从离散广播数据中连续跟踪足球运动员
Matthew J Penn1, Christl A Donnelly1,2, Samir Bhatt3,4
1Department of Statistics, University of Oxford, Oxford, UK.
Royal Society open science
|October 27, 2025
概括
这项研究引入了一种新的方法,可以从低质量的广播录像中生成连续的球员跟踪数据,从而使更多的足球队可以访问高级分析. 这种方法弥合了俱乐部缺乏计算机视觉应用的高质量视频的差距.
科学领域:
- 运动分析 运动分析
- 计算机视觉 计算机视觉
- 足球技术 足球技术
背景情况:
- 职业足球队往往无法获得高质量的球员跟踪数据,因为计算机视觉的视频源质量受到限制.
- 现有的事件数据广泛可用,但不提供高级分析所需的持续跟踪见解.
研究的目的:
- 开发和验证一种方法,通过从广播录像中获得的离散数据来估计连续的全音调播放器跟踪数据.
- 为更广泛的足球俱乐部提供先进的球员跟踪分析,包括半职业水平.
主要方法:
- 开发了一种新的方法,将广播录像中的离散数据处理为连续播放器跟踪数据.
- 该方法使用开源跟踪数据进行了测试.
- 该方法的一种版本应用于200多个游戏的数据集,其中有离散数据.
主要成果:
- 该研究成功展示了一种方法,通过低质量的广播源来估计连续播放器跟踪数据.
- 拟议的方法提供了一个成本效益高的解决方案,与事件数据收集相美.
- 使用现有数据集进行了验证,显示了该方法的适用性.
结论:
- 开发的方法可以弥合足球队在玩家跟踪数据可访问性方面的差距,视频质量有限.
- 这一创新允许以更低的成本进行更全面的绩效分析.
- 这种方法有可能使足球中的高级体育分析民主化.
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