The model missed the final outcome (Al Nasar win 0–3).
The model had projected Al Shabab at 37.1%, but the full-time result went the other way.
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 Shabab Al Nasar ✖ Incorrect
- Correct Score Insights 1-1, 1-0, 0-1, 2-1, 0-0 0-3 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Al Shabab | 37.1% | 25.6% | +11.5 pp | Model Edge |
| Draw | 30.1% | 26.3% | +3.8 pp | Aligned |
| Al Nasar | 32.8% | 48.1% | -15.2 pp | Market Higher |
The closing market estimates Al Nasar's win probability at 48.1%, compared with the model's estimate of 32.8%, a difference of 15.2 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 result was Al Nasar win 0–3.
Market Assessment
The market is materially more optimistic about Al Nasar 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 2.53) — 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
The closing market differed from the model on Al Shabab by 15.2 percentage points (52.3% vs model 37.1%) — in this case the market view proved closer.
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Jun 17, 2026 · 17:44 UTC Forecast generated
- Model 1X2 · Al Shabab 37.1% · Draw 30.1% · Al Nasar 32.8%
- xG · Al Shabab 1.31 — Al Nasar 1.22
-
Jun 17, 2026 · 17:14 UTC Opening odds snapshot PRE30
- 1X2 odds · Al Shabab 3.53 · Draw 3.43 · Al Nasar 1.88
- Implied 1X2 · Al Shabab 25.6% · Draw 26.3% · Al Nasar 48.1%
- Bookmaker · Pinnacle
-
Jun 17, 2026 · 17:44 UTC Closing snapshot recorded PRE1
- 1X2 odds · Al Shabab 3.53 · Draw 3.43 · Al Nasar 1.88
- Implied 1X2 · Al Shabab 25.6% · Draw 26.3% · Al Nasar 48.1%
- Bookmaker · Pinnacle
-
Jun 17, 2026 · 17:45 UTC Kickoff
-
FT Full-time result Al Nasar win · 0–3
-
FT Prediction missed 1X2 lean did not match 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 (15 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 | Al Kuwait | 18 | 13 | 5 | 0 | 44 |
| 2 | Al Qadsia | 18 | 9 | 5 | 4 | 32 |
| 3 | Kazma | 18 | 8 | 6 | 4 | 30 |
| 4 | Al Arabi | 18 | 8 | 6 | 4 | 30 |
| 5 | Al Salmiyah | 18 | 8 | 6 | 4 | 30 |
| 6 | Al Fahaheel | 18 | 6 | 3 | 9 | 21 |
| 7 | Al Tadhamon | 18 | 5 | 3 | 10 | 18 |
| 8 | Al Shabab | 18 | 4 | 6 | 8 | 18 |
| 9 | Al Nasar | 18 | 4 | 4 | 10 | 16 |
| 10 | Al Jahra | 18 | 2 | 2 | 14 | 8 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Al Kuwait | 18 | 46 | 10 | +36 | 44 |
| 2 | Al Qadsia | 18 | 32 | 14 | +18 | 32 |
| 3 | Al Arabi | 18 | 28 | 13 | +15 | 30 |
| 4 | Kazma | 18 | 22 | 17 | +5 | 30 |
| 5 | Al Salmiyah | 18 | 21 | 12 | +9 | 30 |
| 6 | Al Fahaheel | 18 | 21 | 36 | -15 | 21 |
| 7 | Al Nasar | 18 | 17 | 24 | -7 | 16 |
| 8 | Al Tadhamon | 18 | 16 | 28 | -12 | 18 |
| 9 | Al Shabab | 18 | 12 | 33 | -21 | 18 |
| 10 | Al Jahra | 18 | 11 | 39 | -28 | 8 |
