A network approach to characterize the teammates� interactions on footballa single match analysis

  1. Clemente, Filipe
  2. Martins, Fernando Manuel Lourenço
  3. Santos Couceiro, Micael
  4. Mendes, Rui Sousa
  5. Figueiredo, António José
Journal:
Cuadernos de psicología del deporte

ISSN: 1578-8423 1989-5879

Year of publication: 2014

Volume: 14

Issue: 3

Pages: 141-147

Type: Article

DOI: 10.4321/S1578-84232014000300015 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

More publications in: Cuadernos de psicología del deporte

Abstract

*e aim of this case study was to apply a set of network metrics in order to characterize the teammates� cooperation in a football team. *ese metrics were applied in three levels of analysis: i) micro (individual analysis); ii) meso (players� contribution for the team); and iii) macro (global inter-action of the team). One-single case study match was observed and from such procedure were analysed 131 attacking plays. Results from the macro analysis showed a moderate heterogeneity between teammates, thus sug-gesting the emergence of clusters within the team. *e players with highest connections with their teammates were the right defender, central defender from the left side, defensive mid+elder, right mid+elder and the forward player. Finally, in the micro analysis was observed that right defender, cen-tral defender, right mid+elder and the forward can be considered the cen-troid players during attacking plays, thus being the most prominent in the attacking building. In sum, the network metrics allowed to characterize the teammates� interaction during the attacking plays, providing an important and di<erent information that can be useful for the future of match analysis

Bibliographic References

  • 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.
  • Carling, C., Reilly, T., & Williams, A. (2009). Performance assessment for field sports. London: Routledge.
  • Clemente, F. M., Couceiro, M. S., Martins, F. M., & Mendes, R. (2013). An Online Tactical Metrics Applied to Football Game. Research Journal of Applied Sciences, Engineering and Technology, 5(5), 1700-1719.
  • Clemente, F. M., Couceiro, M. S., Martins, F. M., Mendes, R., & Figueiredo, A. J. (2013). Measuring tactical behaviour using technological metrics: Case study of a football game. International Journal of Sports Science & Coaching, 8(4), 723-739.
  • Couceiro, M. S., Clemente, F. M., & Martins, F. M. L. (2013). Towards the Evaluation of Research Groups based on Scienti$c Co-authorship Networks: The RoboCorp Case Study. Arab Gulf Journal of Scientific Research, 31(1), 36-52.
  • 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.
  • Fajen, B. R., Riley, M. R., & Turvey, M. T. (2009). Information, a"ordances, and control in sport. International Journal of Sports Psychology, 40, 79-107.
  • 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.
  • Hespanha, J. P. (2004). An e+cient MATLAB Algorithm for Graph Partitioning. Santa Barbara, CA, USA: University of California.
  • Horvath, S. (2011). Weighted Network Analysis: Applications in Genomics and Systems Biology. New York: Springer.
  • Hughes, M., & Franks, I. (2005). Analysis of passing sequences, shots and goals in soccer. Journal of Sports Sciences, 23(5), 509-514.
  • Martins, F. M. L., Clemente, F. M., & Couceiro, M. S. (2013). From the individual to the collective analysis at the football game. Paper presented at the Proceedings Mathematical Methods in Engineering International Conference, Porto.
  • Parrish, J., & Edelstein-Keshet, L. (1999). Complexity, Pattern, and Evolutionary Trade-o"s in Animal Aggregation. Science, 284(2), 99-101.
  • 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.
  • Reimer, T., Park, E. S., & Hinsz, V. B. . (2006). Shared and coordinated cognition in competitive and dynamic task environments: An information- processing perspective for team sports. International Journal of Sport and Exercise Psychology, 4, 376-400.
  • 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.
  • Salas, E., Dickinson, T. L., Converse, S. A., & Tannenbaum, S. I. (1992). Toward an understanding of team performance and training. In R. W. Swezey & E. Salas (Eds.), Teams: Their training and performance (pp. 3-29). Norwood, NJ: Ablex.
  • Vilar, L., Araújo, D., Davids, K., & Bar-Yam, Y. (2013). Science of winning football: emergent pattern-forming dynamics in association football. Journal of Systems Science and Complexity, 26, 73-84.
  • Wu, M. (2009). wgPlot-Weighted Graph Plot. MatLab Central File Exchange. Retrieved January 10, 2012, from http://www.mathworks. com/matlabcentral/$leexchange/24035