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Current Approaches to Tactical Performance Analyses in Soccer Using Position Data

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Abstract

Tactical match performance depends on the quality of actions of individual players or teams in space and time during match-play in order to be successful. Technological innovations have led to new possibilities to capture accurate spatio-temporal information of all players and unravel the dynamics and complexity of soccer matches. The main aim of this article is to give an overview of the current state of development of the analysis of position data in soccer. Based on the same single set of position data of a high-level 11 versus 11 match (Bayern Munich against FC Barcelona) three different promising approaches from the perspective of dynamic systems and neural networks will be presented: Tactical performance analysis revealed inter-player coordination, inter-team and inter-line coordination before critical events, as well as team-team interaction and compactness coefficients. This could lead to a multi-disciplinary discussion on match analyses in sport science and new avenues for theoretical and practical implications in soccer.

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References

  1. Ali A. Measuring soccer skill performance: a review. Scand J Med Sci Sports. 2011;11:170–83.

    Article  Google Scholar 

  2. Walter F, Lames M, McGarry T. Analysis of sports performance as a dynamic system by means of relative phase. Int J Comput Sci Sport. 2007;6:35–41.

    Google Scholar 

  3. Memmert D. Testing of tactical performance in youth elite soccer. J Sports Sci Med. 2010;9:199–205.

    PubMed  PubMed Central  Google Scholar 

  4. Vilar L, Araújo D, Davids K, et al. The role of ecological dynamics in analysing performance in team sports. Sports Med. 2012;42:1–10.

    Article  PubMed  Google Scholar 

  5. Williams AM, Ford PR. Expertise and expert performance in sport. Int Rev Sport Exerc Psychol. 2008;1:4–18.

    Article  Google Scholar 

  6. Brefeld U, Knauf K, Memmert D. Spatio-temporal convolution kernels. Mach Learn. 2016;102(2):247–73. doi:10.1007/s10994-015-5520-1.

    Article  Google Scholar 

  7. Kempe M, Grunz A, Memmert D. Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks. Eur J Sport Sci. 2015;15:249–55. doi:10.1080/17461391.2014.933882.

    Article  PubMed  Google Scholar 

  8. Lago C. The influence of match location, quality of opposition, and match status on possession strategies in professional association football. J Sports Sci. 2009;27:1463–9. doi:10.1080/02640410903131681.

    Article  PubMed  Google Scholar 

  9. Perl J, Memmert D. Special issue: Network approaches in complex environments. Hum Mov Sci. 2012;31:267–70.

    Article  PubMed  Google Scholar 

  10. Lemmink KAPM, Frencken WGP. Tactical performance analysis in invasion games: Perspectives from a dynamical system approach with examples from soccer. In: McGarry T, O’Donoghue P, Sampaio J, editors. Routledge handbook of sports performance analysis. London: Routledge; 2013. p. 89–100.

    Google Scholar 

  11. Memmert D. Teaching tactical creativity in team and racket sports: research and practice. Routledge; Abingdon; 2015.

    Google Scholar 

  12. Franks I. Qualitative and quantitative analysis. Coach Rev. 1985;8:48–50.

    Google Scholar 

  13. Soccer Tenga A. In: McGarry T, O’Donoghue P, Sampaio J, editors. Routledge handbook of sports performance analysis. London: Routledge; 2013. p. 323–37.

    Google Scholar 

  14. Olthof SBH, Frencken WGP, Lemmink KAPM. The older, the wider: on-field tactical behavior of elite-standard youth soccer players in small-sided games. Hum Mov Sci. 2015;41:92–102.

    Article  PubMed  Google Scholar 

  15. Gréhaigne JF, Godbout P. Collective variables for analysing performance in team sports. In: McGarry T, O’Donoghue P, Sampaio J, editors. Routledge handbook of sports performance analysis. London: Routledge; 2013. p. 101–14.

    Google Scholar 

  16. Baca A. Tracking motion in sport—trends and limitations. In: Hammond J, editor. Proc. of the 9th Australasian Conf. on Mathematics and Computers in Sport. MathSport (ANZIAM). 2008. p. 1–7.

  17. Perl J, Memmert D, Baca A, et al. Sensors, monitoring, and model-based data analysis in sports, exercise and rehabilitation. In: Lai DTH, Begg RK, Palaniswami M, editors. Sensor networks – challenges towards practical application. Boca Raton: Taylor and Francis; 2012. pp. 375–405.

    Google Scholar 

  18. Baca A, Dabnichki P, Heller M, et al. Ubiquitous computing in sports: a review and analysis. J Sports Sci. 2009;27:1335–46.

    Article  PubMed  Google Scholar 

  19. Castellano J, Figueira B, Coutinho D, et al. Identifying the effects from the quality of opposition in a football team positioning strategy. Int J Perform Anal Sport. 2013;13(3):822–32.

    Google Scholar 

  20. Moura FA, Martins LEB, Anido RO, et al. A spectral analysis of team dynamics and tactics in Brazilian football. J Sports Sci. 2013;31(14):1568–77.

    Article  PubMed  Google Scholar 

  21. Fujimura A, Sugihara K. Geometric analysis and quantitative evaluation of sport teamwork. Syst Comp Jpn. 2005;35(6):49–58.

    Article  Google Scholar 

  22. Fonseca S, Milho J, Travassos B, et al. Spatial dynamics of team sports exposed by Voronoi diagrams. Hum Mov Sci. 2012;31(6):1652–9.

