Abstract
Association Football is probably the world’s most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.
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Acknowledgements
This work is partially financed financed by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project LA/P/0063/2020 and grant SFRH/BD/136525/2018. Also, we would like to thank the reviewers for their thoughtful insight.
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Barbosa, A., Ribeiro, P., Dutra, I. (2023). Improving the Characterization and Comparison of Football Players with Spatial Flow Motifs. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_45
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