Predictions / Football / Germany. DFB Pokal / SpVgg Greuther Fürth vs VfL Bochum

Prediction Audit: SpVgg Greuther Fürth vs VfL Bochum Prediction, Odds & AI Betting Tips

SpVgg Greuther Fürth SpVgg Greuther Fürth
Aug 23, 2026 - 11:00
1 1.31
2 1.29
xG Accuracy: 76%
VfL Bochum VfL Bochum

The model missed the final outcome (VfL Bochum win 1–2).

The model had projected SpVgg Greuther Fürth at 35.6%, but the full-time result went the other way.

Tracked markets vs full-time result

Prediction grade F

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 SpVgg Greuther Fürth VfL Bochum ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 1-2 ✔ Correct

Model vs Closing Market

Broadly Aligned

The model and closing market are broadly aligned.

Largest probability gap: VfL Bochum -3.4 pp

Outcome Model Closing Market Difference Signal
SpVgg Greuther Fürth 35.6% 33.9% +1.8 pp Aligned
Draw 29.6% 28.0% +1.6 pp Aligned
VfL Bochum 34.7% 38.1% -3.4 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 VfL Bochum win 1–2.

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 2.60) — 3 goals materialised
  • Both Teams To Score (Yes) matched the full-time result
  • Exact score 1–2 fell within the model's highlighted bins

What failed

  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (3 goals)
  • 1X2: model leaned SpVgg Greuther Fürth; match finished VfL Bochum

Market lesson

The full-time result was VfL Bochum win 1–2, 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 23, 2026 · 11:00 UTC Forecast generated
    • Model 1X2 · SpVgg Greuther Fürth 35.6% · Draw 29.7% · VfL Bochum 34.7%
    • xG · SpVgg Greuther Fürth 1.31 — VfL Bochum 1.29
  2. Aug 23, 2026 · 10:31 UTC Opening odds snapshot PRE30
    • 1X2 odds · SpVgg Greuther Fürth 2.81 · Draw 3.40 · VfL Bochum 2.50
    • Implied 1X2 · SpVgg Greuther Fürth 33.9% · Draw 28.0% · VfL Bochum 38.1%
    • Bookmaker · Pinnacle
  3. Aug 23, 2026 · 11:00 UTC Closing snapshot recorded PRE1
    • 1X2 odds · SpVgg Greuther Fürth 2.81 · Draw 3.40 · VfL Bochum 2.50
    • Implied 1X2 · SpVgg Greuther Fürth 33.9% · Draw 28.0% · VfL Bochum 38.1%
    • Bookmaker · Pinnacle
  4. Aug 23, 2026 · 11:00 UTC Kickoff
  5. FT Full-time result VfL Bochum win · 1–2
  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: Monitor
Historical Decision Monitor
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 77/100 · High
  • Validation: Pass
Evidence ★★★★★
  • No strong statistical edge
  • Direction agrees with model lean
  • Validation passed
Market Compatibility 29/100
Monitoring Confidence 34/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 17, 2026 · 01:01 UTC Snapshot ID: dp-4360330

Closing Odds 2.5
AI Fair Odds —
CLV Pending
Final Result VfL Bochum win · SpVgg Greuther Fürth 1–2 VfL Bochum
Prediction ✖ Missed
Decision Grade F

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: DFB Pokal
  • Fixture: SpVgg Greuther Fürth vs VfL Bochum
  • Kickoff: 2026-08-23 11:00:00
  • 1X2 (model): Home 35.6% · Draw 29.7% · Away 34.7%
  • xG (showing): SpVgg Greuther Fürth 1.31 — VfL Bochum 1.29 (total xG ≈ 2.6)
  • 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 Under 2.5 (Under 2.5 51.8% · Over 2.5 48.2%); BTTS Yes (Yes 54.5% · No 45.5%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.5% · No 45.5%
  • Correct score (top bin): 1-1 (12.6%)

The decision block shows no default Primary: Primary needs consensus EV ≥ +5.0% plus strength/reliability/calibration gates. A separate Lean/tracked gate is +2.0% (selective only). Lean labels are directional only — not bankroll-sized recommendations.

Most likely correct score stays a low-probability tail: use it for context, not as a must-bet story.

Historical Recommendation

Historical Decision: Monitor

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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