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Identification and optimization of high-performance passing networks in football
Phys. Rev. E 111, 044313 – Published 18 April, 2025
DOI: https://doi.org/10.1103/PhysRevE.111.044313
Abstract
This paper explores the relationship between the performance of a football team and the topological parameters of temporal passing networks. To achieve this, we propose a method to identify moments of high and low team performance based on the analysis of match events. This approach enables the construction of sets of temporal passing networks associated with each performance context. By analyzing topological metrics such as clustering, eigenvector centrality, and betweenness across both sets, significant structural differences are identified between moments of high and low performance. These differences reflect changes in the interaction dynamics among players and, consequently, in the team's playing system. Subsequently, a logistic regression model is employed to classify high- and low-performance networks. The analysis of the model coefficients identifies which metrics need to be adjusted to promote the emergence of structures associated with better performance. This framework provides quantitative tools to guide tactical decisions and optimize playing dynamics. Finally, the proposed method is applied to address the “blocked player” problem, optimizing passing relationships to minimize the emergence of structures associated with low performance, thereby ensuring more robust dynamics against contextual changes.
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References (36)
- K. K. Peng, X. J. Hu, and T. B. Swartz, On the time of corner kicks in soccer: an analysis of event history data, Computational Statics (Springer Nature, 2024).
- A. Chacoma and O. V. Billoni, Simple mechanism rules the dynamics of volleyball, J. Phys.: Complexity 3, 035006 (2022).
- A. Chacoma and O. V. Billoni, Probabilistic model for padel games dynamics, Chaos, Solitons Fract. 174, 113784 (2023).
- A. Chacoma and O. V. Billoni, Emergent complexity in the decision-making process of chess players, arXiv:2406.15463.
- C. Zappalà, A. E. Biondo, A. Pluchino, and A. Rapisarda, The paradox of talent: how chance affects success in tennis tournaments, Chaos, Solitons Fract. 176, 114088 (2023).
- C. Zappalà, A. Pluchino, A. Rapisarda, A. E. Biondo, and P. Sobkowicz, On the role of chance in fencing tournaments: An agent-based approach, PLoS ONE 17, e0267541 (2022).
- A. Clauset, M. Kogan, and S. Redner, Safe leads and lead changes in competitive team sports, Phys. Rev. E 91, 062815 (2015).
- H. V. Ribeiro, S. Mukherjee, and X. H. T. Zeng, Anomalous diffusion and long-range correlations in the score evolution of the game of cricket, Phys. Rev. E 86, 022102 (2012).
- S. J. Ibáñez, A. Mazo, J. Nascimento, and J. García-Rubio, The relative age effect in under-18 basketball: Effects on performance according to playing position, PLoS ONE 13, e0200408 (2018).
- A. Chacoma, N. Almeira, J. I. Perotti, and O. V. Billoni, Modeling ball possession dynamics in the game of football, Phys. Rev. E 102, 042120 (2020).
- A. Chacoma, N. Almeira, J. I. Perotti, and O. V. Billoni, Stochastic model for football's collective dynamics, Phys. Rev. E 104, 024110 (2021).
- G. Ichinose, T. Tsuchiya, and S. Watanabe, Robustness of football passing networks against continuous node and link removals, Chaos, Solitons Fract. 147, 110973 (2021).
- J. H. Martínez, D. Garrido, J. L. Herrera-Diestra, J. Busquets, R. Sevilla-Escoboza, and J. M. Buldú, Spatial and temporal entropies in the spanish football league: A network science perspective, Entropy 22, 172 (2020).
- K. Yamamoto, S. Uezu, K. Kagawa, Y. Yamazaki, and T. Narizuka, Theory and data analysis of player and team ball possession time in football, Phys. Rev. E 109, 014305 (2024).
- S. Cao, Passing path predicts shooting outcome in football, Sci. Rep. 14, 9572 (2024).
