AI FOOTBALLPREDICTION LAB
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All predictionsGroup · 2026-06-24
Pre-tournament snapshothigh model confidence

Panama vs Croatia AI prediction

The model’s strongest regulation-time outcome is Croatia win at 67%. Explore the complete probability picture and test alternative scoring assumptions below.

PAPanamaHome
Most likely score0–211% probability
CRCroatiaAway
14%19%67%
Panama winDrawCroatia win
Both teams scoreYes52%
2.5 goalsOver60%
Expected goals0.89 · 2.28Home · away
Interactive scenario

Prediction playground

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

Win probability
13%18%69%
PanamaDrawCroatia
Over 2.5 goals61%
BTTS: yes53%
Likely scores0–2 · 1–2 · 0–1

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

Panama 14% · Draw 19% · Croatia 67%

Both teams to score

Yes 52% · No 48%

Over / under 2.5

Over 60% · Under 40%

Most likely exact scores

Top five cells in the published score distribution.

010–211%020–110%031–29%041–19%050–38%
Complete exact-score matrix

Rows show Panama goals; columns show Croatia goals. All 256 published cells are included.

H \ A0123456789101112131415
05.2%9.5%10.7%8.0%4.7%2.3%1.0%0.4%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
13.7%8.9%9.1%6.8%4.0%1.9%0.8%0.3%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
21.8%3.8%4.1%3.0%1.8%0.9%0.4%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
30.6%1.2%1.3%0.9%0.5%0.3%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
40.1%0.3%0.3%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%
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
Continue exploring

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