Predictions / Football / Iraq. Iraqi League / Diyala vs Al-Karma

Prediction Audit: Diyala vs Al-Karma Prediction, Odds & AI Betting Tips

Diyala Diyala
Sep 03, 2026 - 15:45
1 1.19
2 1.41
xG Accuracy: 81%
Al-Karma Al-Karma

AI correctly predicted the Al-Karma win.

The match finished 1–2, validating the model's directional assessment.

Tracked markets vs full-time result

Prediction grade B-

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 (3 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Al-Karma Al-Karma ✔ Correct
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 0-0 1-2 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Al-Karma higher than the statistical model.

Largest probability gap: Al-Karma -11.5 pp

Outcome Model Closing Market Difference Signal
Diyala 30.1% 21.5% +8.6 pp Model Higher
Draw 29.4% 26.6% +2.9 pp Aligned
Al-Karma 40.4% 51.9% -11.5 pp Market Higher

The closing market estimates Al-Karma's win probability at 51.9%, compared with the model's estimate of 40.4%, a difference of 11.5 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-Karma win 1–2).

Market Assessment

The market is materially more optimistic about Al-Karma 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) — 3 goals materialised
  • Both Teams To Score (Yes) matched the full-time result
  • Exact score 1–2 fell within the model's highlighted bins

What failed

  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (3 goals)

Market lesson

Large model–market gaps do not automatically mean the market is right. Here the closing market priced Al-Karma higher (51.9% vs model 40.4%, 11.5 pp), but the model's lean was validated (Al-Karma win 1–2).

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Prediction Timeline

How this prediction moved from forecast to full-time review.

  1. Sep 03, 2026 · 15:44 UTC Forecast generated
    • Model 1X2 · Diyala 30.1% · Draw 29.4% · Al-Karma 40.5%
    • xG · Diyala 1.19 — Al-Karma 1.41
  2. Sep 03, 2026 · 15:14 UTC Opening odds snapshot PRE30
    • 1X2 odds · Diyala 4.13 · Draw 3.34 · Al-Karma 1.71
    • Implied 1X2 · Diyala 21.5% · Draw 26.6% · Al-Karma 51.9%
    • Bookmaker · Pinnacle
  3. Sep 03, 2026 · 15:44 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Diyala 4.13 · Draw 3.34 · Al-Karma 1.71
    • Implied 1X2 · Diyala 21.5% · Draw 26.6% · Al-Karma 51.9%
    • Bookmaker · Pinnacle
  4. Sep 03, 2026 · 15:45 UTC Kickoff
  5. FT Full-time result Al-Karma win · 1–2
  6. FT Prediction validated Directional lean matched full-time result
  7. Archived Prediction review

Historical Snapshot

Frozen at kickoff — the model output as it stood before the match started.

Historical verdict: Monitor
Historical Decision Monitor
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 57/100 · Moderate
  • Validation: Warning
  • Large market gap (12 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Market has already priced much of the edge
  • Validation warning
Pricing proximity (inverse gap) 42/100
Betting Confidence 45/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 28, 2026 · 01:47 UTC Snapshot ID: dp-5733527

Closing Odds 1.71
AI Fair Odds —
CLV Pending
Final Result Al-Karma win · Diyala 1–2 Al-Karma
Prediction ✔ Correct
Decision Grade B-

Model 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.

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: Iraqi League
  • Fixture: Diyala vs Al-Karma
  • Kickoff: 2026-09-03 15:45:00
  • 1X2 (model): Home 30.1% · Draw 29.4% · Away 40.5%
  • xG (showing): Diyala 1.19 — Al-Karma 1.41 (total xG ≈ 2.6)
  • Value headline: None — no positive EV on tracked lines at current best prices (same as the decision block: no default bet).
  • Structural leans (not bets): Structural lean (model): O/U 2.5 Under 2.5 (Under 2.5 51.8% · Over 2.5 48.2%); BTTS Yes (Yes 54.2% · No 45.8%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.2% · No 45.8%
  • Correct score (top bin): 1-1 (12.5%)

Saying “no value” on a snapshot is a feature, not a bug: it protects readers from forcing a play when the edge is not there.

If lines move materially, re-run generation or refresh — implied probabilities and any future EV readouts will change first.

Historical Recommendation

Historical Decision: Monitor

Outcome: Validated — Pre-match lean validated against the full-time result.

Risk Factors Considered Before Kickoff

  • Price movement: implied probabilities and EV move with odds.
  • Sample / data gaps: low-information leagues widen forecast bands.
  • In-play state: goals and red cards are not modelled here.
  • Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Last Updated

September 29, 2026 (UTC)

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Iraqi League 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