Prediction Audit: Juventus U23 vs Forli Prediction, Odds & AI Betting Tips

Aug 16, 2026 - 16:00
1(2) 1.37
1(4) 1.23
xG Accuracy: 85%

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

The model had projected Juventus U23 at 38.5%, 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 Juventus U23 Draw ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 1-1 ✔ Correct

Model vs Closing Market

Broadly Aligned

The model and closing market are broadly aligned.

Largest probability gap: Forli +1.9 pp

Outcome Model Closing Market Difference Signal
Juventus U23 38.5% 40.2% -1.7 pp Aligned
Draw 29.6% 29.8% -0.2 pp Aligned
Forli 31.9% 30.1% +1.9 pp Aligned

The model and closing market are broadly aligned, with probability differences below 5 percentage points.

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 and model are broadly aligned. Any small pricing gap likely reflects rounding or bookmaker margin, not a structural disagreement.

  • Current pricing remains close to the model baseline.

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 Juventus U23; match finished Draw

Market lesson

The full-time result was Draw 1–1, 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.

  1. Aug 16, 2026 · 16:02 UTC Forecast generated
    • Model 1X2 · Juventus U23 38.5% · Draw 29.6% · Forli 31.9%
    • xG · Juventus U23 1.37 — Forli 1.23
  2. Aug 16, 2026 · 15:34 UTC Opening odds snapshot PRE30
    • 1X2 odds · Juventus U23 2.31 · Draw 3.12 · Forli 3.09
    • Implied 1X2 · Juventus U23 40.2% · Draw 29.8% · Forli 30.1%
    • Bookmaker · Pinnacle
  3. Aug 16, 2026 · 16:02 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Juventus U23 2.31 · Draw 3.12 · Forli 3.09
    • Implied 1X2 · Juventus U23 40.2% · Draw 29.8% · Forli 30.1%
    • Bookmaker · Pinnacle
  4. Aug 16, 2026 · 16:00 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: Observe
Historical Decision No Primary Bet
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 78/100 · High
  • Validation: Pass
Evidence ★★★★★
  • No strong statistical edge
  • Direction agrees with model lean
  • Validation passed
Market Compatibility 90/100
Betting Confidence 81/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 11, 2026 · 01:45 UTC Snapshot ID: dp-3611878

Closing Odds 3.09
AI Fair Odds —
CLV Pending
Final Result Draw · Juventus U23 1–1 Forli
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

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

  • League: Coppa Italia Serie C
  • Fixture: Juventus U23 vs Forli
  • Kickoff: 2026-08-16 16:00:00
  • 1X2 (model): Home 38.5% · Draw 29.6% · Away 31.9%
  • xG (showing): Juventus U23 1.37 — Forli 1.23 (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.

Most likely correct score stays a low-probability tail: use it for context, not as a must-bet story.

Historical Recommendation

Historical Decision: No Primary Bet

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

October 02, 2026 (UTC)

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