Automatic event detection in football using tracking data
- URL: http://arxiv.org/abs/2202.00804v1
- Date: Tue, 1 Feb 2022 23:20:40 GMT
- Title: Automatic event detection in football using tracking data
- Authors: Ferran Vidal-Codina, Nicolas Evans, Bahaeddine El Fakir, Johsan
Billingham
- Abstract summary: We propose a framework to automatically extract football events using tracking data, namely the coordinates of all players and the ball.
Our approach consists of two models: (1) the possession model evaluates which player was in possession of the ball at each time, as well as the distinct player configurations in the time intervals where the ball is not in play.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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 computational framework to
automatically extract football events using tracking data, namely the
coordinates of all players and the ball. Our approach consists of two models:
(1) the possession model evaluates which player was in possession of the ball
at each time, as well as the distinct player configurations in the time
intervals where the ball is not in play; (2) the event detection model relies
on the changes in ball possession to determine in-game events, namely passes,
shots, crosses, saves, receptions and interceptions, as well as set pieces.
First, analyze the accuracy of tracking data for determining ball possession,
as well as the accuracy of the time annotations for the manually collected
events. Then, we benchmark the auto-detected events with a dataset of manually
annotated events to show that in most categories the proposed method achieves
$+90\%$ detection rate. Lastly, 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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