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Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football

Author

Listed:
  • Bruno Gonçalves
  • Diogo Coutinho
  • Sara Santos
  • Carlos Lago-Penas
  • Sergio Jiménez
  • Jaime Sampaio

Abstract

Understanding how youth football players base their game interactions may constitute a solid criterion for fine-tuning the training process and, ultimately, to achieve better individual and team performances during competition. The present study aims to explore how passing networks and positioning variables can be linked to the match outcome in youth elite association football. The participants included 44 male elite players from under-15 and under-17 age groups. A passing network approach within positioning-derived variables was computed to identify the contributions of individual players for the overall team behaviour outcome during a simulated match. Results suggested that lower team passing dependency for a given player (expressed by lower betweenness network centrality scores) and high intra-team well-connected passing relations (expressed by higher closeness network centrality scores) were related to better outcomes. The correlation between the dyads’ positioning regularity and the passing density showed a most likely higher correlation in under-15 (moderate effect), indicating a possible more dependence of the ball position rather than in the under-17 teams (small/unclear effects). Overall, this study emphasizes the potential of coupling notational analyses with spatial-temporal relations to produce a more functional and holistic understanding of teams’ sports performance. Also, the social network analysis allowed to reveal novel key determinants of collective performance.

Suggested Citation

  • Bruno Gonçalves & Diogo Coutinho & Sara Santos & Carlos Lago-Penas & Sergio Jiménez & Jaime Sampaio, 2017. "Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-13, January.
  • Handle: RePEc:plo:pone00:0171156
    DOI: 10.1371/journal.pone.0171156
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    Citations

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    Cited by:

    1. Medina, Pablo & Carrasco, Sebastián & Rogan, José & Montes, Felipe & Meisel, Jose D. & Lemoine, Pablo & Lago Peñas, Carlos & Valdivia, Juan Alejandro, 2021. "Is a social network approach relevant to football results?," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Bruno Gonçalves & Diogo Coutinho & Juliana Exel & Bruno Travassos & Carlos Lago & Jaime Sampaio, 2019. "Extracting spatial-temporal features that describe a team match demands when considering the effects of the quality of opposition in elite football," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    3. Antonio Cordón-Carmona & Abraham García-Aliaga & Moisés Marquina & Jorge Lorenzo Calvo & Daniel Mon-López & Ignacio Refoyo Roman, 2020. "What Is the Relevance in the Passing Action between the Passer and the Receiver in Soccer? Study of Elite Soccer in La Liga," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    4. Li, Yuesen & Ma, Runqing & Gonçalves, Bruno & Gong, Bingnan & Cui, Yixiong & Shen, Yanfei, 2020. "Data-driven team ranking and match performance analysis in Chinese Football Super League," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    5. Sergio Caicedo-Parada & Carlos Lago-Peñas & Enrique Ortega-Toro, 2020. "Passing Networks and Tactical Action in Football: A Systematic Review," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    6. Marius Ötting & Roland Langrock & Antonello Maruotti, 2023. "A copula-based multivariate hidden Markov model for modelling momentum in football," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 9-27, March.
    7. Tomás Rodríguez & Jorge Tovar, 2023. "The hedgehog or the fox: Versatility and performance in professional soccer," Documentos CEDE 20757, Universidad de los Andes, Facultad de Economía, CEDE.
    8. Valerio Ficcadenti & Roy Cerqueti & Ciro Hosseini Varde’i, 2023. "A rank-size approach to analyse soccer competitions and teams: the case of the Italian football league “Serie A"," Annals of Operations Research, Springer, vol. 325(1), pages 85-113, June.
    9. Riccardo Ievoli & Aldo Gardini & Lucio Palazzo, 2023. "The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 153-175, March.
    10. 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.
    11. Jonas Lutz & Daniel Memmert & Dominik Raabe & Rolf Dornberger & Lars Donath, 2019. "Wearables for Integrative Performance and Tactic Analyses: Opportunities, Challenges, and Future Directions," IJERPH, MDPI, vol. 17(1), pages 1-26, December.
    12. Külah, Emre & Alemdar, Hande, 2020. "Quantifying the value of sprints in elite football using spatial cohesive networks," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).

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