AI correctly predicted the Al Karkh win.
The match finished 3–2, 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 Over 2.5 Over 2.5 (5 goals) ✔ Correct
- Both Teams To Score BTTS Yes Yes ✔ Correct
- 1X2 Al Karkh Al Karkh ✔ Correct
- Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 3-2 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Al Karkh | 36.6% | 26.0% | +10.6 pp | Model Edge |
| Draw | 29.6% | 27.5% | +2.1 pp | Aligned |
| Duhok | 33.8% | 46.5% | -12.8 pp | Market Higher |
The closing market estimates Duhok's win probability at 46.5%, compared with the model's estimate of 33.8%, a difference of 12.8 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 (Al Karkh win 3–2).
Market Assessment
The market is materially more optimistic about Duhok 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
- Over 2.5 goals aligned with the xG profile
What failed
- 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 Al Karkh higher (49.4% vs model 36.6%, 12.8 pp), but the model's lean was validated (Al Karkh win 3–2).
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Sep 13, 2026 · 18:01 UTC Forecast generated
- Model 1X2 · Al Karkh 36.6% · Draw 29.6% · Duhok 33.8%
- xG · Al Karkh 1.33 — Duhok 1.27
-
Sep 13, 2026 · 17:32 UTC Opening odds snapshot PRE30
- 1X2 odds · Al Karkh 3.42 · Draw 3.23 · Duhok 1.91
- Implied 1X2 · Al Karkh 26.0% · Draw 27.5% · Duhok 46.5%
- Bookmaker · Pinnacle
-
Sep 13, 2026 · 18:01 UTC Closing snapshot recorded PRE1
- 1X2 odds · Al Karkh 3.42 · Draw 3.23 · Duhok 1.91
- Implied 1X2 · Al Karkh 26.0% · Draw 27.5% · Duhok 46.5%
- Bookmaker · Pinnacle
-
Sep 13, 2026 · 18:00 UTC Kickoff
-
FT Full-time result Al Karkh win · 3–2
-
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 (13 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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Iraqi League — Standings
| # | TEAM | MP | W | D | L | PTS |
|---|---|---|---|---|---|---|
| 1 | Al-Karma | 8 | 6 | 2 | 0 | 20 |
| 2 | Al Zawra'a | 8 | 5 | 3 | 0 | 18 |
| 3 | Erbil | 8 | 5 | 2 | 1 | 17 |
| 4 | Newroz | 8 | 4 | 3 | 1 | 15 |
| 5 | Al Shorta | 8 | 4 | 3 | 1 | 15 |
| 6 | Naft | 8 | 4 | 3 | 1 | 15 |
| 7 | Al-Jolan SC | 8 | 2 | 4 | 2 | 10 |
| 8 | Duhok | 8 | 1 | 6 | 1 | 9 |
| 9 | Diyala | 8 | 2 | 3 | 3 | 9 |
| 10 | Al Minaa Basra | 8 | 2 | 3 | 3 | 9 |
| 11 | Zakho | 8 | 2 | 3 | 3 | 9 |
| 12 | Al Quwa Al Jawiya | 8 | 1 | 5 | 2 | 8 |
| 13 | Ghaz Al Shamal | 8 | 1 | 5 | 2 | 8 |
| 14 | Mosul | 8 | 2 | 2 | 4 | 8 |
| 15 | Al Karkh | 8 | 2 | 1 | 5 | 7 |
| 16 | Al Kahrabaa | 8 | 1 | 4 | 3 | 7 |
| 17 | Al Talaba | 8 | 1 | 4 | 3 | 7 |
| 18 | Naft Maysan | 8 | 1 | 4 | 3 | 7 |
| 19 | Gharraf | 8 | 1 | 2 | 5 | 5 |
| 20 | Karbala | 8 | 1 | 2 | 5 | 5 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Al Zawra'a | 8 | 15 | 3 | +12 | 18 |
| 2 | Al-Karma | 8 | 14 | 5 | +9 | 20 |
| 3 | Newroz | 8 | 13 | 4 | +9 | 15 |
| 4 | Diyala | 8 | 13 | 14 | -1 | 9 |
| 5 | Erbil | 8 | 12 | 3 | +9 | 17 |
| 6 | Duhok | 8 | 11 | 11 | 0 | 9 |
| 7 | Al Quwa Al Jawiya | 8 | 11 | 12 | -1 | 8 |
| 8 | Al Karkh | 8 | 10 | 14 | -4 | 7 |
| 9 | Al Shorta | 8 | 9 | 4 | +5 | 15 |
| 10 | Naft | 8 | 9 | 5 | +4 | 15 |
| 11 | Mosul | 8 | 9 | 12 | -3 | 8 |
| 12 | Al Talaba | 8 | 9 | 14 | -5 | 7 |
| 13 | Al-Jolan SC | 8 | 8 | 9 | -1 | 10 |
| 14 | Al Minaa Basra | 8 | 8 | 10 | -2 | 9 |
| 15 | Zakho | 8 | 7 | 11 | -4 | 9 |
| 16 | Al Kahrabaa | 8 | 6 | 10 | -4 | 7 |
| 17 | Naft Maysan | 8 | 6 | 11 | -5 | 7 |
| 18 | Karbala | 8 | 6 | 15 | -9 | 5 |
| 19 | Ghaz Al Shamal | 8 | 4 | 5 | -1 | 8 |
| 20 | Gharraf | 8 | 4 | 12 | -8 | 5 |