关于体育分析的观点,以告知约束操纵,代表性学习和实践设计中的功能变异性
Ben Teune1, Harjiv Singh2, Sam Robertson3
1Women's Tennis Association, St Petersburg, Florida, United States.
Human movement science
|December 22, 2025
概括
机器学习为体育技能获取提供了强大的工具. 决策树和聚类等技术可以帮助教练优化练习设计,并通过数据驱动的洞察力提高运动员的表现.
科学领域:
- 运动科学 运动科学 运动科学
- 技能获取 技能获得 技能获取
- 数据分析数据分析数据分析.
背景情况:
- 越来越多的技术和数据在体育中的使用为实践设计提供了机会.
- 分析技术可以增强技能获取框架,如约束引导方法.
- 机器学习 (ML) 是获得详细见解的可行方法,因为它能够处理复杂的数据关系.
研究的目的:
- 探索机器学习算法如何运行技能获取原则.
- 为了证明ML技术的应用,以告知体育实践设计.
- 支持教练和应用体育科学家利用技术和数据.
主要方法:
- 应用各种机器学习算法,包括决策树,随机森林,规则关联和集群.
- 使用ML分析体育数据中的多变量和非线性关系.
- 通过ML运行约束操纵,代表性学习和功能可变性的运行原则.
主要成果:
- 决策树/随机森林可以预测玩家行为的最佳约束操纵.
- 规则的关联识别了在实践中复制的交互约束.
- 集群技术将类似的球员运动组合在一起,有助于训练变化处方.
结论:
- 机器学习为增强体育练习设计提供了有价值的工具.
- 这些技术支持教练和应用体育科学家在数据驱动的决策中.
- ML可以导致更有效和个性化的运动员训练计划.
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