Network structure and centralization tendencies in professional football teams from Spanish La Liga and English Premier Leagues

  1. Filipe Clemente 1
  2. FÁBIO JOSÉ
  3. Nuno Oliveira
  4. Fernando Manuel Lourenço Martins
  5. Rui Sousa Mendes
  6. António José Figueiredo 1
  7. Del P. Wong
  8. Dimitris Kalamaras
  1. 1 Universidade de Coimbra
    info

    Universidade de Coimbra

    Coímbra, Portugal

    ROR https://ror.org/04z8k9a98

Aldizkaria:
Journal of Human Sport and Exercise: JHSE

ISSN: 1988-5202

Argitalpen urtea: 2016

Alea: 11

Zenbakia: 3

Orrialdeak: 376-389

Mota: Artikulua

DOI: 10.14198/JHSE.2016.113.06 DIALNET GOOGLE SCHOLAR lock_openRUA editor

Beste argitalpen batzuk: Journal of Human Sport and Exercise: JHSE

Laburpena

The aim of this study was to analyse the variance of different competitive leagues, score status, and tactical position in the centrality levels of degree prestige, degree centrality and page rank in football players. A total of 20 matches from the Spanish La Liga League (10 matches) and English Premier League (10 matches) were analysed and codified in this study. In this study only the top four teams and their opponents per each competitive league were analysed. A total of 14,738 passes between teammates were recorded and processed. The multivariate MANOVA revealed statistical differences in centrality among tactical positions (λ = 0.958; F(15,1212) = 37.898; p-value = 0.001; = 0.319; Moderate Effect Size). Midfielders had the greatest centrality values, followed by the external and central defenders. The lowest values of centrality were found in goalkeepers and forwards. No statistical differences were found in centrality between different competitive leagues (λ = 0.001; F(3,402) = 0.050; p-value = 0.985; = 0.001; Very Small Effect Size) and score status (λ = 0.003; F(6,806) = 0.175; p-value = 0.983; = 0.001; Very Small Effect Size).

