Prediction Audit: Dila vs Samgurali Prediction, Odds & AI Betting Tips

Aug 26, 2026 - 15:00
2 1.38
1 1.22
xG Accuracy: 85%

AI correctly predicted the Dila win.

The match finished 2–1, validating the model's directional assessment.

Tracked markets vs full-time result

Prediction grade B-

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 Yes ✔ Correct
  • 1X2 Dila Dila ✔ Correct
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 2-1 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Samgurali higher than the statistical model.

Largest probability gap: Samgurali -11.1 pp

Outcome Model Closing Market Difference Signal
Dila 39.0% 28.8% +10.2 pp Model Edge
Draw 29.5% 28.6% +0.9 pp Aligned
Samgurali 31.5% 42.6% -11.1 pp Market Higher

The closing market estimates Samgurali's win probability at 42.6%, compared with the model's estimate of 31.5%, 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 (Dila win 2–1).

Market Assessment

The market is materially more optimistic about Samgurali 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
  • Both Teams To Score (Yes) matched the full-time result
  • Exact score 2–1 fell within the model's highlighted bins

What failed

  • 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 Dila higher (50.1% vs model 39.0%, 11.1 pp), but the model's lean was validated (Dila win 2–1).

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Prediction Timeline

How this prediction moved from forecast to full-time review.

  1. Aug 26, 2026 · 14:59 UTC Forecast generated
    • Model 1X2 · Dila 39.0% · Draw 29.5% · Samgurali 31.5%
    • xG · Dila 1.38 — Samgurali 1.22
  2. Aug 26, 2026 · 14:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Dila 3.02 · Draw 3.04 · Samgurali 2.04
    • Implied 1X2 · Dila 28.8% · Draw 28.6% · Samgurali 42.6%
    • Bookmaker · Pinnacle
  3. Aug 26, 2026 · 14:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Dila 3.02 · Draw 3.04 · Samgurali 2.04
    • Implied 1X2 · Dila 28.8% · Draw 28.6% · Samgurali 42.6%
    • Bookmaker · Pinnacle
  4. Aug 26, 2026 · 15:00 UTC Kickoff
  5. FT Full-time result Dila win · 2–1
  6. FT Prediction validated Directional lean matched full-time result
  7. Archived Prediction review

Historical Snapshot

Frozen at kickoff — the model output as it stood before the match started.

Historical verdict: Monitor
Historical Decision Monitor
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 57/100 · Moderate
  • Validation: Warning
  • Large market gap (11 pp)
Evidence
  • No strong statistical edge
  • Market has already priced much of the edge
  • Validation warning
Pricing proximity (inverse gap) 15/100
Monitoring Confidence 20/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 20, 2026 · 08:32 UTC Snapshot ID: dp-4701357

Closing Odds 2.04
AI Fair Odds
CLV Pending
Final Result Dila win · Dila 2–1 Samgurali
Prediction ✔ Correct
Decision Grade B-

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): +37.5%

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.

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: David Kipiani Cup
  • Fixture: Dila vs Samgurali
  • Kickoff: 2026-08-26 15:00:00
  • 1X2 (model): Home 39.0% · Draw 29.5% · Away 31.5%
  • xG (showing): Dila 1.38 — Samgurali 1.22 (total xG ≈ 2.6)
  • Value headline: None — no positive EV on tracked lines at current best prices (same as the decision block: no default bet).
  • Structural leans (not bets): Structural lean (model): O/U 2.5 Under 2.5 (Under 2.5 51.8% · Over 2.5 48.2%); BTTS Yes (Yes 54.4% · No 45.6%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.4% · No 45.6%
  • Correct score (top bin): 1-1 (12.5%)

Saying “no value” on a snapshot is a feature, not a bug: it protects readers from forcing a play when the edge is not there.

If lines move materially, re-run generation or refresh — implied probabilities and any future EV readouts will change first.

Historical Recommendation

Historical Decision: Monitor

Outcome: Validated — Pre-match lean validated against the full-time result.

Risk Factors Considered Before Kickoff

  • Price movement: implied probabilities and EV move with odds.
  • Sample / data gaps: low-information leagues widen forecast bands.
  • In-play state: goals and red cards are not modelled here.
  • Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Last Updated

September 15, 2026 (UTC)

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