用基于图形的深度学习来分析男性精英手球的情境化强度
Manuel Bassek1, Dominik Raabe1, Alexander Banning1
1Institute of Exercise Training and Sport Informatics, German Sport University Cologne, Cologne, Germany.
Journal of sports sciences
|October 18, 2023
概括
手动注释限制了体育数据分析. 一个新的机器学习框架自动化了手球的表现分析,使得对比赛动态和球员强度的大规模洞察成为可能.
科学领域:
- 运动科学 运动科学 运动科学
- 机器学习 机器学习
- 数据分析数据分析数据分析.
背景情况:
- 在体育运动中手动注释数据是耗时的,并且限制了分析范围.
- 自动化性能分析可以从大型数据集中获得更深入的见解.
研究的目的:
- 引入FAUPA-ML (机器学习自动升级性能分析框架) 来自动化体育表现分析.
- 利用图形神经网络将专家知识扩展到大型数据集.
主要方法:
- 在手动注释手球比赛数据上训练有素的图形神经网络.
- 应用了最好的网络来分析539场精英手球比赛 (2019-2022).
- 对攻击者和防御者的计算性能指标 (距离,速度,代谢功率/工作).
主要成果:
- 在对应数据的上下文化方面实现了86%的平衡准确性.
- 反击攻击比位置攻击更短,更少频繁,更强烈.
- 攻击行动被发现比防御行动更强烈.
结论:
- FAUPA-ML准确地复制了性能分析的专家知识.
- 能够进行大规模的,上下文化的分析,这是以前无法实现的.
- 促进对影响团队体育表现的因素的未来研究.
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