Erreferentzia bibliografikoak

  • 1. Bourbousson, J., Poizat, G., Saury, J., & Seve, C. (2010). Team Coordination in Basketball: Description of the Cognitive Connections Among Teammates. Journal of Applied Sport Psychology, 22(2), 150–166.
  • 2. Bourbousson, J., Sève, C., & McGarry, T. (2010). Space-time coordination dynamics in basketball: Part 2 The interaction between the two teams. Journal of Sports Sciences, 28(3), 349–358.
  • 3. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1-7), 107–117. doi:10.1016/S0169-7552(98)00110-X
  • 4. Carling, C., Williams, A. M., & Reilly, T. (2005). Handbook of Soccer Match Analysis: A Systematic Approach to Improving Performance. London & New York: Taylor & Francis Group.
  • 5. Clemente, F. M., Couceiro, M. S., Martins, F. M. L., Mendes, R., & Figueiredo, A. J. (2013). Measuring Collective Behaviour in Football Teams: Inspecting the impact of each half of the match on ball possession. International Journal of Performance Analysis in Sport, 13(3), 678–689.
  • 6. Clemente, F. M., Couceiro, M. S., Martins, F. M. L., & Mendes, R. S. (2014). Using network metrics to investigate football team players ’ connections: A pilot study. Motriz, 20(3), 262–271. doi:dx.doi.org/10.1590/S1980-65742014000300004
  • 7. Clemente, F. M., Couceiro, M. S., Martins, F. M. L., Mendes, R. S., & Figueiredo, A. J. (2014). Practical Implementation of Computational Tactical Metrics for the Football Game: Towards an Augmenting Perception of Coaches and Sport Analysts. In Murgante, Misra, Rocha, Torre, Falcão, Taniar, … Gervasi (Eds.), Computational Science and Its Applications (pp. 712–727). Springer.
  • 8. Cotta, C., Mora, A. M., Merelo, J. J., & Merelo-Molina, C. (2013). A network analysis of the 2010 FIFA world cup champion team play. Journal of Systems Science and Complexity, 26(1), 21–42.
  • 9. Di Salvo, V., Baron, R., Tschan, H., Calderon Montero, F. J., Bachl, N., & Pigozzi, F. (2007). Performance characteristics according to playing position in elite soccer. Int J Sports Med, 28, 222– 227.
  • 10. Duarte, R., Araújo, D., Correia, V., & Davids, K. (2012). Sports Teams as Superorganisms: Implications of Sociobiological Models of Behaviour for Research and Practice in Team Sports Performance Analysis. Sports Medicine, 42(8), 633–642.
  • 11. Duch, J., Waitzman, J. S., & Amaral, L. A. (2010). Quantifying the performance of individual players in a team activity. PloS One, 5(6), e10937.
  • 12. Frencken, W., Lemmink, K., Delleman, N., & Visscher, C. (2011). Oscillations of centroid position and surface area of football teams in small-sided games. European Journal of Sport Science, 11(4), 215–223.
  • 13. Gréhaigne, J. F., Bouthier, D., & David, B. (1997). Dynamic-system analysis of opponent relationship in collective actions in football. Journal of Sports Sciences, 15(2), 137–149.
  • 14. Gréhaigne, J. F., Richard, J. F., & Griffin, L. (2005). Teaching and learning team sports and games. New York, USA: Routledge Falmar.
  • 15. Grund, T. U. (2012). Network structure and team performance: The case of English Premier League soccer teams. Social Networks, 34(4), 682–690.
  • 16. Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal of Sports Sciences, 20(10), 739–754.
  • 17. Hughes, M., & Franks, I. (2005). Analysis of passing sequences, shots and goals in soccer. Journal of Sports Sciences, 23(5), 509–514.
  • 18. Jonsson, G. K., Anguera, M. T., Blanco-Villaseñor, Á., Losada, J. L., Hernández-Mendo, A., Ardá, T., … Castellano, J. (2006). Hidden patterns of play interaction in soccer using SOF-CODER. Behavior Research Methods, 38(3), 372–381.
  • 19. Kalamaras, D. (2014). Social Networks Visualizer (SocNetV): Social network analysis and visualization software. Social Networks Visualizer. Homepage: http://socnetv.sourceforge.net .
  • 20. Malta, P., & Travassos, B. (2014). Characterization of the defense-attack transition of a soccer team. Motricidade, 10(1), 27–37.
  • 21. Nieminen, J. (1974). On the centrality in a graph. Scandinavian Journal of Psychology, 15(1), 332– 336.
  • 22. O’Donoghue, P. (2012). Statistics for sport and exercise studies: An introduction. London and New York, UK and USA: Routledge Taylor & Francis Group.
  • 23. Pallant, J. (2011). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using the SPSS Program. Australia: Allen & Unwin.
  • 24. Passos, P., Davids, K., Araújo, D., Paz, N., Minguéns, J., & Mendes, J. (2011). Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport, 14(2), 170–176.
  • 25. Peña, J. L., & Touchette, H. (2012). A network theory analysis of football strategies. In arXiv preprint arXiv (p. 1206.6904).
  • 26. Pierce, C. A., Block, R. A., & Aguinis, H. (2004). Cautionary Note on Reporting Eta-Squared Values from Multifactor ANOVA Designs. Educational and Psychological Measurement, 64(6), 916–924. doi:10.1177/0013164404264848
  • 27. Reilly, T., & Thomas, V. (1976). A motion analysis of work-rate in different positional roles in professional football match-play. Journal of Human Movement Studies, 2, 87–97.
  • 28. Robinson, G., & O’Donoghue, P. (2007). A weighted kappa statistic for reliability testing in performance analysis of sport. International Journal of Performance Analysis in Sport, 7(1), 12–19.
  • 29. Sarmento, H., Marcelino, R., Anguera, M. T., CampaniÇo, J., Matos, N., & LeitÃo, J. C. (2014). Match analysis in football: a systematic review. Journal of Sports Sciences, 32(20), 1831–1843. doi:10.1080/02640414.2014.898852
  • 30. Travassos, B., Davids, K., Araújo, D., & Esteves, P. T. (2013). Performance analysis in team sports : Advances from an Ecological Dynamics approach. International Journal of Performance Analysis in Sport, 13(1), 83–95.
  • 31. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York, USA: Cambridge University Press.
Datuen iturria: Dialnet