用SHAP解释篮球比赛表现:来自中国篮球协会的见解
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
概括
机器学习模型准确地预测了中国篮球协会 (CBA) 的比赛结果. 关键绩效指标,如射门百分比和篮板显著影响结果,突出了CBA季后赛的进攻策略.
科学领域:
- 运动分析 运动分析
- 运动中的机器学习
- 篮球表现分析 篮球表现分析
背景情况:
- 了解影响职业篮球比赛结果的因素对于团队战略和绩效提升至关重要.
- 中国篮球协会 (CBA) 为分析比赛动态和关键绩效指标 (KPIs) 提供了一个独特的背景.
研究的目的:
- 识别和分析影响中国篮球协会 (CBA) 比赛结果的关键绩效指标 (KPI).
- 开发和评估用于预测CBA游戏结果的机器学习模型.
- 通过使用夏普利添加式扩展 (SHAP) 方法,提供可解释的洞察力,了解性能指标和游戏结果之间的关系.
主要方法:
- 收集了来自10个赛季 (2013-2023) 的4100场CBA比赛的数据.
- 构建并比较了七个机器学习模型:XGBoost,LightGBM,决策树,随机森林,支持矢量机器,物流回归和K-最近邻居.
- 应用了夏普利添加式扩展 (SHAP) 方法来解释最佳预测模型并识别有影响力的KPI.
主要成果:
- XGBoost成为预测CBA游戏结果的高性能算法.
- 确定的关键绩效指标包括有效野外进球百分比 (eFG%),三分百分比 (3P%),两分百分比 (2P%),进攻反弹百分比 (ORB%),防守反弹 (DRB) 和转盘百分比 (TOV%).
- 分析表明,在CBA季后赛比赛中,有利于进攻策略而不是防御策略的趋势.
结论:
- 机器学习和SHAP分析的结合为理解CBA游戏动态提供了一个强大的和可解释的框架.
- 识别的关键关键指标为提高职业篮球团队表现和战略决策提供了可操作的见解.
- 该研究建立了通过利用数据驱动的见解来提高CBA的表现的科学基础.
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