长距离步道跑的实时性能预测:基于地形难度和步调变量的实用模型
Héctor Gutiérrez1, Eduardo Piedrafita1, Pablo Jesús Bascuas1
1Facultad de Ciencias de la Salud, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca, km 299, Villanueva de Gállego, 50830 Zaragoza, Spain.
Sports (Basel, Switzerland)
|November 26, 2025
概括
这项研究引入了一种实时预测模型,用于赛道跑步比赛,使用比赛中的数据来估计终点时间. 该模型准确地预测了表现,帮助运动员在马拉松和超级跑道比赛中调整策略.
科学领域:
- 运动科学 运动科学 运动科学
- 耐力运动分析 耐力运动分析
- 性能预测建模的模型.
背景情况:
- 传统的赛道跑的性能预测模型通常依赖于实验室测试或赛前数据,限制了实际的实时应用.
- 准确预测马拉松和超级赛道比赛表现对于战略规划和伤害预防至关重要.
研究的目的:
- 开发和验证使用比赛数据对马拉松和超级赛道比赛实时预测模型.
- 评估模型在不同性别和种族类型的预测能力.
- 为运动员和教练提供一个实用的工具来监控和优化比赛策略.
主要方法:
- 分析了947名参加"特纳山谷步道"活动的跑步者.
- 使用比赛前三分之一的数据开发预测方程.
- 包括诸如加权时间 (WTn),步调可变性 (WTVn,n+2) 和检查点百分位数排名 (CPRn) 等变量.
主要成果:
- 开发的模型表现出强大的预测能力,调整后的R平方值大于0.95.
- 该模型有效地估计了仅使用早期比赛数据的总比赛时间.
- 关键变量 (WTn,WTVn,n+2,CPRn) 反映了跑者管理高度和节奏一致性的能力.
结论:
- 实时预测模型在现场条件下非常适用,为实验室方法提供了切实可行的替代方案.
- 该模型为耐力赛期间的疲劳管理和性能优化提供了宝贵的见解.
- 建议在类似活动中进行进一步验证,以确认其对培训和比赛规划的有用性.
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