专业足球运动员主观疲劳的预测:一个数据驱动的方法来优化训练方法到比赛
Carlo Simonelli1,2, Athos Trecroci3, Damiano Formenti1
1University of Insubria.
Research quarterly for exercise and sport
|September 23, 2025
概括
预测足球运动员的疲劳是表现的关键. 机器学习模型确定前一天的压力和情绪是比赛日疲劳的首要预测因素,有助于训练优化.
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:
- 优化足球运动员的表现需要准确的疲劳预测.
- 每天监测球员的健康状况对于训练调整至关重要.
研究的目的:
- 确定职业足球运动员日常和比赛日疲劳的关键预测因素.
- 利用大数据分析和机器学习来预测疲劳.
主要方法:
- 收集了来自意大利六个职业足球队的日常主观数据 (疲劳,睡眠,疼痛,压力,情绪).
- 计算的训练负载 (TL) 使用会话评级的感知劳动和持续时间.
- 在超过3万个数据点上训练并测试了四种机器学习模型 (决策树,XGBoost,随机森林,物流回归).
主要成果:
- 机器学习模型在预测主观玩家疲劳时达到70-82%的准确性.
- 前一天的压力和情绪是预测比赛日疲劳的最有影响的因素.
- 调解分析显示,情绪和肌肉疼痛与训练负载与比赛日疲劳感知有关.
结论:
- 使用机器学习和大数据开发了对玩家疲劳的预测框架.
- 前一天的心理状态显著影响了比赛日的表现.
- 这种框架可以帮助教练模拟训练效果,提高球员的准备能力.
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