在IRONMAN®中建模Pe2rformance 70.3年龄组三项运动员
Mabliny Thuany1, David Valero2, Elias Villiger3
1Department of Physical Education, State University of Para, Pará, Brazil.
Sports medicine - open
|November 25, 2025
概括
机器学习模型可以预测IRONMAN® 70.3三项比赛的表现. 来自瑞士或丹麦的年轻男性运动员,在特定的欧洲或澳大利亚赛事中比赛,往往表现最好.
科学领域:
- 体育科学 运动科学 运动科学
- 数据科学数据科学数据科学
- 耐力运动分析 耐力运动分析
背景情况:
- 记录了影响年龄组三项运动员表现的个别因素.
- 应用机器学习 (ML) 来根据这些因素预测性能存在有限的研究.
- 这项研究通过使用ML来分析IRONMAN® 70.3性能预测器来解决这一差距.
研究的目的:
- 开发和分析用于预测IRONMAN® 70.3性能的ML回归模型.
- 确定影响比赛时间的关键个体因素 (性别,年龄,国家,地点).
- 了解影响三项运动员表现的复杂相互作用.
主要方法:
- 分析了823,464个IRONMAN® 70.3完成者记录 (2004-2020年).
- 利用三项运动员的性别,年龄,原籍国和赛事地点作为预测因素.
- 构建并评估了四个ML回归模型,选择了表现最佳的模型进行进一步分析.
主要成果:
- 随机森林回归模型展示了最高的预测准确性.
- 模型的可解释性显示,来自瑞士或丹麦的年轻男性 (30岁以下) 取得了最佳表现.
- 最佳的比赛地点包括IRONMAN® 70.3奥地利/圣. 波尔顿,瑞士,阳光海岸和布塞尔顿.
结论:
- ML模型有效地揭示了影响三项运动员表现的复杂,非线性关系.
- 这些见解可以帮助IRONMAN® 70.3年龄组三项运动员在战略比赛规划中.
- 该研究强调了ML在体育表现分析中的潜力.
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