年轻女短跑运动员的纵向表现轨迹:预测表现进展的新工具
Michael Romann1,2, Marie Javet1, Julia Hernandez1,2
1Department of Elite Sport, Swiss Federal Institute of Sport Magglingen, Magglingen, Switzerland.
Frontiers in sports and active living
|January 7, 2025
概括
线性混合效应模型 (LMM) 允许对女性60米冲刺表现进行可靠的跟踪,帮助识别人才和预测未来. 这种方法为体育科学应用提供了超越传统方法的个性化洞察力.
科学领域:
- 体育科学 运动科学 运动科学
- 田径运动绩效分析分析
- 生物机械数据建模 生物机械数据建模
背景情况:
- 纵向性能跟踪对于人才识别和预测未来的运动成功至关重要.
- 传统方法在体育科学中面临着不平衡的数据集和不一致的重复测量的挑战.
- 准确的性能分析对于优化训练和运动员发展至关重要.
研究的目的:
- 用线性混合效应模型 (LMM) 分析女性60米短跑者的纵向性能发展.
- 为教练和研究人员创建一个实用的工具,用于绩效基准和预测.
- 建立可靠的基准并预测年轻运动员未来的表现轨迹.
主要方法:
- 分析了从2006年至2021年期间,来自8,732名女性60米短跑选手 (年龄在6-15岁) 的41,123场比赛结果.
- 利用线性混合效应模型 (LMM) 来生成性能轨迹,基准和个人预测.
- 开发了一个用于个人绩效预测的软件工具,并将LMM与Lambda-Mu-Sigma (LMS) 百分位曲线进行比较.
主要成果:
- LMM有效地处理了数据的复杂性,产生了强大的纵向性能轨迹.
- 混合模型方法提供了个性化的性能发展和未来预测,优于LMS的回顾性分析.
- 包括日志转换年龄在内的最佳模型解释了59% (边际R2) 和93% (条件R2) 的性能差异.
结论:
- 线性混合效应模型为体育中的性能基准和预测提供了有价值的纵向数据.
- 开发的软件工具有助于教练设定训练目标并识别有前途的运动员.
- LMM提供了一种强大的方法来理解和预测田径运动的发展.
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