AI correctly predicted the Viimsi win.
The match finished 5–0, 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 (5 goals) ✖ Incorrect
- Both Teams To Score BTTS No No ✔ Correct
- 1X2 Viimsi Viimsi ✔ Correct
- Correct Score Insights 1-1, 1-0, 2-1, 2-0, 0-1 5-0 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Viimsi | 44.3% | 55.4% | -11.1 pp | Market Higher |
| Draw | 29.0% | 22.4% | +6.6 pp | Model Higher |
| Elva | 26.6% | 22.2% | +4.5 pp | Aligned |
The closing market estimates Viimsi's win probability at 55.4%, compared with the model's estimate of 44.3%, a difference of 11.1 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 (Viimsi win 5–0).
Market Assessment
The market is materially more optimistic about Viimsi 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) — 5 goals materialised
- Viimsi attacking xG significantly stronger (1.49 vs 1.11)
- Both Teams To Score (No) matched the full-time result
What failed
- Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (5 goals)
- 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 Viimsi higher (55.4% vs model 44.3%, 11.1 pp), but the model's lean was validated (Viimsi win 5–0).
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Sep 10, 2026 · 15:59 UTC Forecast generated
- Model 1X2 · Viimsi 44.3% · Draw 29.0% · Elva 26.7%
- xG · Viimsi 1.49 — Elva 1.11
-
Sep 10, 2026 · 15:29 UTC Opening odds snapshot PRE30
- 1X2 odds · Viimsi 1.60 · Draw 3.95 · Elva 4.00
- Implied 1X2 · Viimsi 55.4% · Draw 22.4% · Elva 22.2%
- Bookmaker · Pinnacle
-
Sep 10, 2026 · 15:59 UTC Closing snapshot recorded PRE1
- 1X2 odds · Viimsi 1.60 · Draw 3.95 · Elva 4.00
- Implied 1X2 · Viimsi 55.4% · Draw 22.4% · Elva 22.2%
- Bookmaker · Pinnacle
-
Sep 10, 2026 · 16:00 UTC Kickoff
-
FT Full-time result Viimsi win · 5–0
-
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 (11 pp)
- No strong statistical edge
- Market has already priced much of the edge
- 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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Esiliiga A — Standings
| # | TEAM | MP | W | D | L | PTS |
|---|---|---|---|---|---|---|
| 1 | Tartu Welco | 30 | 19 | 5 | 6 | 62 |
| 2 | Viimsi | 30 | 15 | 5 | 10 | 50 |
| 3 | Flora II | 30 | 14 | 6 | 10 | 48 |
| 4 | Elva | 30 | 14 | 6 | 10 | 48 |
| 5 | FCI Levadia II | 29 | 13 | 2 | 14 | 41 |
| 6 | Maardu | 28 | 12 | 5 | 11 | 41 |
| 7 | Tallinna Kalev | 29 | 10 | 7 | 12 | 37 |
| 8 | Nõmme United II | 30 | 10 | 6 | 14 | 36 |
| 9 | FC Tallinn | 30 | 8 | 8 | 14 | 32 |
| 10 | Nõmme Kalju II | 30 | 6 | 4 | 20 | 22 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Tartu Welco | 30 | 81 | 39 | +42 | 62 |
| 2 | Flora II | 30 | 72 | 53 | +19 | 48 |
| 3 | FCI Levadia II | 29 | 60 | 55 | +5 | 41 |
| 4 | Viimsi | 30 | 56 | 31 | +25 | 50 |
| 5 | Tallinna Kalev | 29 | 52 | 55 | -3 | 37 |
| 6 | Nõmme United II | 30 | 50 | 62 | -12 | 36 |
| 7 | Elva | 30 | 47 | 44 | +3 | 48 |
| 8 | Maardu | 28 | 47 | 59 | -12 | 41 |
| 9 | FC Tallinn | 30 | 39 | 65 | -26 | 32 |
| 10 | Nõmme Kalju II | 30 | 38 | 79 | -41 | 22 |
