Previsões de resultados em partidas do campeonato brasileiro de futebol

Detalhes bibliográficos
Autor(a) principal: Santos, João Marcos Amorim dos
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/27672
Resumo: Predicting football (soccer) results is a problem that has been explored for decades. The results can be seen from two points of view, predict the score or just to predict the result: win, draw or defeat. When we modeling the number of goals from each team, both points of view can be contemplated, score and result. Since 1950, many approaches have been proposed in order to model the number of goals scored by each team in a match. One of the most explored approaches considers the number of goals scored by each team as a variable following a Poisson distribution. From the first works, a underlying hypothesis was that the number of goals scored by the home team and away team was independent. However, some authors have used approaches that consider correlation in the score of the two teams, either through the use of Bivariate Poisson or the adaptation of the independent model. However, the vast majority of these works were limited to the data about the teams playing the matches and the number of goals scored and concede only. This thesis aims to explore the predictive capacity of different Poisson models proposed in the literature to predict the number of goals scored by each of the teams in a match, in addition to making use of more explanatory variables, such as number of shots, number of shots on target, tackles, all those variables coming from Cartola FC. Each one of the explored models was analyzed from the point of view to correct the true scoreboard of the game, as well as to correct the true result of the match, win, draw or defeat.
id FGV_783dd54a58d4bdbd9895250858eacb2b
oai_identifier_str oai:repositorio.fgv.br:10438/27672
network_acronym_str FGV
network_name_str Repositório Institucional do FGV (FGV Repositório Digital)
repository_id_str 3974
spelling Santos, João Marcos Amorim dosEscolas::EMApDemais unidades::RPCATargino, Rodrigo dos SantosCarvalho, Paulo Cezar P.Dana, SamySilva, Moacyr Alvim Horta Barbosa da2019-07-05T14:00:12Z2019-07-05T14:00:12Z2019-04-29https://hdl.handle.net/10438/27672Predicting football (soccer) results is a problem that has been explored for decades. The results can be seen from two points of view, predict the score or just to predict the result: win, draw or defeat. When we modeling the number of goals from each team, both points of view can be contemplated, score and result. Since 1950, many approaches have been proposed in order to model the number of goals scored by each team in a match. One of the most explored approaches considers the number of goals scored by each team as a variable following a Poisson distribution. From the first works, a underlying hypothesis was that the number of goals scored by the home team and away team was independent. However, some authors have used approaches that consider correlation in the score of the two teams, either through the use of Bivariate Poisson or the adaptation of the independent model. However, the vast majority of these works were limited to the data about the teams playing the matches and the number of goals scored and concede only. This thesis aims to explore the predictive capacity of different Poisson models proposed in the literature to predict the number of goals scored by each of the teams in a match, in addition to making use of more explanatory variables, such as number of shots, number of shots on target, tackles, all those variables coming from Cartola FC. Each one of the explored models was analyzed from the point of view to correct the true scoreboard of the game, as well as to correct the true result of the match, win, draw or defeat.Prever resultados de partidas de futebol é um problema que vem sendo explorado há décadas. Tais resultados podem ser vistos por dois pontos de vista, prever o placar ou apenas prever o resultado: vitória, empate ou derrota. Quando se tem modelos que buscam prever a quantidade de gols marcados por cada uma das equipes, ambos pontos de vista do resultado de uma partida podem ser contemplados, placar e resultado. Desde 1950, diferentes abordagens e modelos foram propostos com intuito de modelar a quantidade de gols marcadas por cada time em uma