AI correctly predicted the Polessya win.
The match finished 5–1, 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 (6 goals) ✔ Correct
- Both Teams To Score BTTS Yes Yes ✔ Correct
- 1X2 Polessya Polessya ✔ Correct
- Correct Score Insights 1-0, 2-0, 1-1, 2-1, 3-0 5-1 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Polessya | 60.9% | 76.8% | -15.9 pp | Market Higher |
| Draw | 24.1% | 15.5% | +8.6 pp | Model Higher |
| Kudrivka | 15.0% | 7.7% | +7.2 pp | Model Higher |
The closing market estimates Polessya's win probability at 76.8%, compared with the model's estimate of 60.9%, a difference of 15.9 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 model's directional lean matched the result (Polessya win 5–1).
Market Assessment
The market is materially more optimistic about Polessya 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 2.80) — 6 goals materialised
- Polessya attacking xG significantly stronger (1.93 vs 0.87)
- Both Teams To Score (Yes) matched the full-time result
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 Polessya higher (76.8% vs model 60.9%, 15.9 pp), but the model's lean was validated (Polessya win 5–1).
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Sep 04, 2026 · 14:59 UTC Forecast generated
- Model 1X2 · Polessya 61.0% · Draw 24.1% · Kudrivka 15.0%
- xG · Polessya 1.93 — Kudrivka 0.87
-
Sep 04, 2026 · 14:30 UTC Opening odds snapshot PRE30
- 1X2 odds · Polessya 1.22 · Draw 6.05 · Kudrivka 12.12
- Implied 1X2 · Polessya 76.8% · Draw 15.5% · Kudrivka 7.7%
- Bookmaker · Pinnacle
-
Sep 04, 2026 · 14:59 UTC Closing snapshot recorded PRE1
- 1X2 odds · Polessya 1.22 · Draw 6.05 · Kudrivka 12.12
- Implied 1X2 · Polessya 76.8% · Draw 15.5% · Kudrivka 7.7%
- Bookmaker · Pinnacle
-
Sep 04, 2026 · 15:00 UTC Kickoff
-
FT Full-time result Polessya win · 5–1
-
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)
- Strong model lean
- 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 | Polessya | 7 | 6 | 0 | 1 | 18 |
| 2 | Shakhtar Donetsk | 7 | 6 | 0 | 1 | 18 |
| 3 | Karpaty | 7 | 4 | 2 | 1 | 14 |
| 4 | Dynamo Kyiv | 6 | 4 | 1 | 1 | 13 |
| 5 | Metalist 1925 Kharkiv | 6 | 4 | 0 | 2 | 12 |
| 6 | Epitsentr Dunayivtsi | 7 | 2 | 2 | 3 | 8 |
| 7 | LNZ Cherkasy | 7 | 2 | 2 | 3 | 8 |
| 8 | Zorya Luhansk | 7 | 2 | 2 | 3 | 8 |
| 9 | Livyi Bereh | 6 | 1 | 4 | 1 | 7 |
| 10 | Bukovyna | 7 | 1 | 4 | 2 | 7 |
| 11 | Veres Rivne | 6 | 1 | 3 | 2 | 6 |
| 12 | Chornomorets | 6 | 1 | 2 | 3 | 5 |
| 13 | Kolos Kovalivka | 6 | 1 | 2 | 3 | 5 |
| 14 | Kryvbas KR | 6 | 1 | 2 | 3 | 5 |
| 15 | Obolon'-Brovar | 7 | 0 | 4 | 3 | 4 |
| 16 | Kudrivka | 6 | 0 | 2 | 4 | 2 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Shakhtar Donetsk | 7 | 15 | 4 | +11 | 18 |
| 2 | Polessya | 7 | 15 | 5 | +10 | 18 |
| 3 | Karpaty | 7 | 15 | 5 | +10 | 14 |
| 4 | Metalist 1925 Kharkiv | 6 | 12 | 4 | +8 | 12 |
| 5 | Dynamo Kyiv | 6 | 9 | 5 | +4 | 13 |
| 6 | Kryvbas KR | 6 | 7 | 10 | -3 | 5 |
| 7 | Zorya Luhansk | 7 | 7 | 11 | -4 | 8 |
| 8 | Epitsentr Dunayivtsi | 7 | 6 | 7 | -1 | 8 |
| 9 | Kolos Kovalivka | 6 | 6 | 10 | -4 | 5 |
| 10 | LNZ Cherkasy | 7 | 5 | 8 | -3 | 8 |
| 11 | Livyi Bereh | 6 | 4 | 3 | +1 | 7 |
| 12 | Veres Rivne | 6 | 4 | 5 | -1 | 6 |
| 13 | Bukovyna | 7 | 4 | 6 | -2 | 7 |
| 14 | Kudrivka | 6 | 4 | 16 | -12 | 2 |
| 15 | Chornomorets | 6 | 3 | 7 | -4 | 5 |
| 16 | Obolon'-Brovar | 7 | 2 | 12 | -10 | 4 |
