Artificial intelligence techniques applied to predict teams position of the Brazilian football championship
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of Physical Education (Maringá) |
Texto Completo: | https://periodicos.uem.br/ojs/index.php/RevEducFis/article/view/50871 |
Resumo: | This study presents a classifier prediction in groups for the Brazilian Football Championship of both A and B leagues, from the results of the first half of each championship. With assertive predictions of the group where a team will end the championship, strategic planning can be performed in the squad, such as new hiring, specific training for athletes, and possible championships that the team will be entitled to participate in according to the group classification. In order to find the predictions, two techniques of artificial intelligence were applied: Multi-Layer Perceptron (MLP), which is a type of artificial neural network, and Support Vector Machine (SVM). Preliminary results show that the proposed methodology is very promising, with more than 40% successful cases with MLP and almost 50% with SVM. Moreover, results indicate that the methodology is able to make a reasonable prediction by missing one group of the true group at the end of the championship. The SVM technique was slightly better than MLP. A post-processing analysis of the SVM results was applied to the 2018 A league data from the Brazilian championship, resulting in 85% success indicator of groups. |
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Journal of Physical Education (Maringá) |
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Artificial intelligence techniques applied to predict teams position of the Brazilian football championshipTécnicas de inteligência artificial aplicadas para previsão da posição de times do campeonato brasileiro de futebol Brazilian Footbal Championshipreasonable predictionspost-processing resultsCampeonato Brasileiro de Futebolprevisão de grupospós processamento de resultadosThis study presents a classifier prediction in groups for the Brazilian Football Championship of both A and B leagues, from the results of the first half of each championship. With assertive predictions of the group where a team will end the championship, strategic planning can be performed in the squad, such as new hiring, specific training for athletes, and possible championships that the team will be entitled to participate in according to the group classification. In order to find the predictions, two techniques of artificial intelligence were applied: Multi-Layer Perceptron (MLP), which is a type of artificial neural network, and Support Vector Machine (SVM). Preliminary results show that the proposed methodology is very promising, with more than 40% successful cases with MLP and almost 50% with SVM. Moreover, results indicate that the methodology is able to make a reasonable prediction by missing one group of the true group at the end of the championship. The SVM technique was slightly better than MLP. A post-processing analysis of the SVM results was applied to the 2018 A league data from the Brazilian championship, resulting in 85% success indicator of groups.Este trabalho apresenta uma previsão de classificação em grupos para as equipes do campeonato brasileiro de futebol tanto da série A quanto da série B a partir dos resultados do primeiro turno de cada campeonato. Com previsões assertivas do grupo onde um time irá finalizar o campeonato, pode-se realizar um planejamento estratégico no elenco tal como novas contratações, treinos específicos dos atletas e possíveis campeonatos que o time terá direito de participar de acordo com o grupo em que se classificar. Para encontrar as previsões, aplicou-se as técnicas rede neural artificial Multi Layer Perceptron (MLP) e Support Vector Machine (SVM). Resultados preliminares indicam que a metodologia proposta é bastante promissora, acertando em mais de 40% dos casos com a MLP e quase 50% com o SVM. Além disso, os resultados indicam que a metodologia também é capaz de realizar uma boa previsão errando em um grupo do verdadeiro grupo ao final do campeonato. A técnica SVM se mostrou um pouco superior à MLP. Um pós processamento nos resultados do SVM é aplicado aos dados do ano de 2018 da série A do campeonato brasileiro, resultando em 85% de acertos dos grupos.Department of Physical Education - State University of Maringá (UEM), Maringá-PR, Brazil2021-07-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.uem.br/ojs/index.php/RevEducFis/article/view/5087110.4025/jphyseduc.v32i1.3254Journal of Physical Education; Vol 32 No 1 (2021): Journal of Physical Education; e-3254Journal of Physical Education; Vol. 32 Núm. 1 (2021): Journal of Physical Education; e-3254Journal of Physical Education; v. 32 n. 1 (2021): Journal of Physical Education; e-32542448-2455reponame:Journal of Physical Education (Maringá)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttps://periodicos.uem.br/ojs/index.php/RevEducFis/article/view/50871/751375152401Copyright (c) 2021 Journal of Physical Education https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessKleina, MarianaNoronha dos Santos, MateusNoronha dos Santos, TiagoMendes Marques, Marcos Augusto Silva, Wiliam de Assis2021-10-25T13:02:04Zoai:periodicos.uem.br/ojs:article/50871Revistahttp://periodicos.uem.br/ojs/index.php/RevEducFis/indexPUBhttps://old.scielo.br/oai/scielo-oai.php||revdef@uem.br2448-24552448-2455opendoar:2021-10-25T13:02:04Journal of Physical Education (Maringá) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship Técnicas de inteligência artificial aplicadas para previsão da posição de times do campeonato brasileiro de futebol |
