通过机器学习方法预测足球运动员的健康状况
Mauro Mandorino1,2, Jo Clubb3, Mathieu Lacome1,4
1Performance and Analytics Department, Parma Calcio 1913, Parma, Italy.
International journal of sports physiology and performance
|February 25, 2024
概括
这项研究为足球运动员开发了一种机器学习的健身指数,显示了与传统测试的强烈相关性. 这种"隐形监控"有助于个性化培训和伤害预防.
科学领域:
- 运动科学 运动科学 运动科学
- 在田径运动中的机器学习
- 足球表现分析 足球表现分析
背景情况:
- 评估足球运动员的健康状况对于表现和伤害预防至关重要.
- 传统的健身测试可能无法完全捕捉训练中的生理反应.
- 机器学习为监控运动员状态提供了新的方法.
研究的目的:
- 开发一个机器学习指数来预测足球运动员的健康状况.
- 根据次最大运行测试 (SMFT) 验证索引.
- 分析培训负载对指数和SMFT结果的影响.
主要方法:
- 收集了50名职业足球运动员的训练负载数据 (外部和内部).
- 利用机器学习来预测训练期间的心率反应.
- 根据实际与预测的心率计算了一个健身指数.
- 将健身指数与SMFT结果相关联.
主要成果:
- 随机森林回归是表现最好的机器学习算法.
- 健身指数的关键预测指标包括平均速度,训练时间和工作:休息比.
- 健身指数与SMFT结果有很强的相关性 (r = .70).
- 在赛季期间,该指数和SMFT之间的差异表明了不同的健身方面.
结论:
- 引入了一种"隐形监测"方法,用于足球训练中的健身评估.
- 健身指数补充了传统的测试,以全面了解球员的准备.
- 可实现个性化训练调整,并支持伤害预防策略.
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