The road to MetLife.
Hey! Like many of you, I'm a massive futbol fan lucky enough to have tickets to the 2026 World Cupโbut I desperately wanted to know who I could actually end up going to see, or where my country is likely to head next. So, I built this. This simulator models every single knockout matchup across 40,000 simulations, powered by current Elo ratings, live results, host-nation edge, and FIFA's real Round-of-32 allocation rules to help you track your tickets or your team's path.
Find your match
Pick any tie โ by number, date or venue โ and see the matchup the model lands on most often, the likeliest alternatives for each slot, and who'd be favoured. You can also click any card in the bracket below.
Model's most likely tie: South Korea vs Switzerland. If it plays out that way, Switzerland are favoured to go through โ about 54.1% to win.
Follow your country
Pick any nation and see every Round-of-32 venue they could land at, how likely each one is, and who they'd probably meet there.
Across 40,000 simulations, USA show up in 2 of the 16 Round-of-32 ties โ a combined 92.3% chance of reaching the knockouts.
- M81Levi's Stadium, San Francisco Bay AreaWed Jul 1 ยท likely opponent ๐ฉ๐ฟ Algeria69.7%lands here
- M88AT&T Stadium, DallasFri Jul 3 ยท likely opponent ๐ฎ๐ท Iran22.5%lands here
Round of 32, projected
The 16 ties. Each card shows the model's most-likely occupant of both slots, the split bar is single-game win probability, and the meter is how often that exact country lands there across 40,000 simulations.
Title favourites
Share of simulations won outright. Three teams sit within a whisker of each other at the top โ this is one of the most open World Cups in years by the numbers.
Every team's odds
All 48 teams, ranked by title probability. 'Win group' and the survival columns are how often each country clears each stage.
| Team | Grp | Elo | Win grp | Reach R32 | Reach QF | Reach SF | Champion |
|---|---|---|---|---|---|---|---|
| ๐ฆ๐ทArgentina | J | 1877 | 72% | 97% | 38% | 25% | 15.4% |
| ๐ซ๐ทFrance | I | 1871 | 66% | 97% | 40% | 25% | 15.4% |
| ๐ช๐ธSpain | H | 1875 | 72% | 99% | 37% | 24% | 15.0% |
| ๐ด๓ ง๓ ข๓ ฅ๓ ฎ๓ ง๓ ฟEngland | L | 1828 | 58% | 95% | 30% | 17% | 9.7% |
| ๐ง๐ทBrazil | C | 1765 | 44% | 94% | 20% | 10% | 4.8% |
| ๐ต๐นPortugal | K | 1768 | 52% | 93% | 20% | 10% | 4.7% |
| ๐บ๐ธUSA | D | 1689 | 70% | 99% | 20% | 10% | 4.5% |
| ๐ฒ๐ฆMorocco | C | 1756 | 40% | 94% | 19% | 9% | 4.3% |
| ๐ง๐ชBelgium | G | 1742 | 54% | 95% | 17% | 8% | 3.7% |
| ๐ฉ๐ชGermany | E | 1736 | 72% | 100% | 17% | 8% | 3.6% |
| ๐ณ๐ฑNetherlands | F | 1754 | 42% | 90% | 16% | 8% | 3.5% |
| ๐ฒ๐ฝMexico | A | 1701 | 66% | 99% | 15% | 6% | 2.6% |
| ๐ญ๐ทCroatia | L | 1715 | 31% | 86% | 13% | 6% | 2.4% |
| ๐จ๐ดColombia | K | 1698 | 35% | 87% | 11% | 5% | 1.9% |
| ๐ธ๐ณSenegal | I | 1684 | 23% | 82% | 11% | 4% | 1.7% |
| ๐บ๐พUruguay | H | 1673 | 24% | 88% | 9% | 4% | 1.4% |
| ๐ฏ๐ตJapan | F | 1662 | 28% | 84% | 8% | 3% | 1.0% |
| ๐จ๐ญSwitzerland | B | 1641 | 39% | 79% | 6% | 2% | 0.8% |
| ๐ฎ๐ทIran | G | 1620 | 27% | 84% | 6% | 2% | 0.7% |
| ๐ฐ๐ทSouth Korea | A | 1613 | 31% | 95% | 7% | 2% | 0.6% |