partida. Uma das abordagens mais exploradas foi caracterizar a quantidade de gols marcados por cada uma das equipes como uma variável que segue a distribuição de Poisson. Desde os primeiros trabalhos, uma hipótese trabalhada foi que a quantidade de gols marcados pelo time mandante e visitante seriam independentes. Porém, alguns autores utilizaram abordagens que consideram correlação no placar das duas equipes, sejam elas através do uso da Poisson Bivariada ou da adaptação do modelo independente. Contudo, a grande maioria desses trabalhos esteve limitada a usar como informação apenas os times participantes das partidas e a quantidade de gols marcados e sofridos por cada uma delas. Esta dissertação tem por objetivo explorar a capacidade preditiva de diferentes modelos de Poisson propostos na literatura para prever a quantidade de gols marcados por cada uma das equipes em uma partida, além de fazer uso de mais variáveis explicativas, tais como número de finalizações, número de finalizações certas, roubadas de bola, variáveis essas provenientes do Cartola FC. Cada um dos modelos explorados é analisado tanto do ponto de vista de acertar o verdadeiro placar da partida quanto acertar o verdadeiro resultado da partida, vitória, empate ou derrota.porGoal modelingPoisson distributionBivariate poissonBinomial distributionBinomial distributionMétodos estatísticosModelagem de golsDistribuição de PoissonPoisson BivariadaDistribuição BinomialCampeonato BrasileiroMatemáticaCampeonato Brasileiro (Futebol) - Métodos estatísticosFutebol - Brasil - Modelos matemáticosFutebol - Brasil - Métodos estatísticosPrevisões de resultados em partidas do campeonato brasileiro de futebolinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessFGV EMAp - Dissertações, Mestrado em Modelagem MatemáticaProjetos de Pesquisa AplicadaEsportes em númerosORIGINALjoao_marcos_amorim_dos_santos.pdfjoao_marcos_amorim_dos_santos.pdfapplication/pdf1017153https://repositorio.fgv.br/bitstreams/95e5dac8-5724-488c-8e47-54f81bbd85c4/downloada22e3621740daaf1b6298202af1dce49MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84707https://repositorio.fgv.br/bitstreams/0dae387f-a2c3-4665-b6f2-e22e4f3a0513/downloaddfb340242cced38a6cca06c627998fa1MD52TEXTjoao_marcos_amorim_dos_santos.pdf.txtjoao_marcos_amorim_dos_santos.pdf.txtExtracted texttext/plain104576https://repositorio.fgv.br/bitstreams/2ab163f9-ba4c-43ab-a198-aca630813422/download90f5aebd2ffc2d39b2a1a35dd7dae8e2MD55THUMBNAILjoao_marcos_amorim_dos_santos.pdf.jpgjoao_marcos_amorim_dos_santos.pdf.jpgGenerated Thumbnailimage/jpeg2828https://repositorio.fgv.br/bitstreams/c13b4e3f-0747-46b0-91b5-a514ea8245aa/downloadfd2cecec9da09e5f34e087ebe147fb56MD5610438/276722023-11-25 02:27:29.746open.accessoai:repositorio.fgv.br:10438/27672https://repositorio.fgv.brRepositório InstitucionalPRIhttp://bibliotecadigital.fgv.br/dspace-oai/requestopendoar:39742023-11-25T02:27:29Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV)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
dc.title.por.fl_str_mv Previsões de resultados em partidas do campeonato brasileiro de futebol
title Previsões de resultados em partidas do campeonato brasileiro de futebol
spellingShingle Previsões de resultados em partidas do campeonato brasileiro de futebol
Santos, João Marcos Amorim dos
Goal modeling
Poisson distribution
Bivariate poisson
Binomial distribution
Binomial distribution
Métodos estatísticos
Modelagem de gols
Distribuição de Poisson
Poisson Bivariada
Distribuição Binomial
Campeonato Brasileiro
Matemática
Campeonato Brasileiro (Futebol) - Métodos estatísticos
Futebol - Brasil - Modelos matemáticos
Futebol - Brasil - Métodos estatísticos
title_short Previsões de resultados em partidas do campeonato brasileiro de futebol
title_full Previsões de resultados em partidas do campeonato brasileiro de futebol
title_fullStr Previsões de resultados em partidas do campeonato brasileiro de futebol
title_full_unstemmed Previsões de resultados em partidas do campeonato brasileiro de futebol
title_sort Previsões de resultados em partidas do campeonato brasileiro de futebol
author Santos, João Marcos Amorim dos
author_facet Santos, João Marcos Amorim dos
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
Demais unidades::RPCA
dc.contributor.member.none.fl_str_mv Targino, Rodrigo dos Santos
Carvalho, Paulo Cezar P.
