Export Ready — 

Predicting soccer outcome with machine learning based on weather condition

Bibliographic Details
Main Author: Palinggi, Denny Asarias
Publication Date: 2019
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/64182
Summary: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
id RCAP_e44b616d838a242d35a6afb256591225
oai_identifier_str oai:run.unl.pt:10362/64182
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:38:05Zoai:run.unl.pt:10362/64182Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:09:16.706549Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596472891801600

Similar Items