使用XGBoost模型量化网球运动员的势头和影响因素
Donghong Wang1,2, Xu Zhang3, Yuneng Xie4
1School of Public Administration, Zhongnan University of Economics and Law, Wuhan, China. wdhong@hbue.edu.cn.
Scientific reports
|May 19, 2025
概括
网球势头显著影响比赛结果,受到球员运动和球速等因素的影响. 这项研究量化了动量,揭示了它的动量.
科学领域:
- 运动科学 运动科学 运动科学
- 定量分析 定量分析
- 体育心理学在体育中的心理学
背景情况:
- 势头是网球中的一个关键因素,但往往无法量化.
- 动量的波动可以改变游戏动态和玩家的表现.
- 了解势头的驱动因素是改善玩家战略和匹配结果的关键.
研究的目的:
- 在网球中量化定义和分析势头.
- 研究势头转移对比赛轨迹和得分的影响.
- 确定影响势头和球员成功的关键因素.
主要方法:
- 建立了动量二次指标系统.
- 使用专家分析,CRITIC和层次分析确定指标权重.
- 采用随机步行模型进行动量验证.
- 使用的XGBoost和Shapley特征对于因子分析很重要.
主要成果:
- 势头不是随机的,而是受到特定因素的影响.
- 重要的因素包括运动员的长跑和击球速度.
- 开发的模型实现了高精度 (R2 = 0.9814) 和概括能力.
- 模型在预测比赛结果方面表现出色.
结论:
- 势头波动与球员的成功有很大关系.
- 定量分析为理解网球势头提供了一个强大的框架.
- 机器学习模型准确地识别了网球中的关键绩效指标.
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