用贝叶斯混合模型对可解释的预期目标进行建模
Loïc Iapteff1, Sebastian Le Coz1, Maxime Rioland1
1Seenovate, Montpellier, France.
Frontiers in sports and active living
|May 8, 2025
概括
本研究介绍了一种简单,可解释的贝叶斯模型,用于预测足球比赛中的预期进球 (xG). 该模型实现了与复杂方法相美的性能,为体育分析和投注提供了宝贵的见解.
科学领域:
- 运动分析 运动分析
- 统计建模 统计建模
- 运动中的机器学习
背景情况:
- 体育团队和博彩公司寻求了解球员/球队的活动和比赛结果.
- 预期目标 (xG) 模型提供了对绩效的洞察力,但往往缺乏可解释性.
- 复杂的统计和机器学习模型目前用于结果预测.
研究的目的:
- 开发一种简单可解释的预期目标 (xG) 建模方法.
- 将拟议模型的性能与现有方法进行比较.
- 用有限的数据利用转移学习来分析团队的优缺点.
主要方法:
- 贝叶斯概括的线性混合效应模型为xG.
- 利用了七个关键变量:射击类型,位置和对手的距离.
- 员工将学习转移给预先训练的模型.
主要成果:
- 贝叶斯式xG模型的性能与StatsBomb模型的性能相当 (AUC=0.781对比0.801).
- 该模型有效地使用一组有限的变量进行预测.
- 预先训练的模型可以通过小数据集来轻松识别团队的优点/缺点.
结论:
- 对于xG,一个简单的,可解释的贝叶斯模型是可行的和有效的.
- 这种方法增强了高级分析在体育中的实际应用.
- 转移学习为分析团队绩效动态提供了一个强大的工具.
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