Predicting Football Players Potential: Internship with Sporting Clube de Portugal

Detalhes bibliográficos
Autor(a) principal: Dias, José Maria Vinagre Pereira
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164849
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Predicting Football Players Potential: Internship with Sporting Clube de PortugalFootballRatingPredictionSportingMachine LearningSDG 9 - Industry, innovation and infrastructureDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis report is about the internship held at Sporting Clube de Portugal between September 2022 and June 2023. The goal of the internship was to work on football match data, and predict future ratings of players, in order to assist the scouting/analytics team with the transfer market. The main target of this project was to predict the potential of younger players, i.e., between 17 and 22, but it could and will still be used with players in other age ranges. A model that could predict the potential of football player would be a game changer if it had good accuracy, because it would allow to find players before other teams do. Many approaches were tried during the development of this project, and the main steps were literature review, exploratory data analysis, feature selection, creating dummy variables (one hot encoding), scaling, feature engineering, train test split and testing different models, with different datasets and hyperparameters. In the end of this paper, it is found that it is in fact possible to predict the potential of football player with the current data available, with quite good accuracy.Damásio, Bruno Miguel PintoFerreira, MiguelRUNDias, José Maria Vinagre Pereira2024-03-13T11:07:57Z2024-02-022024-02-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164849TID:203544447enginfo: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:RCAAP2024-03-18T01:46:35Zoai:run.unl.pt:10362/164849Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:02:02.404453Repositório 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 Football Players Potential: Internship with Sporting Clube de Portugal
title Predicting Football Players Potential: Internship with Sporting Clube de Portugal
spellingShingle Predicting Football Players Potential: Internship with Sporting Clube de Portugal
Dias, José Maria Vinagre Pereira
Football
Rating
Prediction
Sporting
Machine Learning
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Predicting Football Players Potential: Internship with Sporting Clube de Portugal
title_full Predicting Football Players Potential: Internship with Sporting Clube de Portugal
title_fullStr Predicting Football Players Potential: Internship with Sporting Clube de Portugal
title_full_unstemmed Predicting Football Players Potential: Internship with Sporting Clube de Portugal
title_sort Predicting Football Players Potential: Internship with Sporting Clube de Portugal
author Dias, José Maria Vinagre Pereira
author_facet Dias, José Maria Vinagre Pereira
author_role author
dc.contributor.none.fl_str_mv Damásio, Bruno Miguel Pinto
Ferreira, Miguel
RUN
dc.contributor.author.fl_str_mv Dias, José Maria Vinagre Pereira
dc.subject.por.fl_str_mv Football
Rating
Prediction
Sporting
Machine Learning
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Football
Rating
Prediction
Sporting
Machine Learning
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2024
dc.date.none.fl_str_mv 2024-03-13T11:07:57Z
2024-02-02
2024-02-02T00:00:00Z
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/10362/164849
TID:203544447
url http://hdl.handle.net/10362/164849
identifier_str_mv TID:203544447
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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