<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Instituto Politécnico de Viana do Castelo

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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....

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

Published Online: Jul 22, 2017

Page range: 39 - 50

DOI: https://doi.org/10.1515/ijcss-2017-0003

Keywords
Applied Mathematics, graph theory, soccer, football, match analysis

© 2017 Clemente, F. M. et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Jul 22, 2017

Page range: 39 - 50

DOI: https://doi.org/10.1515/ijcss-2017-0003

KeywordsApplied Mathematics, graph theory, soccer, football, match analysis

© 2017 Clemente, F. M. et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Applied Mathematics, graph theory, soccer, football, match analysis