Statistics / Football / Germany. 2. Bundesliga / Karlsruher SC vs VfL Wolfsburg

Karlsruher SC vs VfL Wolfsburg Statistics & Analysis

Karlsruher SC Karlsruher SC
Aug 29, 2026 - 11:00
2 1.30
5 1.37
xG Accuracy: 30%
VfL Wolfsburg VfL Wolfsburg
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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 Under 2.5 Over 2.5 (7 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 VfL Wolfsburg VfL Wolfsburg ✔ Correct
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 2-1 2-5 ✖ Incorrect

Validation Report

Immutable Snapshot

Prediction Time: Aug 23, 2026 · 01:37 UTC Snapshot ID: dp-5126610

Closing Odds 2.06
AI Fair Odds —
CLV +0.0%
Final Result VfL Wolfsburg win · Karlsruher SC 2–5 VfL Wolfsburg
Prediction ✔ Correct
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 Karlsruher SC (1X2), Draw (1X2), Under 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 Under 2.5 goals by about 12.0 percentage points — the clearest mispricing signal summarized on this page.

Model vs. closing implied

Market Model % Closing impl. % Gap (pp)
Karlsruher SC (1X2) 31.9 26.9 +5.0
Draw (1X2) 28.1 26.1 +2.0
VfL Wolfsburg (1X2) 40.0 47.0 -7.0
Over 2.5 goals 49.9 61.9 -12.0
Under 2.5 goals 50.1 38.1 +12.0
What this means

In plain terms: the model lands near 50.1% on Under 2.5 goals, while the closing snapshot implied about 38.1%. The difference — about 12.0 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)
Karlsruher SC (1X2) 3.6 3.6 0.0
Draw (1X2) 3.71 3.71 0.0
VfL Wolfsburg (1X2) 2.06 2.06 0.0
Over 2.5 goals 1.56 1.56 0.0
Under 2.5 goals 2.53 2.53 0.0

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: 2. Bundesliga
  • Fixture: Karlsruher SC vs VfL Wolfsburg
  • Kickoff: 2026-08-29 11:00:00
  • 1X2 (model): Home 31.9% · Draw 28.1% · Away 40.0%
  • xG (showing): Karlsruher SC 1.3 — VfL Wolfsburg 1.37 (total xG ≈ 2.67)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 50.1% · Implied: 37.6% · Probability edge: +12.5 pts · Est. EV: +29.3%
  • BTTS (model): Yes N/A · No N/A
  • Correct score (top bin): N/A

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

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

Historical Recommendation

Historical Decision: Primary Bet

Outcome: Validated — Pre-match lean validated against 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