ITD rate. Edwards fits this profile precisely.", patternMechanics: [ "Home crowd energy correlates with finishing urgency in rematches", "Opponents who duck low expose themselves to level-change head kicks" ], marketThesis: "The market over-indexes Edwards' decision-heavy resume while ignoring the Usman KO shift. His striking has evolved, and Muhammad's defensive tendencies create the exact opening Edwards exploited before." }, { id: "ufc304-2", bet_type: "ITD", label: "Aspinall ITD", odds_american: "-200", P_sim: 0.72, P_imp: 0.667, edge_pct: 5.3, stability_score: 0.84, confidence_label: "High", confidencePct: 84, varianceTag: "low", reason: "Aspinall's speed and power should overwhelm Blaydes early.", detailedReason: { marketInefficiency: "Rematch line is conservative given first fight injury context. Aspinall was dominating before freak knee injury — skill gap is massive.", keyDrivers: [ "Aspinall: 92% finish rate in UFC — doesn't let fights go distance", "Blaydes' chin showed cracks vs Lewis and Ngannou", "Speed differential: Aspinall lands 2.1x faster than typical HW", ], riskFactors: [ "Blaydes' wrestling is elite if he commits to grinding", "First fight ended before we saw Blaydes' adjustments", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "72% ITD exceeds threshold" }, { name: "Market Efficiency", signal: "pass", desc: "5.3% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "speed advantage dominant" }, { name: "Risk Filters", signal: "pass", desc: "low variance outcome" }, ], patternInsight: "In rematches where the first fight ended via injury stoppage and the returning fighter has a 90%+ finish rate, they finish inside the distance 78% of the time. Aspinall's 92% finish rate makes this pattern highly applicable.", patternMechanics: [ "Unfinished business creates aggressive opening-round approaches", "Speed differentials compound against wrestlers who commit to clinch entries" ], marketThesis: "The market is treating the first fight as data when it provided zero competitive information. Aspinall was dominating before the freak injury — this is a skill mismatch the line doesn't reflect." }, { id: "ufc304-3", bet_type: "ML", label: "Holloway ML", odds_american: "-185", P_sim: 0.68, P_imp: 0.649, edge_pct: 3.1, stability_score: 0.73, confidence_label: "Medium", confidencePct: 73, varianceTag: "medium", reason: "Volume and cardio advantage should outlast Allen over 5 rounds.", detailedReason: { marketInefficiency: "Market respects Allen's power but underweights Holloway's elite cardio and volume. Five-round fights heavily favor Holloway's style.", keyDrivers: [ "Holloway: 8.5 sig strikes per minute — highest in division", "Allen's output drops 40% after R3 in his losses", "Championship experience gap is significant", ], riskFactors: [ "Allen's power is real — could end fight with one shot", "Holloway moving up in weight could affect chin", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "68% win probability" }, { name: "Market Efficiency", signal: "pass", desc: "3.1% edge detected" }, { name: "Matchup Fit", signal: "pass", desc: "cardio advantage verified" }, { name: "Risk Filters", signal: "neutral", desc: "power threat flagged" }, ], }, { id: "ufc304-4", bet_type: "ML", label: "Pimblett ML", odds_american: "-180", P_sim: 0.55, P_imp: 0.643, edge_pct: -9.3, stability_score: 0.42, confidence_label: "Low", confidencePct: 42, varianceTag: "high", reason: "Pimblett is overrated. Green has advantages.", detailedReason: { marketInefficiency: "Line heavily favors Pimblett due to name recognition. Green's wrestling is a problem.", keyDrivers: ["Green's wrestling should control fight", "Pimblett's cardio is suspect"], riskFactors: ["Pimblett's power could land", "Home crowd factor"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "55% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -9.3% edge" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling advantage noted" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, { id: "ufc304-5", bet_type: "ML", label: "Umar ML", odds_american: "-225", P_sim: 0.73, P_imp: 0.69, edge_pct: 4.0, stability_score: 0.75, confidence_label: "High", confidencePct: 75, varianceTag: "medium", reason: "Umar's wrestling and control should dominate Shore.", detailedReason: { marketInefficiency: "Line slightly undervalues Umar's wrestling dominance.", keyDrivers: ["Umar averages 5+ takedowns per fight", "Shore has weak TDD"], riskFactors: ["Shore has power", "Umar can be passive"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "73% exceeds threshold" }, { name: "Market Efficiency", signal: "pass", desc: "4% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling dominance verified" }, { name: "Risk Filters", signal: "pass", desc: "control-based low variance" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-305": { name: "UFC 305: Du Plessis vs Adesanya", date: "August 17, 2024", location: "Perth, Australia", fights: [ { id: "fight-1", matchup: "Du Plessis vs Adesanya", odds: "+115 / -135" }, { id: "fight-2", matchup: "Tuivasa vs Rozenstruik", odds: "-150 / +125" }, { id: "fight-3", matchup: "Volk vs Rodriguez", odds: "-350 / +275" }, { id: "fight-4", matchup: "Murphy vs Maverick", odds: "-200 / +165" }, { id: "fight-5", matchup: "Prates vs Morales", odds: "-165 / +140" }, ], simulationBets: [ { id: "ufc305-1", bet_type: "ML", label: "Du Plessis ML", odds_american: "+120", P_sim: 0.52, P_imp: 0.455, edge_pct: 6.5, stability_score: 0.78, confidence_label: "High", confidencePct: 78, varianceTag: "medium", reason: "Pressure wrestling nullifies striking — 52% win rate vs implied 45.5%.", detailedReason: { marketInefficiency: "Market overvalues Adesanya's striking pedigree while ignoring his struggles vs pressure fighters. Du Plessis's style is exactly what troubles Izzy.", keyDrivers: [ "Du Plessis: 89% win rate when he lands 2+ takedowns", "Adesanya's TDD dropped to 67% vs high-output wrestlers", "Clinch time favors DDP — Izzy has never faced this pressure style at MW", ], riskFactors: [ "Adesanya's counter-striking is still elite", "If Du Plessis can't close distance, he gets picked apart", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "52% underdog value" }, { name: "Market Efficiency", signal: "pass", desc: "6.5% edge detected" }, { name: "Matchup Fit", signal: "pass", desc: "pressure nullifies striking" }, { name: "Risk Filters", signal: "neutral", desc: "counter-striking threat flagged" }, ], patternInsight: "In title fights where a pressure wrestler faces a counter-striker with declining TDD (below 70%), the pressure fighter wins 64% of the time. Du Plessis fits this archetype exactly.", patternMechanics: [ "Sustained clinch pressure degrades counter-striking timing", "Championship rounds favor cardio-based grinders over precision strikers" ], marketThesis: "The market is anchored to Adesanya's reputation rather than his recent performance data. His struggles against Pereira (a pressure fighter) exposed the blueprint Du Plessis will follow.", // Line movement lineMovement: { opening: +135, current: +120, history: [135, 130, 128, 125, 122, 120], sharpAction: "toward", }, }, { id: "ufc305-2", bet_type: "Over", label: "Tuivasa vs Rozenstruik Over 1.5", odds_american: "+100", P_sim: 0.55, P_imp: 0.5, edge_pct: 5.0, stability_score: 0.68, confidence_label: "Medium", confidencePct: 68, varianceTag: "medium", reason: "Both fighters have shown durability — not a quick finish.", detailedReason: { marketInefficiency: "Market assumes HW = quick finish, but both fighters have gone deep in recent bouts. Neither has true one-punch power vs elite chins.", keyDrivers: [ "Tuivasa went 5 rounds with Gane — chin is tested", "Rozenstruik's recent fights show more patient approach", "Combined 6 fights going past R2 in last 8 appearances", ], riskFactors: [ "HW fights are inherently volatile — one punch changes everything", "Both fighters have been KO'd recently", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "55% over probability" }, { name: "Market Efficiency", signal: "pass", desc: "5% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "durability profiles align" }, { name: "Risk Filters", signal: "neutral", desc: "HW volatility flagged" }, ], patternInsight: "In heavyweight bouts where both fighters absorb under 1.2 knockdowns per round and exceed 10 minutes of average fight time, fights go Over 1.5 rounds 68% of the time.", patternMechanics: [ "Durability suppresses early KO volatility", "Pace + patience outweigh raw power" ], marketThesis: "The market reflexively prices HW fights for early finishes. This specific matchup features two durable fighters who have both survived late-round scenarios against elite competition.", // Line movement lineMovement: { opening: -110, current: +100, history: [-110, -105, -102, 100, 100, 100], sharpAction: "away", }, }, { id: "ufc305-3", bet_type: "ITD", label: "Volk ITD", odds_american: "-180", P_sim: 0.71, P_imp: 0.643, edge_pct: 6.7, stability_score: 0.83, confidence_label: "High", confidencePct: 83, varianceTag: "low", reason: "Skill gap creates finish opportunities — 71% ITD vs implied 64.3%.", detailedReason: { marketInefficiency: "Line undervalues Volk's finishing ability against lower-ranked opponents. When skill gap is this wide, Volk doesn't coast.", keyDrivers: [ "Volk: 75% finish rate vs opponents outside top 5", "Rodriguez's defense has holes that elite strikers exploit", "Simulations show R3-R4 accumulation leads to stoppage", ], riskFactors: ["Rodriguez has power to make it interesting", "Volk coming off losses could be mentally off"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "71% ITD exceeds threshold" }, { name: "Market Efficiency", signal: "pass", desc: "6.7% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "skill gap exploitable" }, { name: "Risk Filters", signal: "pass", desc: "low variance confirmed" }, ], patternInsight: "When former champions face opponents ranked outside the top 5 with a 15%+ skill differential, they finish inside the distance 72% of the time. Volk's finishing rate in mismatches is even higher at 75%.", patternMechanics: [ "Elite-level volume and accuracy compounds damage over rounds", "Defensive gaps become exploitable under sustained pressure" ], marketThesis: "The market is pricing Volk's recent losses without adjusting for opponent quality. His losses came against the best P4P fighter — this opponent represents a significant step down in competition.", // Line movement lineMovement: { opening: -165, current: -180, history: [-165, -168, -172, -175, -178, -180], sharpAction: "toward", }, }, { id: "ufc305-4", bet_type: "ML", label: "Murphy ML", odds_american: "-200", P_sim: 0.53, P_imp: 0.667, edge_pct: -13.7, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Murphy is a decent fighter but Maverick's experience is too much.", detailedReason: { marketInefficiency: "Line overvalues Murphy's recent win streak. Maverick is tested.", keyDrivers: ["Maverick's experience at elite level", "Murphy's defense has holes"], riskFactors: ["Murphy's power advantage", "Maverick has faded in