超越比赛位置:根据比赛特定的跑步表现对足球运动员进行分类,使用机器学习
Michel de Haan1, Stephan van der Zwaard1,2, Jurrit Sanders3
1Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands.
Journal of sports science & medicine
|September 11, 2025
概括
无监督机器学习更好地通过运行表现来对足球运动员进行分类,而不是传统的比赛位置. 这种方法提高了球员的评价,并优化了精英运动员的体育训练计划.
科学领域:
- 运动科学 运动科学 运动科学
- 绩效分析 绩效分析
- 运动中的机器学习
背景情况:
- 根据位置对足球运动员的传统分类可能不能准确地反映身体比赛表现.
- 了解玩家特定的运行需求对于有效的训练和绩效评估至关重要.
研究的目的:
- 基于跑步表现的无监督机器学习与比赛位置对足球运动员进行分类的有效性进行比较.
- 为了确定哪种分类方法更好地识别出具有相似物理需求的不同玩家子组.
主要方法:
- 在两个赛季内收集了40名精英男性足球运动员的比赛特定的运行数据.
- 使用的k-means基于运行性能指标 (总距离,低,中等,高强度运行,冲刺距离) 的集群.
- 将聚类结果与传统的游戏位置类别进行比较,分析子组之间的差异和标准化差异.
主要成果:
- 基于跑步表现的聚类显示,与打球位置相比,群体内的差异明显较小,群体之间的差异更大.
- 不同的集群显示了冲刺能力和高强度跑步的显著差异,这些差异在比赛位置之间并不明显.
- 在已识别的冲刺和高强度耐力集群中,冲刺速度有所不同.
结论:
- 无监督机器学习提供了一种更强大的方法,可以根据比赛特定的运行表现对足球运动员进行分类.
- 这种数据驱动的方法有助于更准确的绩效评估和个性化的体育训练计划优化.
- 基于机器学习的分类提供了与传统的位置分组相比,对玩家的身体形状有更好的洞察力.
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