Advertisement

Sports Medicine

, Volume 47, Issue 9, pp 1689–1696 | Cite as

Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice

  • João Ribeiro
  • Pedro Silva
  • Ricardo Duarte
  • Keith Davids
  • Júlio Garganta
Current Opinion

Abstract

This paper discusses how social network analyses and graph theory can be implemented in team sports performance analyses to evaluate individual (micro) and collective (macro) performance data, and how to use this information for designing practice tasks. Moreover, we briefly outline possible limitations of social network studies and provide suggestions for future research. Instead of cataloguing discrete events or player actions, it has been argued that researchers need to consider the synergistic interpersonal processes emerging between teammates in competitive performance environments. Theoretical assumptions on team coordination prompted the emergence of innovative, theoretically driven methods for assessing collective team sport behaviours. Here, we contribute to this theoretical and practical debate by re-conceptualising sports teams as complex social networks. From this perspective, players are viewed as network nodes, connected through relevant information variables (e.g. a ball-passing action), sustaining complex patterns of interaction between teammates (e.g. a ball-passing network). Specialised tools and metrics related to graph theory could be applied to evaluate structural and topological properties of interpersonal interactions of teammates, complementing more traditional analysis methods. This innovative methodology moves beyond the use of common notation analysis methods, providing a richer understanding of the complexity of interpersonal interactions sustaining collective team sports performance. The proposed approach provides practical applications for coaches, performance analysts, practitioners and researchers by establishing social network analyses as a useful approach for capturing the emergent properties of interactions between players in sports teams.

Keywords

Social Network Analysis Cluster Coefficient Team Performance Team Sport Interpersonal Interaction 
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.

Notes

Acknowledgements

The authors would like to acknowledge João Cláudio Machado and three anonymous reviewers for the valuable insights that enhanced the quality of this manuscript.

Compliance with Ethical Standards

Funding

No financial support was received for the planning or conduct of the research presented in this article.

Conflict of interest

João Ribeiro, Pedro Silva, Ricardo Duarte, Keith Davids and Júlio Garganta declare that they have no conflicts of interest relevant to the content of this article.