past"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "53% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -13.7% edge" }, { name: "Matchup Fit", signal: "neutral", desc: "experience vs power unclear" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, { id: "ufc305-5", bet_type: "ML", label: "Prates ML", odds_american: "-165", P_sim: 0.57, P_imp: 0.623, edge_pct: -5.3, stability_score: 0.48, confidence_label: "Low", confidencePct: 48, varianceTag: "high", reason: "Prates is a decent striker but Morales's grappling and pace should win.", detailedReason: { marketInefficiency: "Line slightly undervalues Morales's wrestling advantage.", keyDrivers: ["Morales's grappling edge", "Prates has shown cardio issues"], riskFactors: ["Prates's power can land", "Morales can be passive"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "57% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -5.3% edge" }, { name: "Matchup Fit", signal: "pass", desc: "grappling advantage" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-306": { name: "UFC 306: O'Malley vs Dvalishvili", date: "September 14, 2024", location: "Las Vegas, NV (Sphere)", fights: [ { id: "fight-1", matchup: "O'Malley vs Dvalishvili", odds: "-145 / +125" }, { id: "fight-2", matchup: "Shevchenko vs Grasso 3", odds: "-165 / +140" }, { id: "fight-3", matchup: "Royval vs Pantoja", odds: "+185 / -225" }, { id: "fight-4", matchup: "Emmett vs Topuria", odds: "+280 / -360" }, { id: "fight-5", matchup: "Aldana vs Irene", odds: "-135 +115" }, ], simulationBets: [ { id: "ufc306-1", bet_type: "ML", label: "O'Malley ML", odds_american: "-165", P_sim: 0.65, P_imp: 0.623, edge_pct: 2.7, stability_score: 0.71, confidence_label: "Medium", confidencePct: 71, varianceTag: "medium", reason: "Reach and precision striking advantage — 65% win rate vs implied 62.3%.", detailedReason: { marketInefficiency: "Market respects Merab's wrestling but overweights it. O'Malley's takedown defense has improved dramatically, and his length creates problems.", keyDrivers: [ "O'Malley: 82% TDD in recent fights vs elite wrestlers", "Reach advantage of 4 inches creates jab-dominant gameplan", "Merab has never fought a striker with O'Malley's timing", ], riskFactors: [ "Merab's cardio and pressure is relentless — 5 rounds is a lot", "O'Malley's legs have been compromised before", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "65% win probability" }, { name: "Market Efficiency", signal: "pass", desc: "2.7% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "reach/TDD advantage verified" }, { name: "Risk Filters", signal: "neutral", desc: "5-round cardio flagged" }, ], patternInsight: "Champions with 4+ inch reach advantages and 80%+ TDD against pressure wrestlers win 71% of the time. O'Malley's combination of length and improved wrestling defense fits this archetype.", patternMechanics: [ "Reach creates jab-dominant gameplans that frustrate pressure fighters", "High TDD forces grapplers into striking exchanges they can't win" ], marketThesis: "The market is pricing Merab's wrestling threat without accounting for O'Malley's defensive improvements. His TDD has risen 15% since the Yan fight, and his reach nullifies Merab's pressure entries." }, { id: "ufc306-2", bet_type: "Decision", label: "Shevchenko Dec", odds_american: "-110", P_sim: 0.58, P_imp: 0.524, edge_pct: 5.6, stability_score: 0.77, confidence_label: "High", confidencePct: 77, varianceTag: "medium", reason: "Technical chess match goes to cards — 58% decision vs implied 52.4%.", detailedReason: { marketInefficiency: "Trilogy fights historically go to decision as both fighters know each other's tendencies. Neither has finishing power to stop the other.", keyDrivers: [ "First two fights: 1 decision, 1 split — pattern established", "Both fighters showed improved defense in fight 2", "Judge scorecards favor Shevchenko's aggression style", ], riskFactors: [ "Grasso's submission threat from clinch is live", "Shevchenko age could be factor in deep rounds", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "58% decision probability" }, { name: "Market Efficiency", signal: "pass", desc: "5.6% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "trilogy pattern established" }, { name: "Risk Filters", signal: "neutral", desc: "submission threat flagged" }, ], patternInsight: "In trilogy fights at women's flyweight where both previous fights went to decision, the third fight goes to decision 82% of the time. Familiarity breeds caution.", patternMechanics: [ "Fighters who know each other's timing avoid risky exchanges", "Defensive adjustments from film study compound across rematches" ], marketThesis: "The market is pricing finish potential that doesn't exist in this matchup. Both fighters have shown they can survive each other's best shots — this is a technical battle that goes to the cards." }, { id: "ufc306-3", bet_type: "ITD", label: "Topuria ITD", odds_american: "-180", P_sim: 0.72, P_imp: 0.643, edge_pct: 7.7, stability_score: 0.85, confidence_label: "High", confidencePct: 85, varianceTag: "low", reason: "Power differential creates early finish — 72% ITD vs implied 64.3%.", detailedReason: { marketInefficiency: "Emmett is being fed to Topuria to build hype. The skill gap is massive, and Topuria doesn't let opponents survive when he hurts them.", keyDrivers: [ "Topuria: 100% finish rate when he drops opponents", "Emmett's chin has been cracked multiple times", "Simulations show R1-R2 finish in 65% of Topuria wins", ], riskFactors: [ "Emmett's power means one punch can change everything", "Topuria has never fought at this pace — could tire", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "72% ITD exceeds threshold" }, { name: "Market Efficiency", signal: "pass", desc: "7.7% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "skill gap exploitable" }, { name: "Risk Filters", signal: "pass", desc: "low variance confirmed" }, ], patternInsight: "Champions with a 100% finish rate when they drop opponents facing fighters with 2+ KO losses finish inside the distance 85% of the time. Topuria has never let a hurt opponent survive.", patternMechanics: [ "Elite finishers recognize and capitalize on hurt opponents immediately", "Compromised chins compound under sustained damage" ], marketThesis: "The market is pricing this as a competitive fight when it's a showcase. Emmett is being fed to Topuria to build hype for a bigger fight — the skill gap is massive and the line doesn't reflect it." }, { id: "ufc306-4", bet_type: "ML", label: "Aldana ML", odds_american: "-135", P_sim: 0.53, P_imp: 0.574, edge_pct: -4.4, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Aldana is tough but Irene's wrestling and pace should win.", detailedReason: { marketInefficiency: "Line slightly undervalues Irene's wrestling advantage.", keyDrivers: ["Irene's grappling edge", "Aldana's TDD is a weakness"], riskFactors: ["Aldana's power can land", "Irene can be hesitant"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "53% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -4.4% edge" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling advantage" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, { id: "ufc306-5", bet_type: "Under", label: "O'Malley vs Dvalishvili Under 3.5", odds_american: "+125", P_sim: 0.48, P_imp: 0.444, edge_pct: 3.6, stability_score: 0.58, confidence_label: "Low", confidencePct: 58, varianceTag: "high", reason: "Low edge, high variance fight. Could go either way.", detailedReason: { marketInefficiency: "Slight value on early finish given both fighters' styles.", keyDrivers: ["O'Malley's KO power", "Merab's relentless pressure"], riskFactors: ["O'Malley's cardio in later rounds", "Merab's chin has been tested"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "48% below threshold" }, { name: "Market Efficiency", signal: "pass", desc: "3.6% edge detected" }, { name: "Matchup Fit", signal: "neutral", desc: "styles clash unpredictably" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-307": { name: "UFC 307: Pereira vs Rountree", date: "October 5, 2024", location: "Salt Lake City, UT", fights: [ { id: "fight-1", matchup: "Pereira vs Rountree", odds: "-600 / +425" }, { id: "fight-2", matchup: "Raquel vs Kayla", odds: "+195 / -240" }, { id: "fight-3", matchup: "Chimaev vs Costa", odds: "-275 / +220" }, { id: "fight-4", matchup: "Poatan vs Ankalaev", odds: "-180 / +155" }, { id: "fight-5", matchup: "Ribas vs Maverick", odds: "-145 / +125" }, ], simulationBets: [ { id: "ufc307-1", bet_type: "ITD", label: "Pereira ITD", odds_american: "-145", P_sim: 0.64, P_imp: 0.592, edge_pct: 4.8, stability_score: 0.8, confidence_label: "High", confidencePct: 80, varianceTag: "medium", reason: "Power differential creates finish opportunities — 64% ITD vs implied 59.2%.", detailedReason: { marketInefficiency: "Rountree is a stepping stone opponent. Pereira's power and precision should find the finish against a fighter who leaves himself open.", keyDrivers: [ "Pereira: 89% KO rate at LHW — division-leading", "Rountree's defense is porous — absorbed 5.4 sig strikes per min", "Simulations show R2-R3 counter KO as most likely outcome", ], riskFactors: [ "Rountree's power is real — could catch Pereira early", "If Rountree survives R3, cardio becomes a factor", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "64% ITD probability" }, { name: "Market Efficiency", signal: "pass", desc: "4.8% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "power vs porous defense" }, { name: "Risk Filters", signal: "neutral", desc: "opponent power flagged" }, ], }, { id: "ufc307-2", bet_type: "ML", label: "Kayla ML", odds_american: "-240", P_sim: 0.73, P_imp: 0.706, edge_pct: 2.4, stability_score: 0.72, confidence_label: "Medium", confidencePct: 72, varianceTag: "medium", reason: "Wrestling dominance should control fight against Raquel.", detailedReason: { marketInefficiency: "Olympic-level wrestling advantage is being underweighted. Kayla's ability to dictate where fight takes place is significant.", keyDrivers: [ "Kayla: 90% takedown accuracy — elite level control", "Raquel's TDD is 65% — not enough vs Olympic wrestler", "Ground control time should rack up significant points", ], riskFactors: ["Raquel's power on feet is dangerous", "Kayla's MMA experience is still limited"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "73% win probability" }, { name: "Market Efficiency", signal: "neutral", desc: "2.4% edge — borderline" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling dominance verified" }, { name: "Risk Filters", signal: "neutral", desc: "MMA experience limited" }, ], }, { id: "ufc307-3", bet_type: "ITD", label: "Chimaev ITD", odds_american: "-130", P_sim: 0.58, P_imp: 0.565, edge_pct: 1.5, stability_score: 0.67, confidence_label: "Low", confidencePct: 67, varianceTag: "high", reason: "Grappling pressure creates submission opportunities vs Costa.", detailedReason: { marketInefficiency: "Costa's grappling defense is untested against elite wrestlers. Chimaev should dominate on the ground once he gets there.", keyDrivers: [ "Chimaev: 100% finish rate via ground-and-pound or submission", "Costa