True Probability vs Vegas Odds: Who's More Accurate?Table of ContentsShow/Hide ContentsTrue Probability vs Vegas Odds — Who's More Accurate?What We Mean by True Probability and Vegas OddsDefining True ProbabilityDefining Vegas Odds and Implied ProbabilityConverting Odds to Vegas Implied Probability (and Removing the Vig)The Conversion Cheat-SheetThe Crucial Step: Removing the Vig (Normalization)How to Measure Accuracy — Brier, Log Loss, and CalibrationBrier Score (Our Primary Metric)Calibration (The "Reliability" Check)Supporting MetricsExperimental Design for Testing Market Efficiency Sports BettingStep 1 — Data SourcesStep 2 — The Golden Rule: Chronological SplitsStep 3 — Pre-processing & MetricsStep 4 — Statistical Significance with BootstrappingSharp vs Public Betting — How to Detect and Why It MattersDefining the PlayersDetecting Sharp ActionCase Study: A Season-Level Comparison (EPL 2021-22)The SetupThe ResultsPractical Takeaways — When to Follow the Market and When to Trust ModelsWhen Vegas Odds Accuracy ShinesWhen Models Can Beat the MarketThe Best of Both Worlds — Model ShrinkageFAQAre Vegas odds accurate measures of true probability?How do I calculate vegas implied probability from odds?What does market efficiency sports betting mean and how do I test it?How do I tell sharp vs public betting and why does it change accuracy?TL;DR & Reproducible ResourcesActionable Checklist for Bettors and AnalystsResources for Further LearningSample Python Code Pipeline1. Load your datadf should have columns: 'home_odds', 'draw_odds', 'away_odds', 'outcome'outcome is coded as 0 (away), 1 (draw), 2 (home)2. Convert odds to implied probabilities3. Remove the vig (normalize)4. Calculate Brier ScoreFor home wins (binary)5. Create calibration plot6. Bootstrap confidence intervalsTrue Probability vs Vegas Odds — Who's More Accurate?What We Mean by True Probability and Vegas OddsDefining True ProbabilityDefining Vegas Odds and Implied ProbabilityConverting Odds to Vegas Implied Probability (and Removing the Vig)The Conversion Cheat-SheetThe Crucial Step: Removing the Vig (Normalization)How to Measure Accuracy — Brier, Log Loss, and CalibrationBrier Score (Our Primary Metric)Calibration (The "Reliability" Check)Supporting MetricsExperimental Design for Testing Market Efficiency Sports BettingStep 1 — Data SourcesStep 2 — The Golden Rule: Chronological SplitsStep 3 — Pre-processing & MetricsStep 4 — Statistical Significance with BootstrappingSharp vs Public Betting — How to Detect and Why It MattersDefining the PlayersDetecting Sharp ActionCase Study: A Season-Level Comparison (EPL 2021-22)The SetupThe ResultsPractical Takeaways — When to Follow the Market and When to Trust ModelsWhen Vegas Odds Accuracy ShinesWhen Models Can Beat the MarketThe Best of Both Worlds — Model ShrinkageFAQAre Vegas odds accurate measures of true probability?How do I calculate vegas implied probability from odds?What does market efficiency sports betting mean and how do I test it?How do I tell sharp vs public betting and why does it change accuracy?TL;DR & Reproducible ResourcesActionable Checklist for Bettors and AnalystsResources for Further LearningSample Python Code Pipeline1. Load your datadf should have columns: 'home_odds', 'draw_odds', 'away_odds', 'outcome'outcome is coded as 0 (away), 1 (draw), 2 (home)2. Convert odds to implied probabilities3. Remove the vig (normalize)4. Calculate Brier ScoreFor home wins (binary)5. Create calibration plot6. Bootstrap confidence intervalsShare ArticleTwitterFacebookLinkedInCopy LinkAd