References

  1. 1.
    Wagner JA. Studies of individualism-collectivism: effects on cooperation in groups. Acad Manag J. 1995;38(1):152–72.CrossRefGoogle Scholar
  2. 2.
    Wu B, Zhou D, Fu F, et al. Evolution of cooperation on stochastic dynamical networks. PLoS One. 2010;. doi: 10.1371/journal.pone.001187.Google Scholar
  3. 3.
    Duarte R, Araújo D, Correia V, et al. Sport teams as superorganisms: implications of biological models for research and practice in team sports performance analysis. Sports Med. 2012;42(8):633–42.CrossRefPubMedGoogle Scholar
  4. 4.
    Parrish J, Edelstein-Keshet L. Complexity, pattern, and evolutionary trade-offs in animal aggregations. Science. 1999;284(2):99–101.CrossRefPubMedGoogle Scholar
  5. 5.
    Sarmento H, Marcelino R, Anguera MT, et al. Match analysis in football: a systematic review. J Sports Sci. 2014;. doi: 10.1080/02640414.2014.898852.PubMedGoogle Scholar
  6. 6.
    Balague N, Torrents C, Hristovsky R, et al. Overview of complex systems in sport. J Syst Sci Complex. 2013;26(1):4–13.CrossRefGoogle Scholar
  7. 7.
    Glazier PS. Game, set and match? Substantive issues and future directions in performance analysis. Sports Med. 2010;40(8):625–34.CrossRefPubMedGoogle Scholar
  8. 8.
    Vilar L, Araújo D, Davids K, et al. The role of ecological dynamics in analysing performance in team sports. Sports Med. 2012;42(1):1–10.CrossRefPubMedGoogle Scholar
  9. 9.
    Glazier PS. Towards a grand unified theory of sports performance. Hum Mov Sci. 2015;. doi: 10.1016/j.humov.2015.08.001.PubMedGoogle Scholar
  10. 10.
    Couceiro M, Dias G, Araújo D, et al. The ARCANE project: how an ecological dynamics framework can enhance performance assessment and prediction in football. Sports Med. 2016;. doi: 10.1007/s40279-016-0549-2.PubMedGoogle Scholar
  11. 11.
    Grund TU. Network structure and team performance: the case of English Premier League soccer teams. Soc Netw. 2012;34(4):682–90.CrossRefGoogle Scholar
  12. 12.
    Mukherjee S. Complex network analysis in cricket: community structure, player’s role and performance index. Adv Complex Syst. 2013;. doi: 10.1142/S0219525913500318.Google Scholar
  13. 13.
    Clemente FM, Martins FML, Couceiro MC, et al. A network approach to characterize the teammates’ interactions on football: a single match analysis. Cuadernos de Psicología del Deporte. 2014;14(3):141–8.CrossRefGoogle Scholar
  14. 14.
    Wellman B, Wasserman S. Social networks. In: Kazdin A, editor. Encyclopedia of psychology. New York: American Psychological Association and Oxford University Press; 2000. p. 351–3.Google Scholar
  15. 15.
    Aguiar M, Gonçalves B, Botelho G, et al. Footballers’ movement behaviour during 2-,3-,4- and 5-a-side small-sided games. J Sports Sci. 2015;33(12):1259–66.CrossRefPubMedGoogle Scholar
  16. 16.
    Silva P, Travassos B, Vilar L, et al. Numerical relations and skill level constrain co-adaptive behaviours of agents in sports teams. PLoS One. 2014;. doi: 10.1371/journal.pone.0107112.Google Scholar
  17. 17.
    Kelso JAS. Synergies: atoms of brain and behaviour. Adv Exp Med Biol. 2009;629:83–91.CrossRefPubMedGoogle Scholar
  18. 18.
    Kelso JAS. Multistability and metastability: understanding dynamic coordination in the brain. Philos Trans R Soc Lond B Biol Sci. 2012;367:906–18.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Salas E, Dickinson TL, Converse SA, et al. Toward an understanding of team performance and training. In: Swezey RW, Salas E, editors. Norwood. NJ: Ablex; 1992. p. 3–29.Google Scholar
  20. 20.
    Brannick MT, Prince A, Prince C, et al. The measurement of team processes. Hum Factors. 1995;37:641–51.CrossRefPubMedGoogle Scholar
  21. 21.
    Silva P, Chung D, Carvalho T, et al. Practice effects on intra-team synergies in football teams. Hum Mov Sci. 2016;46:39–51.CrossRefPubMedGoogle Scholar
  22. 22.
    Silva P, Garganta J, Araújo D, et al. Shared knowledge or shared affordances? Insights from an ecological dynamics approach to team coordination in sports. Sports Med. 2013;43:765–72.CrossRefPubMedGoogle Scholar
  23. 23.
    Barabási AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;. doi: 10.1038/nrg1272.PubMedGoogle Scholar
  24. 24.
    Henttonen K. Exploring social networks on the team level: a review of the empirical literature. J Eng Technol Manag. 2010;27:74–109.CrossRefGoogle Scholar
  25. 25.
    Quatman C, Chelladurai P. Social network theory and analysis: a complementary lens for inquiry. J Sport Manag. 2008;22:338–60.CrossRefGoogle Scholar
  26. 26.
    Freeman LC. The development of social network analysis: a study in the sociology of science. Vancouver: Empirical Press; 2004.Google Scholar
  27. 27.
