Skip to main content
SpringerLink
Log in

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

  • Current Opinion
  • Published:
Sports Medicine Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Wagner JA. Studies of individualism-collectivism: effects on cooperation in groups. Acad Manag J. 1995;38(1):152–72.

    Article  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  4. Parrish J, Edelstein-Keshet L. Complexity, pattern, and evolutionary trade-offs in animal aggregations. Science. 1999;284(2):99–101.

    Article  CAS  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  6. Balague N, Torrents C, Hristovsky R, et al. Overview of complex systems in sport. J Syst Sci Complex. 2013;26(1):4–13.

    Article  Google Scholar 

  7. Glazier PS. Game, set and match? Substantive issues and future directions in performance analysis. Sports Med. 2010;40(8):625–34.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  9. Glazier PS. Towards a grand unified theory of sports performance. Hum Mov Sci. 2015;. doi:10.1016/j.humov.2015.08.001.

    PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  11. Grund TU. Network structure and team performance: the case of English Premier League soccer teams. Soc Netw. 2012;34(4):682–90.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  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. Kelso JAS. Synergies: atoms of brain and behaviour. Adv Exp Med Biol. 2009;629:83–91.

    Article  PubMed  Google Scholar 

  18. Kelso JAS. Multistability and metastability: understanding dynamic coordination in the brain. Philos Trans R Soc Lond B Biol Sci. 2012;367:906–18.

    Article  PubMed  PubMed Central  Google Scholar 

  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. Brannick MT, Prince A, Prince C, et al. The measurement of team processes. Hum Factors. 1995;37:641–51.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  23. Barabási AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;. doi:10.1038/nrg1272.

    PubMed  Google Scholar 

  24. Henttonen K. Exploring social networks on the team level: a review of the empirical literature. J Eng Technol Manag. 2010;27:74–109.

    Article  Google Scholar 

  25. Quatman C, Chelladurai P. Social network theory and analysis: a complementary lens for inquiry. J Sport Manag. 2008;22:338–60.

    Article  Google Scholar 

  26. Freeman LC. The development of social network analysis: a study in the sociology of science. Vancouver: Empirical Press; 2004.

    Google Scholar 

  27. Wasserman S, Galaskiewicz J. Advances in social network analysis: research from the social and behavioural sciences. Newbury Park: Sage Publications; 1994.

    Book  Google Scholar 

  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. Lusher D, Robins G, Kremer P. The application of social network analysis to team sports. Meas Phys Educ Exerc Sci. 2010;14:211–24.

    Article  Google Scholar 

  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. Malta P, Travassos B. Characterization of the defense-attack transition of a soccer team. Motricidade. 2014;10(1):27–37.

    Article  Google Scholar 

  32. Clemente FM, Couceiro MC, Martins FML, et al. Using network metrics in soccer: a macro-analysis. J Hum Kinet. 2015;45:123–34.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yamamoto Y, Yokoyama K. Common and unique network dynamics in football games. PLoS One. 2011;6(12):1–6.

    Google Scholar 

  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.

    PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  36. Warner S, Bowers MT, Dixon MA. Team dynamics: a social network perspective. J Sport Manag. 2012;26:53–66.

    Article  Google Scholar 

  37. Zhu J. Power systems applications of graph theory. New York: Nova Science Publishers Inc; 2011.

    Google Scholar 

  38. Bondy JA, Murty USR. Graph theory with applications. Elsevier Science Ltd: North-Holland; 1976.

    Book  Google Scholar 

  39. Ruohonen K. Graph theory. Tampere: Tampere University of Technology; 2008.

    Google Scholar 

  40. Voloshin VI. Introduction to graph theory. New York: Nova Science Publishers Inc; 2009.

    Google Scholar 

  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. Molm LD. Dependence and risk: transforming and structure of social exchange. Soc Psychol Q. 1994;57(3):163–76.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Cummings JN, Cross R. Structural properties of work groups and their consequences for performance. Soc Netw. 2003;25:197–210.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393(6684):440–2.

    Article  CAS  PubMed  Google Scholar 

  50. Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002;74(1):47–97.

    Article  Google Scholar 

  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. Freeman LC. Centrality in social networks: conceptual clarification. Soc Netw. 1979;1:215–39.

    Article  Google Scholar 

  53. Gudmundsson J, Horton M. Spatial-temporal analysis of team sports—a survey. 2016;arXiv:1602.06994v1[cs.OH].

  54. Borgatti SP. Centrality and network flow. Soc Netw. 2005;27:55–71.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. CIFI2D, Centre of Research, Education, Innovation and Intervention in Sport, Faculdade de Desporto, Universidade do Porto, Rua Dr. Plácido Costa, 91, 4200-450, Porto, Portugal

    João Ribeiro, Pedro Silva & Júlio Garganta

  2. Shanghai SIPG FC, Xangai, China

    Pedro Silva

  3. CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Cruz Quebrada, Dafundo, 1499-002, Lisboa, Portugal

    Ricardo Duarte

  4. CSER, Sheffield Hallam University, Broomgrove Teaching Block, Broomgrove Road, S10 2LX, Sheffield, UK

    Keith Davids

Authors
  1. João Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricardo Duarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keith Davids
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Júlio Garganta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to João Ribeiro.

Ethics declarations

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ribeiro, J., Silva, P., Duarte, R. et al. Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice. Sports Med 47, 1689–1696 (2017). https://doi.org/10.1007/s40279-017-0695-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40279-017-0695-1

Keywords

Search

Navigation