Predictions / Football / Germany. 2. Bundesliga / VfL Bochum vs SpVgg Greuther Fürth

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

VfL Bochum VfL Bochum
Sep 12, 2026 - 11:00
1 1.37
1 1.23
xG Accuracy: 85%
SpVgg Greuther Fürth SpVgg Greuther Fürth

The model missed the final outcome (Draw 1–1).

The model had projected VfL Bochum at 38.5%, 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 Under 2.5 (2 goals) ✔ Correct
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 VfL Bochum Draw ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 1-1 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices VfL Bochum higher than the statistical model.

Largest probability gap: VfL Bochum -14.3 pp

Outcome Model Closing Market Difference Signal
VfL Bochum 38.5% 52.8% -14.3 pp Market Higher
Draw 29.6% 23.9% +5.7 pp Model Higher
SpVgg Greuther Fürth 31.9% 23.3% +8.6 pp Model Higher

The closing market estimates VfL Bochum's win probability at 52.8%, compared with the model's estimate of 38.5%, a difference of 14.3 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: PRE1.

After full time, the result was Draw 1–1.

Market Assessment

The market is materially more optimistic about VfL Bochum than the current fair estimate.

  • Investors may be incorporating information not fully reflected in the baseline model.
  • Tournament-specific context can shift market pricing.

Post Match Insights

What worked

  • Under 2.5 goals aligned with the xG profile
  • Exact score 1–1 fell within the model's highlighted bins

What failed

  • Both Teams To Score: model leaned BTTS No; match finished BTTS Yes
  • 1X2: model leaned VfL Bochum; match finished Draw

Market lesson

The closing market differed from the model on VfL Bochum by 14.3 percentage points (52.8% vs model 38.5%) — 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 12, 2026 · 11:01 UTC Forecast generated
    • Model 1X2 · VfL Bochum 38.5% · Draw 29.6% · SpVgg Greuther Fürth 31.9%
    • xG · VfL Bochum 1.37 — SpVgg Greuther Fürth 1.23
  2. Sep 12, 2026 · 10:31 UTC Opening odds snapshot PRE30
    • 1X2 odds · VfL Bochum 1.96 · Draw 3.85 · SpVgg Greuther Fürth 3.77
    • Implied 1X2 · VfL Bochum 49.3% · Draw 25.1% · SpVgg Greuther Fürth 25.6%
    • Bookmaker · Pinnacle
  3. Sep 12, 2026 · 11:01 UTC Closing snapshot recorded PRE1
    • 1X2 odds · VfL Bochum 1.83 · Draw 4.05 · SpVgg Greuther Fürth 4.15
    • Implied 1X2 · VfL Bochum 52.8% · Draw 23.9% · SpVgg Greuther Fürth 23.3%
    • Bookmaker · Pinnacle
  4. Sep 12, 2026 · 11:00 UTC Kickoff
  5. FT Full-time result Draw · 1–1
  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: Monitor
Historical Decision Monitor
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 56/100 · Moderate
  • Validation: Warning
  • Large market gap (14 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Market has already priced much of the edge
  • Validation warning
Pricing proximity (inverse gap) 28/100
Betting Confidence 44/100

Validation Report

Immutable Snapshot

Prediction Time: Sep 07, 2026 · 00:58 UTC Snapshot ID: dp-7422600

Closing Odds 1.83
AI Fair Odds —
CLV Pending
Final Result Draw · VfL Bochum 1–1 SpVgg Greuther Fürth
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: 2. Bundesliga
  • Fixture: VfL Bochum vs SpVgg Greuther Fürth
  • Kickoff: 2026-09-12 11:00:00
  • 1X2 (model): Home 38.5% · Draw 29.6% · Away 31.9%
  • xG (showing): VfL Bochum 1.37 — SpVgg Greuther Fürth 1.23 (total xG ≈ 2.6)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Under 2.5 goals
  • Model: 51.8% · Implied: 40.9% · Probability edge: +10.9 pts · Est. EV: +23.3%
  • BTTS (model): Yes 54.4% · No 45.6%
  • Correct score (top bin): 1-1 (12.5%)

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: 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 30, 2026 (UTC)

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2. Bundesliga 2. Bundesliga — Standings
# TEAM MP W D L PTS
1 Hertha BSC 6 6 0 0 18
2 1. FC Nürnberg 6 5 1 0 16
3 1. FC Heidenheim 6 4 1 1 13
4 VfL Wolfsburg 6 3 2 1 11
5 1. FC Kaiserslautern 6 3 2 1 11
6 1. FC Magdeburg 6 3 1 2 10
7 Energie Cottbus 6 2 2 2 8
8 FC St. Pauli 6 1 4 1 7
9 VfL Bochum 6 2 1 3 7
10 Hannover 96 6 2 1 3 7
11 VfL Osnabrück 6 2 1 3 7
12 SpVgg Greuther Fürth 6 1 3 2 6
13 Arminia Bielefeld 6 1 2 3 5
14 Karlsruher SC 6 1 2 3 5
15 Eintracht Braunschweig 6 1 1 4 4
16 Holstein Kiel 6 0 4 2 4
17 Dynamo Dresden 6 1 1 4 4
18 SV Darmstadt 98 6 1 1 4 4
# TEAM MP GS GC +/- PTS
1 Hertha BSC 6 17 7 +10 18
2 1. FC Nürnberg 6 16 6 +10 16
3 VfL Wolfsburg 6 15 8 +7 11
4 1. FC Heidenheim 6 14 12 +2 13
5 Energie Cottbus 6 14 13 +1 8
6 Eintracht Braunschweig 6 12 13 -1 4
7 1. FC Magdeburg 6 11 9 +2 10
8 Arminia Bielefeld 6 10 12 -2 5
9 SpVgg Greuther Fürth 6 9 11 -2 6
10 VfL Osnabrück 6 9 12 -3 7
11 FC St. Pauli 6 8 8 0 7
12 Dynamo Dresden 6 8 14 -6 4
13 Hannover 96 6 7 9 -2 7
14 Holstein Kiel 6 7 10 -3 4
15 1. FC Kaiserslautern 6 6 4 +2 11
16 Karlsruher SC 6 6 12 -6 5
17 VfL Bochum 6 5 6 -1 7
18 SV Darmstadt 98 6 5 13 -8 4
# TEAM MP xG xGC +/- PTS
1 Arminia Bielefeld 6 5.9 2.9 +3.0 5
2 Hannover 96 6 6.3 4.0 +2.3 7
3 VfL Wolfsburg 6 6.0 3.7 +2.3 11
4 Hertha BSC 6 7.5 5.6 +1.9 18
5 1. FC Heidenheim 6 7.7 6.0 +1.7 13
6 Holstein Kiel 6 5.6 4.1 +1.5 4
7 FC St. Pauli 6 5.7 4.3 +1.4 7
8 VfL Bochum 6 3.8 2.6 +1.2 7
9 Eintracht Braunschweig 6 9.4 8.5 +0.9 4
10 SpVgg Greuther Fürth 6 6.9 7.0 -0.1 6
11 1. FC Kaiserslautern 6 2.6 3.2 -0.6 11
12 1. FC Nürnberg 6 4.2 5.2 -1.0 16
13 Karlsruher SC 6 3.8 5.3 -1.5 5
14 VfL Osnabrück 6 5.7 7.7 -2.0 7
15 SV Darmstadt 98 6 2.0 4.3 -2.3 4
16 Dynamo Dresden 6 3.4 5.8 -2.4 4
17 1. FC Magdeburg 6 4.7 7.7 -3.0 10
18 Energie Cottbus 6 5.0 8.3 -3.3 8