Date Approved

11-10-2025

Embargo Period

11-10-2025

Document Type

Thesis

Degree Name

M.S. Data Science

Department

Computer Science

College

College of Science & Mathematics

Advisor

Vasil Hnatyshin, Ph.D.

Committee Member 1

Umashanger Thayasivam, Ph.D.

Committee Member 2

Shen-Shyang Ho, Ph.D.

Keywords

Event Classification;Machine Learning;Soccer;Spatio-Temporal Data;Sports Analytics

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Abstract

Classifying soccer ball events, such as pass, shot, ball loss, and ball out, are crucial for advancing game analytics and tactical insights. This thesis investigates the application of machine learning to classify these ball events using player and ball spatio-temporal data, as well as additional features. We implement and compare traditional machine learning algorithms (AdaBoost, Logistic Regression, and Random Forest) with several deep learning approaches, including Feed-Forward Neural Network (FFNN), sequence-to-sequence (seq2seq) recurrent models (Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)), and Transformer. Our experiments, evaluated on a dataset comprising of three professional soccer matches using accuracy, precision, recall, and F1-score, demonstrate that the Transformer model performs the best. It outperforms all other models by effectively capturing the complex, long-range spatio-temporal dependencies inherent in player and ball movements. This study shows the effectiveness of Transformer models for soccer event classification and provides a baseline benchmark for future research in spatio-temporal sports analytics.

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