Network analysis in basketball : inspecting the prominent players using centrality metrics
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/10400.26/46710 |
Resumo: | The aim of this study was to analyse the team-members cooperation in basketball by using centrality metrics of network. Different ages were compared in this study. Forty players (10 players of under-14; 10 players of under16; 10 players of under-18 and 10 players in amateurs with more than 20 years) voluntarily participated in this study. A total of 326 units of attack were generated based on the team-members interactions and then converted in final graphs. The one-way ANOVA for the factor tactical position found statistical differences in the dependent variables of %DCentrality (F(4,15) = 13.622; p-value = 0.001; n2 = 0.784; Large Effect Size) and %DPrestige (F(4,15) = 20.590; p-value = 0.001; n2 = 0.846; Large Effect Size). In conclusion this study showed that point guard was the prominent position during the attacking organization and that social network analysis it is a useful approach to identify the patterns of interactions in the game of basketball. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Network analysis in basketball : inspecting the prominent players using centrality metricscollective behaviourmatch analysisnetworkmetricstechnical performancebasketballThe aim of this study was to analyse the team-members cooperation in basketball by using centrality metrics of network. Different ages were compared in this study. Forty players (10 players of under-14; 10 players of under16; 10 players of under-18 and 10 players in amateurs with more than 20 years) voluntarily participated in this study. A total of 326 units of attack were generated based on the team-members interactions and then converted in final graphs. The one-way ANOVA for the factor tactical position found statistical differences in the dependent variables of %DCentrality (F(4,15) = 13.622; p-value = 0.001; n2 = 0.784; Large Effect Size) and %DPrestige (F(4,15) = 20.590; p-value = 0.001; n2 = 0.846; Large Effect Size). In conclusion this study showed that point guard was the prominent position during the attacking organization and that social network analysis it is a useful approach to identify the patterns of interactions in the game of basketball.Editura Universitatea din PitestRepositório ComumManuel Clemente, FilipeM. L. Martins, FernandoKalamaras, DimitrisMendes, Rui2023-09-22T12:00:32Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/46710enginfo: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:RCAAP2023-09-28T02:16:47Zoai:comum.rcaap.pt:10400.26/46710Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:31:33.146375Repositório 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 |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
title |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
spellingShingle |
Network analysis in basketball : inspecting the prominent players using centrality metrics Manuel Clemente, Filipe collective behaviour match analysis network metrics technical performance basketball |
title_short |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
title_full |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
title_fullStr |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
title_full_unstemmed |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
title_sort |
Network analysis in basketball : inspecting the prominent players using centrality metrics |
author |
Manuel Clemente, Filipe |
author_facet |
Manuel Clemente, Filipe M. L. Martins, Fernando Kalamaras, Dimitris Mendes, Rui |
author_role |
author |
author2 |
M. L. Martins, Fernando Kalamaras, Dimitris Mendes, Rui |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
Manuel Clemente, Filipe M. L. Martins, Fernando Kalamaras, Dimitris Mendes, Rui |
dc.subject.por.fl_str_mv |
collective behaviour match analysis network metrics technical performance basketball |
topic |
collective behaviour match analysis network metrics technical performance basketball |
description |
The aim of this study was to analyse the team-members cooperation in basketball by using centrality metrics of network. Different ages were compared in this study. Forty players (10 players of under-14; 10 players of under16; 10 players of under-18 and 10 players in amateurs with more than 20 years) voluntarily participated in this study. A total of 326 units of attack were generated based on the team-members interactions and then converted in final graphs. The one-way ANOVA for the factor tactical position found statistical differences in the dependent variables of %DCentrality (F(4,15) = 13.622; p-value = 0.001; n2 = 0.784; Large Effect Size) and %DPrestige (F(4,15) = 20.590; p-value = 0.001; n2 = 0.846; Large Effect Size). In conclusion this study showed that point guard was the prominent position during the attacking organization and that social network analysis it is a useful approach to identify the patterns of interactions in the game of basketball. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2023-09-22T12:00:32Z |
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/10400.26/46710 |
url |
http://hdl.handle.net/10400.26/46710 |
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 |
Editura Universitatea din Pitest |
publisher.none.fl_str_mv |
Editura Universitatea din Pitest |
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 |
|
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1799133583293546496 |