使用预测性XG提升模型对10天超级马拉松的分析
Beat Knechtle1,2, Elias Villiger3, David Valero4
1Medbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001, St. Gallen, Switzerland. beat.knechtle@hispeed.ch.
BMC research notes
|December 20, 2024
概括
来自美国的超级马拉松运动员最常见,但最快的10天比赛运动员来自芬兰和以色列. 赛道表面显著影响速度,青比泥路更快.
科学领域:
- 运动科学 运动科学 运动科学
- 耐力运动研究 耐力运动研究
- 超级马拉松跑步分析
背景情况:
- 超级马拉松跑步包括从6小时到10天的比赛.
- 关于10天比赛中的运动员人口统计,表现和赛事具体情况的知识有限.
- 了解影响超级马拉松表现的因素对于运动员的发展和赛事组织至关重要.
研究的目的:
- 调查10天超级马拉松跑者的起源和表现特征.
- 为了确定10天超级马拉松最快的比赛地点.
- 用机器学习模型分析各种因素对超级马拉松跑步速度的影响.
主要方法:
- 使用XGBoost算法的机器学习模型的开发.
- 根据运动员的年龄,性别,原籍国,比赛地点,比赛类型和跑步表面来预测跑步速度.
- 应用模型可解释性工具来确定每个变量对运行速度的影响.
主要成果:
- 运动员的原籍国成为跑步速度的最重要的预测因素.
- 跑步速度受到年龄组,跑步表面 (泥路减速,青增加速度),性别和活动地点的影响.
- 大多数10天超级马拉松运动员来自美国,大多数比赛都在美国举行.
- 最快的10天超级马拉松跑步者来自芬兰和以色列,最快的比赛发生在希腊.
结论:
- 虽然大多数10天超级马拉松运动员来自美国,但来自芬兰和以色列的运动员展示了精英速度.
- 赛道的特点,特别是跑道,显著影响了超级马拉松的表现.
- 在为期10天的超级马拉松中,青表面促进了较快的跑步速度,而不是泥土路径.
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