World Cup forecasts, with the receipts.
Every match starts with a statistical baseline — the same method for every team, fit on decades of results. Then an AI agent checks what the model can’t see: injuries, line-ups, the week’s news, and the match-day conditions that swing a game.
For each fixture, the agent says whether the evidence is strong enough to move the forecast, and by how much — with the sources behind it. Once the match is played, we score it against the model. The agent gets an opinion; the scoreboard gets the last word.
France WWWLW (4 wins in last 5); Iraq LWWDL (2 wins, 1 draw, 2 losses in last 5).
Calibrated on 47,000+ international matches · scored against real results as they come in · the agent reads each group-stage fixture as its kickoff nears.
What changed
Champion odds over the last day- France v Iraq +12.1 pp
- Portugal v DR Congo +10.7 pp
- Mexico v South Africa +10.6 pp
The agent, live
For each fixture the agent explains what sits behind the model’s number and adjusts it where the match-day context warrants — usually by a few points, the size of the evidence. Most calls stay close to the baseline; its sharpest move so far is France +12.1 pp.
Once a match is played, we show whether the agent’s adjustment helped or hurt versus the model. Method →
USA recent form WLLWL, -0.03 pts/match vs expectation vs avg opp rank #11; Paraguay LWWLW, +0.35 pts/match vs expectation vs avg opp rank #51. Paraguay overperforming against weaker opposition, USA slightly underperforming against strong opposition.
Qatar has LDLLD in last 5, -1.02 pts/match vs expectation vs avg opp rank #85; Switzerland has DLDWD, -0.42 pts/match vs expectation vs avg opp rank #37. Both underperform, but Qatar's underperformance is more severe and against weaker opposition.
Brazil's recent form is DLWWW (+0.18 pts/match vs expectation) against opponents averaging strength-rank #35; Morocco's is DWWWD (-0.00 pts/match) vs opponents averaging #61. Both are in line with expectations, but Brazil faced stronger opposition.
The forecast, in brief
Full 48-team table →The top of the field — the baseline the agent reasons from. Each market’s de-vigged champion price sits beside the model’s, never blended. Top 8 ≈ 70% of the title mass — an unusually flat field.
| # | Team | Champion | Final | Semi | Polymarket | Kalshi |
|---|---|---|---|---|---|---|
| 01 | Spain | 14.2% | 22.8% | 34.8% | 16.5% | 15.7% |
| 02 | Brazil | 12.9% | 20.6% | 33.6% | 8.2% | 7.8% |
| 03 | Argentina | 11.8% | 19.3% | 31.5% | 7.7% | 8.2% |
| 04 | England | 9.3% | 15.9% | 26.8% | 9.4% | 9.9% |
| 05 | France | 6.5% | 12.8% | 23.4% | 15.7% | 15.9% |
| 06 | Portugal | 6.5% | 12.4% | 22.6% | 10.3% | 10.0% |
| 07 | Germany | 5.0% | 10.5% | 20.9% | 5.0% | 4.7% |
| 08 | Colombia | 4.6% | 9.2% | 18.0% | 1.6% | 1.5% |
How the forecast agent works
Team strength estimated from 47,000+ historical international results, with recent matches weighted most. Simulated tournaments are an output of that strength, not a teacher. The same disciplined method for every match, no opinions in it.
For each fixture it searches the web and weighs what the model structurally can’t see — injuries, line-ups, suspensions, the week’s news — and the match-day conditions that bite: altitude, heat, a roof that closes. It cites what it found and says which way it points.
Every adjustment is scored against the model once the match is played. Soccer is hard to call — low-scoring, and national sides barely play together — so we keep expectations honest and show how the agent does, win or lose. The method →
Every fixture the agent has weighed in on, ranked by how far it moves the baseline.
Champion and stage odds for all 48 teams, with the market prices beside them.
Fixtures, groups, and the bracket as it fills in.
Where the model disagrees with Polymarket and Kalshi — published ahead, scored later.
When the model says 60%, does it happen 60% of the time? The public record.
How the model is built and scored, the sources, and why soccer is hard to call.