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Capturing complex, non-linear team behaviours during competitive football performance

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Abstract

This study investigated changes in the complexity (magnitude and structure of variability) of the collective behaviours of association football teams during competitive performance. Raw positional data from an entire competitive match between two professional teams were obtained with the ProZone® tracking system. Five compound positional variables were used to investigate the collective patterns of performance of each team including: surface area, stretch index, team length, team width, and geometrical centre. Analyses involve the coefficient of variation (%CV) and approximate entropy (ApEn), as well as the linear association between both parameters. Collective measures successfully captured the idiosyncratic behaviours of each team and their variations across the six time periods of the match. Key events such as goals scored and game breaks (such as half time and full time) seemed to influence the collective patterns of performance. While ApEn values significantly decreased during each half, the %CV increased. Teams seem to become more regular and predictable, but with increased magnitudes of variation in their organisational shape over the natural course of a match.

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References

  1. Yue Z, Broich H, Seifriz F, and Mester J, Mathematical analysis of a soccer game, Part I: Individual and collective behaviors, Studies in Applied Mathematics, 2008, 121: 223–243.

    Article  MathSciNet  MATH  Google Scholar 

  2. McGarry T, Applied and theoretical perspectives of performance analysis in sport: Scientific issues and challenges, International Journal of Performance Analysis in Sport, 2009, 9: 128–140.

    Google Scholar 

  3. Schöllhorn W, Coordination dynamics and its consequences on sports, International Journal of Computer Science in Sport, 2003, 2: 40–46.

    Google Scholar 

  4. McGarry T, Anderson D, Wallace S, Hughes M, and Franks I, Sport competition as a dynamical self-organizing system, Journal of Sports Sciences, 2002, 15: 171–181.

    Google Scholar 

  5. Schmidt R C, O’Brien B, Sysko R, Self-organization in between-person cooperative tasks and possible applications for sport, International Journal of Sport Psychology, 1999, 30: 558–579.

    Google Scholar 

  6. Frencken W, Lemmink K, Delleman N J, and Visscher C, Oscillations of centroid position and surface area of soccer teams in small-sided games, European Journal of Sport Science, 2011, 4: 215–223.

    Article  Google Scholar 

  7. Lames M, Erdmann J, and Walter F, Oscillations in football — order and disorder in spatial interactions between the two teams, International Journal of Sport Psychology, 2010, 41: 85–86.

    Google Scholar 

  8. Yue Z, Broich H, Seifriz F, and Mester J, Mathematical analysis of a soccer game, Part II: Energy, spectral, and correlation analyses, Studies in Applied Mathematics, 2008, 121: 245–261.

    Article  MathSciNet  MATH  Google Scholar 

  9. Bourbousson J, Séve C, and McGarry T, Space-time coordination dynamics in basketball, Part 2: The interaction between the two teams, Journal of Sports Sciences, 2010, 28: 349–358.

    Article  Google Scholar 

  10. Passos P, Araújo D, Davids K, Gouveia L, Serpa S, Milho J, and Fonseca S, Interpersonal pattern dynamics and adaptive behavior in multiagent neurobiological systems: Conceptual model and data, Journal of Motor Behavior, 2009, 41: 445–459.

    Article  Google Scholar 

  11. Lago C and Martín R, Determinants of possession of the ball in soccer, Journal of Sports Sciences, 2007, 25: 969–974.

    Article  Google Scholar 

  12. Lago C, Casáis L, Domínguez E, and Sampaio J, The influence of situational variables on distance covered at various speed in elite soccer, European Journal of Sport Science, 2010, 10: 103–109.

    Article  Google Scholar 

  13. Di Salvo V, Collins A, McNeill B, and Cardinale M, Validation of Prozone: A new video-based performance analysis system, International Journal of Performance Analysis in Sport, 2006, 6: 108–119.

    Google Scholar 

  14. Kalman R E, A new approach to linear filtering and prediction problems, Transactions of the ASME — Journal of Basic Engineering, 1960, 82: 35–45.