    Wasserman S, Galaskiewicz J. Advances in social network analysis: research from the social and behavioural sciences. Newbury Park: Sage Publications; 1994.CrossRefGoogle Scholar
  28. 28.
    Rice E, Yoshioka-Maxwell A. Social network analysis as a toolkit for the science of social work. J Soc Social Work Res. 2015;. doi: 10.1086/682723.Google Scholar
  29. 29.
    Lusher D, Robins G, Kremer P. The application of social network analysis to team sports. Meas Phys Educ Exerc Sci. 2010;14:211–24.CrossRefGoogle Scholar
  30. 30.
    Gama J, Passos P, Davids K, et al. Network analysis and intra-team activity in attacking phases of professional football. Int J Perf Anal Spor. 2014;14(3):692–708.Google Scholar
  31. 31.
    Malta P, Travassos B. Characterization of the defense-attack transition of a soccer team. Motricidade. 2014;10(1):27–37.CrossRefGoogle Scholar
  32. 32.
    Clemente FM, Couceiro MC, Martins FML, et al. Using network metrics in soccer: a macro-analysis. J Hum Kinet. 2015;45:123–34.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Yamamoto Y, Yokoyama K. Common and unique network dynamics in football games. PLoS One. 2011;6(12):1–6.Google Scholar
  34. 34.
    Duch J, Waitzman JS, Amaral LAN. Quantifying the performance of individual players in a team activity. PLoS One. 2010;. doi: 10.1371/journal.pone.0010937.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Passos P, Davids K, Araújo D, et al. Networks as a novel tool for studying team ball sports as complex social systems. J Sci Med Sport. 2011;14(2):170–6.CrossRefPubMedGoogle Scholar
  36. 36.
    Warner S, Bowers MT, Dixon MA. Team dynamics: a social network perspective. J Sport Manag. 2012;26:53–66.CrossRefGoogle Scholar
  37. 37.
    Zhu J. Power systems applications of graph theory. New York: Nova Science Publishers Inc; 2011.Google Scholar
  38. 38.
    Bondy JA, Murty USR. Graph theory with applications. Elsevier Science Ltd: North-Holland; 1976.CrossRefGoogle Scholar
  39. 39.
    Ruohonen K. Graph theory. Tampere: Tampere University of Technology; 2008.Google Scholar
  40. 40.
    Voloshin VI. Introduction to graph theory. New York: Nova Science Publishers Inc; 2009.Google Scholar
  41. 41.
    Clemente FM, Couceiro MS, Martins F, et al. Using network metrics to investigate football team players’ connections: a pilot study. Motriz. 2014;20(3):262–71.Google Scholar
  42. 42.
    Molm LD. Dependence and risk: transforming and structure of social exchange. Soc Psychol Q. 1994;57(3):163–76.CrossRefGoogle Scholar
  43. 43.
    Sparrowe R, Liden R, Wayne S, et al. Social networks and the performance of individuals and groups. Acad Manag J. 2001;44(2):316–25.CrossRefGoogle Scholar
  44. 44.
    Borgatti SP, Foster PC. The network paradigm in organizational research: a review and typology. J Manag. 2003;29(6):991–1013.Google Scholar
  45. 45.
    Cummings JN, Cross R. Structural properties of work groups and their consequences for performance. Soc Netw. 2003;25:197–210.CrossRefGoogle Scholar
  46. 46.
    Balkundi P, Harrison D. Ties, leaders, and time in teams: strong inference about network structure’s effects on team viability and performance. Acad Manag J. 2006;49(1):49–68.CrossRefGoogle Scholar
  47. 47.
    Gaston ME, DesJardins M. The effect of network structure on dynamic team formation in multi-agent systems. Comput Intell. 2008;24(2):122–57.CrossRefGoogle Scholar
  48. 48.
    Fewell JH, Armbruster D, Ingraham J, et al. Basketball teams as strategic networks. PLoS One. 2012;. doi: 10.1371/journal.pone.0047445.Google Scholar
  49. 49.
    Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393(6684):440–2.CrossRefPubMedGoogle Scholar
  50. 50.
    Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002;74(1):47–97.CrossRefGoogle Scholar
  51. 51.
    Passos P, Araújo D, Travassos B, et al. Interpersonal coordination tendencies induce functional synergies through co-adaptation processes in team sports. In: Davids K, Hristovski R, Araújo D, Serre N, Button C, Passos P, editors. Complex systems in sport. London: Routledge; 2014. p. 117–21.Google Scholar
  52. 52.
    Freeman LC. Centrality in social networks: conceptual clarification. Soc Netw. 1979;1:215–39.CrossRefGoogle Scholar
  53. 53.
    Gudmundsson J, Horton M. Spatial-temporal analysis of team sports—a survey. 2016;arXiv:1602.06994v1[cs.OH].
  54. 54.
    Borgatti SP. Centrality and network flow. Soc Netw. 2005;27:55–71.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.CIFI2D, Centre of Research, Education, Innovation and Intervention in Sport, Faculdade de DesportoUniversidade do PortoPortoPortugal
  2. 2.Shanghai SIPG FCXangaiChina
  3. 3.CIPER, Faculdade de Motricidade HumanaUniversidade de LisboaLisboaPortugal
  4. 4.CSERSheffield Hallam UniversitySheffieldUK

Personalised recommendations