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Collective movement analysis reveals coordination tactics of team players in football matches

Author

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  • Marcelino, Rui
  • Sampaio, Jaime
  • Amichay, Guy
  • Gonçalves, Bruno
  • Couzin, Iain D.
  • Nagy, Máté

Abstract

Collective behavior is a hallmark of every living system and utilizing methods from statistical physics (such as correlation functions) could aid in our understanding of their underlying rules. We analyzed five football (soccer) matches as this game provides a unique but yet mostly unexplored example to study a system of collective cooperation and competition. The aim of our study was to analyze the collective motion patterns exhibited by football players to unfold the underlying coordination among them in order to understand collective strategies associated with team performance. By analyzing pairwise relationships among all the players using spatio-temporal correlation functions we reveal that there exist identifiable collective dynamics that characterize winning and losing teams. Using our metric we find clear and robust differences between the players, indicating a difference in their behavior and their interactions. And this enables us to assign a unique behavioral pattern - a ‘fingerprint’ - for each individual and for each team. Furthermore, we reveal there exists a relationship between the market value of the players and the metrics introduced here, suggesting that these metrics could potentially serve as valuable performance indicators in the future, with applications ranging from talent identification to player scouting. In a broader context team sports could open up new directions for quantitative analyses of human collective behavior.

Suggested Citation

  • Marcelino, Rui & Sampaio, Jaime & Amichay, Guy & Gonçalves, Bruno & Couzin, Iain D. & Nagy, Máté, 2020. "Collective movement analysis reveals coordination tactics of team players in football matches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920302319
    DOI: 10.1016/j.chaos.2020.109831
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    References listed on IDEAS

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    1. Bruno Gonçalves & Diogo Coutinho & Bruno Travassos & Hugo Folgado & Pedro Caixinha & Jaime Sampaio, 2018. "Speed synchronization, physical workload and match-to-match performance variation of elite football players," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
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    4. Keiko Yokoyama & Yuji Yamamoto, 2011. "Three People Can Synchronize as Coupled Oscillators during Sports Activities," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-8, October.
    5. Clemente, Filipe Manuel & Sarmento, Hugo & Aquino, Rodrigo, 2020. "Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    6. Müller, Oliver & Simons, Alexander & Weinmann, Markus, 2017. "Beyond crowd judgments: Data-driven estimation of market value in association football," European Journal of Operational Research, Elsevier, vol. 263(2), pages 611-624.
    7. Gomez, Miguel-Angel & Reus, Marc & Parmar, Nimai & Travassos, Bruno, 2020. "Exploring elite soccer teams’ performances during different match-status periods of close matches’ comebacks," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
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    Cited by:

    1. Külah, Emre & Alemdar, Hande, 2020. "Quantifying the value of sprints in elite football using spatial cohesive networks," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Katalin Ozogány & Viola Kerekes & Attila Fülöp & Zoltán Barta & Máté Nagy, 2023. "Fine-scale collective movements reveal present, past and future dynamics of a multilevel society in Przewalski’s horses," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Ballı, Serkan & Özdemir, Engin, 2021. "A novel method for prediction of EuroLeague game results using hybrid feature extraction and machine learning techniques," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).

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