The effect of playing formation on the passing network characteristics of a professional football team

Purpose The playing formation of football teams influences how players interact with each other via passing. The aim of this study was to use network analysis to determine the passing characteristics of playing positions within a professional a professional football team across two consecutive competitive seasons, when playing two different formations. Methods In season one (2016/2017) the team played 1-4-2-2-2 in 21 matches, and in season two (2017/2018) the team played 1-4-2-3-1 in 21 matches. Network analysis was applied to calculate the individual centrality metrics of indegree centrality (IDC), outdegree centrality (ODC), closeness centrality (CC), and betweenness centrality (BC) for the playing positions, using the Social Network Visualizer (SocNetV). The centrality metrics were compared across the playing formations and as a function of match outcome. Results The forward positions in 1-4-2-2-2 had significantly (p Conclusions The current study shows that subtle changes to playing formation elicit differences in the passing contributions of the players. The results suggest that coaches may adopt the playing formation 1-4-2-2-2 compared with 1-4-2-3-1 owing to the increased passing involvement from the forwards.

[1]  Will G. Hopkins,et al.  Inter-operator reliability of live football match statistics from OPTA Sportsdata , 2013 .

[2]  Paul M. Salmon,et al.  Do inter-continental playing styles exist? Using social network analysis to compare goals from the 2016 EURO and COPA football tournaments knock-out stages , 2017 .

[3]  Keith Davids,et al.  Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice , 2017, Sports Medicine.

[4]  H. Touchette,et al.  A network theory analysis of football strategies , 2012, 1206.6904.

[5]  P. Salmon,et al.  Integrating communication and passing networks in football using social network analysis , 2019 .

[6]  Paul M Salmon,et al.  A social network analysis of the goal scoring passing networks of the 2016 European Football Championships. , 2017, Human movement science.

[7]  Michael J Duncan,et al.  Match play demands of 11 versus 11 professional football using Global Positioning System tracking: Variations across common playing formations. , 2016, Human movement science.

[8]  G. Robins,et al.  The Application of Social Network Analysis to Team Sports , 2010 .

[9]  Thomas U. Grund,et al.  Network structure and team performance: The case of English Premier League soccer teams , 2012, Soc. Networks.

[10]  T. Levine,et al.  Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research , 2002 .

[11]  Filipe Manuel Clemente,et al.  General network analysis of national soccer teams in FIFA World Cup 2014 , 2015 .

[12]  Filipe Manuel Clemente,et al.  Midfielder as the prominent participant in the building attack: A network analysis of national teams in FIFA World Cup 2014 , 2015 .

[13]  Jaime Sampaio,et al.  Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football , 2017, PLoS ONE.

[14]  J. Martin-González,et al.  Applying graphs and complex networks to football metric interpretation. , 2017, Human movement science.

[15]  C. Carling.  Influence of opposition team formation on physical and skill-related performance in a professional soccer team , 2011 .

[16]  U. Brandes,et al.  Social network analysis in sport research: an emerging paradigm , 2017 .

[17]  P. Krustrup,et al.  The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches , 2011, Journal of sports sciences.

[18]  K. Davids,et al.  What Performance Analysts Need to Know About Research Trends in Association Football (2012–2016): A Systematic Review , 2018, Sports Medicine.