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Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona

The application of Network Science to social systems has introduced new methodologies to analyze classical problems such as the emergence of epidemics, the arousal of cooperation between individuals or the propagation of information along social networks. More recently, the organization of football...

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Autores principales: Buldú, J. M., Busquets, J., Echegoyen, I., Seirul.lo, F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6753100/
https://www.ncbi.nlm.nih.gov/pubmed/31537882
http://dx.doi.org/10.1038/s41598-019-49969-2
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author Buldú, J. M.
Busquets, J.
Echegoyen, I.
Seirul.lo, F.
author_facet Buldú, J. M.
Busquets, J.
Echegoyen, I.
Seirul.lo, F.
author_sort Buldú, J. M.
collection PubMed
description The application of Network Science to social systems has introduced new methodologies to analyze classical problems such as the emergence of epidemics, the arousal of cooperation between individuals or the propagation of information along social networks. More recently, the organization of football teams and their performance have been unveiled using metrics coming from Network Science, where a team is considered as a complex network whose nodes (i.e., players) interact with the aim of overcoming the opponent network. Here, we combine the use of different network metrics to extract the particular signature of the F.C. Barcelona coached by Guardiola, which has been considered one of the best teams along football history. We have first compared the network organization of Guardiola’s team with their opponents along one season of the Spanish national league, identifying those metrics with statistically significant differences and relating them with the Guardiola’s game. Next, we have focused on the temporal nature of football passing networks and calculated the evolution of all network properties along a match, instead of considering their average. In this way, we are able to identify those network metrics that enhance the probability of scoring/receiving a goal, showing that not all teams behave in the same way and how the organization Guardiola’s F.C. Barcelona is different from the rest, including its clustering coefficient, shortest-path length, largest eigenvalue of the adjacency matrix, algebraic connectivity and centrality distribution.
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spelling pubmed-67531002019-10-01 Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona Buldú, J. M. Busquets, J. Echegoyen, I. Seirul.lo, F. Sci Rep Article The application of Network Science to social systems has introduced new methodologies to analyze classical problems such as the emergence of epidemics, the arousal of cooperation between individuals or the propagation of information along social networks. More recently, the organization of football teams and their performance have been unveiled using metrics coming from Network Science, where a team is considered as a complex network whose nodes (i.e., players) interact with the aim of overcoming the opponent network. Here, we combine the use of different network metrics to extract the particular signature of the F.C. Barcelona coached by Guardiola, which has been considered one of the best teams along football history. We have first compared the network organization of Guardiola’s team with their opponents along one season of the Spanish national league, identifying those metrics with statistically significant differences and relating them with the Guardiola’s game. Next, we have focused on the temporal nature of football passing networks and calculated the evolution of all network properties along a match, instead of considering their average. In this way, we are able to identify those network metrics that enhance the probability of scoring/receiving a goal, showing that not all teams behave in the same way and how the organization Guardiola’s F.C. Barcelona is different from the rest, including its clustering coefficient, shortest-path length, largest eigenvalue of the adjacency matrix, algebraic connectivity and centrality distribution. Nature Publishing Group UK 2019-09-19 /pmc/articles/PMC6753100/ /pubmed/31537882 http://dx.doi.org/10.1038/s41598-019-49969-2 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Buldú, J. M.
Busquets, J.
Echegoyen, I.
Seirul.lo, F.
Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona
title Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona
title_full Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona
title_fullStr Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona
title_full_unstemmed Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona
title_short Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona
title_sort defining a historic football team: using network science to analyze guardiola’s f.c. barcelona
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6753100/
https://www.ncbi.nlm.nih.gov/pubmed/31537882
http://dx.doi.org/10.1038/s41598-019-49969-2
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