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arXiv 2202.00804cs.LG
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使用跟踪数据的足球中的自动事件检测

Automatic event detection in football using tracking data

Massachusetts Institute of Technology（麻省理工学院）；FIFA（国际足球联合会）

Ferran Vidal-Codina, Nicolas Evans, Bahaeddine El Fakir, Johsan Billingham

备注：
21 pages, 10 figures
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摘要：

近年来被广泛用于分析的足球赛事数据的主要缺点之一是，它仍然需要手动收集，从而限制了其可用性，以减少比赛数量。在这项工作中，我们提出了一种基于确定性决策树的算法来利用跟踪数据自动提取足球事件，该算法包括两个步骤：（1）控球步骤，该步骤评估跟踪数据中的每一帧中哪个球员控球，以及在球不在比赛中的时间间隔期间的不同球员配置，以通知定位球检测;（2）事件检测步骤，其将在第一步骤中计算出的控球变化与足球规则相结合，以确定比赛中事件和定位球。自动生成的事件对手动注释的事件进行基准测试，我们表明，在大多数事件类别中，所提出的方法实现了$+90\%$检测率在不同的比赛和跟踪数据提供商。最后，我们演示了如何利用跟踪数据提供的上下文信息来增加自动检测事件的粒度，并展示了所提出的框架如何用于在足球中进行大量的数据分析。

英文摘要：

One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a deterministic decision tree-based algorithm to automatically extract football events using tracking data, which consists of two steps: (1) a possession step that evaluates which player was in possession of the ball at each frame in the tracking data, as well as the distinct player configurations during the time intervals where the ball is not in play to inform set piece detection; (2) an event detection step that combines the changes in ball possession computed in the first step with the laws of football to determine in-game events and set pieces. The automatically generated events are benchmarked against manually annotated events and we show that in most event categories the proposed methodology achieves $+90\%$ detection rate across different tournaments and tracking data providers. Finally, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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