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.
翻译:近些年来,足球事件数据广泛用于分析分析,其主要缺点之一是,足球事件数据仍需要人工收集,从而将其使用限制在减少的比赛次数;在这项工作中,我们提议采用基于树的确定性决定算法,利用跟踪数据自动提取足球事件,这包括两个步骤:(1) 拥有一个步骤,评价在跟踪数据每个框架哪个球手拥有球,以及在球不起作用的间隔期间不同的播放器配置,以告知碎片探测;(2) 事件探测步骤,将第一步计算出的球拥有量的变化与足球定律相结合,以确定比赛事件和设定碎片;自动生成的事件以手动附加说明的事件为基准,我们表明,在多数情况下,拟议方法在不同比赛和跟踪数据提供者之间达到+90美元检测率;最后,我们展示如何利用跟踪数据提供的背景资料来提高自动探测事件的粒子性,并展示拟议框架如何用于进行足球数据分析。