    Article  PubMed  Google Scholar 

  23. Taki T, Hasegawa JI. Visualization of dominant region in team games and its application to teamwork analysis. In: Computer graphics international, 2000. Proceedings. IEEE. p. 227–235.

  24. Kang CH, Hwang JR, Li KJ. Trajectory analysis for soccer players. In: Data mining workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE. p. 377–381.

  25. Horton M, Gudmundsson J, Chawla S, et al. Classification of passes in football matches using spatiotemporal data. arXiv:1407.5093.

  26. Gudmundsson J, Wolle T. Towards automated football analysis: algorithms and data structures. In: Proc. 10th Australasian Conf. on mathematics and computers in sport.

  27. Wei X, Sha L, Lucey P, et al. Large-scale analysis of formations in soccer. In: Digital image computing: techniques and applications (DICTA), 2013 International Conference on. IEEE. p. 1–8.

  28. Hirano S, Tsumoto S. Grouping of soccer game records by multiscale comparison technique and rough clustering. In: Hybrid intelligent systems, 2005. HIS’05. Fifth International Conference on. IEEE. p. 6.

  29. Gudmundsson J, Wolle T. Football analysis using spatio-temporal tools. Comput Environ Urban Syst. 2014;47:16–27.

    Article  Google Scholar 

  30. Sampaio J, Maçãs V. Measuring tactical behaviour in football. Int J Sports Med. 2012;33:395–401.

    Article  CAS  PubMed  Google Scholar 

  31. Bialkowski A, Lucey P, Carr P, et al. Recognising team activities from noisy data. In: Computer vision and pattern recognition workshops (CVPRW), 2013 IEEE Conference on. IEEE. p. 984–990.

  32. Bialkowski A, Lucey P, Carr P, et al. Large-scale analysis of soccer matches using spatiotemporal tracking data. In: Data mining (ICDM), 2014 IEEE international conference on. IEEE. p. 725–730.

  33. Gonçalves B, Figueira B, Maçãs V, et al. Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. J Sports Sci. 2014;32:191–9.

    Article  PubMed  Google Scholar 

  34. Frencken WGP, Lemmink KAPM, Delleman N, et al. Oscillations of centroid position and surface area of soccer teams in small-sided games. Eur J Sport Sci. 2011;11:215–23.

    Article  Google Scholar 

  35. Frencken WGP, Lemmink KAPM, van de Poel H, et al. Variability of inter team distance associated with match events in elite-standard soccer. J Sports Sci. 2012;30:1207–13.

    Article  PubMed  Google Scholar 

  36. Memmert D, Perl J. Analysis and simulation of creativity learning by means of artificial neural networks. Hum Mov Sci. 2009;28:263–82.

    Article  PubMed  Google Scholar 

  37. Memmert D, Perl J. Game creativity analysis by means of neural networks. J Sport Sci. 2009;27:139–49.

    Article  Google Scholar 

  38. Richman J, Moorman J. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol. 2000;278:H2039–49.

    CAS  Google Scholar 

  39. Pincus S. Approximate entropy as a measure of system-complexity. Proc Natl Acad Sci. 1991;88:2297–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kurz M, Stergiou N. Applied dynamic systems theory for the analysis of movement. In: Stergiou N, editor. Innovative analyses of human movement. Champaign: Human Kinetics; 2004. p. 93–119.

    Google Scholar 

  41. Palut Y, Zanone P. A dynamical analysis of tennis: concepts and data. J Sports Sci. 2005;23:1021–32.

    Article  PubMed  Google Scholar 

  42. Folgado H, Duarte R, Fernandes O, et al. Competing with lower level opponents decreases intra-team movement synchronisation and time-motion demands during pre-season soccer matches. PLOS One. 2014;9:e97145. doi:10.1371/journal.pone.0097145.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Perl J, Tilp M, Baca A, et al. Neural networks for analysing sports games. In: McGarry T, O’Donoghue P, Sampaio J, editors. Routledge Handbook of Sports Performance Analysis. Routledge: Abingdon; 2013. pp. 237–47.

    Google Scholar 

  44. Perl J. A neural network approach to movement pattern analysis. Hum Mov Sci. 2014;23:605–20.

    Article  Google Scholar 

  45. Perl J, Grunz A, Memmert D. Tactics in soccer: an advanced approach. Int J Comput Sci Sport. 2013;12:33–44.

    Google Scholar 

  46. Grunz A, Memmert D, Perl J. Tactical pattern recognition in soccer games by means of special self-organizing maps. Hum Mov Sci. 2012;31:334–43.

    Article  PubMed  Google Scholar 

  47. Glazier PS. Towards a grand unified theory of sports performance. Hum Mov Sci. 2016 (in press).

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Acknowledgements

The content of this leading article is based on three lectures given by the authors to an invited symposium at the European College of Sport Science 2014 Congress held in Amsterdam, The Netherlands.

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Correspondence to Daniel Memmert.

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No financial support was received for the planning or conduct of the research presented in this article. Preparation of the article was supported by a Grant from the German Research Council (DFG, Deutsche Forschungsgemeinschaft) [ME 2678/3-3] to Daniel Memmert.

Conflict of interest

Daniel Memmert, Koen A.P.M. Lemmink and Jaime Sampaio declare that they have no conflicts of interest relevant to the content of this article.

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Memmert, D., Lemmink, K.A.P.M. & Sampaio, J. Current Approaches to Tactical Performance Analyses in Soccer Using Position Data. Sports Med 47, 1–10 (2017). https://doi.org/10.1007/s40279-016-0562-5

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