Multidimensional scaling analysis of soccer dynamics
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/10400.22/23821 |
Resumo: | This paper studies the behavior of teams competing within soccer national leagues. The dissimilarities between teams are measured using the match results at each round and that information feeds a multidimensional scaling (MDS) algorithm for visualizing teams’ performance. Data characterizing four European leagues during season 2014–2015 is adopted and processed using three distinct approaches. In the first, one dissimilarity matrix and one MDS map per round are generated. After, Procrustes analysis is applied to linearly transform the MDS charts for maximum superposition and to build one global MDS representation for the whole season. In the second approach, all data is combined into one dissimilarity matrix leading to a single global MDS chart. In the third approach, the results per round are used to generate time series for all teams. Then, the time series are compared, generating a dissimilarity matrix and the corresponding MDS map. In all cases, the points on the maps represent teams state up to a given round. The set of points corresponding to each team forms a locus representative of its performance versus time. |
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Multidimensional scaling analysis of soccer dynamicsMultidimensional scalingVisualizationLong-term performanceSport dynamicsThis paper studies the behavior of teams competing within soccer national leagues. The dissimilarities between teams are measured using the match results at each round and that information feeds a multidimensional scaling (MDS) algorithm for visualizing teams’ performance. Data characterizing four European leagues during season 2014–2015 is adopted and processed using three distinct approaches. In the first, one dissimilarity matrix and one MDS map per round are generated. After, Procrustes analysis is applied to linearly transform the MDS charts for maximum superposition and to build one global MDS representation for the whole season. In the second approach, all data is combined into one dissimilarity matrix leading to a single global MDS chart. In the third approach, the results per round are used to generate time series for all teams. Then, the time series are compared, generating a dissimilarity matrix and the corresponding MDS map. In all cases, the points on the maps represent teams state up to a given round. The set of points corresponding to each team forms a locus representative of its performance versus time.ElsevierRepositório Científico do Instituto Politécnico do PortoTenreiro Machado, J. A.Lopes, António M.2023-11-02T12:46:54Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/23821eng10.1016/j.apm.2017.01.029info: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:RCAAP2023-11-08T01:46:33Zoai:recipp.ipp.pt:10400.22/23821Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:26:54.643399Repositório 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 |
Multidimensional scaling analysis of soccer dynamics |
title |
Multidimensional scaling analysis of soccer dynamics |
spellingShingle |
Multidimensional scaling analysis of soccer dynamics Tenreiro Machado, J. A. Multidimensional scaling Visualization Long-term performance Sport dynamics |
title_short |
Multidimensional scaling analysis of soccer dynamics |
title_full |
Multidimensional scaling analysis of soccer dynamics |
title_fullStr |
Multidimensional scaling analysis of soccer dynamics |
title_full_unstemmed |
Multidimensional scaling analysis of soccer dynamics |
title_sort |
Multidimensional scaling analysis of soccer dynamics |
author |
Tenreiro Machado, J. A. |
author_facet |
Tenreiro Machado, J. A. Lopes, António M. |
author_role |
author |
author2 |
Lopes, António M. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Tenreiro Machado, J. A. Lopes, António M. |
dc.subject.por.fl_str_mv |
Multidimensional scaling Visualization Long-term performance Sport dynamics |
topic |
Multidimensional scaling Visualization Long-term performance Sport dynamics |
description |
This paper studies the behavior of teams competing within soccer national leagues. The dissimilarities between teams are measured using the match results at each round and that information feeds a multidimensional scaling (MDS) algorithm for visualizing teams’ performance. Data characterizing four European leagues during season 2014–2015 is adopted and processed using three distinct approaches. In the first, one dissimilarity matrix and one MDS map per round are generated. After, Procrustes analysis is applied to linearly transform the MDS charts for maximum superposition and to build one global MDS representation for the whole season. In the second approach, all data is combined into one dissimilarity matrix leading to a single global MDS chart. In the third approach, the results per round are used to generate time series for all teams. Then, the time series are compared, generating a dissimilarity matrix and the corresponding MDS map. In all cases, the points on the maps represent teams state up to a given round. The set of points corresponding to each team forms a locus representative of its performance versus time. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z 2023-11-02T12:46:54Z |
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/10400.22/23821 |
url |
http://hdl.handle.net/10400.22/23821 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.apm.2017.01.029 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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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1817549955860004864 |