Explaining basketball game performance with SHAP: insights from Chinese Basketball Association

Abstract

This study explores the Key Performance Indicators (KPIs) influencing the game outcomes of the Chinese Basketball Association (CBA). Utilizing data from 4100 games across 10 CBA seasons (2013–2023), this study constructs CBA game outcome prediction models using seven machine learning algorithms, including XGBoost, LightGBM, Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors. The SHapley Additive exPlanation (SHAP) method is applied to explain the optimal prediction model and analyze the KPIs. The findings are as follows: (1) XGBoost demonstrates excellent performance in predicting CBA game outcomes. (2) eFG%, 3P%, 2P%, ORB%, DRB, and TOV% are key indicators influencing CBA game outcomes. (3) There is a tendency for offensive play over defensive strategies in CBA playoffs. This combined methodology of machine learning and SHAP analysis not only exhibits superior performance but also strong explainability. It effectively reflects the relationship between game outcomes and performance data, providing a scientific basis for enhancing professional basketball performance.