How team sports behave as a team? : general network metrics applied to sports analysis
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/10400.26/46713 |
Resumo: | 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. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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How team sports behave as a team? : general network metrics applied to sports analysisGraph TheoryAdjacency MatricesNetwork AnalysisPerformanceTeam SportsThe 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.[Routledge]Repositório ComumManuel Clemente, FilipeM. L. Martins, FernandoMendes, Rui2023-09-22T14:34:05Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/46713enginfo: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:RCAAP2024-06-25T16:08:18Zoai:comum.rcaap.pt:10400.26/46713Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-06-25T16:08:18Repositório 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 |
How team sports behave as a team? : general network metrics applied to sports analysis |
title |
How team sports behave as a team? : general network metrics applied to sports analysis |
spellingShingle |
How team sports behave as a team? : general network metrics applied to sports analysis Manuel Clemente, Filipe Graph Theory Adjacency Matrices Network Analysis Performance Team Sports |
title_short |
How team sports behave as a team? : general network metrics applied to sports analysis |
title_full |
How team sports behave as a team? : general network metrics applied to sports analysis |
title_fullStr |
How team sports behave as a team? : general network metrics applied to sports analysis |
title_full_unstemmed |
How team sports behave as a team? : general network metrics applied to sports analysis |
title_sort |
How team sports behave as a team? : general network metrics applied to sports analysis |
author |
Manuel Clemente, Filipe |
author_facet |
Manuel Clemente, Filipe M. L. Martins, Fernando Mendes, Rui |
author_role |
author |
author2 |
M. L. Martins, Fernando Mendes, Rui |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
Manuel Clemente, Filipe M. L. Martins, Fernando Mendes, Rui |
dc.subject.por.fl_str_mv |
Graph Theory Adjacency Matrices Network Analysis Performance Team Sports |
topic |
Graph Theory Adjacency Matrices Network Analysis Performance Team Sports |
description |
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. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2023-09-22T14:34:05Z |
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/10400.26/46713 |
url |
http://hdl.handle.net/10400.26/46713 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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 |
[Routledge] |
publisher.none.fl_str_mv |
[Routledge] |
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) |
collection |
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 |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
_version_ |
1817546166708994048 |