Machine Learning for Sports Analytics
Master Predictive Modeling, Player Performance Analysis, and Game Outcome Forecasting
Build production-ready machine learning models that predict player performance, forecast game outcomes, and uncover hidden patterns in sports data. This practical guide walks you through the complete machine learning workflow applied specifically to sports analytics, from raw data to deployed models. You'll master supervised learning algorithms including linear regression, logistic classification, decision trees, and support vector machines—understanding when and why to use each. Learn feature engineering techniques that transform game statistics into powerful predictors, implement cross-validation strategies that prevent overfitting, and evaluate models using sport-specific metrics. Discover how ensemble methods combine multiple models for superior predictions, explore clustering techniques for athlete segmentation, and understand the critical importance of model bias detection and fairness. Whether you're predicting injury risk, optimizing team rosters, or forecasting game outcomes, this book provides the practical knowledge and code-ready frameworks you need to succeed in sports analytics.
Est. Length
253 pages (14 chapters)
Language
English
Publication date
Mar 17, 2026
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