Prediction Audit: Ravenna vs Forli Prediction, Odds & AI Betting Tips

Sep 01, 2026 - 19:00
2 1.50
1 1.10
xG Accuracy: 85%

AI correctly predicted the Ravenna 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 Ravenna Ravenna ✔ Correct
  • Correct Score Insights 1-1, 1-0, 2-1, 2-0, 0-1 2-1 ✔ Correct

Model vs Closing Market

Moderate Disagreement

The closing market prices Ravenna higher than the statistical model.

Largest probability gap: Ravenna -6.1 pp

Outcome Model Closing Market Difference Signal
Ravenna 44.8% 50.9% -6.1 pp Market Higher
Draw 28.9% 24.8% +4.2 pp Aligned
Forli 26.2% 24.4% +1.9 pp Aligned

The closing market estimates Ravenna's win probability at 50.9%, compared with the model's estimate of 44.8%, a difference of 6.1 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 (Ravenna win 2–1).

Market Assessment

The market and model broadly agree on Ravenna. The remaining divergence may reflect differences in team-strength assumptions rather than a directional disagreement.

  • Monitor line movement before kickoff — not a betting recommendation.

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 2.60) — 3 goals materialised
  • Ravenna attacking xG significantly stronger (1.50 vs 1.10)
  • Both Teams To Score (Yes) matched the full-time result

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 Ravenna higher (50.9% vs model 44.8%, 6.1 pp), but the model's lean was validated (Ravenna win 2–1).

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Prediction Timeline

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

  1. Sep 01, 2026 · 18:59 UTC Forecast generated
    • Model 1X2 · Ravenna 44.8% · Draw 29.0% · Forli 26.2%
    • xG · Ravenna 1.50 — Forli 1.10
  2. Sep 01, 2026 · 18:29 UTC Opening odds snapshot PRE30
    • 1X2 odds · Ravenna 1.80 · Draw 3.70 · Forli 3.76
    • Implied 1X2 · Ravenna 50.9% · Draw 24.8% · Forli 24.4%
    • Bookmaker · Pinnacle
  3. Sep 01, 2026 · 18:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Ravenna 1.80 · Draw 3.70 · Forli 3.76
    • Implied 1X2 · Ravenna 50.9% · Draw 24.8% · Forli 24.4%
    • Bookmaker · Pinnacle
  4. Sep 01, 2026 · 19:00 UTC Kickoff
  5. FT Full-time result Ravenna 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: Observe
Historical Decision No Primary Bet
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 76/100 · High
  • Validation: Pass
Evidence ★★★★★
  • No strong statistical edge
  • Direction agrees with model lean
  • Validation passed
Pricing proximity (inverse gap) 69/100
Betting Confidence 79/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 26, 2026 · 02:39 UTC Snapshot ID: dp-5506767

Closing Odds 1.8
AI Fair Odds —
CLV Pending
Final Result Ravenna win · Ravenna 2–1 Forli
Prediction ✔ Correct
Decision Grade B-

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.

AI match briefing

AI Match Summary

Below is a compact, numbers-first snapshot aligned with the same engine as the cards above.

  • League: Coppa Italia Serie C
  • Fixture: Ravenna vs Forli
  • Kickoff: 2026-09-01 19:00:00
  • 1X2 (model): Home 44.8% · Draw 29.0% · Away 26.2%
  • xG (showing): Ravenna 1.5 — Forli 1.1 (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 53.4% · No 46.6%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 53.4% · No 46.6%
  • Correct score (top bin): 1-1 (12.3%)

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.

Prefer skipping to over-staking when the engine is honest about missing edge.

Historical Recommendation

Historical Decision: No Primary Bet

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 29, 2026 (UTC)

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