Degree level: Masters CS or AI
Supervisor: Mauricio Verano Merino
Problem description
This research project focuses on generating natural language descriptions of football gameplay using tracking data. By leveraging player and ball position data, the system will automatically create detailed, context-aware narratives that describe key events during a match, such as passes, tackles, goals, and formations. Using machine learning and natural language processing (NLP), the project will translate numerical and spatial data into human-readable text, offering real-time or post-game summaries. The goal is to enhance football analytics by providing intuitive, text-based reports for coaches, analysts, and fans, helping them better understand in-game dynamics and performance.