Statistics / Football / Germany. 2. Bundesliga / VfL Bochum vs Hertha BSC

VfL Bochum vs Hertha BSC Statistics & Analysis

VfL Bochum VfL Bochum
Aug 07, 2026 - 18:30
0 2.23
1 1.38
xG Accuracy: 49%
Hertha BSC Hertha BSC
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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 Over 2.5 Under 2.5 (1 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 VfL Bochum Hertha BSC ✖ Incorrect
  • Correct Score Insights 2-1, 1-1, 3-1, 2-0, 2-2 0-1 ✖ Incorrect

Validation Report

Immutable Snapshot

Prediction Time: Aug 01, 2026 · 02:37 UTC Snapshot ID: dp-2681408

Closing Odds 2.39
AI Fair Odds —
CLV +0.0%
Final Result Hertha BSC win · VfL Bochum 0–1 Hertha BSC
Prediction ✖ Missed
Decision Grade F

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

Market intelligence

Supporting read on how the prioritized closing feed moved versus the model — use after the Primary pick above.

Market briefing

Market remained largely stable before kickoff. No meaningful late implied-price shift was detected between PRE30 and PRE1 on the prioritized bookmaker snapshot.

Despite limited late movement, the model still prices VfL Bochum (1X2), Over 2.5 goals meaningfully above what those closing snapshots implied — that gap is a static “model vs. price” read, not a late steam or chase story.

The model still exceeds closing implied on VfL Bochum (1X2) by about 15.5 percentage points — the clearest mispricing signal summarized on this page.

Model vs. closing implied

Market Model % Closing impl. % Gap (pp)
VfL Bochum (1X2) 55.6 40.1 +15.5
Draw (1X2) 22.3 27.3 -5.0
Hertha BSC (1X2) 22.1 32.5 -10.4
Over 2.5 goals 69.9 55.4 +14.5
Under 2.5 goals 30.1 44.6 -14.5
What this means

In plain terms: the model lands near 55.6% on VfL Bochum (1X2), while the closing snapshot implied about 40.1%. The difference — about 15.5 percentage points — is the largest model-vs.-market gap highlighted on this page.

Quick definitions: “closing implied” is the probability for that outcome implied by the final captured odds (after a simple de-vig). “Gap (pp)” is the model percentage minus that implied value, in percentage points (pp).

Closing-window line move

Single prioritized bookmaker per snapshot (not all books). Capture path: PRE30 → PRE1 · Book: Pinnacle

Column tags in parentheses: Closing uses the first available snapshot in PRE1→PRE5→PRE10→PRE30; Early uses the first available in PRE30→PRE10→PRE5 that is not the same capture as Closing.

Detailed capture odds are folded below — movement was negligible on de-vig implied prices.

View full line-by-line capture table
Market Early (PRE30) Closing (PRE1) Implied Δ (pp)
VfL Bochum (1X2) 2.39 2.39 0.0
Draw (1X2) 3.51 3.51 0.0
Hertha BSC (1X2) 2.95 2.95 0.0
Over 2.5 goals 1.73 1.73 0.0
Under 2.5 goals 2.15 2.15 0.0

AI match briefing

AI Match Summary

Below is a compact, numbers-first snapshot aligned with the same engine as the cards above.

  • League: 2. Bundesliga
  • Fixture: VfL Bochum vs Hertha BSC
  • Kickoff: 2026-08-07 18:30:00
  • 1X2 (model): Home 55.6% · Draw 22.3% · Away 22.1%
  • xG (showing): VfL Bochum 2.23 — Hertha BSC 1.38 (total xG ≈ 3.61)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Over 2.5 goals
  • Model: 69.9% · Implied: 55.6% · Probability edge: +14.3 pts · Est. EV: +22.3%
  • BTTS (model): Yes 67.9% · No 32.1%
  • Correct score (top bin): 2-1 (9.3%)

Totals and BTTS are evaluated against current market prices where available.

Early match state can move realised goals away from pre-kick projections.

Historical Recommendation

Historical Decision: AI Value Alert - 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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Back to Statistics
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