Predictions / Football / Germany. 3. Liga / Preußen Münster vs Havelse

Prediction Audit: Preußen Münster vs Havelse Prediction, Odds & AI Betting Tips

Preußen Münster Preußen Münster
Sep 05, 2026 - 12:00
2 1.56
1 1.65
xG Accuracy: 74%
Havelse Havelse

The model missed the final outcome (Preußen Münster win 2–1).

The model had projected Havelse at 38.9%, 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 (3 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Havelse Preußen Münster ✖ Incorrect
  • Correct Score Insights 1-1, 1-2, 2-1, 2-2, 0-1 2-1 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Preußen Münster higher than the statistical model.

Largest probability gap: Preußen Münster -22.8 pp

Outcome Model Closing Market Difference Signal
Preußen Münster 35.0% 57.9% -22.8 pp Market Higher
Draw 26.1% 22.0% +4.1 pp Aligned
Havelse 38.9% 20.2% +18.7 pp Model Edge

The closing market estimates Preußen Münster's win probability at 57.9%, compared with the model's estimate of 35.0%, a difference of 22.8 percentage points. This highlights a disagreement between the model and market consensus, without indicating which view is ultimately correct.

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: PRE5.

After full time, the result was Preußen Münster win 2–1.

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 3.21) — 3 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 Havelse; match finished Preußen Münster

Market lesson

The closing market differed from the model on Havelse by 22.8 percentage points (61.7% vs model 38.9%) — in this case the market view proved closer.

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Prediction Timeline

How this prediction moved from forecast to full-time review.

  1. Sep 05, 2026 · 11:59 UTC Forecast generated
    • Model 1X2 · Preußen Münster 35.1% · Draw 26.0% · Havelse 38.9%
    • xG · Preußen Münster 1.56 — Havelse 1.65
  2. Sep 05, 2026 · 11:34 UTC Opening odds snapshot PRE30
    • 1X2 odds · Preußen Münster 1.64 · Draw 4.32 · Havelse 4.70
    • Implied 1X2 · Preußen Münster 57.9% · Draw 22.0% · Havelse 20.2%
    • Bookmaker · Pinnacle
  3. Sep 05, 2026 · 11:59 UTC Closing snapshot recorded PRE5
    • 1X2 odds · Preußen Münster 1.64 · Draw 4.32 · Havelse 4.70
    • Implied 1X2 · Preußen Münster 57.9% · Draw 22.0% · Havelse 20.2%
    • Bookmaker · Pinnacle
  4. Sep 05, 2026 · 12:00 UTC Kickoff
  5. FT Full-time result Preußen Münster win · 2–1
  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: Cautious / Wait

Historical label: Originally displayed as "Wait for validation".

Historical Decision Wait
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 52/100 · Moderate
  • Validation: Warning
  • Large market gap (23 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Pricing remains divergent
  • Validation warning
Pricing proximity (inverse gap) 0/100
Monitoring Confidence 14/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 30, 2026 · 01:43 UTC Snapshot ID: dp-6133028

Closing Odds 1.64
AI Fair Odds —
CLV Pending
Final Result Preußen Münster win · Preußen Münster 2–1 Havelse
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

Pre-match snapshot for this fixture.

  • League: 3. Liga
  • Fixture: Preußen Münster vs Havelse
  • Kickoff: 2026-09-05 12:00:00
  • 1X2 (model): Home 35.1% · Draw 26.0% · Away 38.9%
  • xG (showing): Preußen Münster 1.56 — Havelse 1.65 (total xG ≈ 3.21)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Havelse
  • Model: 38.9% · Implied: 20.2% · Probability edge: +18.7 pts · Est. EV: +7.5%
  • BTTS (model): Yes 65.2% · No 34.8%
  • Correct score (top bin): 1-1 (10.4%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

Correct score remains high-variance even when a line is most likely on paper.

Historical Recommendation

Historical Decision: Wait

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 16, 2026 (UTC)

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3. Liga 3. Liga — Standings
# TEAM MP W D L PTS
1 MSV Duisburg 5 4 0 1 12
2 Rot-Weiß Essen 5 3 1 1 10
3 FC Saarbrücken 5 3 0 2 9
4 FC Viktoria Köln 5 3 0 2 9
5 Alemannia Aachen 5 2 2 1 8
6 FC Ingolstadt 04 5 2 2 1 8
7 Hansa Rostock 5 2 2 1 8
8 Preußen Münster 4 2 1 1 7
9 Fortuna Düsseldorf 5 2 1 2 7
10 SG Sonnenhof Grossaspach 5 2 1 2 7
11 Hoffenheim II 4 2 0 2 6
12 Stuttgart II 4 2 0 2 6
13 Verl 5 2 0 3 6
14 SV Meppen 5 2 0 3 6
15 Fortuna Köln 4 1 2 1 5
16 Waldhof Mannheim 4 1 1 2 4
17 Havelse 5 1 1 3 4
18 SSV Jahn Regensburg 5 1 1 3 4
19 Würzburger Kickers 5 1 1 3 4
20 SV Wehen 4 0 2 2 2
# TEAM MP GS GC +/- PTS
1 FC Saarbrücken 5 14 8 +6 9
2 MSV Duisburg 5 13 5 +8 12
3 FC Viktoria Köln 5 13 8 +5 9
4 Hansa Rostock 5 11 12 -1 8
5 Rot-Weiß Essen 5 10 7 +3 10
6 Alemannia Aachen 5 10 7 +3 8
7 Hoffenheim II 4 9 5 +4 6
8 SSV Jahn Regensburg 5 9 12 -3 4
9 Würzburger Kickers 5 9 13 -4 4
10 Havelse 5 8 10 -2 4
11 Preußen Münster 4 7 7 0 7
12 Waldhof Mannheim 4 7 8 -1 4
13 Verl 5 7 11 -4 6
14 SV Meppen 5 7 11 -4 6
15 FC Ingolstadt 04 5 6 6 0 8
16 Stuttgart II 4 6 7 -1 6
17 Fortuna Düsseldorf 5 4 5 -1 7
18 SG Sonnenhof Grossaspach 5 4 6 -2 7
19 Fortuna Köln 4 3 3 0 5
20 SV Wehen 4 2 8 -6 2
3. Liga Germany. 3. Liga Predictions