Background
Modern football analysis has undergone significant transformations with the advent of advanced data collection methods and artificial intelligence technologies. While traditional analysis relied solely on event data (passes, shots, tackles), the industry has shifted toward tracking data that captures player and ball positions throughout matches. Both high-quality tracking data and broadcast footage with sufficient quality for analysis remain expensive and require proper licensing agreements. This creates a significant barrier for many clubs, particularly those with limited resources, and for academic researchers seeking to advance the field of football analytics.
Problem Definition
This project aims to develop a comprehensive football analysis framework that maximizes the value extracted from limited available data sources. We will create a pipeline that: (1) extracts 3D tracking data from broadcast footage when available, (2) determines player body orientation to enhance tactical understanding, (3) builds simulation models based on the derived data, and (4) identifies team playing styles by combining event and tracking data.
Task
The project aims to do the following research:
- Review state-of-the-art computer vision and machine learning techniques applicable to football video analysis
- Develop a pipeline for converting broadcast footage to 3D tracking data, including camera calibration and player detection algorithms
- Create methods to determine player body orientation—a critical factor often missing from commercial tracking systems
- Build simulation models to analyze counterfactual scenarios and predict outcomes under varying tactical approaches
- Design clustering and classification algorithms to identify team playing styles and tactical patterns from the combined dataset
Topics
- Computer Vision for Sports Analysis
- Deep Learning for Human Pose Estimation
- Data-driven Simulation Models
- Pattern Recognition in Spatio-temporal Data
- Tactical Decision Analysis
Expected Outcomes
- An open-source tool for converting broadcast footage to 3D tracking data
- Algorithms for player body orientation detection with high accuracy
- Simulation models for predicting player and team behaviors
- Classification framework for analyzing team playing styles
- Case studies demonstrating practical applications for tactical analysis
Potential Impact
This project will enhance the analytical capabilities of organizations with access to limited football data sources. For clubs and researchers able to secure the necessary data rights, our tools will allow them to extract substantially more information from their available footage. The insights into body orientation, tactical patterns, and playing styles will enhance coaching decisions and match preparation. Furthermore, the simulation capabilities will allow teams to explore tactical innovations in a data-driven environment before implementing them in competitive matches.