Abstract
Association football has been the subject of many research studies. In this work we present a study on player similarity using passing sequences extracted from games from the top-5 European football leagues during the 2017/2018 season. We present two different approaches: first, we only count the motifs a player is involved in; then we also take into consideration the specific position a player occupies in each motif. We also present a new way to objectively judge the quality of the generated models in football analytics. Our results show that the study of passing sequences can be used to study player similarity with relative success.
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This research was funded by FCT and INESC-TEC under the grant SFRH/BD/136525/2018, Ref CRM:0067161.
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Barbosa, A., Ribeiro, P., Dutra, I. (2022). Similarity of Football Players Using Passing Sequences. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2021. Communications in Computer and Information Science, vol 1571. Springer, Cham. https://doi.org/10.1007/978-3-031-02044-5_5
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DOI: https://doi.org/10.1007/978-3-031-02044-5_5
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