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Le probabilità si avverano?
Quando il modello assegna a una squadra il 70% di possibilità, quella squadra dovrebbe vincere circa sette volte su dieci. Questa pagina verifica se succede davvero. Ogni partita della Coppa del Mondo 2026 viene valutata non appena giocata, e i numeri qui sotto rispondono a una sola domanda: le probabilità sono oneste, non solo sicure di sé? (Il termine tecnico per questo è calibrazione.)
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.
Coppa del Mondo 2026: tracker live
Across 96 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 96 scored matches
- 0.497
- 0.837
- 0.167
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 |
| 2026-07-15 | 1 | 0.537 | 0.911 |
Updated 2026-07-16.
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 | 96 | 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 | 1 | 0.537 | +0.039 |
| 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% | 47 | 0.540 | +0.043 |
| P_fav 60-80% | 32 | 0.373 | -0.125 |
| P_fav >= 80% | 6 | 0.513 | +0.016 |
Cosa significano i tre numeri
Pensa a un meteorologo. Chiunque può dire "70% di possibilità di pioggia". Quelli bravi hanno ragione circa il 70% delle volte quando lo dicono. Questi tre numeri verificano il modello allo stesso modo.
- Brier score: le probabilità erano vicine alla realtà? Per ogni partita misuriamo quanto la previsione si è discostata da ciò che è realmente accaduto, poi calcoliamo la media. Una sfera di cristallo perfetta segna 0; indovinare alla cieca 1 su 3 ogni volta segna circa 0,667. Più basso è, meglio è.
- Log loss: la stessa idea, ma l'eccessiva sicurezza viene punita duramente. Dichiarare qualcosa quasi certo e poi sbagliare fa schizzare questo numero. È la metrica che mantiene il modello umile. Indovinare alla cieca segna circa 1,099. Più basso è, meglio è.
- ECE: "70%" significa davvero 70%? Raccogliamo tutte le previsioni "circa 70%" e verifichiamo quante volte quegli eventi si sono effettivamente verificati. Lo scarto medio, su ogni livello di confidenza, è l'ECE. Pochi punti percentuali significano che le probabilità dichiarate possono essere prese per buone. Più basso è, meglio è.
I primi due premiano l'essere corretti e audaci; l'ultimo è la verifica di onestà. Un modello può sembrare impressionante e comunque sopravvalutare la propria confidenza: misurare tutti e tre insieme è ciò che lo individua.
Vuoi il meccanismo sottostante, i modelli componenti e il test out-of-sample dietro questi numeri? È tutto nella pagina metodologia.