Learning Real-Life Football Tactics
Using Active Inference to learn optimal football strategies
Project Overview
This research project aims to apply Active Inference principles to learn and optimize football tactics in real-world scenarios. By modeling football players as Active Inference agents, we can develop a framework that learns optimal positioning, movement patterns, and decision-making in dynamic game situations. The project combines cutting-edge AI research with practical sports applications.
Research Goals
- Develop an Active Inference framework for multi-agent coordination in football
- Create realistic simulations of football scenarios using RxEnvironments.jl
- Bridge the gap between theoretical models and practical football tactics
- Validate learned strategies in real-world training scenarios
Technical Approach
- Multi-agent Active Inference implementation using RxInfer.jl
- Real-time position and movement tracking data integration
- Hierarchical model structure for different time scales:
- Strategic level (game plan)
- Tactical level (situational decisions)
- Operational level (individual movements)
- Integration with existing football analytics frameworks
Applications
- Automated tactical analysis of game situations
- Real-time strategy suggestions during matches
- Training program optimization
- Player development through personalized feedback
- Team composition optimization
Student Opportunities
We are currently recruiting Master’s students interested in:
- Machine Learning
- Active Inference
- Multi-agent Systems
- Real-world AI Applications
- Football
Ideal candidates should have:
- Strong programming skills (Julia/Python)
- Interest in sports analytics
- Background in machine learning or AI
- Understanding of football tactics (playing experience is a plus)
Current Status
This is an ongoing research project where we collaborate with football clubs to validate our approaches. We’re particularly interested in:
- Developing new Active Inference models for team sports
- Creating practical tools for coaches and analysts
- Validating theoretical models with real-world data
- Bridging the gap between AI research and sports practice
Interested students can contact me for more information about thesis opportunities in this project.