PresairaPresairaWorld Cup 2026
ForecastMatchesGroupsPredictPlayers

An independent, calibrated AI forecast. Not affiliated with FIFA or any official World Cup organisation.

For interest and analysis only: not betting advice.

Methodology · how the forecast is built.

Built by Mohammed Ehab Elnomany · probabilistic, not an oracle.

  • Forecast
  • Matches
  • Groups
  • Predict
  • Players

June 11 – July 19, 2026 · United States · Canada · Mexico

World Cup 2026 - every stage played

48 nations, 104 matches, one champion. The road from kick-off to the Final, complete.

See how the AI stacked up against the humans.

See the final scores→

The 2026 World Cup is the first with 48 teams and 104 matches, the largest in the tournament's history.

Tournament Forecast

A calibrated, probabilistic AI forecast.

World Cup 2026 Champion

final result
Champion crown
Spain
Champions

From day one, the model’s top two were the eventual finalists: Spain at 10.9%, Argentina at 9.6%. It called the exact Final.

Backtested on 2018 & 2022 · Updated 19 Jul 2026 · Final run, all 104 results in

Title-odds over time

champion probability
100.0%50.0%0.0%
Spain>99.9%
Argentina<0.1%
Brazil<0.1%
England<0.1%
France<0.1%
Morocco<0.1%
KickoffFinal

Each point is a model forecast published during the tournament, conditioned on every result completed at that time; the line connects successive published forecasts (recalcs ran in batches, so some matches share a segment).

How the model saw it

predicted vs actual
#TeamActual finishvsPre-cup
  1. 1SpainChampions=Finished where the model ranked it, among these eight10.9%
  2. 2ArgentinaRunners-up=Finished where the model ranked it, among these eight9.6%

June 11 – July 19, 2026 · United States · Canada · Mexico

World Cup 2026 - every stage played

48 nations, 104 matches, one champion. The road from kick-off to the Final, complete.

See how the AI stacked up against the humans.

See the final scores→

The 2026 World Cup is the first with 48 teams and 104 matches, the largest in the tournament's history.

Tournament Forecast

A calibrated, probabilistic AI forecast.

  • 3BrazilRound of 16▼22 places worse than the model's rank, among these eight7.0%
  • 4FranceFourth place=Finished where the model ranked it, among these eight6.6%
  • 5EnglandThird place▲22 places better than the model's rank, among these eight5.4%
  • 6PortugalRound of 16▲11 place better than the model's rank, among these eight4.2%
  • 7GermanyRound of 32=Finished where the model ranked it, among these eight4.1%
  • 8NetherlandsRound of 32▲11 place better than the model's rank, among these eight4.0%
  • The model's pre-tournament champion odds (before a ball was kicked) against where each side actually finished. Ordered by that pre-tournament number; the green/red chip is how many places better or worse a side finished than the model ranked it, among these eight.

    Golden Boot race

    actual / exp
    • 1Kylian Mbappé10 / 7.0 exp
    • 2Lionel Messi8 / 6.7 exp
    • 3Jude Bellingham7 / 2.9 exp
    • 4Erling Haaland7 / 5.4 exp
    • 5Ousmane Dembélé6 / 2.0 exp
    See all scorers →

    AI vs Humans

    11 brackets
    1. 1rank 1Ahmad Hassan141 / 172 pts
    2. 2rank 2Presaira modelSpainBenchmark138 / 172 pts
    3. 3rank 3Seif Tamer116 / 172 pts
    4. 4rank 4M7mdEhab113 / 172 pts
    5. 5rank 5Mohammed Tarik98 / 172 pts

    Round-weighted points vs the actual bracket. Full comparison →

    See the final AI vs humans board.
    Methodology

    How the forecast is built: Elo, Dixon–Coles, Monte Carlo, and backtests.

    Read →

    World Cup 2026 Champion

    final result
    Champion crown
    Spain
    Champions

    From day one, the model’s top two were the eventual finalists: Spain at 10.9%, Argentina at 9.6%. It called the exact Final.

    Backtested on 2018 & 2022 · Updated 19 Jul 2026 · Final run, all 104 results in

    How the model saw it

    predicted vs actual
    #TeamActual finishvsPre-cup
    1. 1SpainChampions=Finished where the model ranked it, among these eight10.9%
    2. 2ArgentinaRunners-up=Finished where the model ranked it, among these eight9.6%
    3. 3BrazilRound of 16▼22 places worse than the model's rank, among these eight7.0%
    4. 4FranceFourth place=Finished where the model ranked it, among these eight6.6%
    5. 5EnglandThird place▲22 places better than the model's rank, among these eight5.4%
    6. 6PortugalRound of 16▲11 place better than the model's rank, among these eight4.2%
    7. 7GermanyRound of 32=Finished where the model ranked it, among these eight4.1%
    8. 8NetherlandsRound of 32▲11 place better than the model's rank, among these eight4.0%

    The model's pre-tournament champion odds (before a ball was kicked) against where each side actually finished. Ordered by that pre-tournament number; the green/red chip is how many places better or worse a side finished than the model ranked it, among these eight.

    Golden Boot race

    actual / exp
    • 1Kylian Mbappé10 / 7.0 exp
    • 2Lionel Messi8 / 6.7 exp
    • 3Jude Bellingham7 / 2.9 exp
    • 4Erling Haaland7 / 5.4 exp
    • 5Ousmane Dembélé6 / 2.0 exp
    See all scorers →

    Title-odds over time

    champion probability
    100.0%50.0%0.0%
    >99.9%
    <0.1%
    <0.1%
    <0.1%
    <0.1%
    <0.1%
    KickoffFinal

    Each point is a model forecast published during the tournament, conditioned on every result completed at that time; the line connects successive published forecasts (recalcs ran in batches, so some matches share a segment).

    AI vs Humans

    11 brackets
    1. 1rank 1Ahmad Hassan141 / 172 pts
    2. 2rank 2Presaira modelSpainBenchmark138 / 172 pts
    3. 3rank 3Seif Tamer116 / 172 pts
    4. 4rank 4M7mdEhab113 / 172 pts
    5. 5rank 5Mohammed Tarik98 / 172 pts

    Round-weighted points vs the actual bracket. Full comparison →

    See the final AI vs humans board.
    Methodology

    How the forecast is built: Elo, Dixon–Coles, Monte Carlo, and backtests.

    Read →