AI FOOTBALLPREDICTION LAB
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All predictionsGroup · 2026-06-13
Pre-tournament snapshothigh model confidence

Qatar vs Switzerland AI prediction

The model’s strongest regulation-time outcome is Switzerland win at 87%. Explore the complete probability picture and test alternative scoring assumptions below.

QAQatarHome
Most likely score0–310% probability
SWSwitzerlandAway
4%9%87%
Qatar winDrawSwitzerland win
Both teams scoreNo56%
2.5 goalsOver76%
Expected goals0.64 · 3.62Home · away
Interactive scenario

Prediction playground

Change either scoring rate. Every probability below recalculates immediately with a Poisson scoreline model.

Win probability
3%7%90%
QatarDrawSwitzerland
Over 2.5 goals79%
BTTS: yes46%
Likely scores0–3 · 0–4 · 0–2

Play this fixture once with the probabilities above. Every run is different.

Scenario controls run locally and leave the published model snapshot unchanged.
Probability at a glance

What the score grid says

Aggregate markets below are calculated directly from the same exact score distribution—not produced as separate tips.

Match result

Qatar 4% · Draw 9% · Switzerland 87%

Both teams to score

Yes 44% · No 56%

Over / under 2.5

Over 76% · Under 24%

Most likely exact scores

Top five cells in the published score distribution.

010–310%020–210%030–49%040–57%050–16%
Complete exact-score matrix

Rows show Qatar goals; columns show Switzerland goals. All 256 published cells are included.

H \ A0123456789101112131415
02.4%6.2%9.7%10.4%9.0%6.6%4.3%2.6%1.4%0.7%0.4%0.2%<0.1%<0.1%<0.1%<0.1%
11.1%3.9%5.7%6.1%5.3%3.9%2.6%1.6%0.9%0.4%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%
20.4%1.2%1.9%2.0%1.8%1.3%0.9%0.5%0.3%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
30.1%0.3%0.5%0.5%0.4%0.3%0.2%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
4<0.1%<0.1%<0.1%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
5<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
6<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
7<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
8<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
9<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
10<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
11<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
12<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
13<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
14<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
15<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
How this was made

From team strength to a score distribution.

  1. 01
    Fit team strengths

    The upstream Bayesian model estimates attack, defence and home-advantage parameters from international results.

  2. 02
    Build match probabilities

    Expected scoring rates produce an exact-score probability grid; 1X2, BTTS and goal totals are derived from it.

  3. 03
    Simulate the tournament

    Repeated complete tournament paths produce progression and title probabilities for every team.

Forecast wc2026-2026-06-04-2026-100 · Data through 2026-06-04 · 100 tournament runs Source at pinned commit
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