Building Predictive Models for Sports

Building Robust Sports Prediction Models

Creating accurate predictive models for sports requires careful attention to feature engineering, proper validation techniques, and understanding the unique challenges of sports data including small sample sizes and high variance.

Feature Engineering for Sports

The quality of your features often matters more than your choice of algorithm. Key considerations:

• Rolling averages: Use recent performance windows (last 10, 30, 100 games)
• Opponent adjustments: Adjust stats for quality of competition
• Park/venue factors: Account for environmental effects
• Rest and fatigue: Days of rest, travel distance
• Platoon splits: Handedness matchups in baseball

Model Validation in Sports

Standard cross-validation can lead to data leakage in time-series sports data. Use:

• Time-based splits: Train on past, test on future
• Walk-forward validation: Retrain as new data arrives
• Season holdouts: Test on completely unseen seasons

Avoiding Overfitting

Key Takeaways

• Domain expertise drives good feature engineering
• Always use time-based validation for sports data
• Simple models often outperform complex ones out-of-sample
• Regularization is essential with limited sports data
• Backtest thoroughly before deploying any model