| ๐ฆ๐บAustralia | D | 1579 | 27% | 95% | 5% | 1% | 0.3% |
| ๐ช๐จEcuador | E | 1599 | 3% | 64% | 3% | 1% | 0.3% |
| ๐ฆ๐นAustria | J | 1597 | 14% | 71% | 3% | 1% | 0.2% |
| ๐ช๐ฌEgypt | G | 1562 | 18% | 75% | 3% | 1% | 0.2% |
| ๐น๐ทTurkiye | D | 1606 | 3% | 45% | 2% | 1% | 0.2% |
| ๐ฉ๐ฟAlgeria | J | 1571 | 12% | 66% | 3% | 1% | 0.2% |
| ๐จ๐ฎIvory Coast | E | 1541 | 26% | 95% | 3% | 1% | 0.2% |
| ๐จ๐ฆCanada | B | 1552 | 32% | 74% | 3% | 1% | 0.1% |
| ๐ณ๐ดNorway | I | 1557 | 9% | 59% | 2% | 1% | 0.1% |
| ๐ธ๐ชSweden | F | 1510 | 29% | 94% | 2% | 0% | 0.1% |
| ๐ต๐ฆPanama | L | 1539 | 9% | 57% | 2% | 1% | 0.1% |
| ๐ด๓ ง๓ ข๓ ณ๓ ฃ๓ ด๓ ฟScotland | C | 1519 | 16% | 78% | 2% | 0% | 0.1% |
| ๐ถ๐ฆQatar | B | 1459 | 16% | 59% | 1% | 0% | 0.0% |
| ๐จ๐ฟCzechia | A | 1485 | 2% | 43% | 1% | 0% | 0.0% |
| ๐จ๐ฉDR Congo | K | 1474 | 7% | 46% | 1% | 0% | 0.0% |
| ๐ต๐พParaguay | D | 1488 | 1% | 28% | 0% | 0% | 0.0% |
| ๐จ๐ปCape Verde | H | 1378 | 2% | 32% | 0% | 0% | 0.0% |
| ๐บ๐ฟUzbekistan | K | 1445 | 6% | 39% | 0% | 0% | 0.0% |
| ๐ง๐ฆBosnia and Herzegovina | B | 1435 | 13% | 53% | 0% | 0% | 0.0% |
| ๐ฎ๐ถIraq | I | 1410 | 2% | 26% | 0% | 0% | 0.0% |
| ๐ฌ๐ญGhana | L | 1400 | 3% | 26% | 0% | 0% | 0.0% |
| ๐ญ๐นHaiti | C | 1335 | 0% | 6% | 0% | 0% | 0.0% |
| ๐ฏ๐ดJordan | J | 1400 | 3% | 29% | 0% | 0% | 0.0% |
| ๐จ๐ผCuracao | E | 1320 | 0% | 11% | 0% | 0% | 0.0% |
| ๐น๐ณTunisia | F | 1476 | 2% | 14% | 0% | 0% | 0.0% |
| ๐ณ๐ฟNew Zealand | G | 1276 | 1% | 16% | 0% | 0% | 0.0% |
| ๐ธ๐ฆSaudi Arabia | H | 1395 | 2% | 36% | 0% | 0% | 0.0% |
| ๐ฟ๐ฆSouth Africa | A | 1392 | 1% | 26% | 0% | 0% | 0.0% |
How the model works
No black box. Here is exactly what goes in and how the bracket comes out.
- A. Ratings in, goals out
Elo โ Poisson
Each team carries a World Football Elo rating, snapshotted 14 Jun 2026 โ already absorbing every result so far. For a given match the Elo gap becomes expected goal supremacy, and each side's goals are drawn from a Poisson distribution around that. Realistic scorelines, win/draw rates, and the goal differences the tiebreakers need.
- B. Results already count
Matchday 1 locked
Matchday-1 games are locked to their real scores โ Mexico's 2โ0, the USA's 4โ1, Germany's 7โ1, Sweden's 5โ1, Australia over Tรผrkiye, the three Group B draws. Only unplayed fixtures are simulated, then added on top of points already banked.
- C. Home advantage
USA ยท Mexico ยท Canada
The three host nations get an Elo bump in matches on their own soil, in the group stage and at knockout venues in their country. It is why the USA and Mexico project so strongly out of their groups.
- D. The new 32-team maths
Annex C allocation
Top two from each of the 12 groups, plus the 8 best third-placed teams ranked by points, goal difference, goals, then rating. The eight thirds are slotted using FIFA's real Annex C allocation, which guarantees no group-mate rematch in the Round of 32.
- E. Simulate, repeat, count
40,000 tournaments
The whole tournament โ group stage through the final โ is played out 40,000 times. For each slot the model records who landed there; the country shown is simply the most frequent occupant, and the meter is how dominant that pick was.