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Stop trying to beat the market. Start learning from it.
Most sports betting models fail not because they are unsophisticated, but because they ignore the single most important source of information available: the betting market itself.
In Bayesian Betting, Adam Wickwire presents a practical, quantitative framework for NBA player prop betting that treats the market as an informed prior rather than an opponent. Using Bayesian updating, statistical models and market prices are combined mathematically to produce more accurate and better-calibrated predictions.
Buy on Amazon →This book walks step-by-step through the full pipeline:
- → Pulling and cleaning NBA data using R
- → Engineering predictive features while avoiding data leakage
- → Building a multivariate Bayesian model that captures correlations between player stats
- → Incorporating sportsbook odds as probabilistic beliefs
- → Blending model predictions with market consensus
- → Identifying and filtering edges with realistic confidence
Rather than selling a black-box betting system, this book focuses on principles: uncertainty, calibration, information asymmetry, and probabilistic thinking. The methods are transparent, reproducible, and grounded in real-world constraints.
Bayesian Betting is written for technically inclined bettors, data scientists, and analysts who want a deeper understanding of how quantitative models and markets interact. No hype. No guarantees. Just a rigorous framework for thinking clearly about sports betting.
Enter the access code from the book to download the companion code.