在室内游戏中,在没有全球定位系统的情况下预测运动场距离:对机器学习技术的比较研究
Casey J Metoyer1, Jonathon R Lever1, Alan Huebner1,2
1Sports Performance, University of Notre Dame, Notre Dame, IN, USA.
International journal of sports physiology and performance
|April 29, 2025
概括
机器学习准确地预测了在没有GPS的室内运动中运动员的距离. XGBoost Regressor在总,冲刺和跑步距离方面表现最好,有助于性能优化和伤害预防.
科学领域:
- 运动科学 运动科学 运动科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确的运动员距离跟踪对于性能分析和伤害预防至关重要.
- 全球定位系统 (GPS) 通常不适合室内运动.
- 开发用于距离预测的替代方法是必不可少的.
研究的目的:
- 评估机器学习技术,用于在没有GPS的室内运动中预测运动员距离.
- 为了比较XGBoost回归器,ElasticNet,Ridge和Lasso回归的有效性.
- 分析预测总,冲刺和跑步距离在不同体育和性别的准确性.
主要方法:
- 使用了机器学习模型,包括XGBoost回归器,ElasticNet,Ridge和Lasso回归.
- 使用了来自诺特达姆大学男女足球和拉克罗斯运动员的数据.
- 绩效使用根-平均-平方误差,标准偏差,平均值和95%置信区间来评估.
主要成果:
- XGBoost回归器实现了总距离 (97.962 ± 12.973) 的最小平方根平均误差.
- 在预测冲刺距离 (91.616 ± 4.234) 和跑步距离 (137.103 ± 2.789) 方面,XGBoost还表现出卓越的性能.
- 运动,性别和背景 (游戏与实践) 的表现各不相同.
结论:
- 机器学习,特别是XGBoost Regressor,为预测室内运动员距离提供了可行的解决方案.
- 准确的距离预测可以为优化团队表现,预防伤病和管理球员条件的策略提供信息.
- 模型选择应根据特定的运动,性别和活动背景进行量身定制,以获得最佳结果.
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