title |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship |
spellingShingle |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship Kleina, Mariana Brazilian Footbal Championship reasonable predictions post-processing results Campeonato Brasileiro de Futebol previsão de grupos pós processamento de resultados |
title_short |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship |
title_full |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship |
title_fullStr |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship |
title_full_unstemmed |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship |
title_sort |
Artificial intelligence techniques applied to predict teams position of the Brazilian football championship |
author |
Kleina, Mariana |
author_facet |
Kleina, Mariana Noronha dos Santos, Mateus Noronha dos Santos, Tiago Mendes Marques, Marcos Augusto Silva, Wiliam de Assis |
author_role |
author |
author2 |
Noronha dos Santos, Mateus Noronha dos Santos, Tiago Mendes Marques, Marcos Augusto Silva, Wiliam de Assis |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Kleina, Mariana Noronha dos Santos, Mateus Noronha dos Santos, Tiago Mendes Marques, Marcos Augusto Silva, Wiliam de Assis |
dc.subject.por.fl_str_mv |
Brazilian Footbal Championship reasonable predictions post-processing results Campeonato Brasileiro de Futebol previsão de grupos pós processamento de resultados |
topic |
Brazilian Footbal Championship reasonable predictions post-processing results Campeonato Brasileiro de Futebol previsão de grupos pós processamento de resultados |
description |
This study presents a classifier prediction in groups for the Brazilian Football Championship of both A and B leagues, from the results of the first half of each championship. With assertive predictions of the group where a team will end the championship, strategic planning can be performed in the squad, such as new hiring, specific training for athletes, and possible championships that the team will be entitled to participate in according to the group classification. In order to find the predictions, two techniques of artificial intelligence were applied: Multi-Layer Perceptron (MLP), which is a type of artificial neural network, and Support Vector Machine (SVM). Preliminary results show that the proposed methodology is very promising, with more than 40% successful cases with MLP and almost 50% with SVM. Moreover, results indicate that the methodology is able to make a reasonable prediction by missing one group of the true group at the end of the championship. The SVM technique was slightly better than MLP. A post-processing analysis of the SVM results was applied to the 2018 A league data from the Brazilian championship, resulting in 85% success indicator of groups. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/RevEducFis/article/view/50871 10.4025/jphyseduc.v32i1.3254 |
url |
https://periodicos.uem.br/ojs/index.php/RevEducFis/article/view/50871 |
identifier_str_mv |
10.4025/jphyseduc.v32i1.3254 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/RevEducFis/article/view/50871/751375152401 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Journal of Physical Education https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Journal of Physical Education https://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Department of Physical Education - State University of Maringá (UEM), Maringá-PR, Brazil |
publisher.none.fl_str_mv |
Department of Physical Education - State University of Maringá (UEM), Maringá-PR, Brazil |
dc.source.none.fl_str_mv |
Journal of Physical Education; Vol 32 No 1 (2021): Journal of Physical Education; e-3254 Journal of Physical Education; Vol. 32 Núm. 1 (2021): Journal of Physical Education; e-3254 Journal of Physical Education; v. 32 n. 1 (2021): Journal of Physical Education; e-3254 2448-2455 reponame:Journal of Physical Education (Maringá) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Journal of Physical Education (Maringá) |
collection |
Journal of Physical Education (Maringá) |
repository.name.fl_str_mv |
Journal of Physical Education (Maringá) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||revdef@uem.br |
_version_ |
1754732546184183808 |