Predictions / Football / Germany. 3. Liga / Havelse vs Waldhof Mannheim

Prediction Audit: Havelse vs Waldhof Mannheim Prediction, Odds & AI Betting Tips

Havelse Havelse
Aug 30, 2026 - 14:30
3 1.47
2 2.15
xG Accuracy: 63%
Waldhof Mannheim Waldhof Mannheim

The model missed the final outcome (Havelse win 3–2).

The model had projected Waldhof Mannheim at 52.1%, 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 (5 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Waldhof Mannheim Havelse ✖ Incorrect
  • Correct Score Insights 1-2, 1-1, 2-2, 1-3, 2-1 3-2 ✖ Incorrect

Model vs Closing Market

Broadly Aligned

The model and closing market are broadly aligned.

Largest probability gap: Waldhof Mannheim -3.1 pp

Outcome Model Closing Market Difference Signal
Havelse 25.0% 23.3% +1.7 pp Aligned
Draw 22.9% 21.5% +1.4 pp Aligned
Waldhof Mannheim 52.1% 55.2% -3.1 pp Aligned

The model and closing market are broadly aligned, with probability differences below 5 percentage points.

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: PRE1.

After full time, the result was Havelse win 3–2.

Market Assessment

The market and model are broadly aligned. Any small pricing gap likely reflects rounding or bookmaker margin, not a structural disagreement.

  • Current pricing remains close to the model baseline.

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 3.62) — 5 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 Waldhof Mannheim; match finished Havelse
  • Exact score: outside the model's top score bins

Market lesson

The full-time result was Havelse win 3–2, which did not match the model's pre-match lean on 1X2.

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Prediction Timeline

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

  1. Aug 30, 2026 · 14:30 UTC Forecast generated
    • Model 1X2 · Havelse 25.0% · Draw 22.9% · Waldhof Mannheim 52.1%
    • xG · Havelse 1.47 — Waldhof Mannheim 2.15
  2. Aug 30, 2026 · 14:02 UTC Opening odds snapshot PRE30
    • 1X2 odds · Havelse 3.84 · Draw 4.34 · Waldhof Mannheim 1.78
    • Implied 1X2 · Havelse 24.7% · Draw 21.9% · Waldhof Mannheim 53.4%
    • Bookmaker · Pinnacle
  3. Aug 30, 2026 · 14:30 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Havelse 4.07 · Draw 4.41 · Waldhof Mannheim 1.72
    • Implied 1X2 · Havelse 23.3% · Draw 21.5% · Waldhof Mannheim 55.2%
    • Bookmaker · Pinnacle
  4. Aug 30, 2026 · 14:30 UTC Kickoff
  5. FT Full-time result Havelse win · 3–2
  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: Observe
Historical Decision No Primary Bet
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 71/100 · High
  • Validation: Pass
  • High xG deviation from league baseline (59%+)
Evidence ★★★★★
  • Moderate model lean
  • Direction agrees with model lean
  • Validation passed
Market Compatibility 84/100
Betting Confidence 74/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 24, 2026 · 01:55 UTC Snapshot ID: dp-5296542

Closing Odds 1.72
AI Fair Odds —
CLV Pending
Final Result Havelse win · Havelse 3–2 Waldhof Mannheim
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

Quick read on how the model reads this matchup.

  • League: 3. Liga
  • Fixture: Havelse vs Waldhof Mannheim
  • Kickoff: 2026-08-30 14:30:00
  • 1X2 (model): Home 25.0% · Draw 22.9% · Away 52.1%
  • xG (showing): Havelse 1.47 — Waldhof Mannheim 2.15 (total xG ≈ 3.62)
  • 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 29.9% · Over 2.5 70.1%); BTTS Yes (Yes 69.1% · No 30.9%) Value lean (pricing): O/U 2.5 Over 2.5; BTTS Yes
  • BTTS (model): Yes 69.1% · No 30.9%
  • Correct score (top bin): 1-2 (9.1%)

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