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Consistency and identifiability of football teams: a network science perspective
We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given s...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661721/ https://www.ncbi.nlm.nih.gov/pubmed/33184412 http://dx.doi.org/10.1038/s41598-020-76835-3 |
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author | Garrido, D. Antequera, D. R. Busquets, J. López del Campo, R. Resta Serra, R. Jos Vielcazat, S. Buldú, J. M. |
author_facet | Garrido, D. Antequera, D. R. Busquets, J. López del Campo, R. Resta Serra, R. Jos Vielcazat, S. Buldú, J. M. |
author_sort | Garrido, D. |
collection | PubMed |
description | We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams’ matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match. |
format | Online Article Text |
id | pubmed-7661721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76617212020-11-13 Consistency and identifiability of football teams: a network science perspective Garrido, D. Antequera, D. R. Busquets, J. López del Campo, R. Resta Serra, R. Jos Vielcazat, S. Buldú, J. M. Sci Rep Article We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams’ matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match. Nature Publishing Group UK 2020-11-12 /pmc/articles/PMC7661721/ /pubmed/33184412 http://dx.doi.org/10.1038/s41598-020-76835-3 Text en © The Author(s) 2020 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Garrido, D. Antequera, D. R. Busquets, J. López del Campo, R. Resta Serra, R. Jos Vielcazat, S. Buldú, J. M. Consistency and identifiability of football teams: a network science perspective |
title | Consistency and identifiability of football teams: a network science perspective |
title_full | Consistency and identifiability of football teams: a network science perspective |
title_fullStr | Consistency and identifiability of football teams: a network science perspective |
title_full_unstemmed | Consistency and identifiability of football teams: a network science perspective |
title_short | Consistency and identifiability of football teams: a network science perspective |
title_sort | consistency and identifiability of football teams: a network science perspective |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661721/ https://www.ncbi.nlm.nih.gov/pubmed/33184412 http://dx.doi.org/10.1038/s41598-020-76835-3 |
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