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


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.


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.



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


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.


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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

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