Bayesian Sports Analytics with R: Predictive Modeling for Betting & Performance is a beginner-friendly, end-to-end guide to applying Bayesian statistics to real sports data in R. Blending clear theory with hands-on code, the book teaches you how to move from raw data to calibrated, defensible predictions for both performance analysis and betting.
You’ll learn the core building blocks—Bayes’ theorem, priors and posteriors, likelihoods, and credible intervals—before progressing to practical modeling with tidyverse, brms, rstanarm, bayesplot, and sport-specific tools like footBayes. Step-by-step tutorials show how to clean and visualize data, fit models, diagnose MCMC, and communicate uncertainty with posterior predictive checks and interval estimates.
Covering multiple sports—soccer, basketball, American football, baseball, and tennis—the book demonstrates:
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Goal and score models (Poisson/Dixon–Coles), Bradley–Terry/Elo-style ratings, and hierarchical player/team effects (partial pooling).
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Bayesian updating for live and season-long win probabilities, plus simulation-based forecasting.
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Predictive modeling for betting: converting odds to implied probabilities, identifying value, bankroll sizing with the Kelly criterion, and evaluating performance with Brier score, log loss, and calibration plots.
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Model validation and comparison via LOO/WAIC, along with robust diagnostics (R-hat, effective sample size, divergent transitions).
Every chapter includes reproducible R code, interpretable charts/tables, and practical exercises, culminating in full case studies (e.g., a soccer match predictor and a simulated betting strategy). Designed for analysts, students, and bettors, this resource provides a complete workflow—from data ingestion and feature engineering to model specification, fitting, checking, and deployment—so you can build reliable, transparent, and actionable Bayesian sports models in R.
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