San Francisco City vs Academica Statistics & Analysis

San Francisco City

Jul 05, 2026 - 22:00

5 1.38

1 1.22

xG Accuracy: 35%

Academica

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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 Under 2.5 Over 2.5 (6 goals) ✖ Incorrect
Both Teams To Score BTTS Yes Yes ✔ Correct
1X2 San Francisco City San Francisco City ✔ Correct
Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 5-1 ✖ Incorrect

Validation Report

Immutable Snapshot

Prediction Time: Jul 24, 2026 · 16:49 UTC Snapshot ID: dp-1618554

Closing Odds Pending

AI Fair Odds —

CLV Pending

Final Result San Francisco City win · San Francisco City 5–1 Academica

Prediction ✔ Correct

Decision Grade B-

Model Performance

This prediction contributes to:

Primary Bets ROI (180d): -100.0%

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

League: USL League Two
Fixture: San Francisco City vs Academica
Kickoff: 2026-07-05 22:00:00
1X2 (model): Home 39.0% · Draw 29.5% · Away 31.5%
xG (showing): San Francisco City 1.38 — Academica 1.22 (total xG ≈ 2.6)
Value headline: None — no positive EV could be estimated on tracked lines at current best prices (missing odds or thin book depth).
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%)

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.

If lines move materially, re-run generation or refresh — implied probabilities and any future EV readouts will change first.

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

October 01, 2026 (UTC)

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