Original Reddit post

I built an AI platform that predicts football matches and tracks its own accuracy. After 265 matches, here’s what I found. The stack:

  • Frontend: Next.js 15 + React 19 + Tailwind CSS
  • Backend: FastAPI + SQLAlchemy + PostgreSQL
  • ML: XGBoost + Random Forest + Logistic Regression ensemble
  • LLM: Groq (Llama 3.3 70B) for tactical analysis
  • Deployed on Railway, 5 languages (EN/IT/ES/FR/ZH) What it does:
  • Predicts match outcomes (1X2, Over/Under, BTTS, corners, cards) for 17 leagues
  • Updates predictions every 2 minutes with fresh data
  • LLM reviews each prediction and writes tactical analysis
  • Live in-play probability updates every 15 seconds during matches
  • Value bet detection (model probability vs bookmaker odds)
  • Auto-generates blog articles for SEO Accuracy after 265 tracked matches: | League | Matches | 1X2 | Over 2.5 | BTTS | |--------|---------|-----|----------|------| | Champions League | 16 | 62.5% | 75.0% | 62.5% | | La Liga | 30 | 60.0% | 53.3% | 56.7% | | Serie B | 19 | 57.9% | 47.4% | 47.4% | | Championship | 14 | 57.1% | 57.1% | 35.7% | | Bundesliga | 27 | 51.9% | 59.3% | 59.3% | | Serie A | 30 | 50.0% | 56.7% | 70.0% | Overall 1X2 is 47.9% — not great. But Over/Under (53.6%) and BTTS (54%) are more consistent. The model struggles badly with Ligue 1 (26.9%) and Premier League (38.9%). Biggest challenges: Getting accurate data for international friendlies (no standings, no odds = garbage predictions) Balancing ML model confidence vs LLM corrections — sometimes they disagree Keeping costs low — Groq API, API-Football, The Odds API all add up Check it out: [pronostats.it] https://www.pronostats.it/ Would love feedback on the UX or prediction methodology. What would you want to see in a tool like this? submitted by /u/Aware_Stay2054

Originally posted by u/Aware_Stay2054 on r/ArtificialInteligence