has never faced a wrestler of Chimaev's caliber", "Weight cut concerns for Costa could affect durability", ], riskFactors: [ "Costa's power on feet is significant threat", "Chimaev's recent inactivity could affect timing", ], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "58% ITD probability" }, { name: "Market Efficiency", signal: "neutral", desc: "1.5% edge — marginal" }, { name: "Matchup Fit", signal: "pass", desc: "grappling mismatch" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, { id: "ufc307-4", bet_type: "ML", label: "Ankalaev ML", odds_american: "-180", P_sim: 0.66, P_imp: 0.643, edge_pct: 1.7, stability_score: 0.71, confidence_label: "Medium", confidencePct: 71, varianceTag: "medium", reason: "Ankalaev's technical striking and wrestling should control the fight.", detailedReason: { marketInefficiency: "Line reflects a close fight, but Ankalaev's consistency gives edge.", keyDrivers: ["Ankalaev's well-rounded game", "Poatan's striking is dangerous but limited defense"], riskFactors: ["Poatan's power is immense", "Ankalaev has been hesitant in past"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "66% win probability" }, { name: "Market Efficiency", signal: "neutral", desc: "1.7% edge — slim" }, { name: "Matchup Fit", signal: "pass", desc: "technical advantage verified" }, { name: "Risk Filters", signal: "neutral", desc: "power threat flagged" }, ], }, { id: "ufc307-5", bet_type: "ML", label: "Ribas ML", odds_american: "-145", P_sim: 0.57, P_imp: 0.59, edge_pct: -2.0, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Ribas is tough but Maverick's experience and well-rounded game should win.", detailedReason: { marketInefficiency: "Line reflects a close fight, slight edge to Maverick.", keyDrivers: ["Maverick's experience advantage", "Ribas's cardio can fade"], riskFactors: ["Ribas's aggression", "Maverick can be susceptible to pressure"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "57% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -2% edge" }, { name: "Matchup Fit", signal: "neutral", desc: "experience vs aggression" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-308": { name: "UFC 308: Topuria vs Holloway", date: "October 26, 2024", location: "Abu Dhabi, UAE", fights: [ { id: "fight-1", matchup: "Topuria vs Holloway", odds: "-165 / +140" }, { id: "fight-2", matchup: "Whittaker vs Chimaev", odds: "+185 / -225" }, { id: "fight-3", matchup: "Rakic vs Ankalaev", odds: "+210 / -260" }, { id: "fight-4", matchup: "Lerone vs Buckley", odds: "-145 / +125" }, { id: "fight-5", matchup: "Oliveira vs Tsarukyan", odds: "+155 / -185" }, ], simulationBets: [ { id: "ufc308-1", bet_type: "ML", label: "Topuria ML", odds_american: "-165", P_sim: 0.66, P_imp: 0.623, edge_pct: 3.7, stability_score: 0.77, confidence_label: "Medium", confidencePct: 77, varianceTag: "medium", reason: "Power and wrestling combo favors Topuria — 66% win rate vs implied 62.3%.", detailedReason: { marketInefficiency: "Market respects Holloway's volume but underweights Topuria's power and wrestling. Topuria can win in multiple ways; Holloway needs points.", keyDrivers: [ "Topuria: 78% KO rate — highest in FW division", "Holloway's chin has shown cracks at 145 recently", "Simulations show Topuria's wrestling as key differentiator", ], riskFactors: ["Holloway's volume and cardio can overwhelm", "Five rounds favor the cardio machine"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "66% win probability" }, { name: "Market Efficiency", signal: "pass", desc: "3.7% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "power + wrestling combo" }, { name: "Risk Filters", signal: "neutral", desc: "5-round cardio flagged" }, ], }, { id: "ufc308-2", bet_type: "ITD", label: "Chimaev ITD", odds_american: "-110", P_sim: 0.55, P_imp: 0.524, edge_pct: 2.6, stability_score: 0.69, confidence_label: "Medium", confidencePct: 69, varianceTag: "medium", reason: "Wrestling pressure should create finish vs aging Whittaker.", detailedReason: { marketInefficiency: "Whittaker's TDD hasn't been tested by a wrestler of Chimaev's caliber. If fight hits the mat, Chimaev's GnP is devastating.", keyDrivers: [ "Chimaev: 100% finish rate when he maintains top position 2+ min", "Whittaker's wrestling defense is theoretical vs this level", "Simulations show R2-R3 ground finish as primary path", ], riskFactors: ["Whittaker's counter-striking is elite", "If Chimaev can't get takedowns, he's in trouble"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "55% ITD probability" }, { name: "Market Efficiency", signal: "pass", desc: "2.6% edge detected" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling mismatch" }, { name: "Risk Filters", signal: "neutral", desc: "counter-striking threat" }, ], }, { id: "ufc308-3", bet_type: "ML", label: "Ankalaev ML", odds_american: "-260", P_sim: 0.75, P_imp: 0.722, edge_pct: 2.8, stability_score: 0.76, confidence_label: "Medium", confidencePct: 76, varianceTag: "medium", reason: "Technical advantage should outclass Rakic over 3 rounds.", detailedReason: { marketInefficiency: "Line is fair but slightly undervalues Ankalaev's technical dominance. Rakic has shown defensive holes that elite strikers exploit.", keyDrivers: [ "Ankalaev: 82% significant strike accuracy — elite precision", "Rakic's last two losses came via KO — durability questions", "Southpaw advantage creates angles Rakic struggles with", ], riskFactors: ["Rakic's power is real — can end fights", "Ankalaev's passivity could lead to close rounds"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "75% win probability" }, { name: "Market Efficiency", signal: "pass", desc: "2.8% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "technical dominance verified" }, { name: "Risk Filters", signal: "neutral", desc: "opponent power flagged" }, ], }, { id: "ufc308-4", bet_type: "ML", label: "Lerone ML", odds_american: "-145", P_sim: 0.57, P_imp: 0.59, edge_pct: -2.0, stability_score: 0.45, confidence_label: "Low", confidencePct: 45, varianceTag: "high", reason: "Lerone is a tough fighter, but Buckley's power and aggression should win.", detailedReason: { marketInefficiency: "Line reflects a close fight, slight edge to Buckley.", keyDrivers: ["Buckley's power advantage", "Lerone's defense can be porous"], riskFactors: ["Lerone's durability", "Buckley can be wild"], }, agentSignals: [ { name: "Model Probability", signal: "fail", desc: "57% below threshold" }, { name: "Market Efficiency", signal: "fail", desc: "negative -2% edge" }, { name: "Matchup Fit", signal: "neutral", desc: "power vs durability unclear" }, { name: "Risk Filters", signal: "fail", desc: "high variance blocked" }, ], }, { id: "ufc308-5", bet_type: "ML", label: "Tsarukyan ML", odds_american: "-185", P_sim: 0.68, P_imp: 0.652, edge_pct: 2.8, stability_score: 0.75, confidence_label: "High", confidencePct: 75, varianceTag: "medium", reason: "Tsarukyan's improved wrestling and pace should overcome Oliveira.", detailedReason: { marketInefficiency: "Rematch line undervalues Tsarukyan's improvements since last fight.", keyDrivers: ["Tsarukyan's wrestling advantage", "Oliveira's grappling defense has declined"], riskFactors: ["Oliveira's submission threat is always live", "Oliveira's experience"], }, agentSignals: [ { name: "Model Probability", signal: "pass", desc: "68% win probability" }, { name: "Market Efficiency", signal: "pass", desc: "2.8% edge confirmed" }, { name: "Matchup Fit", signal: "pass", desc: "wrestling advantage verified" }, { name: "Risk Filters", signal: "neutral", desc: "submission threat flagged" }, ], }, ], bestBets: [], systemParlays: [], }, "ufc-309": { name: "UFC 309: Edwards vs Covington 2", date: "December 14, 2024", location: "Las Vegas, NV", fights: [], simulationBets: [], bestBets: [], systemParlays: [], }, } // EV + Mispricing thresholds const MIN_MISPRICING_PROB = 0.05 // 5% const qualifyBets = (bets: SimulationBet[]): SimulationBet[] => { // First pass: individual qualification checks including EV const qualifiedPass1 = bets.map((bet) => { const rejectReasons: string[] = [] const passCount = bet.agentSignals.filter((s) => s.signal === "pass").length const agentPassRate = passCount / bet.agentSignals.length // Compute EV and mispricing for filtering const marketOdds = parseInt(bet.odds_american.replace("+", "")) const marketImpliedProb = marketOdds > 0 ? 100 / (marketOdds + 100) : (-marketOdds) / ((-marketOdds) + 100) const decimalOdds = marketOdds > 0 ? 1 + (marketOdds / 100) : 1 + (100 / -marketOdds) const evPerUnit = (bet.P_sim * (decimalOdds - 1)) - ((1 - bet.P_sim) * 1) const mispricingProb = bet.P_sim - marketImpliedProb // Check qualification rules - use plain language for reject reasons if (bet.P_sim < MIN_MAF_PROB) { rejectReasons.push("Model confidence too low") } if (bet.edge_pct < MIN_EDGE_PCT) { rejectReasons.push("No clear betting edge") } if (agentPassRate < MIN_AGENT_CONSENSUS_PASS_RATE) { rejectReasons.push("Weak model agreement") } if (bet.varianceTag === "high" && bet.confidencePct / 100 < BLOCK_HIGH_VARIANCE_IF_CONFIDENCE_BELOW) { rejectReasons.push("Fight outcome too unpredictable") } // EV-based filtering if (evPerUnit <= 0) { rejectReasons.push("No statistical advantage") } if (mispricingProb < MIN_MISPRICING_PROB) { rejectReasons.push("Odds already priced correctly") } return { ...bet, status: rejectReasons.length === 0 ? "qualified" : "filtered", rejectReasons, // Store computed pricing fields ev_per_unit: evPerUnit, mispricing_prob: mispricingProb, market_implied_prob: marketImpliedProb, } as SimulationBet }) // Second pass: correlation filtering for qualified bets const qualifiedBetsOnly = qualifiedPass1.filter(b => b.status === "qualified") const seenFamilyByFighter: Record> = {} return qualifiedPass1.map((bet) => { if (bet.status === "filtered") return bet // Check for correlation with already-kept bets const fighter = bet.label.split(" ")[0].toLowerCase() const family = bet.market_family || "method_victory" const key = `${fighter}_${family}` // Initialize tracking if (!seenFamilyByFighter[fighter]) { seenFamilyByFighter[fighter] = new Set() } // Check if we already have a bet in this family for this fighter // Keep the one with highest edge (first one seen after sorting) const sortedQualified = [...qualifiedBetsOnly].sort((a, b) => b.edge_pct - a.edge_pct) const isHighestEdgeInFamily = sortedQualified.find( b => b.label.split(" ")[0].toLowerCase() === fighter && (b.market_family || "method_victory") === family )?.id === bet.id if (seenFamilyByFighter[fighter].has(family) && !isHighestEdgeInFamily) { return { ...bet, status: "filtered" as const, rejectReasons: [...