一个整体的方法来预测在大学田径的表现:球员,团队和会议的观点
Christopher B Taber1, Srishti Sharma2, Mehul S Raval2
1Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT, USA.
Scientific reports
|January 12, 2024
概括
这项研究使用机器学习来预测女子篮球中的球员,球队和会议表现. 预测分析可以提高运动员的准备和训练策略.
科学领域:
- 运动科学 运动科学 运动科学
- 数据分析数据分析数据分析.
- 机器学习 机器学习
背景情况:
- 现有的研究往往单独分析球员或团队的表现.
- 为了获得全面的见解,需要跨多个层面的整体绩效评估.
研究的目的:
- 通过机器学习,全面评估第一师女子篮球队的球员,球队和会议水平的表现.
- 量化和预测各种水平的体育表现,以改善训练和监测.
主要方法:
- 从训练,压力,睡眠,恢复 (WHOOP),游戏统计 (极地) 和反运动的数据中收集的数据,在竞争一年中跳跃.
- 使用极端梯度提升 (XGB) 分类器来预测反应强度指数 (RSI) 和游戏得分 (GS),以及XGB回归器用于玩家效率评分 (PER).
- 利用数据平衡,集合方法 (随机森林,XGB),相关性分析和部分依赖图表来进行特征重要性和影响分析.
主要成果:
- 在使用XGB分类器预测RSI和GS时获得了>90%的准确性和0.9F1得分.
- XGB回归器预测PER的平均平方误差 (MSE) 为0.026和R平方 (R2) 为0.680.
- 通过整体特征重要性分析,通过玩家,团队和会议层面确定了关键绩效指标.
结论:
- 机器学习模型可以在多个层面上准确预测运动表现指标.
- 量化和预测表现使教练能够监测运动员的准备,并优化训练干预措施.
- 这种整体方法为体育数据分析和性能提升提供了一条革命性的途径.
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