All predictionsGroup · 2026-06-25
Pre-tournament snapshothigh model confidence

Curaçao vs Ivory Coast AI prediction

The model’s strongest regulation-time outcome is Ivory Coast win at 76%. Explore the complete probability picture and test alternative scoring assumptions below.

CUCuraçaoHome
Most likely score0211% probability
IVIvory CoastAway
9%15%76%
Curaçao winDrawIvory Coast win
Both teams scoreNo50%
2.5 goalsOver67%
Expected goals0.79 · 2.71Home · away
Interactive scenario

Prediction playground

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

Win probability
8%14%78%
CuraçaoDrawIvory Coast
Over 2.5 goals68%
BTTS: yes51%
Likely scores0–2 · 0–3 · 1–2

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

Curaçao 9% · Draw 15% · Ivory Coast 76%

Both teams to score

Yes 50% · No 50%

Over / under 2.5

Over 67% · Under 33%

Most likely exact scores

Top five cells in the published score distribution.

010211%020310%03128%04018%05137%
Complete exact-score matrix

Rows show Curaçao goals; columns show Ivory Coast goals. All 256 published cells are included.

H \ A0123456789101112131415
03.8%8.2%11.0%9.7%6.6%3.8%1.9%0.8%0.3%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
12.3%6.9%8.4%7.3%5.0%2.8%1.4%0.6%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
21.1%2.6%3.3%2.9%2.0%1.1%0.5%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
30.3%0.7%0.9%0.8%0.5%0.3%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
4<0.1%0.2%0.2%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%
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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