Network structure of UEFA Champions League teams: association with classical notational variables and variance between different levels of success

Abstract

The aim of this study was to analyse the general properties of the network of elite football teams that participated in UEFA Champions League 2015-2016. Analysis of variance of the general network measures between performances in competition was made. Moreover, the association between performance variables (goals, shots, and percentage of ball possession) and general network measures also was tested. The best sixteen teams that participated in UEFA Champions League 2015-2016 were analysed in a total of 109 official matches. Statistically significant differences between maximum stages in competition were found in total links (p = 0.003; ES = 0.087), network density (p = 0.003; ES = 0.088), and clustering coefficient (p = 0.007; ES = 0.078). Total links (r = 0.439; p = 0.001), network density (r = 0.433; p = 0.001) and clustering coefficient (r = 0.367; p = 0.001) had a moderate positive correlations with percentage of ball possession. This study revealed that teams that achieved the quarterfinals and finals had greater values of general network measures than the remaining teams, thus suggesting that higher values of homogeneity in network process may improve the success of the teams. Moderate correlations were found between ball possession and the general network measures suggesting that teams with more capacity to perform longer passing sequences may involve more players in a more homogeneity manner.

Passing Network Analysis of Positional Attack Formations in Handball

“…All analyses were conducted with Matlab at a 5% significance level. Following Ferguson (2009) and Clemente and Martins (2017), was reported to interpret the effect size according to the following criteria: no effect ( < 0.04); small effect (0.04 ≤ < 0.25); moderate effect (0.25 ≤ < 0.64); strong effect ( ≥ 0.64).…”

“…Social network analysis (SNA) has proven successful in the study of ball passing dynamics by breaking down the complexity within the web of interactions between players (Passos et al, 2011). As a match analysis tool, SNA is able to quantify the contribution of individual players to the general interplay as well as detect patterns in the passing structure of teams (Clemente and Martins, 2017).…”

“…As midfielders are responsible for building attacks, they are identified as the most prominent players in the majority of studies (Clemente et al, 2015a;Pena and Touchette, 2012). Clemente and Martins (2017) also consider different tactical formations in their computation of network metrics in professional soccer. At a macro-level, focusing on general team performance, several studies suggest a strong correlation between successful team performance and frequent but also balanced interplay between players (Clemente et al, 2015b;Duch et al, 2010;Grund, 2012).…”

Characterizing different team sports using network analysis

“…The statistical analyses were all conducted at a significance level of p < .05 using Matlab®. Following Ferguson (2009) and Clemente and Martins (2017), η 2 is reported to interpret the effect size according to the following criteria: no effect (η 2 < .04); small effect (.04 ≤ η 2 < .25); moderate effect (.25 ≤ η 2 < .64); strong effect (η 2 ≥ .64).…”

“…The complexity of matches and team dynamics makes breaking down such patterns difficult, creating an ongoing challenge for performance analysis in team sports. There is an increasing interest in applying Social Network Analysis (SNA), a method that exploits familiar performance variables such as passes, in order to detect patterns in the interplay of teams (Clemente & Martins, 2017). Network approaches focus on breaking down the web of interactions in systems of multiple agents also referred to as nodes (Passos et al, 2011).…”