(bet.rejectReasons || []), "Similar bet already recommended"], } } seenFamilyByFighter[fighter].add(family) return bet }) } const currentEvent = eventData[selectedEvent] const currentFights = currentEvent?.fights || [] // Run the market scanner for the current event const [scannerResults, setScannerResults] = useState<{ allResults: ScannerResult[] qualified: ScannerResult[] filtered: ScannerResult[] lastScan: Date | null }>({ allResults: [], qualified: [], filtered: [], lastScan: null }) // Scanner effect - runs on event change or refresh trigger // Note: We use selectedEvent instead of currentFights to avoid infinite loop // (currentFights is derived from selectedEvent and recreated each render) useEffect(() => { const fights = eventData[selectedEvent]?.fights || [] if (!fights.length) return // Scan all fights in the event const allScanResults: ScannerResult[] = [] for (const fight of fights) { const [fighter1, fighter2] = fight.matchup.split(" vs ") const selections = generateMockSelections(fight.id, fighter1.trim(), fighter2.trim()) const fightResults = scanFightMarkets(fight.id, selections) allScanResults.push(...fightResults) } // Sort by ranking score and select top picks with correlation filtering const sortedResults = allScanResults.sort((a, b) => b.ranking_score - a.ranking_score) const { qualified, filtered } = selectTopInefficiencies(sortedResults, 5) setScannerResults({ allResults: sortedResults, qualified, filtered, lastScan: new Date(), }) }, [selectedEvent, scannerRefreshTrigger]) // Live feed effect - generates dynamic feed entries (AI radar style) useEffect(() => { const fights = eventData[selectedEvent]?.fights || [] const fighterNames = fights.flatMap(f => f.matchup.split(" vs ").map(n => n.trim())) const matchups = fights.map(f => f.matchup) const oddsOptions = ["+140", "+165", "-120", "+185", "+210", "-145", "+125", "+175"] const marketTypes = ["ML", "ITD", "KO/TKO R1", "KO/TKO R2", "Dec", "Over 1.5", "Over 2.5", "Under 2.5"] const generateEntry = (id: number) => { const fighter = fighterNames[Math.floor(Math.random() * fighterNames.length)] || "Fighter" const matchup = matchups[Math.floor(Math.random() * matchups.length)] || "Main Event" const market = marketTypes[Math.floor(Math.random() * marketTypes.length)] const odds = oddsOptions[Math.floor(Math.random() * oddsOptions.length)] const prob = (55 + Math.random() * 30).toFixed(0) const drift = (Math.random() > 0.5 ? "+" : "-") + (5 + Math.random() * 20).toFixed(0) // AI radar-style feed entries const entryTypes = [ { feedType: "edge", title: `Edge detected — ${fighter} ${market} ${odds}`, detail: `Model probability: ${prob}%` }, { feedType: "movement", title: `Market movement — ${fighter} line drifting`, detail: `${drift} points in last hour` }, { feedType: "value", title: `New value found — ${market} ${matchup.split(" vs ").join("/")}`, detail: `Edge: +${(2 + Math.random() * 6).toFixed(1)}%` }, { feedType: "alert", title: `Sharp action — ${fighter} ${market}`, detail: `Line moving toward model price` }, ] const entry = entryTypes[Math.floor(Math.random() * entryTypes.length)] return { id, time: new Date().toLocaleTimeString("en-US", { hour: "numeric", minute: "2-digit" }), feedType: entry.feedType, title: entry.title, detail: entry.detail, isNew: true, } } // Initialize with a few entries const initialEntries = Array.from({ length: 4 }, (_, i) => ({ ...generateEntry(i), time: new Date(Date.now() - (i + 1) * 45000).toLocaleTimeString("en-US", { hour: "numeric", minute: "2-digit" }), isNew: false, })) setLiveFeedEntries(initialEntries) setFeedIdCounter(4) // Add new entries periodically const interval = setInterval(() => { setFeedIdCounter(prev => { const newId = prev + 1 const newEntry = generateEntry(newId) setLiveFeedEntries(entries => { // Mark all existing as not new const updated = entries.map(e => ({ ...e, isNew: false })) // Add new entry at the top, keep max 8 return [newEntry, ...updated].slice(0, 8) }) // Remove "new" status after animation setTimeout(() => { setLiveFeedEntries(entries => entries.map(e => e.id === newId ? { ...e, isNew: false } : e) ) }, 2000) return newId }) }, 8000 + Math.random() * 7000) // 8-15 seconds between updates return () => clearInterval(interval) }, [selectedEvent]) // Enhanced: Merge scanner results with existing simulation bets const allBets = qualifyBets(currentEvent?.simulationBets || []) const qualifiedBets = allBets.filter((b) => b.status === "qualified") const filteredBets = allBets.filter((b) => b.status === "filtered") const avgConfidence = qualifiedBets.length > 0 ? qualifiedBets.reduce((sum, b) => sum + b.confidencePct, 0) / qualifiedBets.length : 0 const avgEdge = qualifiedBets.length > 0 ? qualifiedBets.reduce((sum, b) => sum + b.edge_pct, 0) / qualifiedBets.length : 0 const hasHighVariance = qualifiedBets.some((b) => b.varianceTag === "high") const riskLevel = hasHighVariance || avgConfidence < 55 ? "High" : avgConfidence < 62 ? "Medium" : "Low" // Fractional Kelly Calculator (25% Kelly) const calculateKellyStake = (modelProb: number, americanOdds: number, variance: string, confidence: number): { units: number; method: string; isPass: boolean } => { // Convert American to decimal const decimal = americanOdds > 0 ? 1 + (americanOdds / 100) : 1 + (100 / -americanOdds) const impliedProb = americanOdds > 0 ? 100 / (americanOdds + 100) : (-americanOdds) / ((-americanOdds) + 100) const edge = modelProb - impliedProb // Full Kelly = (p * b - q) / b where b = decimal - 1, p = model prob, q = 1 - p const b = decimal - 1 const fullKelly = (modelProb * b - (1 - modelProb)) / b // Apply 25% fractional Kelly let kellyFraction = fullKelly * 0.25 // Variance adjustment: reduce stake by 25-50% for high variance if (variance === "High") kellyFraction *= 0.5 else if (variance === "Medium") kellyFraction *= 0.75 // Safety filters if (edge < 0.02 || confidence < 55) return { units: 0, method: "Pass", isPass: true } if (kellyFraction <= 0) return { units: 0, method: "Pass", isPass: true } // Convert to units (cap at 3.0, min increment 0.25) let units = Math.min(3.0, Math.max(0.25, Math.round(kellyFraction * 100 * 4) / 4)) return { units, method: "25% Kelly", isPass: false } } const fightBreakdowns: Record = { "fight-1": selectedEvent === "ufc-305" ? { trueLine: "-145 / +125", marketLine: "-130 / +110", mispricing: "+3.2%", bet: "Du Plessis ITD +140", ev: "+16.3%", confidence: "81%", risk: "Medium", stake: "Strong", modelProb: 0.59, marketOdds: 140, fighter1Name: "Du Plessis", fighter2Name: "Adesanya", fighter1Notes: "Pressure-driven output with elite cardio durability. 21-1 with 13 finishes.", fighter2Notes: "Counter-striking profile showing late-round decline against sustained pressure.", pathToVictory: "Du Plessis: TKO R3-R5 (48%) | Adesanya: KO R1-R2 (28%) | Decision (24%)", marketAnalysis: "Market overweights Adesanya name equity and early-KO narratives in pressure-resistant matchups.", varianceReason: "late-finish dependency", primaryRisk: "Early Adesanya counter KO before pressure accumulation establishes rhythm.", mafsIntelligence: [ { type: "Style Interaction", text: "Sustained forward pressure forces extended exchanges against reactive counter-striker who prefers to reset" }, { type: "Fight Dynamic", text: "Edge compounds after R2 as cardio differential widens and Adesanya's output historically dips 30%+ in championship rounds" }, { type: "Market Bias", text: "Market overweights Adesanya's name equity and early-KO narrative in pressure-resistant matchups with durable opponents" }, ], simulationPaths: [ { name: "Pressure Accumulation Finish", pct: 48, desc: "Du Plessis volume overwhelms Adesanya in R3-R5 as output fades, leading to TKO stoppage.", impacts: "Late-finish props, ITD markets" }, { name: "Early Counter Window", pct: 28, desc: "Adesanya lands clean counter during early linear pressure entries before rhythm establishes.", impacts: "R1-R2 KO props, underdog ML" }, { name: "Attritional Decision", pct: 18, desc: "Pressure wins rounds but lacks finishing sequences; fight reaches scorecards.", impacts: "Decision markets, Over rounds" }, { name: "Chaos Swing", pct: 6, desc: "Momentum shift via flash knockdown or injury disrupts expected fight flow.", impacts: "Prop volatility" }, ], lineMovement: { firstObserved: -110, current: -130, history: [-110, -115, -118, -122, -125, -130], edgeAtFirst: 1.2, edgeNow: 3.2, }, } : selectedEvent === "ufc-300" ? { trueLine: "-420 / +330", marketLine: "-400 / +310", mispricing: "+2.8%", bet: "Pereira ITD -180", ev: "+7.7%", confidence: "85%", risk: "Low", stake: "Strong", modelProb: 0.72, marketOdds: -180, fighter1Name: "Pereira", fighter2Name: "Hill", fighter1Notes: "Elite counter-timing with devastating power. KO machine at LHW with predictable setup windows.", fighter2Notes: "Forward pressure profile creates timing windows. Chin durability overweighted by market.", pathToVictory: "Pereira: KO R2-R3 (58%) | Pereira: Decision (22%) | Hill: Decision (20%)", marketAnalysis: "Market anchors to Hill durability narrative while underpricing Pereira's counter-timing against forward pressure.", varianceReason: "heavy favorite volatility", primaryRisk: "Hill clinch control neutralizes Pereira's counter-striking range.", mafsIntelligence: [ { type: "Style Interaction", text: "Hill's forward pressure creates predictable timing windows for Pereira's counter-striking with devastating power" }, { type: "Fight Dynamic", text: "Pereira capitalizes on aggressive entries; Hill must sustain pressure through R2 to have path, but exchanges favor counter-striker" }, { type: "Market Bias", text: "Market overweights chin durability at LHW while underpricing Pereira's unique counter-timing against linear pressure" }, ], simulationPaths: [ { name: "Counter KO Finish", pct: 58, desc: "Pereira lands clean counter during Hill's forward entries, resulting in R2-R3 stoppage.", impacts: "ITD props, KO method" }, { name: "Decision Control", pct: 22, desc: "Hill survives but loses exchanges; Pereira coasts to comfortable decision.", impacts: "Decision props, Over rounds" }, { name: "Pressure Survival", pct: 14, desc: "Hill's clinch work and durability frustrate Pereira to split decision.", impacts: "Underdog ML, clinch props" }, { name: "Chaos Swing", pct: 6, desc: "Hill catches Pereira early or clinch-heavy gameplan neutralizes power.", impacts: "Prop volatility" }, ], lineMovement: { firstObserved: -165, current: -180, history: [-165, -168, -172, -175, -178, -180], edgeAtFirst: 5.2, edgeNow: 7.7, }, } : { trueLine: "-145 / +125", marketLine: "-130 / +110", mispricing: "+2.5%", bet: "Default Bet", ev: "+12.5%", confidence: "70%", risk: "Medium", stake: "1.5 units", fighter1Name: "Fighter A", fighter2Name: "Fighter B", fighter1Notes: "Analysis pending.", fighter2Notes: "Analysis pending.", pathToVictory: "TBD", marketAnalysis: "Analysis pending.", }, "fight-2": selectedEvent === "ufc-305" ? { trueLine: "-165 / +140", marketLine: "-150 / +125", mispricing: "+3.8%", bet: "Tuivasa ITD +145", ev: "+21.8%", confidence: "74%", risk: "High", stake: "1.5 units", modelProb: 0.52, marketOdds: 145, fighter1Name: "Tuivasa", fighter2Name: "Rozenstruik", fighter1Notes: "Power puncher with 85% KO rate. Vulnerable to technical strikers but devastating when he lands.", fighter2Notes: "Technical striker with knockout power. Both fighters typically finish fights early.", pathToVictory: "Tuivasa: KO (45%) | Rozenstruik: KO (38%) | Decision (17%)", marketAnalysis: "Plus odds on finish offers value in heavyweight bout with combined 85% finish rate.", mafsIntelligence: [ { type: "Style Interaction", text: "Both fighters rely on power over technique; first clean shot likely ends the fight in either direction" }, { type: "Fight Dynamic", text: "83% combined finish rate means decision is unlikely; value exists on either ITD outcome at plus odds" }, { type: "Market Bias", text: "Market underweights Tuivasa's power advantage in pure brawler matchup despite recent losses to technical strikers" }, ], simulationPaths: [ { name: "Tuivasa Power Shot", pct: 45, desc: "Tuivasa lands first clean power shot in early exchanges, ending fight via KO.", impacts: "ITD props, Tuivasa KO method" }, { name: "Rozenstruik Counter", pct: 38, desc: "Rozenstruik's superior technique catches Tuivasa during wild exchanges.", impacts: "Rozenstruik ITD, counter KO props" }, { name: "Unlikely Decision", pct: 17, desc: "Both fighters gas or cautious approach leads to decision (rare at HW).", impacts: "Over rounds, decision props" }, ], lineMovement: { firstObserved: -110, current: 100, history: [-110, -105, -102, 100, 100, 100], edgeAtFirst: 4.8, edgeNow: 2.1, }, } : selectedEvent === "ufc-300" ? { trueLine: "-185 / +155", marketLine: "-185 / +155", mispricing: "+0.0%", bet: "Zhang ML -185", ev: "+0.0%", confidence: "0%", risk: "Low", stake: "0 units", modelProb: 0.65, marketOdds: -185, fighter1Name: "Zhang", fighter2Name: "Yan", fighter1Notes: "Champion with devastating power. 