Social Network Analysis in Football: A Systematic Review of Performance and

Ricardo Alves^1*Gonçalo Dias^2,3Nuno André Nunes⁴Sérgio M. Querido⁵Vasco Vaz^5,6

• ¹Faculty of Sport Sciences and Physical Education, University of Coimbra, 3040-248 Coimbra, Portugal, Coimbra, Portugal
• ²Escola Superior de Educação de Coimbra, Instituto Politécnico de Coimbra,, Coimbra, Portugal
• ³SPRINT Sport Physical Activity and Health Research & Innovation Center, 3030-329 Coimbra, Coimbra, Portugal
• ⁴Department of Sport and Health, Southampton Solent University, Southampton SO14 0YN, United Kingdom, Southampton, United Kingdom
• ⁵CIPER, Faculty of Human Kinetics, University of Lisbon, 1499-002 Cruz-Quebrada-Dafundo, Portugal, Lisboa, Portugal
• ⁶Faculty of Sport Sciences and Physical Education, University of Coimbra, 3040-248 Coimbra, coimbra, Portugal

This systematic review aims to critically examine the application of social network analysis (SNA) in football, with a focus on its contribution to evaluating team performance, tactical behaviour, and player interactions. Following PRISMA guidelines, a comprehensive search was conducted across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus) from January 2017 to October 2024. Fifty-five peerreviewed studies met the inclusion criteria, addressing network analysis in official men's professional football matches. Data were extracted and summarised regarding methodological quality, network metrics used, tactical context, and practical implications.Most studies demonstrated that cohesive network structures, characterised by high density, clustering coefficients, and centrality, are associated with successful team performance. Centrality metrics were frequently used to identify key tactical players, typically central defenders and midfielders. Recent methodological advances included dynamic time-window analysis, pitch-passing networks, and spatial-temporal integration using tracking data. However, there remains an overrepresentation of elite men's football and offensive phases, with limited focus on defensive networks, youth categories, and women's football. SNA offers a powerful framework to decode the complexity of football performance, evolving from static graphs to dynamic, role-sensitive, and context-rich models. Future research should adopt longitudinal designs, multi-layer network approaches, and closer collaboration with practitioners to enhance the operational utility of network insights in coaching and performance analysis.