    Article  Google Scholar 

  15. Hartley R and Zisserman A, Multiple View Geometry in Computer Vision, Cambridge, Cambridge University Press, 2002.

    Google Scholar 

  16. Mullineaux D, Bartlett R, and Bennett S, Research design and statistics in biomechanics and motor control, Journal of Sports Sciences, 2001, 19: 739–760.

    Article  Google Scholar 

  17. Mohr M, Krustrup P, and Bangsbo J, Fatigue in soccer: A brief review, Journal of Sports Sciences, 2005, 23: 593–599.

    Article  Google Scholar 

  18. Pincus S M, Approximate entropy as a measure of system complexity, Proceedings of the National Academy of Science USA, 1991, 88: 2297–2301.

    Article  MathSciNet  MATH  Google Scholar 

  19. Pincus S M and Goldberger A L, Physiological time-series analysis: What does regularity quantify? American Journal of Physiology, 1994, 266: 1643–1656.

    Google Scholar 

  20. Stergiou N, Buzzi U H, Kurz M J, and Heidel J, Nonlinear Tools in Human Movement, Innovative Analyses of Human Movement (ed. by Stergiou N), Champaign, Human Kinetics Publishers, 2004, 63–90.

    Google Scholar 

  21. Harbourne R T and Stergiou N, Movement variability and the use of nonlinear tools: Principles to guide physical therapist practice, Physical Therapy, 2009, 89: 267–282.

    Article  Google Scholar 

  22. Kugiumtzis D and Tsimpiris A, Measures of analysis of time series (mats): A Matlab toolkit for computation of multiple measures on time series data bases, Journal of Statistical Software, 2010, 33: 1–30.

    Google Scholar 

  23. Franks I M, Analysis of Association Football, Coaching Soccer (ed. by Schum T), Indianapolis, Master Press, 1996, 29–37.

    Google Scholar 

  24. Hughes M and Franks I M, Analysis of passing sequences, shots and goals in soccer, Journal of Sports Sciences, 2005, 23: 509–514.

    Article  Google Scholar 

  25. Duarte R and Frias T, Collective intelligence: An incursion into the tactical performance of football teams, Proceedings of the First International Conference in Science and Football (ed. by Jemni M, Bianco A, Palma A), Palermo, Italy, 2011.

    Google Scholar 

  26. Royal K A, Farrow D, Mujika I, Halson S L, Pyne D, and Abernethy B, The effects of fatigue on decision making and shooting skill performance in water polo players, Journal of Sports Sciences, 2006, 24: 807–815.

    Article  Google Scholar 

  27. Passos P, Milho J, Fonseca S, Borges J, Araújo D, and Davids K, Interpersonal distance regulates functional grouping tendencies of agents in team sports, Journal of Motor Behavior, 2011, 43: 155–163.

    Article  Google Scholar 

  28. Bernstein N A, The Co-Ordination and Regulation of Movements, Pergamon Press, Oxford, 1967.

    Google Scholar 

  29. Higuchi T, Imanaka K, and Hatayama T, Freezing degrees of freedom under stress: Kinematic evidence of constrained movement strategies, Human Movement Science, 2002, 21: 155–163.

    Article  Google Scholar 

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Correspondence to Duarte Araújo.

Additional information

The first author was supported by a grant of the Portuguese Foundation for Science and Technology (SFRH/BD/43994/2008). The authors are grateful to Prozone (Prozone®, ProZone Holdings Ltd, UK) for their help providing the data sets and encouraging the development of the study.

This paper was recommended for publication by Editors FENG Dexing and HAN Jing.

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Duarte, R., Araújo, D., Folgado, H. et al. Capturing complex, non-linear team behaviours during competitive football performance. J Syst Sci Complex 26, 62–72 (2013). https://doi.org/10.1007/s11424-013-2290-3

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  • DOI: https://doi.org/10.1007/s11424-013-2290-3

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