Using network metrics to investigate football team players’ connections : a pilot study
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.26/46784 |
Resumo: | The aim of this pilot study was propose a set of network methods to measure the specific properties of football teams. These metrics were organized on “meso” and “micro” analysis levels. Five official matches of the same team on the First Portuguese Football League were analyzed. An overall of 577 offensive plays were analyzed from the five matches. From the adjacency matrices developed per each offensive play it were computed the scaled connectivity, the clustering coefficient and the centroid significance and centroid conformity. Results showed that the highest values of scaled connectivity were found in lateral defenders and central and midfielder players and the lowest values were found in the striker and goalkeeper. The highest values of clustering coefficient were generally found in midfielders and forwards. In addition, the centroid results showed that lateral and central defenders tend to be the centroid players in the attacking process. In sum, this study showed that network metrics can be a powerful tool to help coaches to understanding the specific team’s properties, thus supporting decision-making and improving sports training based on match analysis. |
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Using network metrics to investigate football team players’ connections : a pilot studymatch analysisfootballnetworkmetricsperformanceanálise de jogofutebolnetworkmétricasrendimentoThe aim of this pilot study was propose a set of network methods to measure the specific properties of football teams. These metrics were organized on “meso” and “micro” analysis levels. Five official matches of the same team on the First Portuguese Football League were analyzed. An overall of 577 offensive plays were analyzed from the five matches. From the adjacency matrices developed per each offensive play it were computed the scaled connectivity, the clustering coefficient and the centroid significance and centroid conformity. Results showed that the highest values of scaled connectivity were found in lateral defenders and central and midfielder players and the lowest values were found in the striker and goalkeeper. The highest values of clustering coefficient were generally found in midfielders and forwards. In addition, the centroid results showed that lateral and central defenders tend to be the centroid players in the attacking process. In sum, this study showed that network metrics can be a powerful tool to help coaches to understanding the specific team’s properties, thus supporting decision-making and improving sports training based on match analysis.UNESPRepositório ComumManuel Clemente, FilipeCouceiro, MicaelM. L. Martins, FernandoMendes, Rui2023-09-27T09:40:51Z20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/46784engdx.doi.org/10.1590/S1980-65742014000300004info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-09-28T02:17:17Zoai:comum.rcaap.pt:10400.26/46784Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:31:35.480676Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Using network metrics to investigate football team players’ connections : a pilot study |
title |
Using network metrics to investigate football team players’ connections : a pilot study |
spellingShingle |
Using network metrics to investigate football team players’ connections : a pilot study Manuel Clemente, Filipe match analysis football network metrics performance análise de jogo futebol network métricas rendimento |
title_short |
Using network metrics to investigate football team players’ connections : a pilot study |
title_full |
Using network metrics to investigate football team players’ connections : a pilot study |
title_fullStr |
Using network metrics to investigate football team players’ connections : a pilot study |
title_full_unstemmed |
Using network metrics to investigate football team players’ connections : a pilot study |
title_sort |
Using network metrics to investigate football team players’ connections : a pilot study |
author |
Manuel Clemente, Filipe |
author_facet |
Manuel Clemente, Filipe Couceiro, Micael M. L. Martins, Fernando Mendes, Rui |
author_role |
author |
author2 |
Couceiro, Micael M. L. Martins, Fernando Mendes, Rui |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
Manuel Clemente, Filipe Couceiro, Micael M. L. Martins, Fernando Mendes, Rui |
dc.subject.por.fl_str_mv |
match analysis football network metrics performance análise de jogo futebol network métricas rendimento |
topic |
match analysis football network metrics performance análise de jogo futebol network métricas rendimento |
description |
The aim of this pilot study was propose a set of network methods to measure the specific properties of football teams. These metrics were organized on “meso” and “micro” analysis levels. Five official matches of the same team on the First Portuguese Football League were analyzed. An overall of 577 offensive plays were analyzed from the five matches. From the adjacency matrices developed per each offensive play it were computed the scaled connectivity, the clustering coefficient and the centroid significance and centroid conformity. Results showed that the highest values of scaled connectivity were found in lateral defenders and central and midfielder players and the lowest values were found in the striker and goalkeeper. The highest values of clustering coefficient were generally found in midfielders and forwards. In addition, the centroid results showed that lateral and central defenders tend to be the centroid players in the attacking process. In sum, this study showed that network metrics can be a powerful tool to help coaches to understanding the specific team’s properties, thus supporting decision-making and improving sports training based on match analysis. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 2014-01-01T00:00:00Z 2023-09-27T09:40:51Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.26/46784 |
url |
http://hdl.handle.net/10400.26/46784 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
dx.doi.org/10.1590/S1980-65742014000300004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
UNESP |
publisher.none.fl_str_mv |
UNESP |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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