6 fight win streak with 4 KOs.", fighter2Notes: "Technical striker but power gap significant. Never faced Zhang's level.", pathToVictory: "Zhang: KO (52%) | Zhang: Decision (32%) | Yan: Decision (16%)", marketAnalysis: "Zhang is clearly superior, but market reflects odds accurately.", mafsIntelligence: [ { type: "Style Interaction", text: "Both durable fighters prefer technical exchanges over power shots, reducing early finish probability" }, { type: "Fight Dynamic", text: "Yan's output has declined since bantamweight; Zhang's chin allows absorption without finish risk" }, { type: "Market Bias", text: "Market pricing is accurate—no exploitable edge exists on this fight" }, ], simulationPaths: [ { name: "Zhang KO", pct: 52, desc: "Zhang's power advantage creates finish opportunity through accumulation.", impacts: "KO props, ITD markets" }, { name: "Zhang Decision", pct: 32, desc: "Zhang outpoints Yan over 5 rounds without finding finish.", impacts: "Decision props, Over rounds" }, { name: "Yan Upset", pct: 16, desc: "Yan's technique frustrates Zhang to split decision.", impacts: "Underdog ML" }, ], lineMovement: { firstObserved: -175, current: -185, history: [-175, -178, -180, -182, -184, -185], edgeAtFirst: 0.5, edgeNow: 0.0, }, } : { trueLine: "-145 / +125", marketLine: "-130 / +110", mispricing: "+2.5%", bet: "Default Bet", ev: "+12.5%", confidence: "70%", risk: "Medium", stake: "1.5 units", fighter1Name: "Fighter A", fighter2Name: "Fighter B", fighter1Notes: "Analysis pending.", fighter2Notes: "Analysis pending.", pathToVictory: "TBD", marketAnalysis: "Analysis pending.", lineMovement: { firstObserved: -125, current: -130, history: [-125, -126, -127, -128, -129, -130], edgeAtFirst: 2.0, edgeNow: 2.5, }, }, "fight-3": selectedEvent === "ufc-305" ? { trueLine: "-380 / +300", marketLine: "-350 / +275", mispricing: "+2.8%", bet: "Volk ML -350", ev: "+15.4%", confidence: "71%", risk: "Low", stake: "2.0 units", modelProb: 0.79, marketOdds: -350, fighter1Name: "Volkanovski", fighter2Name: "Rodriguez", fighter1Notes: "P4P great with elite striking and wrestling. 10 fight win streak at featherweight.", fighter2Notes: "Dangerous striker but significant skill gap against champion-level fighters.", pathToVictory: "Volk: Decision (55%) | Volk: KO (32%) | Rodriguez: KO (13%)", marketAnalysis: "Heavy favorite still offers value given overwhelming skill advantage.", mafsIntelligence: [ { type: "Style Interaction", text: "Volkanovski's wrestling and volume neutralize Rodriguez's one-shot power; skill gap is significant across all ranges" }, { type: "Fight Dynamic", text: "Volk's cardio and pace create cumulative damage; Rodriguez fades against sustained pressure in later rounds" }, { type: "Market Bias", text: "Heavy favorite pricing still underweights complete skill differential at championship level" }, ], simulationPaths: [ { name: "Decision Dominance", pct: 55, desc: "Volkanovski outworks Rodriguez over 5 rounds with wrestling and volume.", impacts: "Decision props, Over rounds" }, { name: "Late Stoppage", pct: 32, desc: "Accumulated damage leads to R3-R5 TKO as Rodriguez output collapses.", impacts: "ITD props, late-round finishes" }, { name: "Rodriguez Flash KO", pct: 13, desc: "Rodriguez lands one-shot power in early rounds before Volk establishes rhythm.", impacts: "Underdog ITD, early KO props" }, ], lineMovement: { firstObserved: -320, current: -350, history: [-320, -328, -335, -340, -345, -350], edgeAtFirst: 1.9, edgeNow: 2.8, }, } : selectedEvent === "ufc-300" ? { trueLine: "-170 / +145", marketLine: "-170 / +145", mispricing: "+0.0%", bet: "Tsarukyan ML -170", ev: "+2.0%", confidence: "72%", risk: "Medium", stake: "1.0 units", modelProb: 0.63, marketOdds: -170, fighter1Name: "Tsarukyan", fighter2Name: "Oliveira", fighter1Notes: "Tsarukyan's wrestling and cardio should control pace against aging Oliveira.", fighter2Notes: "Oliveira's submission threat is always live, but defense has holes.", pathToVictory: "Tsarukyan: Wrestling Control (55%) | Oliveira: Submission (25%) | Decision (20%)", marketAnalysis: "Wrestling advantage is key, but Oliveira's danger is ever-present.", mafsIntelligence: [ { type: "Style Interaction", text: "Tsarukyan's chain wrestling and top control neutralize Oliveira's submission game from guard" }, { type: "Fight Dynamic", text: "Cardio differential widens in championship rounds; Oliveira's output historically drops 40%+ after R3" }, { type: "Market Bias", text: "Market overweights Oliveira's finishing reputation while underpricing wrestling control path" }, ], simulationPaths: [ { name: "Wrestling Control", pct: 55, desc: "Tsarukyan's chain wrestling dominates positioning; Oliveira can't create submission entries.", impacts: "Decision props, wrestling ML" }, { name: "Oliveira Submission", pct: 25, desc: "Oliveira catches Tsarukyan in transition or off back; always live threat.", impacts: "Submission props, underdog ITD" }, { name: "Decision Grind", pct: 15, desc: "Fight stays competitive but Tsarukyan edges rounds on control time.", impacts: "Over rounds, decision markets" }, { name: "Chaos Swing", pct: 5, desc: "Knockdown or injury creates scramble opportunity for either fighter.", impacts: "Prop volatility" }, ], lineMovement: { firstObserved: -155, current: -170, history: [-155, -158, -162, -165, -168, -170], edgeAtFirst: 1.5, edgeNow: 2.0, }, } : { trueLine: "-170 / +145", marketLine: "-155 / +130", mispricing: "+2.3%", bet: "Default Bet", ev: "+12.5%", confidence: "70%", risk: "Medium", stake: "1.5 units", fighter1Name: "Fighter A", fighter2Name: "Fighter B", fighter1Notes: "Analysis pending.", fighter2Notes: "Analysis pending.", pathToVictory: "TBD", marketAnalysis: "Analysis pending.", lineMovement: { firstObserved: -150, current: -155, history: [-150, -151, -152, -153, -154, -155], edgeAtFirst: 2.0, edgeNow: 2.3, }, }, "fight-4": selectedEvent === "ufc-305" ? { trueLine: "+165 / -195", marketLine: "+180 / -215", mispricing: "+3.1%", bet: "Murphy ML +180", ev: "+16.2%", confidence: "68%", risk: "Medium", stake: "1.5 units", fighter1Name: "Murphy", fighter2Name: "Maverick", fighter1Notes: "Decent fighter, but Maverick's experience is too much.", fighter2Notes: "Veteran fighter with good technique, but showing age.", pathToVictory: "Maverick: Decision (50%) | Murphy: KO (30%) | Maverick: Submission (20%)", marketAnalysis: "Maverick's experience should win rounds, but Murphy's power is a threat.", lineMovement: { firstObserved: 165, current: 180, history: [165, 168, 172, 175, 178, 180], edgeAtFirst: 2.5, edgeNow: 3.1, }, } : selectedEvent === "ufc-300" ? { trueLine: "-220 / +180", marketLine: "-220 / +180", mispricing: "+0.0%", bet: "Sterling ML -220", ev: "-16.8%", confidence: "45%", risk: "High", stake: "0 units", fighter1Name: "Sterling", fighter2Name: "Kattar", fighter1Notes: "Sterling's wrestling is questionable at featherweight. Kattar is a dangerous striker.", fighter2Notes: "Kattar has solid boxing and power, but can be outworked.", pathToVictory: "Kattar: KO (35%) | Sterling: Decision (30%) | Kattar: Decision (20%)", marketAnalysis: "Overvalued favorite. Kattar has a clearer path to victory.", lineMovement: { firstObserved: -240, current: -220, history: [-240, -235, -230, -225, -222, -220], edgeAtFirst: -20.0, edgeNow: -16.8, }, } : { trueLine: "-195 / +165", marketLine: "-180 / +155", mispricing: "+2.4%", bet: "Default Bet", ev: "+12.5%", confidence: "70%", risk: "Medium", stake: "1.5 units", fighter1Name: "Fighter A", fighter2Name: "Fighter B", fighter1Notes: "Analysis pending.", fighter2Notes: "Analysis pending.", pathToVictory: "TBD", marketAnalysis: "Analysis pending.", lineMovement: { firstObserved: -175, current: -180, history: [-175, -176, -177, -178, -179, -180], edgeAtFirst: 2.1, edgeNow: 2.4, }, }, "fight-5": selectedEvent === "ufc-305" ? { trueLine: "-165 / +140", marketLine: "-155 / +130", mispricing: "+2.3%", bet: "Prates ML -155", ev: "+14.0%", confidence: "70%", risk: "Medium", stake: "1.5 units", fighter1Name: "Prates", fighter2Name: "Morales", fighter1Notes: "Solid striker with power, Morales's grappling is the key.", fighter2Notes: "Grappler with good pressure, but striking is limited.", pathToVictory: "Prates: KO (40%) | Morales: Decision (35%) | Prates: Decision (25%)", marketAnalysis: "Prates's striking should win rounds, but Morales can make it ugly.", lineMovement: { firstObserved: -145, current: -155, history: [-145, -148, -150, -152, -154, -155], edgeAtFirst: 1.8, edgeNow: 2.3, }, } : selectedEvent === "ufc-300" ? { trueLine: "-165 / +140", marketLine: "-165 / +140", mispricing: "+0.0%", bet: "Figueiredo ML -165", ev: "+0.0%", confidence: "0%", risk: "Low", stake: "0 units", fighter1Name: "Figueiredo", fighter2Name: "Garbrandt", fighter1Notes: "Figueiredo has power, but Garbrandt's speed is a problem.", fighter2Notes: "Garbrandt has speed and power, but his chin is compromised.", pathToVictory: "Figueiredo: KO (40%) | Garbrandt: KO (35%) | Decision (25%)", marketAnalysis: "This fight is a coin flip. High variance.", lineMovement: { firstObserved: -155, current: -165, history: [-155, -158, -160, -162, -164, -165], edgeAtFirst: 0.0, edgeNow: 0.0, }, } : { trueLine: "-145 / +125", marketLine: "-135 / +115", mispricing: "+2.3%", bet: "Default Bet", ev: "+12.5%", confidence: "70%", risk: "Medium", stake: "1.5 units", fighter1Name: "Fighter A", fighter2Name: "Fighter B", fighter1Notes: "Analysis pending.", fighter2Notes: "Analysis pending.", pathToVictory: "TBD", marketAnalysis: "Analysis pending.", lineMovement: { firstObserved: -130, current: -135, history: [-130, -131, -132, -133, -134, -135], edgeAtFirst: 2.0, edgeNow: 2.3, }, }, } return ( <> {/* Mobile Splash Screen */} {isMobile && showSplash && ( setShowSplash(false)} duration={2400} /> )}

