MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Pesquisa operacional (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200333 |
Resumo: | ABSTRACT This article aims to analyze the technical performance of football teams in the FA Premier League during the 2015/2016 season. Data of twenty clubs over 38 matches for each club are considered using 23 variables. These variables have been explored in the football literature and address different features of technical performance. The different configuration of the data for teams in detached segments motivated the multi-criteria approach, which enables identification of strong and weak sectors in each segment. The uncertainty as to the outcome of football matches and the imprecision of the measures indicated the use of Composition of Probabilistic Preferences (CPP) to model the problem. “R” software was used in the modeling and computation. The CPP global scores obtained were more consistent with the final classification than those of other methods. CPP scores revealed different performances of particular groups of variables indicating aspects to be improved and explored. |
id |
SOBRAPO-1_8a779fa1a7b8586c9acdcb41b3356e89 |
---|---|
oai_identifier_str |
oai:scielo:S0101-74382017000200333 |
network_acronym_str |
SOBRAPO-1 |
network_name_str |
Pesquisa operacional (Online) |
repository_id_str |
|
spelling |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016match analysisprobabilistic composition of preferencesfootballABSTRACT This article aims to analyze the technical performance of football teams in the FA Premier League during the 2015/2016 season. Data of twenty clubs over 38 matches for each club are considered using 23 variables. These variables have been explored in the football literature and address different features of technical performance. The different configuration of the data for teams in detached segments motivated the multi-criteria approach, which enables identification of strong and weak sectors in each segment. The uncertainty as to the outcome of football matches and the imprecision of the measures indicated the use of Composition of Probabilistic Preferences (CPP) to model the problem. “R” software was used in the modeling and computation. The CPP global scores obtained were more consistent with the final classification than those of other methods. CPP scores revealed different performances of particular groups of variables indicating aspects to be improved and explored.Sociedade Brasileira de Pesquisa Operacional2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200333Pesquisa Operacional v.37 n.2 2017reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2017.037.02.0333info:eu-repo/semantics/openAccessPrincipe,VitorGavião,Luiz OctávioHenriques,RobertoLobo,VictorLima,Gilson Brito AlvesSant’anna,Annibal Parrachoeng2017-09-22T00:00:00Zoai:scielo:S0101-74382017000200333Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2017-09-22T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false |
dc.title.none.fl_str_mv |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
title |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
spellingShingle |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 Principe,Vitor match analysis probabilistic composition of preferences football |
title_short |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
title_full |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
title_fullStr |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
title_full_unstemmed |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
title_sort |
MULTICRITERIA ANALYSIS OF FOOTBALL MATCH PERFORMANCES: COMPOSITION OF PROBABILISTIC PREFERENCES APPLIED TO THE ENGLISH PREMIER LEAGUE 2015/2016 |
author |
Principe,Vitor |
author_facet |
Principe,Vitor Gavião,Luiz Octávio Henriques,Roberto Lobo,Victor Lima,Gilson Brito Alves Sant’anna,Annibal Parracho |
author_role |
author |
author2 |
Gavião,Luiz Octávio Henriques,Roberto Lobo,Victor Lima,Gilson Brito Alves Sant’anna,Annibal Parracho |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Principe,Vitor Gavião,Luiz Octávio Henriques,Roberto Lobo,Victor Lima,Gilson Brito Alves Sant’anna,Annibal Parracho |
dc.subject.por.fl_str_mv |
match analysis probabilistic composition of preferences football |
topic |
match analysis probabilistic composition of preferences football |
description |
ABSTRACT This article aims to analyze the technical performance of football teams in the FA Premier League during the 2015/2016 season. Data of twenty clubs over 38 matches for each club are considered using 23 variables. These variables have been explored in the football literature and address different features of technical performance. The different configuration of the data for teams in detached segments motivated the multi-criteria approach, which enables identification of strong and weak sectors in each segment. The uncertainty as to the outcome of football matches and the imprecision of the measures indicated the use of Composition of Probabilistic Preferences (CPP) to model the problem. “R” software was used in the modeling and computation. The CPP global scores obtained were more consistent with the final classification than those of other methods. CPP scores revealed different performances of particular groups of variables indicating aspects to be improved and explored. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-08-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200333 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200333 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0101-7438.2017.037.02.0333 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
dc.source.none.fl_str_mv |
Pesquisa Operacional v.37 n.2 2017 reponame:Pesquisa operacional (Online) instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO |
instname_str |
Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
instacron_str |
SOBRAPO |
institution |
SOBRAPO |
reponame_str |
Pesquisa operacional (Online) |
collection |
Pesquisa operacional (Online) |
repository.name.fl_str_mv |
Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
repository.mail.fl_str_mv |
||sobrapo@sobrapo.org.br |
_version_ |
1750318018160754688 |