ARTIFICIAL INTELLIGENCE
Modern Football Player Skills Analysis with AI
Artificial intelligence plays an increasingly important role in the world of sports, delivering tools that revolutionize player evaluation methods. Thanks to advanced AI technologies, it is now possible to build systems capable of automatically detecting, tracking, and analyzing player behavior on the pitch in real time.
In this project, delivered by ALTEN Polska for an innovative US-based analytics company, a comprehensive framework for football skill assessment was created. The solution integrated modules for object detection, tracking, and 2D/3D pose estimation, significantly streamlining the analytical process. The system operates across local environments (CPU, GPU), cloud platforms, and mobile devices, ensuring both flexibility and scalability.
Challenge
To build a flexible, scalable football player assessment system that combines detection, tracking, and position analysis in real time.
Solution
Designing a modular AI-based framework enabling integration and configuration of various analytical models (YOLOv5, HRNet, RTMPose).
Benefits
- Faster player skill analysis
- Automated talent evaluation and comparison
- Scalability through Docker technology
- Flexible parameter configuration for various needs
- Objective candidate assessment based on precise data
The Challenge and Its Impact on Users
Real-time sports analytics demands reliability, speed, and precision. The key challenge was to create a system that not only detects and analyzes player movement accurately but also allows for rapid adjustment of analytical parameters without rebuilding the entire environment. This was particularly crucial for end users—sports scouts—who require intuitive tools for evaluating multiple candidates quickly and across different devices.
Tools and Technologies Used
The project developed by ALTEN Polska was built in Python, using libraries such as OpenCV, PyTorch, and NumPy. Modern detection and pose estimation models—YOLOv4, YOLOv5, HRNet, and RTMPose—were integrated into a modular environment deployed via Docker containers. This made it possible to easily implement the framework across various platforms—from local computers to mobile devices and the cloud. A key feature was the ability to swiftly change module configurations, greatly increasing the system’s flexibility.
Client and End-User Benefits
Thanks to the implemented solution, the client—a US-based analytics company—gained an advanced tool enabling fast and objective player skill evaluation. The system allowed for:
- Independence from specific hardware—operating both locally and in the cloud
- Expanding the talent pool through automatic analysis of multiple players
- Delivering measurable, comparable metrics for candidate evaluation
- Improving scout efficiency by reducing video analysis time