{/* AI Simulation Overlay */}

{/* Compact Hero */}

Live

MMA Analytics Platform

Multi-Agent Fight Simulator

AI-powered analysis using multi-agent simulation to model outcomes and identify statistical patterns.

{/* Event Selector - Above the fold */}

Select Event

{currentEvent?.location}

{currentFights.length} fights on card

{/* Results Section */} {showResults && (

{/* Compliance Disclaimer */}

Educational purposes only

Analysis Mode: Active

MAFS filters low-confidence scenarios. Only statistically notable outcomes are displayed.

Markets Scanned

Total betting markets analyzed across all fights on this card.

{scannerResults.allResults.length} betting markets analyzed

Model Confidence

How strongly the simulation agents agree on outcomes.

{avgConfidence}% agreement across simulations

Model Edge

Average pricing advantage detected vs. market odds.

{avgEdge >= 6 ? "Strong pricing advantage detected" : avgEdge >= 3 ? "Moderate edge detected" : "Market efficiently priced"}

Prediction Stability

How consistent outcomes are across simulation runs.

{riskLevel === "Low" ? "Stable" : riskLevel === "Medium" ? "Variable" : "Volatile"}

{riskLevel === "Low" ? "Outcome distribution consistent" : riskLevel === "Medium" ? "Some outcome variation" : "Wide outcome distribution"}

{/* MAFS AI Picks Section */}

MAFS AI Picks

{qualifiedBets.length} High-Edge Opportunit{qualifiedBets.length !== 1 ? 'ies' : 'y'} Detected

Highest-edge opportunities identified by the MAFS simulation engine.

{scannerResults.lastScan && (

Simulation complete — updated {Math.floor((Date.now() - scannerResults.lastScan.getTime()) / 1000)}s ago

)}

{qualifiedBets.length > 0 ? (

{qualifiedBets.slice(0, 3).map((bet, idx) => { const hasRiskFlag = bet.agentSignals.some(s => s.signal === "neutral" || s.signal === "fail") const passedSignals = bet.agentSignals.filter(s => s.signal === "pass").length return ( {/* Top rank badge for first card */} {idx === 0 && (

Top Pick

)} {/* RECOMMENDATION HEADER */}

{bet.label}

{bet.odds_american} • Market: {(bet.P_imp * 100).toFixed(0)}% implied

{/* CORE METRICS - Visual hierarchy */}

{/* Model Win Probability - Large circular indicator */}

Model Probability

{/* Model Edge - Visual scale */}

Model Edge

= 10 ? "text-emerald-400" : bet.edge_pct >= 6 ? "text-green-400" : bet.edge_pct >= 3 ? "text-primary" : "text-muted-foreground"}`}> +{bet.edge_pct.toFixed(1)}%

{/* Edge scale bar */}

= 10 ? "bg-gradient-to-r from-emerald-500/60 to-emerald-400" : bet.edge_pct >= 6 ? "bg-gradient-to-r from-green-500/60 to-green-400" : bet.edge_pct >= 3 ? "bg-gradient-to-r from-primary/60 to-primary" : "bg-gradient-to-r from-muted-foreground/40 to-muted-foreground/60" }`} style={{ width: `${Math.min(bet.edge_pct * 8, 100)}%` }} />

= 10 ? "text-emerald-400" : bet.edge_pct >= 6 ? "text-green-400" : bet.edge_pct >= 3 ? "text-primary" : "text-muted-foreground/60" }`}> {bet.edge_pct >= 10 ? "Elite Edge (10%+)" : bet.edge_pct >= 6 ? "Strong Edge (6-9%)" : bet.edge_pct >= 3 ? "Small Edge (3-5%)" : "No Edge"}

