All predictionsGroup · 2026-06-19
Pre-tournament snapshotlow model confidence

Mexico vs South Korea AI prediction

The model’s strongest regulation-time outcome is Mexico win at 45%. Explore the complete probability picture and test alternative scoring assumptions below.

MEMexicoHome
Most likely score1014% probability
SOSouth KoreaAway
45%30%26%
Mexico winDrawSouth Korea win
Both teams scoreNo58%
2.5 goalsUnder63%
Expected goals1.28 · 0.89Home · away
Interactive scenario

Prediction playground

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

Win probability
45%29%26%
MexicoDrawSouth Korea
Over 2.5 goals37%
BTTS: yes43%
Likely scores1–0 · 1–1 · 0–0

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

Mexico 45% · Draw 30% · South Korea 26%

Both teams to score

Yes 42% · No 58%

Over / under 2.5

Over 37% · Under 63%

Most likely exact scores

Top five cells in the published score distribution.

011014%021113%030013%040110%05209%
Complete exact-score matrix

Rows show Mexico goals; columns show South Korea goals. All 256 published cells are included.

H \ A0123456789101112131415
012.5%9.6%4.6%1.4%0.3%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
114.0%13.1%5.6%1.7%0.4%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
29.3%7.9%3.6%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%
34.0%3.5%1.5%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%
41.4%1.2%0.5%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%
50.4%0.3%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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