Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game

From the diversity of applications of Network Science, in this Opinion Paper we are concerned about its potential to analyze one of the most extended group sports: Football (soccer in U.S. terminology). As we will see, Network Science allows addressing different aspects of the team organization and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences "can not be analyzed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole" or, in the classical words of after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur

Identifying the centrality levels of futsal players : a network approach

The aim of this study it was verify the differences of prominence levels between tactical positions in futsal (indoor football). For that reason, it was performed an analysis of variance between competitive levels and tactical positions for the centrality metrics computed by using network analysis. Forty-six futsal players from different competitive levels (U12, U14, U16 and Amateurs) it were analysed during three official futsal matches. Results revealed no differences in centrality metrics between competitive levels (p = 1.00; = 0.001; very small effect size) had no significant statistical differences in the centrality metrics. Nevertheless, tactical position (p = 0.001; = 0.593; moderate effect size) had significant main effects on the centrality metrics. Centrality metrics revealed that defenders are the most prominent players in to receive the ball. By the other hand, defenders and wings are the positions with greater centralities in to pass the ball for the teammates.info:eu-repo/semantics/publishedVersio

Network measures and digraph theory applied to soccer analysis : midfielder is the key player in youth teams

Graph and digraph theories have been used to test the relationships between teammates and the network properties of team sports. Nevertheless, no studies in young soccer teams have been found, as far we know. Therefore, the objective of the study was to apply network measures to identify centrality levels of young soccer players during official matches and analyse the variance between tactical positions and tactical line-ups. Seventy young soccer players from under-10 competitive level were observed during 10 matches. Significant statistical differences were found between players’ positions in IDC (p = 0.001; ES = 0.090; minimum effect); ODC (p = 0.001; ES = 0.156; minimum effect); and BC (p = 0.001; ES = 0.110; minimum effect) variables. No significant statistical differences were found between 1-3-2-1 and 1-2-3-1 line-ups for %IDC (p = 0.113; ES = 0.056; minimum effect), %ODC (p = 0.126; ES = 0.048; minimum effect) and %BC (p = 0.204; ES = 0.035; minimum effect). This study found that midfielder is the key position on the field, being a linkage player to attacking building.info:eu-repo/semantics/publishedVersio

Network analysis in basketball : inspecting the prominent players using centrality metrics

The aim of this study was to analyse the team-members cooperation in basketball by using centrality metrics of network. Different ages were compared in this study. Forty players (10 players of under-14; 10 players of under16; 10 players of under-18 and 10 players in amateurs with more than 20 years) voluntarily participated in this study. A total of 326 units of attack were generated based on the team-members interactions and then converted in final graphs. The one-way ANOVA for the factor tactical position found statistical differences in the dependent variables of %DCentrality (F(4,15) = 13.622; p-value = 0.001; n2 = 0.784; Large Effect Size) and %DPrestige (F(4,15) = 20.590; p-value = 0.001; n2 = 0.846; Large Effect Size). In conclusion this study showed that point guard was the prominent position during the attacking organization and that social network analysis it is a useful approach to identify the patterns of interactions in the game of basketball.info:eu-repo/semantics/publishedVersio

Who is the prominent tactical position in rink-hockey? : a network approach based on centrality metrics

The aim of this study was to verify the prominence levels of rink-hockey players in different competitive levels. For that reason, it was analysed the variance of network centrality metrics between competitive levels and tactical positions. Fifty-four rink-hockey players from five different levels (U12, U14, U16, U18 and Elite) were analysed during three official matches. The results did not found statistical differences in centrality levels of players between competitive levels (p-value = 1.00; partial eta square = 0.001; very small effect size). Nevertheless, tactical position (p-value = 0.001; partial eta square = 0.534; moderate effect size) had significant main effects on the centrality metrics. In this study it was found that defender and forward are the positions that most receive balls from the teammates. In other hand, the forward is the position that most passes performed until the U16 and in older levels the defender assumes the centrality in passes performed.info:eu-repo/semantics/publishedVersio

The social network analysis of Switzerland football team on FIFA World Cup 2014

The aim of this study was to apply the social network analysis approach to the football match analysis case. For such, it was analyzed the Switzerland national football team during the FIFA World Cup 2014 tournament. Two general network metrics (total links and network density) and two centrality metrics (degree centrality and degree prestige) were computed. Four matches from Switzerland in FIFA World Cup 2014 were analysed in this study. A total of 334 adjacency matrices corresponding to 334 units of attack were generated based on the teammates’ interactions and then converted in 4 network graphs. A total of 1129 passes were analysed. The greatest value of total links and network density was achieved in the first match (88 total links and 0.80 of density value). Degree centrality revealed that the defenders and midfielders were the players with greatest prominent values in the attacking building. Degree prestige showed that midfielders were the main targets of the team to pass the ball in the attacking process. In summary, this study showed that centrality metrics can be an important tool in match analysis to identify the style of play of football teams, revealing the most prominent tactical roles in the attacking process.info:eu-repo/semantics/publishedVersio

How team sports behave as a team? : general network metrics applied to sports analysis

The aim of this study was to analyse the general properties of networks in different team sports. Therefore, the analysis of variance to the general network properties between different team sports and different competitive levels was carried out. Sixty-six official matches (from Handball, Basketball, Football, Futsal, Rink-Hockey and Volleyball) were observed in five possible competitive levels (U12, U14, U16, U18 and Amateurs with more than 20 years). Analysis of variance revealed that the type of sport (p = 0.001; ��=0.647; moderate effect size) and competitive level(p = 0.001; �� = 0.355; small effect size)had significant statistical differences in the general network metrics. It was also found that football generates more connections between teammates but basketball and volleyball promote better results of density and clustering coefficient.info:eu-repo/semantics/publishedVersio

Who are the prominent players in the UEFA champions league? : an approach based on network analysis

This study aimed to analyze the centrality levels of elite football players. Tactical positions and tactical line-ups were considered factors to be used in analyzing the variance in the prominence of players, measured by social network measures. The best 16 teams from the UEFA Champions league were analyzed during the entire competition. A total of 109 matches were analyzed for this study. Significant statistical differences between positions were found in % indegree (p = 0.001; ES = 0.268, moderate effect), % outdegree (p = 0.001; ES = 0.301, moderate effect) and % betweenness (p = 0.001; ES = 0.114, minimum effect). No statistical differences between tactical line-ups in % outdegree (p = 1.000; ES = 0.001, no effect) or % indegree (p = 1.000; ES = 0.001, no effect) were found. Central midfielders had the greatest values of centrality, thus confirming their importance in the linkage process of the team. Position had great influence on the centrality levels of players.info:eu-repo/semantics/publishedVersio