4.6 Article

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

期刊

PLOS ONE
卷 12, 期 1, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0171156

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资金

  1. Portuguese Foundation for Science and Technology (FCT)
  2. European Social Fund (ESF) under the Human Potential Operating Program (POPH) [SFRH/BD/86044/2012]
  3. project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM) [NORTE-01-0145-FEDER-000026]
  4. Fundo Europeu de Desenvolvimento Regional (FEDER)
  5. NORTE
  6. Fundação para a Ciência e a Tecnologia [SFRH/BD/86044/2012] Funding Source: FCT

向作者/读者索取更多资源

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.

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