Predicting soccer outcome with machine learning based on weather condition

Detalhes bibliográficos
Autor(a) principal: Palinggi, Denny Asarias
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/64182
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Predicting soccer outcome with machine learning based on weather conditionWeatherSoccerFootballMachine LearningK-nearest neighborsSupport vector machineRandom ForestDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesMassive amounts of research have been doing on predicting soccer matches using machine learning algorithms. Unfortunately, there are no prior researches used weather condition as features. In this thesis, three different classification algorithms were investigated for predicting the outcomes of soccer matches by using temperature difference, rain precipitation, and several other historical match statistics as features. The dataset consists of statistic information of soccer matches in La Liga and Segunda division from season 2013-2014 to 2016-2017 and weather information in every host cities. The results show that the SVM model has better accuracy score for predicting the full-time result compare to KNN and RF with 45.32% for temperature difference below 5° and 49.51% for temperature difference above 5°. For over/under 2.5 goals, SVM also has better accuracy with 53.07% for rain precipitation below 5 mm and 56% for rain precipitation above 5 mm.Ramos Romero, José FranciscoHenriques, Roberto André PereiraMateu Mahiques, JorgeRUNPalinggi, Denny Asarias2019-03-22T15:22:21Z2019-03-042019-03-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/64182TID:202201775enginfo: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:RCAAP2024-03-11T04:30:30Zoai:run.unl.pt:10362/64182Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:34:06.393626Repositório 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 Predicting soccer outcome with machine learning based on weather condition
title Predicting soccer outcome with machine learning based on weather condition
spellingShingle Predicting soccer outcome with machine learning based on weather condition
Palinggi, Denny Asarias
Weather
Soccer
Football
Machine Learning
K-nearest neighbors
Support vector machine
Random Forest
title_short Predicting soccer outcome with machine learning based on weather condition
title_full Predicting soccer outcome with machine learning based on weather condition
title_fullStr Predicting soccer outcome with machine learning based on weather condition
title_full_unstemmed Predicting soccer outcome with machine learning based on weather condition
title_sort Predicting soccer outcome with machine learning based on weather condition
author Palinggi, Denny Asarias
author_facet Palinggi, Denny Asarias
author_role author
dc.contributor.none.fl_str_mv Ramos Romero, José Francisco
Henriques, Roberto André Pereira
Mateu Mahiques, Jorge
RUN
dc.contributor.author.fl_str_mv Palinggi, Denny Asarias
dc.subject.por.fl_str_mv Weather
Soccer
Football
Machine Learning
K-nearest neighbors
Support vector machine
Random Forest
topic Weather
Soccer
Football
Machine Learning
K-nearest neighbors
Support vector machine
Random Forest
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2019
dc.date.none.fl_str_mv 2019-03-22T15:22:21Z
2019-03-04
2019-03-04T00:00:00Z
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 http://hdl.handle.net/10362/64182
TID:202201775
url http://hdl.handle.net/10362/64182
identifier_str_mv TID:202201775
dc.language.iso.fl_str_mv eng
language eng
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.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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