- I. Bandara, S. Shelyag, S. Rajasegarar, D. Dwyer, E.-j. Kim, and M. Angelova, Predicting goal probabilities with improved XG models using event sequences in association football, PLoS ONE 19, e0312278 (2024).
- J. E. Teixeira, P. Forte, R. Ferraz, M. Leal, J. Ribeiro, A. J. Silva, T. M. Barbosa, and A. M. Monteiro, Monitoring accumulated training and match load in football: A systematic review, Int. J. Environ. Res. Public Health 18, 3906 (2021).
- Z. Wang, P. Veličković, D. Hennes, N. Tomašev, L. Prince, M. Kaisers, Y. Bachrach, R. Elie, L. K. Wenliang, F. Piccinini et al., Tacticai: an AI assistant for football tactics, Nat. Commun. 15, 1906 (2024).
- X. Cao, X. Zhao, H. Tang, N. Fan, and F. Zereg, Football players' strength training method using image processing based on machine learning, PLoS ONE 18, e0287433 (2023).
- R. Ievoli, A. Gardini, and L. Palazzo, The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models, AStA Adv. Stat. Anal. 107, 153 (2023).
- A. Chacoma, O. V. Billoni, and M. N. Kuperman, Complexity emerges in measures of the marking dynamics in football games, Phys. Rev. E 106, 044308 (2022).
- K. Yamamoto and T. Narizuka, Examination of Markov-chain approximation in football games based on time evolution of ball-passing networks, Phys. Rev. E 98, 052314 (2018).
- B. Gonçalves, D. Coutinho, S. Santos, C. Lago-Penas, S. Jiménez, and J. Sampaio, Exploring team passing networks and player movement dynamics in youth association football, PLoS ONE 12, e0171156 (2017).
- S. Caicedo-Parada, C. Lago-Peñas, and E. Ortega-Toro, Passing networks and tactical action in football: A systematic review, Int. J. Environ. Res. Public Health 17, 6649 (2020).
- J. M. Buldú, J. Busquets, I. Echegoyen et al., Defining a historic football team: Using network science to analyze Guardiola's F.C. Barcelona, Sci. Rep. 9, 13602 (2019).
- L. Pappalardo, P. Cintia, A. Rossi, E. Massucco, P. Ferragina, D. Pedreschi, and F. Giannotti, A public data set of spatio-temporal match events in soccer competitions, Sci. Data 6, 236 (2019).
- G. Fagiolo, Clustering in complex directed networks, Phys. Rev. E 76, 026107 (2007).
- Documentation of library networkx version 3.3, clustering coefficient, https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.cluster.clustering.html.
- A. Bihari and M. K. Pandia, Eigenvector centrality and its application in research professionals' relationship network, in 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), Greater Noida, India (IEEE, New York, 2015), pp. 510–514.
- Documentation of library networkx version 3.3, eigenvector centrality, https://networkx.org/documentation/stable/reference /algorithms/generated/networkx.algorithms.centrality.eigenvector_centrality.html#networkx.algorithms.centrality.eigenvector_centrality.
- M. E. J. Newman, Scientific collaboration networks. II. shortest paths, weighted networks, and centrality, Phys. Rev. E 64, 016132 (2001).
- Documentation of library networkx version 3.3, betweenness centrality, https://networkx.org/documentation/stable/reference /algorithms/generated/networkx.algorithms.centrality.betweenness_centrality.html#networkx.algorithms.centrality.betweenness_centrality.
- Documentation of library sklearn (scikit-learn) version 1.2.1, https://scikit-learn.org/stable.
- Documentation of library networkx version 3.3, https://networkx.org/documentation/stable/reference.
- F. M. Clemente, H. Sarmento, and R. Aquino, Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons, Chaos, Solitons Fract. 133, 109625 (2020).
- Q. Yu, Y. Gai, B. Gong, M.-A. Gomez, and Y. Cui, Using passing network measures to determine the performance difference between foreign and domestic outfielder players in chinese football super league, Int. J. Sports Sci. Coach. 15, 398 (2020).
