Predictions / World Cup 2026 / Spain vs Belgium

Spain vs Belgium

Group H
Jul 10, 2026 - 19:00
2 1.83
1 0.87

Betting Signal

Model says
Spain 58.7%
Market says
Spain 59.7%
Difference
1.0 pp
Status
Direction aligned; check pricing gap separately

OddsGPT Interpretation

The betting market strongly prefers Spain (59.7%).

Spain — market direction supports the model lean.

Model vs Market Comparison

Spain
58.7% vs 59.68%
-1.0 pp
Draw
25.3% vs 24.63%
+0.7 pp
Belgium
16.0% vs 15.69%
+0.3 pp

Largest Gap

Spain -1.0 pp

The market is substantially more bullish on Spain than the model.

Model probabilities are generated from the statistical xG model using a Poisson distribution. Closing market probabilities are derived from consensus closing 1X2 odds after margin removal. Values represent implied probabilities rather than betting recommendations. Closing snapshot: PRE1.

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AI Forecast
Spain 58.7%
Draw 25.3%
Belgium 16.0%

Generated from Elo-adjusted Poisson simulation. Not bookmaker odds.

Show Full Model Data
Poisson goal-line probabilities
Line Over Under
0.5 91.9% 8.1%
1.5 76.5% 23.5%
2.5 50.6% 49.4%
3.5 28.6% 71.4%
4.5 13.7% 86.3%
Match Expectations
Over 2.5 goals
50.6%
Balanced
Under 2.5 goals
49.4%
Both teams to score
50.1%
Balanced
Clean sheet likely
49.9%

Poisson total-goals expectation Σλ = 2.7 (Over 2.5 50.6% · Under 2.5 49.4%).

BTTS Yes 50.1% · No 49.9% — neither side dominates the BTTS split.

Most Likely Scorelines
Score Probability
1-1 12.1%
2-0 11.3%
1-0 10.9%
2-1 9.8%
0-0 8.1%

Top Poisson cell: 1-1 at 12.1% (draw-type scoreline; exact-score variance remains high).

FAQ
How are win probabilities calculated?

Home and away expected goals (λ) are derived from Elo ratings and tournament parameters, then fed into a Dixon–Coles Poisson grid to produce 1X2, goal-line, and scoreline probabilities shown on this page.

Is this page betting advice?

No. OddsGPT displays model probabilities for informational purposes only. We do not recommend wagers or stake sizes on this page.

What does xG / λ mean here?

λ is the model’s pre-match expected goals for each team before variance is simulated. It is an input to the Poisson matrix, not a post-match expected-goals stat.

Why are exact score probabilities low?

Even the most likely scoreline typically sits below 15% because many score combinations share the probability mass — that is normal for Poisson models.

Match Phase: Finished

Decision Lifecycle

Current stage: Awaiting CLV

  1. Prediction Generated
  2. Market Compared
    Entry Pending
  3. Validation Passed
  4. Closing Recorded
    Closing 1.63
  5. CLV Evaluated
    CLV Pending Awaiting CLV calculation
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