I built a massive sports data engine from my truck cabin (Supabase + Monte Carlo)

Paul Iroveanu — Mon, 13 Jul 2026 14:22:57 +0000

Hi DEV community! 👋

I’m Paul, an international truck driver by day, and a solo maker by night. I just launched Statlyzer, a transparent football data analytics engine, and I literally built the entire backend architecture directly from the cabin of my 40-ton truck during my mandatory highway rest breaks across Europe. 🚛💻

🛠️ The Tech Stack & Architecture

I wanted to build something extremely robust that doesn't lag on mobile devices despite running heavy math.

Database: PostgreSQL hosted on Supabase. (Migrated away from Firebase to handle complex relational queries cheaper and faster).
The Engine: It runs high-iteration Monte Carlo simulations, Poisson distribution, and Dixon-Coles models.
The Output: Raw probability percentages for variables like 1X2, Over/Unders, corners, and cards across global fixtures.

🔍 The "Past Analyst" Feature

The sports prediction market is full of fake gurus hiding their losses. I built Statlyzer to be 100% transparent. The app automatically logs and publicly archives every single AI prediction from yesterday against the real-world results. If the mathematical model misses, it stays on the dashboard for everyone to see. No deleted data.

💡 The Model

It's a freemium app. Every user gets 2 Free Credits daily to fully analyze any 2 matches. For power users, I integrated Stripe/Paddle for subscriptions.

I would absolutely love for you guys to test the web demo and try to break it. Give me your brutal feedback on the database responsiveness and the UI!

🔗 Web Demo: [app.statlyzer-studio.com]https://app.statlyzer-studio.com/
🔗 Official Site: https//statlyzer-studio.com

Thanks for reading, and I'll be answering questions in the comments as soon as I park the truck!

DEV Community: Paul Iroveanu

I built a massive sports data engine from my truck cabin (Supabase + Monte Carlo)

🛠️ The Tech Stack & Architecture

🔍 The "Past Analyst" Feature

💡 The Model