ArticleScientific reports2025
Explaining basketball game performance with SHAP: insights from Chinese Basketball Association.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- Article
- Winning beyond scoring: an explainable machine learning framework for identifying key performance indicators in the Chinese Basketball Association.BMC sports science, medicine & rehabilitation · 2026Article
- Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm.Scientific reports · 2025Article
- Interrupt or reinforce? The impact of timeout on momentum in basketball game.Frontiers in psychology · 2025Article
- Key factors and tactical variations in Chinese national women's softball games: a machine learning-based identification.Frontiers in sports and active living · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.