青少年足球运动员方向变化速度的人类测量决定因素:使用决策树回归和机器学习方法进行横截面研究
Akram Fray1, Yassine Negra1, Halil İbrahim Ceylan2
1University of Manouba, Higher Institute of Sport and Physical Education of Ksar-Saïd, Research Laboratory (LR23JS01) "Sport Performance, Health & Society," Tunis, Tunisia.
Medicine
|November 27, 2025
概括
年轻的足球运动员.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 儿科运动医学 儿科运动医学
背景情况:
- 评估方向变化速度 (CODS) 对于青少年足球中的人才识别至关重要.
- 人类测量特征和生物成熟度是CODS的潜在预测因素.
- 可解释的人工智能 (XAI) 为分析复杂的性能数据提供了新的方法.
研究的目的:
- 确定人类特征对年轻足球运动员CODS的预测能力.
- 利用XAI的方法来理解物理特征和CODS之间的关系.
- 确定影响男性青年足球中CODS的关键人体测量和成熟度指标.
主要方法:
- 51名年轻的足球运动员从一个职业学院招募.
- 进行了标准化的人类测量 (例如,腿长,坐高) 和505测试CODS.
- 决策树回归 (DTR) 是一种XAI方法,用于建模和预测CODS,用于模型评估的交叉验证.
主要成果:
- DTR模型准确地预测了CODS,解释了84.3%的性能差异 (R2 = 0.843).
- 确定的主要预测因素包括成熟度偏移 (16.3%),年龄 (16.8%),腿长 (15.8%),坐高 (13.6%),膝高 (10.5%).
- 较为成熟的球员,坐高较短的球员表现出优异的CODS.
结论:
- 人类特征和生物成熟度是年轻男性足球运动员中CODS的重要预测因素.
- XAI,特别是DTR,为影响CODS的因素提供了有价值的见解.
- 这些发现支持将人体测量和成熟度评估纳入青年足球人才识别和发展计划.
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