Automatic event detection in football using tracking data
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12283_2022_Article_381.pdf
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2.45 MB
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Checksum (MD5)
16d0c6530f83bb31f8bd0aa6a7fba079
Author(s)
Vidal-Codina, Ferran
Evans, Nicolas
El Fakir, Bahaeddine
Billingham, Johsan
Date Issued
September 6, 2022
Publisher
Springer London
Citation
Sports Engineering. 2022 Sep 06;25(1):18
Version
Final published version
Abstract
Abstract
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
$$+90\%$$
+
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.
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s12283-022-00381-6
