Checking our work
Do the probabilities come true?
When the model says a team has a 70% chance, that team should win about seven times in ten. This page checks whether it does. Every 2026 World Cup match is graded the moment it's played, and the numbers below ask one question: are the probabilities honest, not just confident? (The technical name for this is calibration.)
Track record
Proven on past tournaments
The short version: when the model says 70%, it happens about 70%. Across these 987 matches its stated chances landed within ~5.6 points of reality, and on average it rated the actual result about 35% more likely than a blind 1-in-3 guess would.
The 2026 tracker below stays empty until the first match kicks off. So to show the model has been tested, not just described, we ran it against tournaments whose results you already know. For each one, the model was rebuilt exactly as it stood the day before kickoff, then graded on every match — it never sees the result it is being marked on. That is what “graded out of sample” means: no peeking, no hindsight.
Each tournament is scored by the production model reconstructed as it stood the day before the tournament's first match: Dixon-Coles and Hierarchical Poisson refit on matches strictly beforehand, Elo rolled forward to each match, and the tournament-tier calibration layer refit on the 24 months of matches before the cutoff. No data from the tournament, or any later match, touches any layer of the fit.
Graded across 987 matches at 24 major tournaments (2014–2024)
- 0.572
- 1.000
- 5.6pp
How close the forecasts landed to reality. Lower is better; blind 1-in-3 guessing scores ≈ 0.667.
Like Brier, but overconfidence is punished harder. Lower is better; blind guessing scores ≈ 1.099.
Does “70%” really mean 70%? The average gap between the two. Lower is better.
Tournament by tournament
One row per tournament: the model rebuilt as it stood the day before it began, then graded on every match through the final (Brier — lower is better, blind 1-in-3 guessing is 0.667). A few thin, early editions sit above that line; the honest measure is all of them pooled, in the box above.
| Tournament | Host | Matches | Brier |
|---|---|---|---|
| Copa América 2024 | United States | 32 | 0.522 |
| Euro 2024 | Germany | 51 | 0.613 |
| AFCON 2024 | Ivory Coast | 52 | 0.651 |
| Asian Cup 2024 | Qatar | 51 | 0.515 |
| Gold Cup 2023 | United States | 31 | 0.566 |
| World Cup 2022 | Qatar | 64 | 0.611 |
| AFCON 2022 | Cameroon | 52 | 0.686 |
| Gold Cup 2021 | United States | 31 | 0.341 |
| Copa América 2021 | Brazil | 28 | 0.481 |
| Euro 2021 | England | 51 | 0.554 |
| AFCON 2019 | Egypt | 52 | 0.546 |
| Gold Cup 2019 | United States | 31 | 0.405 |
| Copa América 2019 | Brazil | 26 | 0.542 |
| Asian Cup 2019 | United Arab Emirates | 51 | 0.496 |
| World Cup 2018 | Russia | 64 | 0.569 |
| Gold Cup 2017 | United States | 25 | 0.456 |
| AFCON 2017 | Gabon | 32 | 0.642 |
| Euro 2016 | France | 51 | 0.668 |
| Copa América 2016 | United States | 32 | 0.502 |
| Gold Cup 2015 | United States | 26 | 0.755 |
| Copa América 2015 | Chile | 26 | 0.686 |
| AFCON 2015 | Equatorial Guinea | 32 | 0.795 |
| Asian Cup 2015 | Australia | 32 | 0.434 |
| World Cup 2014 | Brazil | 64 | 0.565 |
Reliability diagram
Read it like this: each dot is a group of similar forecasts. left-to-right is what the model predicted, bottom-to-top is how often it actually happened. When the model says 70% and that happens about 70% of the time, the dot sits on the dashed line: perfect calibration. The closer the dots hug the line, the more honest the probabilities; bigger dots mean more matches in that group.
Brier by favourite confidence
Matches grouped by how confident the model's favourite was (its biggest of the home / draw / away probabilities) — so you can see whether it is as reliable on toss-ups as on heavy favourites.
| Favourite confidence | Matches | Brier |
|---|---|---|
| P_fav < 40% | 81 | 0.649 |
| P_fav 40-60% | 476 | 0.633 |
| P_fav 60-80% | 318 | 0.512 |
| P_fav >= 80% | 112 | 0.428 |
- Out-of-sample: the calibration layer is refit per tournament on pre-tournament data, so these numbers do not reuse the live shipped calibrator (which has seen these results).
- The uniform 1/3 forecast scores a Brier of 0.667; lower is better. Major-tournament football is high-variance, so a strong model still sits well above a league-season Brier.
- Calibrated and uncalibrated metrics are reported on the same fixtures so the calibration layer's effect is visible.
Built 2026-05-30 · model 1.0.0 · calibration layer refit on the 24 months before each tournament.
2026 World Cup, live tracker
Across 95 graded forecasts so far, the model's overall Brier score is 0.497 (lower is better; blind 1-in-3 guessing scores ≈ 0.667). The reliability diagram below plots what the model predicted against how often it actually came true. The closer to the diagonal, the more honest the probabilities.