{/* AI REASONING + SIMULATION DISTRIBUTION */}

{/* AI Insight */}

AI Insight

{bet.reason || `${bet.label.split(' ')[0]} shows ${(bet.P_sim * 100).toFixed(0)}% win probability in simulations vs ${(bet.P_imp * 100).toFixed(0)}% market implied.`}

{/* Confidence Badge */}

= 75 ? "bg-green-500/15 text-green-400 border border-green-500/20" : bet.confidencePct >= 50 ? "bg-yellow-500/15 text-yellow-400 border border-yellow-500/20" : "bg-muted/30 text-muted-foreground border border-white/10" }`}> {bet.confidencePct >= 75 ? "High Confidence" : bet.confidencePct >= 50 ? "Medium Confidence" : "Volatile"}

{/* Simulation Outcome Distribution */}

Simulation Paths

{(() => { // Generate realistic simulation paths based on bet type const fighterName = bet.label.split(' ')[0] const isFinishBet = bet.label.includes('ITD') || bet.label.includes('KO') || bet.label.includes('Sub') const isDecisionBet = bet.label.includes('Decision') || bet.label.includes('distance') const paths = isFinishBet ? [ { name: "R1 Finish", pct: Math.floor(bet.P_sim * 35 + Math.random() * 10) }, { name: "R2 Finish", pct: Math.floor(bet.P_sim * 25 + Math.random() * 8) }, { name: "R3+ Finish", pct: Math.floor(bet.P_sim * 20 + Math.random() * 5) }, { name: "Decision", pct: Math.floor((1 - bet.P_sim) * 40 + Math.random() * 10) }, ] : isDecisionBet ? [ { name: "UD Win", pct: Math.floor(bet.P_sim * 50 + Math.random() * 10) }, { name: "SD Win", pct: Math.floor(bet.P_sim * 25 + Math.random() * 8) }, { name: "Early Stop", pct: Math.floor((1 - bet.P_sim) * 30 + Math.random() * 5) }, ] : [ { name: `${fighterName} KO/TKO`, pct: Math.floor(bet.P_sim * 45 + Math.random() * 10) }, { name: `${fighterName} Sub`, pct: Math.floor(bet.P_sim * 15 + Math.random() * 5) }, { name: `${fighterName} Dec`, pct: Math.floor(bet.P_sim * 25 + Math.random() * 8) }, { name: "Opponent Win", pct: Math.floor((1 - bet.P_sim) * 100) }, ] // Normalize to ~100% const total = paths.reduce((sum, p) => sum + p.pct, 0) const normalized = paths.map(p => ({ ...p, pct: Math.round(p.pct / total * 100) })) return (

{normalized.slice(0, 4).map((path, pIdx) => (

{path.name} {path.pct}%

))}

) })()}

{/* Line Movement Section */} {bet.lineMovement && (

Line Movement

{/* Mini Sparkline */} {/* First Observed → Current */}

{bet.lineMovement.opening > 0 ? '+' : ''}{bet.lineMovement.opening} {bet.lineMovement.current > 0 ? '+' : ''}{bet.lineMovement.current}

{/* Market Trend Label */} {bet.lineMovement.sharpAction === "toward" && ( Market Moving Toward Bet )} {bet.lineMovement.sharpAction === "away" && ( Market Moving Away )}

)} {/* Outcome Indicators */}

{bet.agentSignals.filter(s => s.signal === "pass").length >= 2 && ( Model Edge )} {bet.edge_pct >= 3 && ( Market Inefficiency )} {bet.agentSignals.find(s => s.name === "Matchup Fit" && s.signal === "pass") && ( Matchup Fit )} {hasRiskFlag && ( Risk Flag )}

{/* Accordion Toggle */} {/* EXPANDABLE SECTION */} {expandedBetIdx === idx && (

{/* Executive Summary - Style + Dynamic + Market Bias */}

{bet.executiveSummary || bet.detailedReason.marketInefficiency}

{/* MAFS Verdict - Key Action Area with Glow */}

MAFS Verdict

{/* Playable Bet */}

{bet.edge_pct >= 3 && passedSignals >= 2 ? `Playable — ${bet.label} up to ${bet.odds_american}` : bet.edge_pct >= 2 ? `Consider — ${bet.label} (reduced stake)` : `Monitor only — edge below threshold` }

{/* Stats Grid */}

Edge

= 10 ? "text-emerald-400" : bet.edge_pct >= 6 ? "text-green-400" : "text-primary" }`}>+{bet.edge_pct.toFixed(1)}%

= 10 ? "text-emerald-400/70" : bet.edge_pct >= 6 ? "text-green-400/70" : "text-primary/70" }`}> {bet.edge_pct >= 10 ? "Elite" : bet.edge_pct >= 6 ? "Strong" : "Small"}

Expected Value

{(() => { const marketOdds = parseInt(bet.odds_american.replace("+", "")) const decimalOdds = marketOdds > 0 ? 1 + (marketOdds / 100) : 1 + (100 / -marketOdds) const ev = (bet.P_sim * (decimalOdds - 1)) - ((1 - bet.P_sim) * 1) return `+${(ev * 100).toFixed(1)}%` })()}

Recommended Stake

{(() => { const marketOdds = parseInt(bet.odds_american.replace("+", "")) const kelly = calculateKellyStake(bet.P_sim, marketOdds, bet.varianceTag || "Medium", bet.confidencePct || 70) return kelly.isPass ? "Pass" : `${kelly.units} Units` })()}

Model Prob

{(bet.P_sim * 100).toFixed(0)}%

{/* AI Signal Checklist with Tooltips */}

AI Signal Checklist

= 3 ? "text-green-400" : passedSignals >= 2 ? "text-primary" : "text-yellow-400"}`}> {passedSignals}/{bet.agentSignals.length} passed

{bet.agentSignals.map((signal, sIdx) => { // Map signal names to spec naming and tooltips const signalDisplayMap = { "Model Edge": { display: "Model Probability Advantage", tooltip: "Model probability exceeds market implied probability" }, "Market Inefficiency": { display: "Market Inefficiency", tooltip: "Detected pricing gap in current market odds" }, "Matchup Fit": { display: "Matchup Fit", tooltip: "Fighter style and attributes align with bet type" }, "Variance Check": { display: "Risk Filters", tooltip: "Outcome consistency and variance across simulation runs" }, "Stylistic": { display: "Stylistic Analysis", tooltip: "Style matchup favors this outcome" }, "Momentum": { display: "Recent Form", tooltip: "Recent performance trajectory supports bet" }, "Historical": { display: "Historical Edge", tooltip: "Past results in similar situations" }, } as const const signalInfo = signalDisplayMap[signal.name as keyof typeof signalDisplayMap] || { display: signal.name, tooltip: "AI analysis signal" } return (

{signal.signal === "pass" ? "✓" : signal.signal === "neutral" ? "⚠" : "✗"} {signalInfo.display}

{/* Tooltip */}

{signalInfo.tooltip}

) })}

{/* MAFS Edge Source - Short insight */}

Edge Source

{bet.edgeSource || bet.detailedReason.marketInefficiency.split('.').slice(0, 2).join('.') + '.'}

{/* Risk Callout (only if risk flag exists) */} {hasRiskFlag && bet.detailedReason.riskFactors[0] && (

Primary risk: {bet.detailedReason.riskFactors[0].toLowerCase()}

)} {/* Market Pricing */} {(() => { // Compute True Line pricing inline const marketOdds = parseInt(bet.odds_american.replace("+", "")) const modelProb = bet.P_sim const trueLineAmerican = modelProb >= 0.5 ? -Math.round((modelProb / (1 - modelProb)) * 100) : Math.round(((1 - modelProb) / modelProb) * 100) const marketImpliedProb = marketOdds > 0 ? 100 / (marketOdds + 100) : (-marketOdds) / ((-marketOdds) + 100) const decimalOdds = marketOdds > 0 ? 1 + (marketOdds / 100) : 1 + (100 / -marketOdds) const evPerUnit = (modelProb * (decimalOdds - 1)) - ((1 - modelProb) * 1) const mispricingProb = modelProb - marketImpliedProb const oddsAge = Math.floor(Math.random() * 45) + 15 return (

Market Pricing

Derived from aggregated market odds

Updated {oddsAge}s ago

{/* Row 1: Market Line + True Line */}

Market Line

{bet.odds_american}

Implied: {(marketImpliedProb * 100).toFixed(0)}%

MAFS True Line

{trueLineAmerican > 0 ? `+${trueLineAmerican}` : trueLineAmerican}

Model: {(modelProb * 100).toFixed(0)}%

{/* Row 2: Mispricing + EV */}

Mispricing

= 0.05 ? "text-positive" : mispricingProb >= 0 ? "text-risk" : "text-red-400"}`}> {mispricingProb >= 0 ? "+" : ""}{(mispricingProb * 100).toFixed(1)}%

Expected Value

0 ? "text-positive" : "text-red-400"}`}> {evPerUnit >= 0 ? "+" : ""}{(evPerUnit * 100).toFixed(1)}%

{/* Row 3: Recommended Stake */}

{(() => { const kelly = calculateKellyStake(modelProb, marketOdds, bet.varianceTag || "Medium", bet.confidencePct || 70) return (

Recommended Stake {kelly.isPass ? "Pass" : `${kelly.units} units`}

Sizing Method {kelly.method}

) })()}

{/* CLV display for historical fights */} {bet.closing_odds && (

Close: {bet.closing_odds > 0 ? `+${bet.closing_odds}` : bet.closing_odds} 0 ? "text-positive" : "text-muted-foreground/50"}`}> CLV: {(bet.clv_prob || 0) > 0 ? "+" : ""}{((bet.clv_prob || 0) * 100).toFixed(1)}%

)}

EV = (Model Prob × Profit) - (1 - Model Prob) | Mispricing = Model Prob - Market Implied

) })()} {/* Related Props - Same fighter only, ranked by EV */}

Related Props

{(() => { // Extract fighter name from bet label (e.g., "Pereira ITD" -> "Pereira") const fighterName = bet.label.split(" ")[0].toLowerCase() // Find scanner results for SAME FIGHTER only (not fight-level props like "Goes Distance") const relatedFromScanner = scannerResults.allResults .filter(r => { const selectionFighter = r.selection_name.split(" ")[0].toLowerCase() return ( selectionFighter === fighterName && r.selection_key !== bet.selection_key && r.ev_per_unit > -0.02 ) }) .sort((a, b) => b.ev_per_unit - a.ev_per_unit) .slice(0, 2) if (relatedFromScanner.length === 0) { return (

No related props available

) } return relatedFromScanner.map((prop, pIdx) => (

{prop.selection_name}

{formatScannerOdds(prop.market_odds)} 0 ? "text-positive" : "text-muted-foreground/50"}`}> EV: {formatScannerEV(prop.ev_per_unit)}

)) })()}

)} ) })}

) : (

No Scenarios Available

All outcomes for this event were filtered by the simulation engine.

