AI FOOTBALLPREDICTION LAB
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All predictionsGroup · 2026-06-25
Pre-tournament snapshotmedium model confidence

South Africa vs South Korea AI prediction

The model’s strongest regulation-time outcome is South Korea win at 51%. Explore the complete probability picture and test alternative scoring assumptions below.

SOSouth AfricaHome
Most likely score1–19% probability
SOSouth KoreaAway
27%22%51%
South Africa winDrawSouth Korea win
Both teams scoreYes65%
2.5 goalsOver66%
Expected goals1.43 · 2.03Home · away
Interactive scenario

Prediction playground

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

Win probability
27%21%51%
South AfricaDrawSouth Korea
Over 2.5 goals67%
BTTS: yes66%
Likely scores1–2 · 1–1 · 2–2

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

South Africa 27% · Draw 22% · South Korea 51%

Both teams to score

Yes 65% · No 35%

Over / under 2.5

Over 66% · Under 34%

Most likely exact scores

Top five cells in the published score distribution.

011–19%021–29%030–27%042–16%050–16%
Complete exact-score matrix

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

H \ A0123456789101112131415
04.0%6.3%6.5%4.4%2.3%1.0%0.4%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
14.4%9.4%8.7%5.9%3.1%1.4%0.5%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
23.4%6.3%6.1%4.1%2.2%0.9%0.4%0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
31.6%3.1%3.0%2.0%1.0%0.5%0.2%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%<0.1%
40.6%1.2%1.1%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%
50.2%0.4%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%
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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