Multidimensional scaling analysis of soccer dynamics

Detalhes bibliográficos
Autor(a) principal: Tenreiro Machado, J. A.
Data de Publicação: 2017
Outros Autores: Lopes, António M.
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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spelling 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
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/23821
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language eng
dc.relation.none.fl_str_mv 10.1016/j.apm.2017.01.029
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publisher.none.fl_str_mv Elsevier
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