AI correctly predicted the Al Qadsia win.
The match finished 3–0, 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 (3 goals) ✖ Incorrect
- Both Teams To Score BTTS Yes No ✖ Incorrect
- 1X2 Al Qadsia Al Qadsia ✔ Correct
- Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 3-0 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Al Qadsia | 38.5% | 54.7% | -16.2 pp | Market Higher |
| Draw | 29.6% | 23.9% | +5.7 pp | Model Higher |
| Al Nasar | 31.9% | 21.4% | +10.5 pp | Model Edge |
The closing market estimates Al Qadsia's win probability at 54.7%, compared with the model's estimate of 38.5%, a difference of 16.2 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 Qadsia win 3–0).
Market Assessment
The market is materially more optimistic about Al Qadsia 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
What failed
- Both Teams To Score: model leaned BTTS Yes; match finished BTTS No
- 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 Qadsia higher (54.7% vs model 38.5%, 16.2 pp), but the model's lean was validated (Al Qadsia win 3–0).
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Sep 04, 2026 · 15:34 UTC Forecast generated
- Model 1X2 · Al Qadsia 38.5% · Draw 29.6% · Al Nasar 31.9%
- xG · Al Qadsia 1.37 — Al Nasar 1.23
-
Sep 04, 2026 · 15:04 UTC Opening odds snapshot PRE30
- 1X2 odds · Al Qadsia 1.65 · Draw 3.78 · Al Nasar 4.22
- Implied 1X2 · Al Qadsia 54.7% · Draw 23.9% · Al Nasar 21.4%
- Bookmaker · Pinnacle
-
Sep 04, 2026 · 15:34 UTC Closing snapshot recorded PRE1
- 1X2 odds · Al Qadsia 1.65 · Draw 3.78 · Al Nasar 4.22
- Implied 1X2 · Al Qadsia 54.7% · Draw 23.9% · Al Nasar 21.4%
- Bookmaker · Pinnacle
-
Sep 04, 2026 · 15:35 UTC Kickoff
-
FT Full-time result Al Qadsia win · 3–0
-
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 (16 pp)
- No strong statistical edge
- Pricing remains divergent
- 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 |
