AI correctly predicted the Kazma win.
The match finished 2–3, validating the model's directional assessment.
Tracked markets vs full-time result
Each row compares the pre-match model lean to the full-time result.
- Market Prediction Result Outcome
- Over / Under 2.5 Under 2.5 Over 2.5 (5 goals) ✖ Incorrect
- Both Teams To Score BTTS Yes Yes ✔ Correct
- 1X2 Kazma Kazma ✔ Correct
- Correct Score Insights 1-1, 0-1, 1-2, 1-0, 0-2 2-3 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Al Shabab | 27.9% | 17.9% | +10.0 pp | Model Edge |
| Draw | 29.2% | 24.9% | +4.3 pp | Aligned |
| Kazma | 42.9% | 57.2% | -14.3 pp | Market Higher |
The closing market estimates Kazma's win probability at 57.2%, compared with the model's estimate of 42.9%, a difference of 14.3 percentage points. This highlights a disagreement between the model and market consensus, without indicating which view is ultimately correct.
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.
After full time, the model's directional lean matched the result (Kazma win 2–3).
Market Assessment
The market is materially more optimistic about Kazma than the current fair estimate.
- Investors may be incorporating information not fully reflected in the baseline model.
- Tournament-specific context can shift market pricing.
Post Match Insights
What worked
- Expected goals projected a high-scoring match (ΣxG 2.60) — 5 goals materialised
- Both Teams To Score (Yes) matched the full-time result
What failed
- Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (5 goals)
- Exact score: outside the model's top score bins
Market lesson
Large model–market gaps do not automatically mean the market is right. Here the closing market priced Kazma higher (57.2% vs model 42.9%, 14.3 pp), but the model's lean was validated (Kazma win 2–3).
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Sep 06, 2026 · 17:49 UTC Forecast generated
- Model 1X2 · Al Shabab 27.9% · Draw 29.2% · Kazma 42.9%
- xG · Al Shabab 1.14 — Kazma 1.46
-
Sep 06, 2026 · 17:19 UTC Opening odds snapshot PRE30
- 1X2 odds · Al Shabab 5.05 · Draw 3.63 · Kazma 1.58
- Implied 1X2 · Al Shabab 17.9% · Draw 24.9% · Kazma 57.2%
- Bookmaker · Pinnacle
-
Sep 06, 2026 · 17:49 UTC Closing snapshot recorded PRE1
- 1X2 odds · Al Shabab 5.05 · Draw 3.63 · Kazma 1.58
- Implied 1X2 · Al Shabab 17.9% · Draw 24.9% · Kazma 57.2%
- Bookmaker · Pinnacle
-
Sep 06, 2026 · 17:50 UTC Kickoff
-
FT Full-time result Kazma win · 2–3
-
FT Prediction validated Directional lean matched full-time result
-
Archived Prediction review
Historical Snapshot
Frozen at kickoff — the model output as it stood before the match started.
Pre-match metrics (historical context)
- Validation: Warning
- Large market gap (14 pp)
- No strong statistical edge
- Market has already priced much of the edge
- Validation warning
Validation Report
Immutable SnapshotModel Performance
This prediction contributes to:
- Primary Bets ROI (180d): -100.0%
Review FAQ
- How accurate was the prediction?
- This page grades directional markets (1X2, Over/Under 2.5, BTTS) against the full-time result. The prediction grade reflects how many of those tracked markets matched reality.
- What does xG Accuracy measure?
- xG Accuracy compares the model's pre-match expected-goals profile to the actual scoreline — not whether every market hit. A strong directional review can coexist with a moderate xG accuracy score.
- Why wasn't the exact score predicted?
- Correct-score outcomes are low-probability tails even when the model reads the match profile well. We highlight top score bins for context; missing the exact line does not invalidate a directional review.
- Does this improve the AI record?
- Each finished match is logged in our validation pipeline. Aggregated hit rates and CLV studies are published separately — this page is the per-match audit trail.
Predictions are for informational purposes only. Always gamble responsibly and within your limits. Past performance does not guarantee future results.
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Premier League — Standings
| # | TEAM | MP | W | D | L | PTS |
|---|---|---|---|---|---|---|
| 1 | Kazma | 3 | 2 | 1 | 0 | 7 |
| 2 | Al Kuwait | 3 | 2 | 1 | 0 | 7 |
| 3 | Al Qadsia | 3 | 2 | 0 | 1 | 6 |
| 4 | Al Salmiyah | 3 | 2 | 0 | 1 | 6 |
| 5 | Al Nasar | 3 | 2 | 0 | 1 | 6 |
| 6 | Al Shabab | 3 | 1 | 1 | 1 | 4 |
| 7 | Al Sahel | 3 | 1 | 1 | 1 | 4 |
| 8 | Al Fahaheel | 3 | 1 | 0 | 2 | 3 |
| 9 | Al Jahra | 3 | 1 | 0 | 2 | 3 |
| 10 | Al Arabi | 3 | 0 | 2 | 1 | 2 |
| 11 | Al Sulaibikhat | 3 | 0 | 2 | 1 | 2 |
| 12 | Al Tadhamon | 3 | 0 | 0 | 3 | 0 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Al Arabi | 3 | 7 | 8 | -1 | 2 |
| 2 | Kazma | 3 | 6 | 3 | +3 | 7 |
| 3 | Al Fahaheel | 3 | 6 | 5 | +1 | 3 |
| 4 | Al Shabab | 3 | 6 | 6 | 0 | 4 |
| 5 | Al Qadsia | 3 | 5 | 2 | +3 | 6 |
| 6 | Al Kuwait | 3 | 5 | 3 | +2 | 7 |
| 7 | Al Salmiyah | 3 | 4 | 2 | +2 | 6 |
| 8 | Al Nasar | 3 | 4 | 5 | -1 | 6 |
| 9 | Al Jahra | 3 | 4 | 5 | -1 | 3 |
| 10 | Al Sulaibikhat | 3 | 4 | 5 | -1 | 2 |
| 11 | Al Tadhamon | 3 | 2 | 9 | -7 | 0 |
| 12 | Al Sahel | 3 | 1 | 1 | 0 | 4 |