Overall, across 95 scored matches
- 0.497
- 0.837
- 0.165
How close the forecasts landed to reality. Lower is better; blind 1-in-3 guessing scores ≈ 0.667.
Like Brier, but overconfidence is punished harder. Lower is better; blind guessing scores ≈ 1.099.
Does “70%” really mean 70%? The average gap between the two. Lower is better.
Rolling Brier, 10-match window
The model's accuracy over its most recent 10 matches, re-figured after each game. Lower is better; the dashed line is what blind 1-in-3 guessing would score, so anything below it is real skill.
Per-matchday breakdown
| Date | Matches | Brier | Log loss |
|---|---|---|---|
| 2026-06-11 | 2 | 0.404 | 0.723 |
| 2026-06-12 | 2 | 0.817 | 1.280 |
| 2026-06-13 | 4 | 0.771 | 1.178 |
| 2026-06-14 | 4 | 0.595 | 1.008 |
| 2026-06-15 | 4 | 1.148 | 1.632 |
| 2026-06-16 | 4 | 0.197 | 0.438 |
| 2026-06-17 | 4 | 0.640 | 1.005 |
| 2026-06-18 | 4 | 0.426 | 0.739 |
| 2026-06-19 | 4 | 0.463 | 0.787 |
| 2026-06-20 | 4 | 0.531 | 0.854 |
| 2026-06-21 | 4 | 0.603 | 0.928 |
| 2026-06-22 | 4 | 0.266 | 0.529 |
| 2026-06-23 | 4 | 0.433 | 0.706 |
| 2026-06-24 | 6 | 0.410 | 0.750 |
| 2026-06-25 | 6 | 0.506 | 0.868 |
| 2026-06-26 | 6 | 0.388 | 0.704 |
| 2026-06-27 | 6 | 0.434 | 0.763 |
| 2026-06-29 | 2 | 0.612 | 0.996 |
| 2026-06-30 | 3 | 0.500 | 0.866 |
| 2026-07-01 | 3 | 0.478 | 0.830 |
| 2026-07-02 | 3 | 0.327 | 0.622 |
| 2026-07-03 | 3 | 0.460 | 0.796 |
| 2026-07-04 | 1 | 0.225 | 0.470 |
| 2026-07-06 | 2 | 0.443 | 0.784 |
| 2026-07-07 | 2 | 0.541 | 0.909 |
| 2026-07-09 | 1 | 0.573 | 0.960 |
| 2026-07-10 | 1 | 0.280 | 0.564 |
| 2026-07-11 | 2 | 0.250 | 0.517 |
Updated 2026-07-14.
Reliability diagram
Read it like this: each dot is a group of similar forecasts. left-to-right is what the model predicted, bottom-to-top is how often it actually happened. When the model says 70% and that happens about 70% of the time, the dot sits on the dashed line: perfect calibration. The closer the dots hug the line, the more honest the probabilities; bigger dots mean more matches in that group.
Brier by competition
| Segment | Matches | Brier | Δ vs overall |
|---|---|---|---|
| World Cup 2026 | 95 | 0.497 | +0.000 |
Brier by tournament stage
| Segment | Matches | Brier | Δ vs overall |
|---|---|---|---|
| Group stage | 72 | 0.516 | +0.019 |
| Round of 32 | 15 | 0.449 | -0.048 |
| Round of 16 | 4 | 0.492 | -0.005 |
| Quarter-final | 4 | 0.338 | -0.159 |
| Semi-final | — | — | — |
| Third-place play-off | — | — | — |
| Final | — | — | — |
Brier by favourite confidence
| Segment | Matches | Brier | Δ vs overall |
|---|---|---|---|
| P_fav < 40% | 11 | 0.669 | +0.172 |
| P_fav 40-60% | 46 | 0.540 | +0.043 |
| P_fav 60-80% | 32 | 0.373 | -0.124 |
| P_fav >= 80% | 6 | 0.513 | +0.016 |
What the three numbers mean
Think of a weather forecaster. Anyone can say “70% chance of rain.” The good ones are actually right about 70% of the time when they say it. These three numbers check the model the same way.
- Brier score: were the probabilities close to reality? For every match we measure how far the forecast landed from what actually happened, then average it. A perfect crystal ball scores 0; blindly guessing 1-in-3 every time scores about 0.667. Lower is better.
- Log loss: the same idea, but overconfidence is punished hard. Call something nearly certain and then get it wrong, and this number jumps. It is the metric that keeps the model humble. Blind guessing scores about 1.099. Lower is better.
- ECE: does “70%” really mean 70%? We gather up every “about 70%” forecast and check how often those things actually happened. The average gap, across every confidence level, is the ECE. A few percentage points means the stated chances can be taken at face value. Lower is better.
The first two reward being right and bold; the last is the honesty check. A model can look impressive and still overstate its confidence. Measuring all three together is what catches that.
Want the machinery underneath, the component models and the held-out test behind these numbers? It is all on the methodology page.