The model missed the final outcome (Suva win 1–2).
The model had projected Navua at 43.2%, 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 Over 2.5 Over 2.5 (3 goals) ✔ Correct
- Both Teams To Score BTTS Yes Yes ✔ Correct
- 1X2 Navua Suva ✖ Incorrect
- Correct Score Insights 1-1, 1-0, 2-1, 0-1, 1-2 1-2 ✔ Correct
Post Match Insights
What worked
- Expected goals projected a high-scoring match (ΣxG 2.72) — 3 goals materialised
- Both Teams To Score (Yes) matched the full-time result
- Over 2.5 goals aligned with the xG profile
What failed
- 1X2: model leaned Navua; match finished Suva
Market lesson
The full-time result was Suva win 1–2, which did not match the model's pre-match lean on 1X2.
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Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Before kickoff Forecast generated
- xG · Navua 1.49 — Suva 1.23
-
May 18, 2025 · 03:00 UTC Kickoff
-
FT Full-time result Suva win · 1–2
-
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.
Historical verdict:
Observe
Historical Decision
No Primary Bet
Outcome
Missed
Pre-match metrics (historical context)
Prediction Reliability
57/100
· Moderate
- Validation: Pending
Evidence
★★★★★
- No strong statistical edge
- Market has already priced much of the edge
- Validation pending
Market Compatibility
100/100
Betting Confidence
59/100
Validation Report
Immutable Snapshot
Closing Odds
Pending
AI Fair Odds
—
CLV
Pending
Final Result
Suva win · Navua 1–2 Suva
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.
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National Football League — Standings
| # | TEAM | MP | W | D | L | PTS |
|---|---|---|---|---|---|---|
| 1 | Rewa | 18 | 13 | 4 | 1 | 43 |
| 2 | Labasa | 18 | 11 | 4 | 3 | 37 |
| 3 | Lautoka | 18 | 11 | 1 | 6 | 34 |
| 4 | Navua | 18 | 9 | 2 | 7 | 29 |
| 5 | Ba | 18 | 7 | 6 | 5 | 27 |
| 6 | Suva | 18 | 5 | 8 | 5 | 23 |
| 7 | Nadroga | 18 | 5 | 4 | 9 | 19 |
| 8 | Nasinu | 18 | 5 | 3 | 10 | 18 |
| 9 | Nadi | 18 | 4 | 5 | 9 | 17 |
| 10 | Tavua | 18 | 0 | 3 | 15 | 3 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Rewa | 18 | 51 | 13 | +38 | 43 |
| 2 | Labasa | 18 | 45 | 24 | +21 | 37 |
| 3 | Ba | 18 | 41 | 23 | +18 | 27 |
| 4 | Lautoka | 18 | 30 | 23 | +7 | 34 |
| 5 | Nadi | 18 | 30 | 40 | -10 | 17 |
| 6 | Suva | 18 | 29 | 24 | +5 | 23 |
| 7 | Nasinu | 18 | 28 | 56 | -28 | 18 |
| 8 | Navua | 18 | 27 | 24 | +3 | 29 |
| 9 | Nadroga | 18 | 26 | 41 | -15 | 19 |
| 10 | Tavua | 18 | 12 | 51 | -39 | 3 |
