Intelligent systems for analyzing soccer gamesThe weighted centroid

  1. Filipe Clemente
  2. Micael Santos Couceiro
  3. Fernando Manuel Lourenço Martins
  4. Rui Sousa Mendes
  5. António José Figueiredo
Revista:
Ingeniería e Investigación

ISSN: 0120-5609

Año de publicación: 2014

Volumen: 34

Número: 3

Páginas: 70-75

Tipo: Artículo

DOI: 10.15446/ING.INVESTIG.V34N3.43602 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Ingeniería e Investigación

Resumen

New, intelligent systems have been developed recently to improve the quality of match analysis. These systems analyze the tactical behavior of the teams. However, the existing methods leave room for improvement. Thus, the main goal of this study is to refine the team centroid metric by considering all of the players on the team and the ball position. Furthermore, this study analyzes the relation-ship between the centroids of the two opposing teams. One 11-on-11 soccer match was analyzed to test the new centroid algorithm. The results provided strong evidence of the positive relation between the centroids of the two teams over time in the x-axis (rs= 0.781) and the x-axis (rs= 0.707). This study confirmed the results of previous studies that analyzed the relationship between team centroids. Furthermore, it was possible to prove the effectiveness of the new tactical metric and its relevance for adding information during a match.

Referencias bibliográficas

  • Abdel-Aziz, Y., & Karara, H. (1971). Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. In ASP symposium on close-range photogrammetry (pp. 1-18). Falls Church, VA: American Society of Photogrammetry.
  • Akritas, M. G., & Papadatos, N. (2004). Heteroscedastioc One-Way ANOVA and Lack-of-Fit Tests. Journal of the American Statistical Association, 99(466), 368-390.
  • Bartlett, R., Button, C., Robins, M., Dutt-Mazumder, A., & Kennedy, G. (2012). Analysing Team Coordination Patterns from Player Movement Trajectories in Soccer: Methodological Considerations. International Journal of Performance Analysis in Sport, 12(2), 398-424.
  • Borrie, A., Jonsson, G., & Magnusson, M. (2002). Temporal pattern analysis and its applicability in sport: An explanation and exemplar data. Journal of Sports Science, 20(10), 845-852.
  • 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.
  • Carling, C., Williams, A.M., & Reilly, T. (2005). Handbook of soccer match analysis: A systematic approach to improving performance. Abingdon, UK: Routledge.
  • Clemente, F. M. (2012). Pedagogical Principles of Teaching Games for Understanding and Nonlinear Pedagogy in the Physical Education Teaching. Movimento, 18(2), 315-335.
  • Clemente, F., Couceiro, M., Martins, F., Dias, G., & Mendes, R. (2012). The influence of task constraints on attacker trajectories during 1v1 sub-phase in soccer practice. SportLogia, 8(1), 13-20.
  • Clemente, F., Couceiro, M., Martins, F., & Mendes, R. (2012). Team's Performance on FIFA U17 World Cup 2011: Study based on Notational Analysis. Journal of Physical Education and Sport, 12(1), 13-17.
  • 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.
  • Costa, I. T., Garganta, J., Greco, P. J., Mesquita, I., & Seabra, A. (2010). Influence of Relative Age Effects and Quality of Tactical Behaviour in the Performance of Youth Soccer Players. International Journal of Performance Analysis in Sport, 10(2), 82-97.
  • Couceiro, M. S., Clemente, F. M., & Martins, F. M. (2013). Analysis of football player's motion in view of fractional calculus. Central European Journal of Physics, 11(6), 714-723.
  • Duarte, R., Araújo, D., Freire, L., Folgado, H., Fernandes, O., & Davids, K. (2012). Intra- and inter-group coordination patterns reveal collective behaviors of football players near the scoring zone. Human Movement Science, 31(6), 1639-1651.
  • Frencken, W., & Lemmink, K. (2008). Team kinematics of small-sided soccer games: A systematic approach. In T. Reilly, & F. Korkusuz (Eds.), Science and Football VI (pp. 161-166). Oxon: Routledge Taylor and Francis Group.
  • Frencken, W., Lemmink, K., Delleman, N., & Visscher, C. (2011). Oscillations of centroid position and surface area of soccer teams in small-sided games. European Journal of Sport Science, 11(4), 215-223.
  • Lames, M., Erdmann, J., & Walter, F. (2010). Oscillations in football - Order and disorder in spatial interactions between the two teams. International Journal of Sport Psychology, 41(4), 85-86.
  • Lees, A. (2012). Technique analysis in sports: a critical review. Journal of Sports Sciences, 20(10), 813-828.
  • Lucchesi, M. (2001). Attacking Soccer: A Tactical Analysis. Auburn, Michigan: Reedswain Publishing.
  • Maroco, J. (2010). Análise Estatística com o PASW Statistics [Statistical Analysis with PASW Statistics]. Lisboa: Edições Sílabo.
  • Maroco, J., & Bispo, R. (2003). Estatística Aplicada às Ciências Sociais e Humanas [Applied Statistics at the Social and Human Sciences]. Lisboa: Climepsi Editores.
  • McGarry, T., Anderson, D., Wallace, S., Hughes, M., & Franks, I. (2002). Sport competition as a dynamical self-organizing system. Journal of Sports Sciences, 20(10), 771-781.
  • Pallant, J. (2011). SPSS Survival Manual: A step by step guide to data analysis using the SPSS program. Australia: Allen and Unwin.
  • 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.
  • Pedrosa, A. C., & Gama, S. M. A. (2004). Introdução computacional à probabilidade e estatística [Introduction Computational Probability and Statistics]. Porto: Porto Editora.
  • Pestana, M. H., & Gageiro, J. N. (2008). Análise de dados para as ciências sociais: A complementaridade do SPSS [Data Analysis for the Social Sciences: The complamentarity of the SPSS] (5th ed.). Lisboa, Portugal: Edições Sílabo.
  • Vincent, W. J. (1999). Statistics in Kinesiology (2nd ed.). Champaign: Human Kinetics.
  • Yue, Z., Broich, H., Seifriz, F., & Mester, J. (2008). Mathematical Analysis of a Soccer Game. Part I: Individual and Collective Behaviors. Studies in Applied Mathematics, 121(3), 223-243.
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