Machine learning methods for sports injury - Athlete injury risk and Injury profiling

Aug 3, 2026

This study analyzed a dataset of elite youth football with injury-related variables, demographic data, and performance metrics to clarify concepts and theories in sports injuries, explain the analytical research approach, and explore machine learning for injury prediction and injury risk profiling. Traditional injury prevention models are limited, so a time-sensitive methodology was used to reflect the cyclical shifting of risk factors and generate a dynamic, recursive view of injury aetiology. Models were built with CRISP-DM and evaluated using stratified 10-fold cross-validation. Results showed muscle injuries as the most common type, while gradient boosting models achieved the highest injury-risk accuracy (F1-scores 87% and 88%). Key predictors included total exposure time, training exposure, and match exposure.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages118

Reading Time4 h 57 min

AccessFree to download

Abstract

Dataset and goals

Methodology (CRISP-DM)

Modeling and validation

Key findings

Conclusions and limitations

Machine learning methods for sports injury Athlete injury risk and Injury profiling Luís Manuel Figueiredo Ribeiro Master Thesis presented as partial requirement for obtaining a Master’s Degree in Information Management NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Machine learning methods for sports injury Athlete injury risk and Injury profiling by Luís Manuel Figueiredo Ribeiro Master Thesis presented as partial requirement for obtaining the Master’s degree in Information Management. Supervised by Vitor Santos, PhD, NOVA Information Management School 11, 2024 STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism, any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School. Lisbon, 18 of November of 2024 Luís Manuel Figueiredo Ribeiro DEDICATION These pages tell the story of a man who dived into the unknown just for the thrill to see what’s on the other side. To my parents for the value driven education and affection. To Mariana for your wisdom, love and support. To Yola for your companionship. Genuinely, couldn’t have done it without you. The pack stands together! ACKNOWLEDGEMENTS To acknowledge everyone who help me along the process of writing this thesis would be as ambitious as having to re-write it in scientific Mandarin. Which makes me realise how lucky I am to have met you. Daniel Moura, thank God for your adductor tendinopathy. If it wasn’t for that we wouldn’t have reunited, and you wouldn’t have introduced Hugo. Damm Hugo, you rock! The beekeeper of the Matrix. Bruna and Falcão really appreciate your openness to help when times were tough. João Pedro Figueira, you were such an honest and helpful guy. May I help you as you helped me. My friend JA, thank you for understanding and supporting me whenever I had to focus on this project. To Dr, Miguel Gouveia e Brito for receiving me and giving me the opportunity to talk and learn a bit more about the football business. To Dr. António Martins I thank you for the sarcastic guidance and all the teaching along the process. Francisco Tavares, your dedication and work precedes you. I am very grateful for the trust which allowed me to pursue my goals. Better late than never, right? To the team of national padel managers, I thank you for being patient and understanding. To Rawayana for getting me in the right mood and stimulating my creation. To all my partners who lost padel games because of my lack of attention during the matches. To the teachers in the Escola Superior de Saúde da Cruz Vermelha Portuguesa, especially Martinho, Ricardo and Rui who gave me all the conditions to use the school facilities to study and work on this. Thank you, guys! To all my colleagues in Fisiogaspar, especially my colleagues who had to deal with my absence and my lack of sleep humor. Proud to share my days with all of you. Prof. Vítor, I know you doubted this. I understand… but never doubt my resilience! Thank you for always standing by my side! João Vaz, you give another meaning to the term “overwhelmed by work”. Thank you for taking the time to help me. If it wasn’t for our mentor Raul Oliveira we would have worked together. To everyone who ever listened to me about the thrill of predicting injuries…Thank you very much! ABSTRACT This study analysed a dataset of elite youth football containing various variables, including injuryrelated data, demographic information, and performance metrics, with the goals of clarify the latest concepts and theories on sports

FAQs

What was the main goal of the study?

The study aimed to apply machine learning to predict sports injuries and to profile athletes’ injury risk, using elite youth football data and performance-related variables.

Which methodology and validation strategy guided the analysis?

The approach followed CRISP-DM and used stratified 10-fold cross-validation to partition the data, then trained multiple machine learning models.

Which models performed best and what features were most influential?

Gradient Boost Classifier and Extreme Gradient Boosting achieved the highest accuracy for injury risk identification, and total exposure time, training exposure, and match exposure had the strongest impact.

Aug 3, 2026

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