Abstract
Analyzing playing style is a recurring task within soccer analytics that plays a crucial role in club activities such as player scouting and match preparation. It involves identifying and summarizing prototypical behaviors of teams and players that reoccur both within and across matches. Current techniques for analyzing playing style are often hindered by the sparsity of event stream data (i.e., the same player rarely performs the same action in the same location more than once). This paper proposes SoccerMix, a soft clustering technique based on mixture models that enables a novel probabilistic representation for soccer actions. SoccerMix overcomes the sparsity of event stream data by probabilistically grouping together similar actions in a data-driven manner. We show empirically how SoccerMix can capture the playing style of both teams and players and present an alternative view of a team’s style that focuses not on the team’s own actions, but rather on how the team forces its opponents to deviate from their usual playing style.
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More details on our approach to select the number of components used in each mixture model can be found in the public implementation.
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Acknowledgements
Tom Decroos is supported by the Research Foundation-Flanders (FWO-Vlaanderen). Maaike Van Roy is supported by the Research Foundation-Flanders under EOS No. 30992574. Jesse Davis is partially supported by KU Leuven Research Fund (C14/17/07), Research Foundation - Flanders (EOS No. 30992574, G0D8819N). Thanks to StatsBomb for providing the data used in this paper.
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Decroos, T., Van Roy, M., Davis, J. (2021). SoccerMix: Representing Soccer Actions with Mixture Models. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_28
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