Statistics / Football / Germany. Oberliga - Hamburg / Tus Dassendorf vs Paloma

Tus Dassendorf vs Paloma Statistics & Analysis

Sep 12, 2026 - 11:00
1 1.43
2 1.17
xG Accuracy: 71%
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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 (3 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Tus Dassendorf Paloma ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 2-1, 0-1, 2-0 1-2 ✖ Incorrect

Validation Report

Immutable Snapshot

Prediction Time: Sep 07, 2026 · 00:57 UTC Snapshot ID: dp-7422497

Closing Odds Pending
AI Fair Odds —
CLV Pending
Final Result Paloma win · Tus Dassendorf 1–2 Paloma
Prediction ✖ Missed
Decision Grade F

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: Oberliga - Hamburg
  • Fixture: Tus Dassendorf vs Paloma
  • Kickoff: 2026-09-12 11:00:00
  • 1X2 (model): Home 41.4% · Draw 29.4% · Away 29.3%
  • xG (showing): Tus Dassendorf 1.43 — Paloma 1.17 (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 54.1% · No 45.9%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.1% · No 45.9%
  • Correct score (top bin): 1-1 (12.4%)

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.

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

September 29, 2026 (UTC)

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