AI FOOTBALLPREDICTION LAB
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All predictionsGroup · 2026-06-17
Pre-tournament snapshotmedium model confidence

Austria vs Jordan AI prediction

The model’s strongest regulation-time outcome is Austria win at 47%. Explore the complete probability picture and test alternative scoring assumptions below.

AUAustriaHome
Most likely score1–112% probability
JOJordanAway
47%25%28%
Austria winDrawJordan win
Both teams scoreYes53%
2.5 goalsOver51%
Expected goals1.61 · 1.16Home · away
Interactive scenario

Prediction playground

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

Win probability
48%25%27%
AustriaDrawJordan
Over 2.5 goals52%
BTTS: yes55%
Likely scores1–1 · 1–0 · 2–1

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

Austria 47% · Draw 25% · Jordan 28%

Both teams to score

Yes 53% · No 47%

Over / under 2.5

Over 51% · Under 49%

Most likely exact scores

Top five cells in the published score distribution.

011–112%021–010%032–19%042–08%050–07%
Complete exact-score matrix

Rows show Austria goals; columns show Jordan goals. All 256 published cells are included.

H \ A0123456789101112131415
07.5%7.1%4.4%1.8%0.6%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
19.7%11.8%6.4%2.6%0.8%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
28.0%8.7%5.0%2.0%0.6%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
34.4%4.8%2.7%1.1%0.3%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
41.9%2.1%1.2%0.5%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
50.7%0.8%0.4%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
60.2%0.2%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
Continue exploring

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