)}

{/* MAFS Daily Edge Feed */}

MAFS Daily Edge Feed

Live

{liveFeedEntries.length} signals

{liveFeedEntries.map((entry) => { const tierStyles = { edge: { bg: "bg-emerald-500/10", border: "border-l-emerald-500", icon: , label: "Edge", labelBg: "bg-emerald-500/20 text-emerald-400" }, alert: { bg: "bg-orange-500/10", border: "border-l-orange-500", icon: , label: "Sharp", labelBg: "bg-orange-500/20 text-orange-400" }, movement: { bg: "bg-yellow-500/10", border: "border-l-yellow-500", icon: , label: "Movement", labelBg: "bg-yellow-500/20 text-yellow-400" }, value: { bg: "bg-green-400/10", border: "border-l-green-400", icon: , label: "Value", labelBg: "bg-green-400/20 text-green-400" }, } const tier = tierStyles[entry.feedType as keyof typeof tierStyles] || tierStyles.edge return (

{tier.icon}

{tier.label} {entry.isNew && ( New )}

{entry.title}

{entry.detail}

{entry.time}

) })} {liveFeedEntries.length === 0 && (

Scanning markets...

)}

{filteredBets.length > 0 && (

These markets were evaluated by the AI but did not meet the statistical edge threshold.

{showFilteredBets && (

{filteredBets.map((bet) => ( {/* Header */}

{bet.label}

{bet.odds_american}

{/* Rejection Tags */} {bet.rejectReasons && bet.rejectReasons.length > 0 && (

{bet.rejectReasons.map((reason, rIdx) => { // Map reasons to tag styles const getTagStyle = (r: any) => { if (r.includes("confidence") || r.includes("probability")) return "bg-yellow-500/10 text-yellow-400/80 border-yellow-500/20" if (r.includes("edge") || r.includes("advantage")) return "bg-muted/50 text-muted-foreground/70 border-white/5" if (r.includes("variance") || r.includes("unpredictable")) return "bg-orange-500/10 text-orange-400/80 border-orange-500/20" if (r.includes("agreement") || r.includes("model")) return "bg-blue-500/10 text-blue-400/80 border-blue-500/20" return "bg-muted/30 text-muted-foreground/60 border-white/5" } return ( {reason} ) })}

)} ))}

)}

)} {/* Fight Table Section */}

All Fights on This Card

Select any fight to view detailed AI analysis

{currentFights.map((fight) => { // Get fight breakdown for confidence indicators const breakdown = fightBreakdowns[fight.id] const confidenceLevel = breakdown?.confidence || "Medium" const mispricingValue = parseFloat(breakdown?.mispricing?.replace('%', '').replace('+', '') || '0') const hasEdge = mispricingValue > 2 // Get confidence percentage for display const confidencePct = confidenceLevel === "High" ? "74%" : confidenceLevel === "Medium" ? "62%" : "48%" return (

{ const isNewSelection = selectedFight !== fight.id setSelectedFight(selectedFight === fight.id ? null : fight.id) if (isNewSelection) { setTimeout(() => { document.getElementById('fight-breakdown-section')?.scrollIntoView({ behavior: 'smooth', block: 'start' }) }, 100) } }} className={`group flex items-center justify-between p-4 cursor-pointer transition-all hover:bg-white/[0.04] ${ selectedFight === fight.id ? "bg-primary/10 border-l-2 border-primary" : "border-l-2 border-transparent" }`} >

{fight.matchup}

{/* Edge percentage + tier badge for quick scanning */}

= 10 ? "text-emerald-400" : mispricingValue >= 6 ? "text-green-400" : mispricingValue >= 3 ? "text-primary" : "text-muted-foreground/50" }`}> {mispricingValue >= 1 ? `+${mispricingValue.toFixed(1)}%` : "—"} {mispricingValue >= 10 && ( Elite Edge )} {mispricingValue >= 6 && mispricingValue < 10 && ( Strong Edge )} {mispricingValue >= 3 && mispricingValue < 6 && ( Small Edge )} {mispricingValue < 3 && ( No Edge )}

{fight.odds}

View Analysis

) })}

{/* Fight Breakdown Section */} {selectedFight && fightBreakdowns[selectedFight] && (

AI Fight Analysis

{fightBreakdowns[selectedFight].fighter1Name} vs {fightBreakdowns[selectedFight].fighter2Name}

{/* SYSTEM SUMMARY */}

System Summary

Model bias favors{" "} {fightBreakdowns[selectedFight].fighter1Name} . {fightBreakdowns[selectedFight].fighter1Notes.split(".")[0]}.{" "} {fightBreakdowns[selectedFight].fighter2Name} {"'"}s path requires {fightBreakdowns[selectedFight].fighter2Notes.toLowerCase().split(".")[0]}.

{/* MODEL LINE VS MARKET LINE */}

Model Line vs Market Line

MAFS True Line

{fightBreakdowns[selectedFight].fighter1Name}: {" "} {fightBreakdowns[selectedFight].trueLine.split(" / ")[0]}

{fightBreakdowns[selectedFight].fighter2Name}: {" "} {fightBreakdowns[selectedFight].trueLine.split(" / ")[1]}

Market Line

{fightBreakdowns[selectedFight].fighter1Name}: {" "} {fightBreakdowns[selectedFight].marketLine.split(" / ")[0]}

{fightBreakdowns[selectedFight].fighter2Name}: {" "} {fightBreakdowns[selectedFight].marketLine.split(" / ")[1]}

Market Mispricing

{fightBreakdowns[selectedFight].mispricing}

Model - Market Implied

EV per Unit

{fightBreakdowns[selectedFight].ev}

Expected return on $1

{/* Line Movement Section */} {fightBreakdowns[selectedFight].lineMovement && (

Line Movement

{/* Mini Sparkline */} {/* First Observed → Current */}

First Observed: {fightBreakdowns[selectedFight].lineMovement.firstObserved > 0 ? '+' : ''}{fightBreakdowns[selectedFight].lineMovement.firstObserved}

Current: {fightBreakdowns[selectedFight].lineMovement.current > 0 ? '+' : ''}{fightBreakdowns[selectedFight].lineMovement.current}

{/* Edge Context */}

Edge at First: +{fightBreakdowns[selectedFight].lineMovement.edgeAtFirst}%

Edge Now: +{fightBreakdowns[selectedFight].lineMovement.edgeNow}%

{/* Market Trend Label */}

{fightBreakdowns[selectedFight].lineMovement.edgeNow > fightBreakdowns[selectedFight].lineMovement.edgeAtFirst ? ( Market Moving Toward Model ) : fightBreakdowns[selectedFight].lineMovement.edgeNow < fightBreakdowns[selectedFight].lineMovement.edgeAtFirst ? ( Market Moving Against Model ) : ( Market Stable )}

)}

{/* KEY WIN CONDITIONS */}

Key Win Conditions

{fightBreakdowns[selectedFight].fighter1Name}

{fightBreakdowns[selectedFight].fighter1Notes}

{fightBreakdowns[selectedFight].fighter2Name}

{fightBreakdowns[selectedFight].fighter2Notes}

{/* OUTCOME DISTRIBUTION */}

Outcome Distribution

{fightBreakdowns[selectedFight].pathToVictory}

{/* SIMULATION INSIGHT */} {fightBreakdowns[selectedFight].mafsIntelligence && (

Simulation Insight

{/* eslint-disable-next-line @typescript-eslint/no-explicit-any */} {(fightBreakdowns[selectedFight].mafsIntelligence as any[]).map((item: any, idx: any) => (

{item.type} {item.text}

))}

)} {/* SIMULATION PATH BREAKDOWN */} {fightBreakdowns[selectedFight].simulationPaths && (

Simulation Path Breakdown

{/* eslint-disable-next-line @typescript-eslint/no-explicit-any */} {(fightBreakdowns[selectedFight].simulationPaths as any[]).map((path: any, idx: any) => (

{path.name} {path.pct}%

{path.desc}

))}

)} {/* MARKET PSYCHOLOGY */}

Market Mispricing Analysis

{fightBreakdowns[selectedFight].marketAnalysis}

{/* QUICK STATS GRID */}

Model Outcome

{fightBreakdowns[selectedFight].bet}

Playable Up To

{fightBreakdowns[selectedFight].playableUpTo || "current line"}

Confidence

{fightBreakdowns[selectedFight].confidence}

Variance

{fightBreakdowns[selectedFight].risk}

Signal

{fightBreakdowns[selectedFight].stake}

Stake

{(() => { const fb = fightBreakdowns[selectedFight] if (fb.modelProb && fb.marketOdds !== undefined) { const kelly = calculateKellyStake( fb.modelProb, fb.marketOdds, fb.risk, parseFloat(fb.confidence) ) return (

{kelly.isPass ? "Pass" : `${kelly.units} units`}

) } return

—

})()}

{/* PRIMARY RISK */}

Primary Risk

{fightBreakdowns[selectedFight].primaryRisk || (() => { const fb = fightBreakdowns[selectedFight] const hasKOPower = fb.fighter2Notes.includes("KO") || fb.fighter2Notes.includes("power") const riskType = hasKOPower ? "knockout" : "momentum shift" return `Early ${fb.fighter2Name} ${riskType} before ${fb.fighter1Name.toLowerCase()} pressure accumulation.` })()}

)}

) }