Predictions / Football / Italy. Coppa Italia / Sassuolo vs Frosinone

Prediction Audit: Sassuolo vs Frosinone Prediction, Odds & AI Betting Tips

Sep 02, 2026 - 13:00
1(4) 1.31
1(3) 1.29
xG Accuracy: 85%

The model missed the final outcome (Draw 1–1).

The model had projected Sassuolo at 35.6%, 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 Under 2.5 Under 2.5 (2 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Sassuolo Draw ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 1-1 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Sassuolo higher than the statistical model.

Largest probability gap: Sassuolo -17.5 pp

Outcome Model Closing Market Difference Signal
Sassuolo 35.6% 53.1% -17.5 pp Market Higher
Draw 29.6% 24.3% +5.3 pp Model Higher
Frosinone 34.7% 22.6% +12.1 pp Model Edge

The closing market estimates Sassuolo's win probability at 53.1%, compared with the model's estimate of 35.6%, a difference of 17.5 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 result was Draw 1–1.

Market Assessment

The market is materially more optimistic about Sassuolo 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

  • Both Teams To Score (Yes) matched the full-time result
  • Under 2.5 goals aligned with the xG profile
  • Exact score 1–1 fell within the model's highlighted bins

What failed

  • 1X2: model leaned Sassuolo; match finished Draw

Market lesson

The closing market differed from the model on Sassuolo by 17.5 percentage points (53.1% vs model 35.6%) — in this case the market view proved closer.

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Prediction Timeline

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

  1. Sep 02, 2026 · 12:59 UTC Forecast generated
    • Model 1X2 · Sassuolo 35.6% · Draw 29.7% · Frosinone 34.7%
    • xG · Sassuolo 1.31 — Frosinone 1.29
  2. Sep 02, 2026 · 12:29 UTC Opening odds snapshot PRE30
    • 1X2 odds · Sassuolo 1.81 · Draw 3.95 · Frosinone 4.26
    • Implied 1X2 · Sassuolo 53.1% · Draw 24.3% · Frosinone 22.6%
    • Bookmaker · Pinnacle
  3. Sep 02, 2026 · 12:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Sassuolo 1.81 · Draw 3.95 · Frosinone 4.26
    • Implied 1X2 · Sassuolo 53.1% · Draw 24.3% · Frosinone 22.6%
    • Bookmaker · Pinnacle
  4. Sep 02, 2026 · 13:00 UTC Kickoff
  5. FT Full-time result Draw · 1–1
  6. FT Prediction missed 1X2 lean did not match 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 Missed
Pre-match metrics (historical context)
Prediction Reliability 54/100 · Moderate
  • Validation: Warning
  • Large market gap (17 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Pricing remains divergent
  • Validation warning
Pricing proximity (inverse gap) 12/100
Betting Confidence 43/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 27, 2026 · 01:49 UTC Snapshot ID: dp-5620442

Closing Odds 1.81
AI Fair Odds —
CLV Pending
Final Result Draw · Sassuolo 1–1 Frosinone
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.

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Coppa Italia
  • Fixture: Sassuolo vs Frosinone
  • Kickoff: 2026-09-02 13:00:00
  • 1X2 (model): Home 35.6% · Draw 29.7% · Away 34.7%
  • xG (showing): Sassuolo 1.31 — Frosinone 1.29 (total xG ≈ 2.6)
  • Value headline: At least one tracked line reaches the headline EV threshold — align with the hero / Primary card if shown.
  • 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 54.5% · No 45.5%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.5% · No 45.5%
  • Correct score (top bin): 1-1 (12.6%)

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: Monitor

Outcome: Missed — Pre-match 1X2 lean did not match 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 30, 2026 (UTC)

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