Degree level: Masters CS or AI
Supervisor: Mauricio Verano Merino, Forward.Football
Problem description
This research project explores the use of generative AI techniques, such as autoencoders and Generative Adversarial Networks (GANs), to detect key football events from positional data. By leveraging these advanced machine learning models, the system will learn patterns from player and ball movement data, automatically identifying important in-game events like passes, shots, tackles, and transitions. The goal is to improve the accuracy and efficiency of event detection in football analytics, offering deeper insights into match dynamics. This project aims to enhance event-based analysis by uncovering hidden patterns in positional data that may be missed by traditional methods. This collaboration aims to push the boundaries of data-driven football analysis and performance evaluation.