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Predicting the risk of injury of professional football players with machine learning

Bibliographic Details
Main Author: Martins, Bruno Gonçalo Pires
Publication Date: 2019
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/62419
Summary: Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
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spelling Predicting the risk of injury of professional football players with machine learningFootballInjury PredictionSports AnalyticsData MiningPredictive AnalyticsImbalanced DataProject Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementSports analytics is quickly changing the way sports are played. With the rise of sensor data and new tracking technologies, data is collected at an unprecedented degree which allows for a plethora of innovative analytics possibilities, with the goal of uncovering hidden trends and developing new knowledge from data sources. This project creates a prediction model which predicts a player’s muscular injury in a professional football team using GPS and self-rating training data, by following a Data Mining methodology and applying machine learning algorithms. Different sampling techniques for imbalanced data are described and used. An analysis of the quality of the results of the different sampling techniques and machine learning algorithms are presented and discussed.Henriques, Roberto André PereiraRUNMartins, Bruno Gonçalo Pires2019-03-06T12:28:40Z2019-02-062019-02-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/62419TID:202183904enginfo: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:RCAAP2024-05-22T17:37:33Zoai:run.unl.pt:10362/62419Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:08:30.292994Repositórios 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 the risk of injury of professional football players with machine learning
title Predicting the risk of injury of professional football players with machine learning
spellingShingle Predicting the risk of injury of professional football players with machine learning
Martins, Bruno Gonçalo Pires
Football
Injury Prediction
Sports Analytics
Data Mining
Predictive Analytics
Imbalanced Data
title_short Predicting the risk of injury of professional football players with machine learning
title_full Predicting the risk of injury of professional football players with machine learning
title_fullStr Predicting the risk of injury of professional football players with machine learning
title_full_unstemmed Predicting the risk of injury of professional football players with machine learning
title_sort Predicting the risk of injury of professional football players with machine learning
author Martins, Bruno Gonçalo Pires
author_facet Martins, Bruno Gonçalo Pires
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Martins, Bruno Gonçalo Pires
dc.subject.por.fl_str_mv Football
Injury Prediction
Sports Analytics
Data Mining
Predictive Analytics
Imbalanced Data
topic Football
Injury Prediction
Sports Analytics
Data Mining
Predictive Analytics
Imbalanced Data
description Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
publishDate 2019
dc.date.none.fl_str_mv 2019-03-06T12:28:40Z
2019-02-06
2019-02-06T00:00:00Z
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/10362/62419
TID:202183904
url http://hdl.handle.net/10362/62419
identifier_str_mv TID:202183904
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
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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
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