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

Juventus W vs Inter Milano W Statistics & Analysis

Juventus W Juventus W
May 10, 2026 - 13:00
3 1.45
3 1.25
xG Accuracy: 40%
Inter Milano W Inter Milano W
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Tracked markets vs full-time result

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

Validation Report

Immutable Snapshot

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

Closing Odds 2.34
AI Fair Odds —
CLV +0.0%
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%

Market intelligence

Supporting read on how the prioritized closing feed moved versus the model — use after the Primary pick above.

Market briefing

Market remained largely stable before kickoff. No meaningful late implied-price shift was detected between PRE30 and PRE1 on the prioritized bookmaker snapshot.

Despite limited late movement, the model still prices Juventus W (1X2), Over 2.5 goals meaningfully above what those closing snapshots implied — that gap is a static “model vs. price” read, not a late steam or chase story.

The model still exceeds closing implied on Juventus W (1X2) by about 2.6 percentage points — the clearest mispricing signal summarized on this page.

Model vs. closing implied

Market Model % Closing impl. % Gap (pp)
Juventus W (1X2) 41.8 39.1 +2.6
Draw (1X2) 25.7 28.0 -2.4
Inter Milano W (1X2) 32.6 32.8 -0.3
Over 2.5 goals 50.6 49.3 +1.3
Under 2.5 goals 49.4 50.7 -1.3
What this means

In plain terms: the model lands near 41.8% on Juventus W (1X2), while the closing snapshot implied about 39.1%. The difference — about 2.6 percentage points — is the largest model-vs.-market gap highlighted on this page.

Quick definitions: “closing implied” is the probability for that outcome implied by the final captured odds (after a simple de-vig). “Gap (pp)” is the model percentage minus that implied value, in percentage points (pp).

Closing-window line move

Single prioritized bookmaker per snapshot (not all books). Capture path: PRE30 → PRE1 · Book: Pinnacle

Column tags in parentheses: Closing uses the first available snapshot in PRE1→PRE5→PRE10→PRE30; Early uses the first available in PRE30→PRE10→PRE5 that is not the same capture as Closing.

Detailed capture odds are folded below — movement was negligible on de-vig implied prices.

View full line-by-line capture table
Market Early (PRE30) Closing (PRE1) Implied Δ (pp)
Juventus W (1X2) 2.34 2.34 0.0
Draw (1X2) 3.27 3.27 0.0
Inter Milano W (1X2) 2.79 2.79 0.0
Over 2.5 goals 1.92 1.92 0.0
Under 2.5 goals 1.87 1.87 0.0

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 — 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 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

September 28, 2026 (UTC)

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Back to Statistics
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
Serie A Women Italy. Serie A Women Predictions