Statistics / Football / USA. USL League Two / Christos vs Patuxent

Christos vs Patuxent Statistics & Analysis

Christos Christos
Jun 13, 2026 - 23:00
2 1.45
0 1.15
xG Accuracy: 63%
Patuxent Patuxent
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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 Under 2.5 (2 goals) ✔ Correct
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Christos Christos ✔ Correct
  • Correct Score Insights 1-1, 1-0, 2-1, 0-1, 2-0 2-0 ✔ Correct

Validation Report

Immutable Snapshot

Prediction Time: Jul 24, 2026 · 00:37 UTC Snapshot ID: dp-1519954

Closing Odds Pending
AI Fair Odds —
CLV Pending
Final Result Christos win · Christos 2–0 Patuxent
Prediction ✔ Correct
Decision Grade B-

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

AI match briefing

AI Match Summary

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

  • League: USL League Two
  • Fixture: Christos vs Patuxent
  • Kickoff: 2026-06-13 23:00:00
  • 1X2 (model): Home 42.4% · Draw 29.2% · Away 28.4%
  • xG (showing): Christos 1.45 — Patuxent 1.15 (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 53.9% · No 46.1%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 53.9% · No 46.1%
  • Correct score (top bin): 1-1 (12.4%)

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: 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

September 29, 2026 (UTC)

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