A format nobody has played.
For the first time, forty-eight nations arrive instead of thirty-two: twelve groups of four, one hundred and four matches across three countries. More debutants, more travel, more chaos. Every model built on the old World Cup is, in a sense, guessing at a new game.
How the machine thinks.
Three layers turn a decade of history into a single probability. A team's strength becomes a distribution of plausible scorelines, which becomes fifty thousand simulated tournaments, each one a complete, alternate July.
Rate every team.
An Elo rating distilled from ten years of internationals (form, opponent quality, margin of victory) gives each of the 48 sides a single number for strength. Play those ratings forward fifty thousand times and they decide how often each nation survives to the last four.
Who the model backs.
Across fifty thousand simulations, Brazil emerge as favourites, lifting the trophy in roughly one in five runs. Yet no nation clears 21%: Argentina, Germany and France crowd in just behind. This is a wide-open tournament, and the model knows it.
The race for the Boot.
Lionel Messi tops the Golden Boot board at 27.6%, ahead of Cristiano Ronaldo and Mbappé. But this is the model's least certain call. On back-tests it reliably names the contenders, never the exact winner.
One path to the trophy.
The single most-likely route through the final eight sends Brazil past Argentina in the final, 34% against 28%. In the other 49,999 simulations it rarely unfolds this cleanly, which is precisely the point.
Forecasts are not fortunes.
A model can tell you Brazil are likely. It cannot tell you about the injury in the eighty-ninth minute, or the debutant who becomes a legend in a single July afternoon. That gap, the space between probable and certain, is the whole reason we watch.
Every number, in one place.
The complete output of fifty thousand simulations: champion odds and deep-run probabilities, every group, and the Golden Boot race in full.
| Team | Champion | Final | Semi |
|---|---|---|---|
| Brazil | 20.9% | 33.7% | 52.6% |
| Argentina | 15.0% | 27.9% | 46.2% |
| Germany | 12.0% | 21.6% | 41.5% |
| France | 11.5% | 22.3% | 40.2% |
| England | 7.9% | 14.8% | 30.3% |
| Spain | 7.5% | 15.3% | 30.5% |
| Portugal | 5.8% | 12.9% | 25.2% |
| Netherlands | 5.1% | 10.8% | 21.0% |
| Player | P(boot) |
|---|---|
| Messi | 27.6% |
| Cristiano Ronaldo | 14.7% |
| Mbappé | 13.1% |
| Kane | 10.0% |
| Neymar | 7.4% |