Hey all, solo dev here. I've been running a football prediction model for ~6 months and wanted to share the approach + actual results (good and bad). Looking for feedback from people who've done similar work.
The stack:
- pi-ratings (Constantinou & Fenton, 2013) as the base team-strength feature — captures recent form better than ELO for football because it weighs goal difference and home/away separately
- CatBoost for the final probability output, trained on [X] seasons across [your leagues]
- Features beyond pi-ratings: [list 2-3, e.g. xG rolling averages, rest days, etc.]
- Calibration check via reliability diagrams before any stake sizing
Stake sizing: Fractional Kelly on Value Bets (anything where model edge > [X]%). I've been experimenting with a separate confidence-weighted approach for a "daily picks" track — happy to discuss if anyone has worked on similar problems.
Results so far: [be honest — ROI, sample size, biggest drawdown, which leagues performed and which didn't]
What didn't work: [share 1-2 failed experiments — this is the part Reddit respects most]
Full disclosure: I run bettingwithai.app where this model is live. Not linking because I'm here for the methodology discussion, not traffic — mods feel free to remove if it's an issue.
Questions I'd love input on:
- How do you handle league-strength normalization across competitions?
- Anyone else found pi-ratings degrades for early-season matches?