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The Interpretable Representation of Football Player Roles Based on Passing/Receiving Patterns
Jönköping University, School of Engineering, JTH, Department of Computing.
Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0412-6199
Jönköping University, School of Engineering, JTH, Department of Computing.
Jönköping University, School of Engineering, JTH, Department of Computing.
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2022 (English)In: Machine Learning and Data Mining for Sports Analytics / [ed] U. Brefeld, J. Davis, J. Van Haaren, A. Zimmermann, Springer, 2022, p. 62-76Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we define a new way of representing football player roles based on passing and receiving interactions. We develop a definition of player roles consisting of a linear combination of 12 common and interpretable passing/receiving patterns. Linear combinations are derived from the decomposition of players’ pitch passing and receiving networks using non-negative matrix factorization (NMF). Our model shows that 43 % of the 1491 players studied in this paper had a maximum weight of less than 50 % in each of the 12 common passing/receiving patterns. This suggests that a substantial percentage of players do not follow the specific passing/receiving patterns typically associated with their conventional role. The model also reveals the underlying differences in passing/receiving patterns amongst players who hold the same conventional role. It shows the intricacies of player patterns optimally when tasked with analyzing the most complex conventional roles such as midfielders, wingers, and forwards. Lastly, we show that the combinations of the 12 common passing/receiving patterns can be used as a footprint to find players with similar passing/receiving styles. For instance, our model found that Shaqiri and Fabinho had the highest similarity in passing/receiving styles to Oxlade-Chamberlain and Henderson. This is consistent with Liverpool FC’s transfers of Shaqiri and Fabinho to replace Oxlade-Chamberlain and Henderson’s positions respectively in the summer of 2018.

Place, publisher, year, edition, pages
Springer, 2022. p. 62-76
Series
Communications in Computer and Information Science, ISSN 1865-0929 ; 1571
Keywords [en]
Football analytics, Passing networks, Player roles, Matrix algebra, Sports, Football analytic, Football players, Interpretable representation, Linear combinations, Nonnegative matrix factorization, Passing network, Player role, Role-based, Non-negative matrix factorization
National Category
Sport and Fitness Sciences
Identifiers
URN: urn:nbn:se:hj:diva-56614DOI: 10.1007/978-3-031-02044-5_6Scopus ID: 2-s2.0-85130252172ISBN: 9783031020438 (print)ISBN: 9783031020445 (electronic)OAI: oai:DiVA.org:hj-56614DiVA, id: diva2:1661756
Conference
8th International Workshop, MLSA 2021, Virtual Event, September 13, 2021, Revised Selected Papers
Funder
Knowledge Foundation, DATAKIND 20190194Available from: 2022-05-30 Created: 2022-05-30 Last updated: 2025-02-11Bibliographically approved

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Sattari, AlexJohansson, UlfJakupovic, Jasmin

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Sattari, AlexJohansson, UlfJakupovic, JasminLarsson-Green, Peter
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JTH, Department of ComputingJönköping AI Lab (JAIL)JTH, Department of Computer Science and Informatics
Sport and Fitness Sciences

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