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亲自行车团队的骑自行车运动员在即将到来的比赛中被分配
Maor Sagi1, Paulo Saldanha2, Guy Shani1
1Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel.
PloS one
|March 4, 2024
概括
这项研究介绍了RaceFit,这是一个新的模型,用于使用最近的训练数据和过去的任务将骑自行车者分配到专业比赛中. RaceFit准确地预测了参与率,表现优于基线方法.
科学领域:
- 运动科学 运动科学 运动科学
- 运动中的机器学习
- 专业自行车运动中的数据分析.
背景情况:
- 专业自行车运动利用传感器数据进行训练和比赛分析.
- 骑自行车的比赛对团队战略至关重要,但往往依赖于启发式.
研究的目的:
- 开发和评估RaceFit,这是一个用于将骑自行车者分配到专业比赛的预测模型.
- 利用最近的训练数据和历史任务来优化团队选择.
主要方法:
- 开发了RaceFit,一种使用对骑自行车者和比赛特征进行训练的二进制分类器的模型.
- 整合了人口统计,最近的炼和种族特定数据.
- 评估了两种方法:阶段级和整个比赛的分配.
主要成果:
- 在来自三支职业自行车队的大数据集上,RaceFit实现了高达80%的精度.
- 无论使用TP还是STRAVA数据,性能都是一致的.
- 具有5周窗口和归算的CatBoost分类器表现最好,表现优于基于人气的基线.
结论:
- RaceFit提供了一种数据驱动的方法来分配骑自行车的比赛,大大改进了传统方法.
- 该模型展示了机器学习在优化职业自行车运动中团队选择方面的有效性.
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