Key Factors Influencing NBA Game Outcomes: A Machine Learning Approach Using Game and Player Statistics

Predicting basketball game outcomes is complex as every game is influenced by many factors including individual players’ performance and health conditions, team dynamics, team strategies, and game conditions. This study aimed to develop a machine-learning approach using game logs, player statistics, and historical data from the 2023–24 and 2024–25 NBA seasons. It incorporated game conditions and momentum indicators and optimized an XGBoost model using team-based train-test-validation, feature selection, and hyperparameter tuning. Key predictors included win streaks, home-court advantage, shooting efficiency, and player trades. SHAP values were used to interpret feature importance. The results suggest momentum, player performance, and rest days significantly influence game outcomes.