Dana, Samy
dc.contributor.author.fl_str_mv Santos, João Marcos Amorim dos
dc.contributor.advisor1.fl_str_mv Silva, Moacyr Alvim Horta Barbosa da
contributor_str_mv Silva, Moacyr Alvim Horta Barbosa da
dc.subject.eng.fl_str_mv Goal modeling
Poisson distribution
Bivariate poisson
Binomial distribution
Binomial distribution
topic Goal modeling
Poisson distribution
Bivariate poisson
Binomial distribution
Binomial distribution
Métodos estatísticos
Modelagem de gols
Distribuição de Poisson
Poisson Bivariada
Distribuição Binomial
Campeonato Brasileiro
Matemática
Campeonato Brasileiro (Futebol) - Métodos estatísticos
Futebol - Brasil - Modelos matemáticos
Futebol - Brasil - Métodos estatísticos
dc.subject.por.fl_str_mv Métodos estatísticos
Modelagem de gols
Distribuição de Poisson
Poisson Bivariada
Distribuição Binomial
Campeonato Brasileiro
dc.subject.area.por.fl_str_mv Matemática
dc.subject.bibliodata.por.fl_str_mv Campeonato Brasileiro (Futebol) - Métodos estatísticos
Futebol - Brasil - Modelos matemáticos
Futebol - Brasil - Métodos estatísticos
description Predicting football (soccer) results is a problem that has been explored for decades. The results can be seen from two points of view, predict the score or just to predict the result: win, draw or defeat. When we modeling the number of goals from each team, both points of view can be contemplated, score and result. Since 1950, many approaches have been proposed in order to model the number of goals scored by each team in a match. One of the most explored approaches considers the number of goals scored by each team as a variable following a Poisson distribution. From the first works, a underlying hypothesis was that the number of goals scored by the home team and away team was independent. However, some authors have used approaches that consider correlation in the score of the two teams, either through the use of Bivariate Poisson or the adaptation of the independent model. However, the vast majority of these works were limited to the data about the teams playing the matches and the number of goals scored and concede only. This thesis aims to explore the predictive capacity of different Poisson models proposed in the literature to predict the number of goals scored by each of the teams in a match, in addition to making use of more explanatory variables, such as number of shots, number of shots on target, tackles, all those variables coming from Cartola FC. Each one of the explored models was analyzed from the point of view to correct the true scoreboard of the game, as well as to correct the true result of the match, win, draw or defeat.
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-07-05T14:00:12Z
dc.date.available.fl_str_mv 2019-07-05T14:00:12Z
dc.date.issued.fl_str_mv 2019-04-29
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/27672
url https://hdl.handle.net/10438/27672
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional do FGV (FGV Repositório Digital)
instname:Fundação Getulio Vargas (FGV)
instacron:FGV
instname_str Fundação Getulio Vargas (FGV)
instacron_str FGV
institution FGV
reponame_str Repositório Institucional do FGV (FGV Repositório Digital)
collection Repositório Institucional do FGV (FGV Repositório Digital)
bitstream.url.fl_str_mv https://repositorio.fgv.br/bitstreams/95e5dac8-5724-488c-8e47-54f81bbd85c4/download
https://repositorio.fgv.br/bitstreams/0dae387f-a2c3-4665-b6f2-e22e4f3a0513/download
https://repositorio.fgv.br/bitstreams/2ab163f9-ba4c-43ab-a198-aca630813422/download
https://repositorio.fgv.br/bitstreams/c13b4e3f-0747-46b0-91b5-a514ea8245aa/download
bitstream.checksum.fl_str_mv a22e3621740daaf1b6298202af1dce49
dfb340242cced38a6cca06c627998fa1
90f5aebd2ffc2d39b2a1a35dd7dae8e2
fd2cecec9da09e5f34e087ebe147fb56
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV)
repository.mail.fl_str_mv
_version_ 1813797708912132096