Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports

Detalhes bibliográficos
Autor(a) principal: Martins, Fernando
Data de Publicação: 2021
Outros Autores: Gomes, Ricardo, Lopes, Vasco, Silva, Frutuoso, Mendes, Rui
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103887
https://doi.org/10.3390/e23081072
Resumo: Pattern analysis is a widely researched topic in team sports performance analysis, using information theory as a conceptual framework. Bayesian methods are also used in this research field, but the association between these two is being developed. The aim of this paper is to present new mathematical concepts that are based on information and probability theory and can be applied to network analysis in Team Sports. These results are based on the transition matrices of the Markov chain, associated with the adjacency matrices of a network with n nodes and allowing for a more robust analysis of the variability of interactions in team sports. The proposed models refer to individual and collective rates and indexes of total variability between players and teams as well as the overall passing capacity of a network, all of which are demonstrated in the UEFA 2020/2021 Champions League Final.
id RCAP_6248a3bb5a41d5a9ddccce5e67d8a95c
oai_identifier_str oai:estudogeral.uc.pt:10316/103887
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Mathematical Models to Measure the Variability of Nodes and Networks in Team Sportsentropyfootballsocial network analysisMarkov chainperformance analysisdynamical systemsPattern analysis is a widely researched topic in team sports performance analysis, using information theory as a conceptual framework. Bayesian methods are also used in this research field, but the association between these two is being developed. The aim of this paper is to present new mathematical concepts that are based on information and probability theory and can be applied to network analysis in Team Sports. These results are based on the transition matrices of the Markov chain, associated with the adjacency matrices of a network with n nodes and allowing for a more robust analysis of the variability of interactions in team sports. The proposed models refer to individual and collective rates and indexes of total variability between players and teams as well as the overall passing capacity of a network, all of which are demonstrated in the UEFA 2020/2021 Champions League Final.MDPI AG2021-08-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103887http://hdl.handle.net/10316/103887https://doi.org/10.3390/e23081072eng1099-4300344412121099-4300Martins, FernandoGomes, RicardoLopes, VascoSilva, FrutuosoMendes, Ruiinfo: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:RCAAP2022-12-07T21:38:34Zoai:estudogeral.uc.pt:10316/103887Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:39.111595Repositório 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 Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
title Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
spellingShingle Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
Martins, Fernando
entropy
football
social network analysis
Markov chain
performance analysis
dynamical systems
title_short Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
title_full Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
title_fullStr Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
title_full_unstemmed Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
title_sort Mathematical Models to Measure the Variability of Nodes and Networks in Team Sports
author Martins, Fernando
author_facet Martins, Fernando
Gomes, Ricardo
Lopes, Vasco
Silva, Frutuoso
Mendes, Rui
author_role author
author2 Gomes, Ricardo
Lopes, Vasco
Silva, Frutuoso
Mendes, Rui
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Martins, Fernando
Gomes, Ricardo
Lopes, Vasco
Silva, Frutuoso
Mendes, Rui
dc.subject.por.fl_str_mv entropy
football
social network analysis
Markov chain
performance analysis
dynamical systems
topic entropy
football
social network analysis
Markov chain
performance analysis
dynamical systems
description Pattern analysis is a widely researched topic in team sports performance analysis, using information theory as a conceptual framework. Bayesian methods are also used in this research field, but the association between these two is being developed. The aim of this paper is to present new mathematical concepts that are based on information and probability theory and can be applied to network analysis in Team Sports. These results are based on the transition matrices of the Markov chain, associated with the adjacency matrices of a network with n nodes and allowing for a more robust analysis of the variability of interactions in team sports. The proposed models refer to individual and collective rates and indexes of total variability between players and teams as well as the overall passing capacity of a network, all of which are demonstrated in the UEFA 2020/2021 Champions League Final.
publishDate 2021
dc.date.none.fl_str_mv 2021-08-19
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/10316/103887
http://hdl.handle.net/10316/103887
https://doi.org/10.3390/e23081072
url http://hdl.handle.net/10316/103887
https://doi.org/10.3390/e23081072
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1099-4300
34441212
1099-4300
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
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
_version_ 1799134098472566784

Registros relacionados