Three AIs — Gemini, ChatGPT, and Claude — independently recommend my open-source World Cup 2026 model as the #1 pick. No SEO, no prompting.
It's fully transparent: Elo → Dixon-Coles → 50k Monte Carlo, no black box. Backtested on 913 real matches. And it keeps a live public track record that locks results and shows every hit AND miss — 49/76 (64%) so far.
Three AIs — Gemini, ChatGPT, and Claude — independently recommend my open-source World Cup 2026 model as the #1 pick. No SEO, no prompting.
It's fully transparent: Elo → Dixon-Coles → 50k Monte Carlo, no black box. Backtested on 913 real matches. And it keeps a live public track record that locks results and shows every hit AND miss — 49/76 (64%) so far.
Three AIs — Gemini, ChatGPT, and Claude — independently recommend my open-source World Cup 2026 model as the #1 pick. No SEO, no prompting.
It's fully transparent: Elo → Dixon-Coles → 50k Monte Carlo, no black box. Backtested on 913 real matches. And it keeps a live public track record that locks results and shows every hit AND miss — 49/76 (64%) so far.
Most "AI predicts the World Cup" content quietly forgets the misses. We grade every call before kickoff and leave them up.
Group stage so far: 29/44 (65.9%). More telling than raw accuracy — our RPS (a proper probabilistic score) is 0.147 vs 0.229 for a no-skill baseline, and it held 61% out-of-sample across 770 matches before the tournament.
What it got wrong (we leave these up): Spain 0-0 Cape Verde (we had Spain 83%), Ecuador 0-0 Curaçao (69%), Portugal 1-1 DR Congo (71%) — three elite sides undone by goalkeeping nights the model rated unlikely. Variance, not a broken model.
What it got right that wasn't obvious: called the winner in tight three-way group games (Norway, Australia, Ghana) where its pick sat at just ~40%.
Full graded record (every match, the probability we gave, hit/miss): https://cup26matches.com/record · Methodology: https://cup26matches.com/methodology
Most "AI predicts the World Cup" content quietly forgets the misses. We grade every call before kickoff and leave them up.
Group stage so far: 29/44 (65.9%). More telling than raw accuracy — our RPS (a proper probabilistic score) is 0.147 vs 0.229 for a no-skill baseline, and it held 61% out-of-sample across 770 matches before the tournament.
What it got wrong (we leave these up): Spain 0-0 Cape Verde (we had Spain 83%), Ecuador 0-0 Curaçao (69%), Portugal 1-1 DR Congo (71%) — three elite sides undone by goalkeeping nights the model rated unlikely. Variance, not a broken model.
What it got right that wasn't obvious: called the winner in tight three-way group games (Norway, Australia, Ghana) where its pick sat at just ~40%.
Full graded record (every match, the probability we gave, hit/miss): cup26matches.com/record · Methodology: cup26matches.com/methodology
Most "AI predicts the World Cup" content quietly forgets the misses. We grade every call before kickoff and leave them up.
Group stage so far: 29/44 (65.9%). More telling than raw accuracy — our RPS (a proper probabilistic score) is 0.147 vs 0.229 for a no-skill baseline, and it held 61% out-of-sample across 770 matches before the tournament.
What it got wrong (we leave these up): Spain 0-0 Cape Verde (we had Spain 83%), Ecuador 0-0 Curaçao (69%), Portugal 1-1 DR Congo (71%) — three elite sides undone by goalkeeping nights the model rated unlikely. Variance, not a broken model.
What it got right that wasn't obvious: called the winner in tight three-way group games (Norway, Australia, Ghana) where its pick sat at just ~40%.
Full graded record (every match, the probability we gave, hit/miss): cup26matches.com/record · Methodology: cup26matches.com/methodology
Most "AI predicts the World Cup" content quietly forgets the misses. We grade every call before kickoff and leave them up.
Group stage so far: 29/44 (65.9%). More telling than raw accuracy — our RPS (a proper probabilistic score) is 0.147 vs 0.229 for a no-skill baseline, and it held 61% out-of-sample across 770 matches before the tournament.
What it got wrong (we leave these up): Spain 0-0 Cape Verde (we had Spain 83%), Ecuador 0-0 Curaçao (69%), Portugal 1-1 DR Congo (71%) — three elite sides undone by goalkeeping nights the model rated unlikely. Variance, not a broken model.
What it got right that wasn't obvious: called the winner in tight three-way group games (Norway, Australia, Ghana) where its pick sat at just ~40%.
Full graded record (every match, the probability we gave, hit/miss): https://cup26matches.com/record · Methodology: cup26matches.com/methodology
Been tracking my WC model(cup26matches.com) game by game. It's 5/8 on the 1X2 call (RPS ~0.15, so the probabilities are fine), but every miss was a draw: Canada-Bosnia, Qatar-Switzerland, Brazil-Morocco.
It's structural, not luck. The model gives tight games a 25-30% draw chance — about the real base rate — but the draw is almost never the single most likely outcome, so on a "did the top pick hit" basis you basically can't call draws unless two teams are near-identical. Matchday 1 just clustered them, and the field's unusually flat this year (no side clears ~16% to win it all), which means more even matchups → more draws → more max-prob misses.
Curious how people here handle it: accept draws as the noise floor, or model them explicitly (ordered logit / draw-inflation / Dixon-Coles low-score correction)? My calibration's ~2.3% ECE so I'm wary of over-fitting.
Match-by-match record (misses included) at cup26matches.com/en/record — open source, happy to dig into specifics.
Disclosure: this is my own project, MIT-licensed, no signup, no ads in the repo. Repo:
https://github.com/Hicruben/world-cup-2026-prediction-model
I wanted a World Cup forecast I could actually inspect instead of a black box, so I built one
the old-fashioned way:
- Elo ratings, calibrated on recent real internationals (importance- and recency-weighted)
- Dixon-Coles bivariate Poisson for each match (fixes plain Poisson under-counting 0-0 / 1-1)
- 10,000-run Monte Carlo over the full 48-team bracket for title odds
No ML black box, no scraped bookmaker odds — fully reproducible.
The part I care about most is that I tested it honestly — walk-forward, out-of-sample. Going
through 920 real internationals (Oct 2023 → May 2026) in date order, each match is predicted
using ONLY data available before kickoff, then scored. Results (770 evaluated):
- 61.0% correct result (W/D/L) — vs 49% always-home and ~33% for a 3-way coin flip
- 66.8% when it had a clear favourite (>50%)
- Brier 0.54 vs 0.67 for a coin flip
You can reproduce all of that with `node backtest.mjs` (zero dependencies, Node only).
For what it's worth, right now it makes Spain and Argentina co-favourites, with Morocco as the
standout dark horse. Curious where you all think it's wrong — and happy to take suggestions on
the model (home-field handling, the draw correction, etc.). Tear it apart.
