Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/10316/101331 https://doi.org/10.3390/math8091543 |
Resumo: | Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behaviorsocial network analysisentropyMarkov chainfootballPattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game.2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/101331http://hdl.handle.net/10316/101331https://doi.org/10.3390/math8091543eng2227-7390Martins, 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:RCAAP2022-08-23T20:39:23Zoai:estudogeral.uc.pt:10316/101331Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:32.834117Repositório 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 |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
title |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
spellingShingle |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior Martins, Fernando social network analysis entropy Markov chain football |
title_short |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
title_full |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
title_fullStr |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
title_full_unstemmed |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
title_sort |
Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior |
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 |
social network analysis entropy Markov chain football |
topic |
social network analysis entropy Markov chain football |
description |
Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
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/10316/101331 http://hdl.handle.net/10316/101331 https://doi.org/10.3390/math8091543 |
url |
http://hdl.handle.net/10316/101331 https://doi.org/10.3390/math8091543 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-7390 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
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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 |
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1799134079787991040 |