Abstract

The purpose of this research is to develop and assess multi-modal machine learning for robust performance analysis to predict athletic performance and evaluate injury risk. The study employed Data Analytics approach, where composite features, i.e., Training Stress and Recovery Score,were modelled to characterize training-recovery correlations. It had four different regression models (MLR, RFR, SVR, DNN) and four different classification models (Logistic Regression, RFC, SVM, DNN). DNN exhibited an increased degree of effectiveness in the forecasting Synthetic Performance Score (R2 =0.9998). Notably, the Random Forest Classifier (RFC) turned out to be the most valid predictor of injuries risk (F1-Score 0.7736; AUC-ROC 0.5572) in case the problem of imbalance between classes was dealt with by upsampling. The findings confirm that non-linear ensemble and engineered features can be used to translate physiological and training data into useful and proactive information to coordinate and optimize athletes. Keywords: Sports Analytics, Machine Learning, Random Forest, Injury Prediction, Athlete

Library of Congress Subject Headings

Athletic ability--Forecasting--Automation; Sports--Physiological aspects--Data processing; Machine learning

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ayman Ibrahim

Campus

RIT Dubai

Plan Codes

PROFST-MS

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