橄球联盟中的机器学习:预测和识别专业橄球联盟球员在基于比赛的工作负载中的关键绩效指标
Xiangyu Ren1,2,3, Simon Boisbluche4, Kilian Philippe5
1Sino-French Joint Research Center of Sport Science, Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, China.
European journal of sport science
|August 23, 2025
概括
机器学习模型,特别是随机森林回归,有效地从全球定位系统 (GPS) 工作负载数据中预测橄球运动员的表现指标. 这些模型揭示了复杂的关系, 有助于优化工作负载管理以提高运动表现.
科学领域:
- 运动科学
- 生物力学
- 体育中的数据科学
背景情况:
- 橄球联盟要求对训练和比赛指标进行分析, 工作量对绩效指标的影响未得到充分研究.
- 时间运动和视频分析提供了洞察力,但量化工作负载与性能之间的联系需要进一步调查.
研究的目的:
- 调查全球定位系统 (GPS) 累积工作负载数据对橄球比赛关键绩效指标 (KPIs) 的影响.
- 在理解工作负载-KPI关系方面比较各种机器学习模型的预测性能.
主要方法:
- 收集了GPS数据来计算7,14和21天的累积工作量.
- 使用主要组件分析 (PCA) 减少维度.
- 使用线性回归,支持向量的回归,随机森林回归和轻GBM来预测KPI,使用R2,RMSE和R的模型进行评估.
主要成果:
- 个别GPS指标与KPI的相关性较弱.
- 机器学习模型,特别是随机森林回归,捕获了复杂的非线性工作负载-KPI交互.
- 模型实现了显著的预测性能 (一些关键指标的R2 0.400.72).
- 通过SHapley添加式解释 (SHAP) 提高了模型的可解释性,并确定了性能的主要工作负载驱动因素.
结论:
- 与简单的相关性相比,机器学习模型可以从工作负载数据中更好地预测橄球KPI.
- 通过可解释的机器学习来理解工作负载-性能动态对于有效的玩家管理至关重要.
- 这些发现为优化训练负载提供了可操作的见解, 以提高橄球运动员的表现.
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