Prediction Audit: Al Urooba vs Al Thaid Prediction, Odds & AI Betting Tips

Aug 21, 2026 - 15:15
1 1.43
1 1.17
xG Accuracy: 85%

The model missed the final outcome (Draw 1–1).

The model had projected Al Urooba at 41.4%, but the full-time result went the other way.

Tracked markets vs full-time result

Prediction grade C

Each row compares the pre-match model lean to the full-time result.

  • Market Prediction Result Outcome
  • Over / Under 2.5 Under 2.5 Under 2.5 (2 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Al Urooba Draw ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 2-1, 0-1, 2-0 1-1 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Al Urooba higher than the statistical model.

Largest probability gap: Al Urooba -16.9 pp

Outcome Model Closing Market Difference Signal
Al Urooba 41.4% 58.4% -16.9 pp Market Higher
Draw 29.4% 24.1% +5.2 pp Model Higher
Al Thaid 29.2% 17.5% +11.7 pp Model Edge

The closing market estimates Al Urooba's win probability at 58.4%, compared with the model's estimate of 41.4%, a difference of 16.9 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 result was Draw 1–1.

Market Assessment

The market is materially more optimistic about Al Urooba 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

  • Both Teams To Score (Yes) matched the full-time result
  • Under 2.5 goals aligned with the xG profile
  • Exact score 1–1 fell within the model's highlighted bins

What failed

  • 1X2: model leaned Al Urooba; match finished Draw

Market lesson

The closing market differed from the model on Al Urooba by 16.9 percentage points (58.3% vs model 41.4%) — in this case the market view proved closer.

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Prediction Timeline

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

  1. Aug 21, 2026 · 15:14 UTC Forecast generated
    • Model 1X2 · Al Urooba 41.4% · Draw 29.4% · Al Thaid 29.3%
    • xG · Al Urooba 1.43 — Al Thaid 1.17
  2. Aug 21, 2026 · 14:44 UTC Opening odds snapshot PRE30
    • 1X2 odds · Al Urooba 1.55 · Draw 3.75 · Al Thaid 5.16
    • Implied 1X2 · Al Urooba 58.4% · Draw 24.1% · Al Thaid 17.5%
    • Bookmaker · Pinnacle
  3. Aug 21, 2026 · 15:14 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Al Urooba 1.55 · Draw 3.75 · Al Thaid 5.16
    • Implied 1X2 · Al Urooba 58.4% · Draw 24.1% · Al Thaid 17.5%
    • Bookmaker · Pinnacle
  4. Aug 21, 2026 · 15:15 UTC Kickoff
  5. FT Full-time result Draw · 1–1
  6. FT Prediction missed 1X2 lean did not match 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 Missed
Pre-match metrics (historical context)
Prediction Reliability 55/100 · Moderate
  • Validation: Warning
  • Large market gap (17 pp)
Evidence
  • No strong statistical edge
  • Pricing remains divergent
  • Validation warning
Pricing proximity (inverse gap) 15/100
Betting Confidence 44/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 15, 2026 · 01:18 UTC Snapshot ID: dp-4024798

Closing Odds 1.55
AI Fair Odds
CLV Pending
Final Result Draw · Al Urooba 1–1 Al Thaid
Prediction ✖ Missed
Decision Grade C

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: Division 1
  • Fixture: Al Urooba vs Al Thaid
  • Kickoff: 2026-08-21 15:15:00
  • 1X2 (model): Home 41.4% · Draw 29.4% · Away 29.3%
  • xG (showing): Al Urooba 1.43 — Al Thaid 1.17 (total xG ≈ 2.6)
  • Value headline: None (actionable) — best tracked EV is about +0.4%, still below the +2.0% minimum for a headline / default stake (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 54.1% · No 45.9%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.1% · No 45.9%
  • Correct score (top bin): 1-1 (12.4%)

When book depth is thin or odds are missing, EV may be unavailable even though the model still prefers one side on totals or BTTS — wait for cleaner prices or skip.

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: Missed — Pre-match 1X2 lean did not match 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 16, 2026 (UTC)

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Division 1 Division 1Standings
# TEAM MP W D L PTS
1 Al Arabi 4 3 1 0 10
2 Al Thaid 4 3 1 0 10
3 Al Bataeh 4 2 1 1 7
4 Al Fujairah SC 3 2 0 1 6
5 Al Urooba 4 1 3 0 6
6 Dibba Al-Fujairah 3 2 0 1 6
7 Forte Virtus 3 2 0 1 6
8 City 4 1 1 2 4
9 Dibba Al Hisn 2 1 0 1 3
10 Gulf United 3 1 0 2 3
11 Emirates Club 4 1 0 3 3
12 Palm City 3 1 0 2 3
13 Al Hamriyah 3 1 0 2 3
14 Al Jazira Al Hamra 4 0 2 2 2
15 Ittifaq 4 0 1 3 1
# TEAM MP GS GC +/- PTS
1 Al Arabi 4 12 5 +7 10
2 Al Thaid 4 11 4 +7 10
3 Al Fujairah SC 3 8 4 +4 6
4 Dibba Al-Fujairah 3 7 6 +1 6
5 Gulf United 3 6 7 -1 3
6 City 4 6 10 -4 4
7 Al Urooba 4 5 3 +2 6
8 Palm City 3 5 9 -4 3
9 Dibba Al Hisn 2 4 1 +3 3
10 Emirates Club 4 4 5 -1 3
11 Al Bataeh 4 3 2 +1 7
12 Forte Virtus 3 3 2 +1 6
13 Al Hamriyah 3 2 6 -4 3
14 Al Jazira Al Hamra 4 2 10 -8 2
15 Ittifaq 4 1 5 -4 1