Predictions / Football / Italy. Serie A Women / Juventus W vs Inter Milano W

Prediction Audit: Juventus W vs Inter Milano W Prediction, Odds & AI Betting Tips

May 10, 2026 - 13:00
3 1.45
3 1.25
xG Accuracy: 40%

The model missed the final outcome (Draw 3–3).

The model had projected Juventus W at 41.8%, 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 (6 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Juventus W Draw ✖ Incorrect
  • Correct Score Insights 1-1, 0-1, 1-2, 0-2, 1-0 3-3 ✖ Incorrect

Model vs Closing Market

Broadly Aligned

The model and closing market are broadly aligned.

Largest probability gap: Juventus W +2.6 pp

Outcome Model Closing Market Difference Signal
Juventus W 41.8% 39.1% +2.6 pp Aligned
Draw 25.7% 28.0% -2.4 pp Aligned
Inter Milano W 32.6% 32.8% -0.3 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 3–3.

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

  • Expected goals projected a high-scoring match (ΣxG 2.70) — 6 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 Juventus W; match finished Draw
  • Exact score: outside the model's top score bins

Market lesson

The full-time result was Draw 3–3, 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. May 10, 2026 · 12:59 UTC Forecast generated
    • Model 1X2 · Juventus W 41.8% · Draw 25.7% · Inter Milano W 32.6%
    • xG · Juventus W 1.45 — Inter Milano W 1.25
  2. May 10, 2026 · 12:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Juventus W 2.34 · Draw 3.27 · Inter Milano W 2.79
    • Implied 1X2 · Juventus W 39.1% · Draw 28.0% · Inter Milano W 32.8%
    • Bookmaker · Pinnacle
  3. May 10, 2026 · 12:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Juventus W 2.34 · Draw 3.27 · Inter Milano W 2.79
    • Implied 1X2 · Juventus W 39.1% · Draw 28.0% · Inter Milano W 32.8%
    • Bookmaker · Pinnacle
  4. May 10, 2026 · 13:00 UTC Kickoff
  5. FT Full-time result Draw · 3–3
  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 86/100
Betting Confidence 81/100

Validation Report

Immutable Snapshot

Prediction Time: Jul 24, 2026 · 03:45 UTC Snapshot ID: dp-1545721

Closing Odds 2.34
AI Fair Odds —
CLV Pending
Final Result Draw · Juventus W 3–3 Inter Milano W
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: Serie A Women
  • Fixture: Juventus W vs Inter Milano W
  • Kickoff: 2026-05-10 13:00:00
  • 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
  • xG (showing): Juventus W 1.45 — Inter Milano W 1.25 (total xG ≈ 2.7)
  • Value headline: None (actionable) — best tracked EV is about +1.2%, still below the +2.0% minimum for a headline / default stake (no default bet).
  • Structural leans (not bets): Structural lean (model): O/U 2.5 Over 2.5 (Under 2.5 48.1% · Over 2.5 51.9%); BTTS Yes (Yes 56.0% · No 44.0%) Value lean (pricing): O/U 2.5 Over 2.5; BTTS Yes
  • BTTS (model): Yes 56.0% · No 44.0%
  • Correct score (top bin): 1-1 (11.9%)

When book depth is thin or odds are missing, EV may be unavailable even though the model still prefers one side on totals or BTTS — wait for cleaner prices or skip.

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

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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Serie A Women Serie A Women — Standings
# TEAM MP W D L PTS
1 Roma W 22 17 4 1 55
2 Inter Milano W 22 13 5 4 44
3 Juventus W 22 11 6 5 39
4 Fiorentina W 22 10 6 6 36
5 Lazio W 22 10 3 9 33
6 Napoli W 22 8 8 6 32
7 AC Milan W 22 9 5 8 32
8 Como W 22 8 6 8 30
9 Sassuolo W 22 4 6 12 18
10 Ternana W 22 4 5 13 17
11 Parma W 22 2 10 10 16
12 Genoa W 22 2 4 16 10
# TEAM MP GS GC +/- PTS
1 Inter Milano W 22 49 26 +23 44
2 Roma W 22 44 19 +25 55
3 Juventus W 22 33 19 +14 39
4 Fiorentina W 22 33 30 +3 36
5 AC Milan W 22 31 26 +5 32
6 Lazio W 22 31 30 +1 33
7 Napoli W 22 30 25 +5 32
8 Como W 22 24 22 +2 30
9 Ternana W 22 19 40 -21 17
10 Genoa W 22 18 43 -25 10
11 Sassuolo W 22 17 34 -17 18
12 Parma W 22 16 31 -15 16