Explaining basketball game performance with SHAP: insights from Chinese Basketball Association.
Yan Ou-Yang1,2, Wei Hong1,2, Liming Peng1,3
1School of Intelligent Sports Engineering, Wuhan Sports University, Wuhan, Hubei, People's Republic of China.
Scientific Reports
|April 21, 2025
Summary
Machine learning models accurately predict Chinese Basketball Association (CBA) game outcomes. Key performance indicators like shooting percentages and rebounds significantly influence results, highlighting offensive strategies in CBA playoffs.
Area of Science:
- Sports Analytics
- Machine Learning in Sports
- Basketball Performance Analysis
Background:
- Understanding factors influencing professional basketball game outcomes is crucial for team strategy and performance enhancement.
- The Chinese Basketball Association (CBA) presents a unique context for analyzing game dynamics and key performance indicators (KPIs).
Purpose of the Study:
- To identify and analyze the Key Performance Indicators (KPIs) that significantly influence Chinese Basketball Association (CBA) game outcomes.
- To develop and evaluate machine learning models for predicting CBA game results.
- To provide explainable insights into the relationship between performance metrics and game outcomes using the SHapley Additive exPlanation (SHAP) method.
Main Methods:
- Collected data from 4100 CBA games spanning 10 seasons (2013-2023).
- Constructed and compared seven machine learning models: XGBoost, LightGBM, Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors.
- Applied the SHapley Additive exPlanation (SHAP) method to interpret the optimal prediction model and identify influential KPIs.
Main Results:
- XGBoost emerged as the top-performing algorithm for predicting CBA game outcomes.
- Key performance indicators identified include effective field goal percentage (eFG%), three-point percentage (3P%), two-point percentage (2P%), offensive rebound percentage (ORB%), defensive rebounds (DRB), and turnovers percentage (TOV%).
- Analysis indicated a trend favoring offensive strategies over defensive ones in CBA playoff games.
Conclusions:
- The combination of machine learning and SHAP analysis provides a robust and interpretable framework for understanding CBA game dynamics.
- Identified KPIs offer actionable insights for enhancing team performance and strategic decision-making in professional basketball.
- The study establishes a scientific basis for improving performance in the CBA by leveraging data-driven insights.
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