Inspiration

What it does

How I built it

Challenges I ran into

Accomplishments that I'm proud of

What I learned

What's next for football-tech oriented

⚽ Project Story: FERWAFA Football Intelligence Platform

💡 Inspiration

This project was inspired by the need to modernize football in Rwanda by using AI to improve match analysis, player tracking, and decision-making for federations, clubs, and academies.

📚 What I Learned

I learned how to combine AI, backend systems, and real-time data processing. I improved my skills in computer vision, API design, WebSockets, database management, and building hybrid AI + human systems.

🛠️ How I Built It

I built the system using Python (FastAPI) for the backend, YOLOv8 and ByteTrack for player tracking, EasyOCR for jersey recognition, PostgreSQL and Redis for data storage, and WebSockets for real-time updates. The system connects an AI Machine with a Match Control dashboard for live football intelligence.

⚠️ Challenges

I faced challenges in real-time processing, player identification accuracy, network instability in stadium environments, and ensuring data consistency between AI and human inputs.

🚀 Outcome

The final result is a real-time AI football intelligence platform that helps analyze matches, track players, manage competitions, and generate official football data.

Built With

• bytetrack
• css;-frameworks:-fastapi/flask-(backend)
• docker
• easyocr
• html
• javascript
• languages:-python
• opencv;-deployment:-aws/gcp
• optional-react.js-(frontend);-apis:-rest-apis-and-websockets-with-hmac-sha256-security;-databases:-postgresql-and-redis;-ai/tools:-yolov8