Sports Medicine

, Volume 47, Issue 1, pp 1–10 | Cite as

Current Approaches to Tactical Performance Analyses in Soccer Using Position Data

  • Daniel MemmertEmail author
  • Koen A. P. M. Lemmink
  • Jaime Sampaio
Leading Article


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.


Critical Event Team Sport Position Data Soccer Match Team Formation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.

Compliance with Ethical Standards


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.


  1. 1.
    Ali A. Measuring soccer skill performance: a review. Scand J Med Sci Sports. 2011;11:170–83.CrossRefGoogle Scholar
  2. 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. 3.
    Memmert D. Testing of tactical performance in youth elite soccer. J Sports Sci Med. 2010;9:199–205.PubMedPubMedCentralGoogle Scholar
  4. 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.CrossRefPubMedGoogle Scholar
  5. 5.
    Williams AM, Ford PR. Expertise and expert performance in sport. Int Rev Sport Exerc Psychol. 2008;1:4–18.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefPubMedGoogle Scholar
  8. 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.CrossRefPubMedGoogle Scholar
  9. 9.
    Perl J, Memmert D. Special issue: Network approaches in complex environments. Hum Mov Sci. 2012;31:267–70.CrossRefPubMedGoogle Scholar
  10. 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. 11.
    Memmert D. Teaching tactical creativity in team and racket sports: research and practice. Routledge; Abingdon; 2015.Google Scholar
  12. 12.
    Franks I. Qualitative and quantitative analysis. Coach Rev. 1985;8:48–50.Google Scholar
  13. 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. 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.CrossRefPubMedGoogle Scholar
  15. 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. 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.Google Scholar
  17. 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. 18.
    Baca A, Dabnichki P, Heller M, et al. Ubiquitous computing in sports: a review and analysis. J Sports Sci. 2009;27:1335–46.CrossRefPubMedGoogle Scholar
  19. 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. 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.CrossRefPubMedGoogle Scholar
  21. 21.
    Fujimura A, Sugihara K. Geometric analysis and quantitative evaluation of sport teamwork. Syst Comp Jpn. 2005;35(6):49–58.CrossRefGoogle Scholar
  22. 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.CrossRefPubMedGoogle Scholar
  23. 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.Google Scholar
  24. 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.Google Scholar
  25. 25.
    Horton M, Gudmundsson J, Chawla S, et al. Classification of passes in football matches using spatiotemporal data. arXiv:1407.5093.Google Scholar
  26. 26.
    Gudmundsson J, Wolle T. Towards automated football analysis: algorithms and data structures. In: Proc. 10th Australasian Conf. on mathematics and computers in sport.Google Scholar
  27. 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.Google Scholar
  28. 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.Google Scholar
  29. 29.
    Gudmundsson J, Wolle T. Football analysis using spatio-temporal tools. Comput Environ Urban Syst. 2014;47:16–27.CrossRefGoogle Scholar
  30. 30.
    Sampaio J, Maçãs V. Measuring tactical behaviour in football. Int J Sports Med. 2012;33:395–401.CrossRefPubMedGoogle Scholar
  31. 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.Google Scholar
  32. 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.Google Scholar
  33. 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.CrossRefPubMedGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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.CrossRefPubMedGoogle Scholar
  36. 36.
    Memmert D, Perl J. Analysis and simulation of creativity learning by means of artificial neural networks. Hum Mov Sci. 2009;28:263–82.CrossRefPubMedGoogle Scholar
  37. 37.
    Memmert D, Perl J. Game creativity analysis by means of neural networks. J Sport Sci. 2009;27:139–49.CrossRefGoogle Scholar
  38. 38.
    Richman J, Moorman J. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol. 2000;278:H2039–49.Google Scholar
  39. 39.
    Pincus S. Approximate entropy as a measure of system-complexity. Proc Natl Acad Sci. 1991;88:2297–301.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 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. 41.
    Palut Y, Zanone P. A dynamical analysis of tennis: concepts and data. J Sports Sci. 2005;23:1021–32.CrossRefPubMedGoogle Scholar
  42. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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. 44.
    Perl J. A neural network approach to movement pattern analysis. Hum Mov Sci. 2014;23:605–20.CrossRefGoogle Scholar
  45. 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. 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.CrossRefPubMedGoogle Scholar
  47. 47.
    Glazier PS. Towards a grand unified theory of sports performance. Hum Mov Sci. 2016 (in press).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Daniel Memmert
    • 1
    Email author
  • Koen A. P. M. Lemmink
    • 2
  • Jaime Sampaio
    • 3
  1. 1.Institute of Cognitive and Team/Racket Sport ResearchGerman Sport UniversityCologneGermany
  2. 2.Center for Human Movement SciencesUniversity Medical Center Groningen/University of GroningenGroningenThe Netherlands
  3. 3.Research Center for Sports Sciences, Health Sciences and Human Development (CIDESD)University of Trás-os-Montes and Alto DouroVila RealPortugal

Personalised recommendations