March madness prediction using machine learning techniques

Detalhes bibliográficos
Autor(a) principal: Fonseca, João Gonçalo Silva Serra
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/33864
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling March madness prediction using machine learning techniquesMarch MadnessNCAABBasketballPredictionClassification problemMachine LearningProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceMarch Madness describes the final tournament of the college basketball championship, considered by many as the biggest sporting event in the United States - moving every year tons of dollars in both bets and television. Besides that, there are 60 million Americans who fill out their tournament bracket every year, and anything is more likely than hit all 68 games. After collecting and transforming data from Sports-Reference.com, the experimental part consists of preprocess the data, evaluate the features to consider in the models and train the data. In this study, based on tournament data over the last 20 years, Machine Learning algorithms like Decision Trees Classifier, K-Nearest Neighbors Classifier, Stochastic Gradient Descent Classifier and others were applied to measure the accuracy of the predictions and to be compared with some benchmarks. Despite of the most important variables seemed to be those related to seeds, shooting and the number of participations in the tournament, it was not possible to define exactly which ones should be used in the modeling and all ended up being used. Regarding the results, when training the entire dataset, the accuracy ranges from 65 to 70%, where Support Vector Classification yields the best results. When compared with picking the highest seed, these results are slightly lower. On the other hand, when predicting the Tournament of 2017, the Support Vector Classification and the Multi-Layer Perceptron Classifier reach 85 and 79% of accuracy, respectively. In this sense, they surpass the previous benchmark and the most respected websites and statistics in the field. Given some existing constraints, it is quite possible that these results could be improved and deepened in other ways. Meanwhile, this project can be referenced and serve as a basis for the future work.Castelli, MauroGonçalves, Ivo Carlos PereiraRUNFonseca, João Gonçalo Silva Serra2018-04-05T13:36:35Z2018-03-262018-03-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/33864TID:201894319enginfo: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:RCAAP2024-03-11T04:18:35Zoai:run.unl.pt:10362/33864Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:30:05.513381Repositório 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 March madness prediction using machine learning techniques
title March madness prediction using machine learning techniques
spellingShingle March madness prediction using machine learning techniques
Fonseca, João Gonçalo Silva Serra
March Madness
NCAAB
Basketball
Prediction
Classification problem
Machine Learning
title_short March madness prediction using machine learning techniques
title_full March madness prediction using machine learning techniques
title_fullStr March madness prediction using machine learning techniques
title_full_unstemmed March madness prediction using machine learning techniques
title_sort March madness prediction using machine learning techniques
author Fonseca, João Gonçalo Silva Serra
author_facet Fonseca, João Gonçalo Silva Serra
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Gonçalves, Ivo Carlos Pereira
RUN
dc.contributor.author.fl_str_mv Fonseca, João Gonçalo Silva Serra
dc.subject.por.fl_str_mv March Madness
NCAAB
Basketball
Prediction
Classification problem
Machine Learning
topic March Madness
NCAAB
Basketball
Prediction
Classification problem
Machine Learning
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2018
dc.date.none.fl_str_mv 2018-04-05T13:36:35Z
2018-03-26
2018-03-26T00:00:00Z
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/10362/33864
TID:201894319
url http://hdl.handle.net/10362/33864
identifier_str_mv TID:201894319
dc.language.iso.fl_str_mv eng
language eng
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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