Predictions / Football / World. Friendlies Clubs / VfL Wolfsburg vs Telstar

Prediction Audit: VfL Wolfsburg vs Telstar Prediction, Odds & AI Betting Tips

VfL Wolfsburg VfL Wolfsburg
Jul 31, 2026 - 15:00
3 1.26
1 1.34
xG Accuracy: 56%
Telstar Telstar

The model missed the final outcome (VfL Wolfsburg win 3–1).

The model had projected Telstar at 37.1%, 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 (4 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Telstar VfL Wolfsburg ✖ Incorrect
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 2-1 3-1 ✖ Incorrect

Model vs Closing Market

Strong Disagreement

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

Largest probability gap: VfL Wolfsburg -25.0 pp

Outcome Model Closing Market Difference Signal
VfL Wolfsburg 33.3% 58.4% -25.0 pp Market Higher
Draw 29.6% 21.8% +7.8 pp Model Higher
Telstar 37.1% 19.8% +17.2 pp Model Edge

The closing market estimates VfL Wolfsburg's win probability at 58.4%, compared with the model's estimate of 33.3%, a difference of 25.0 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 VfL Wolfsburg win 3–1.

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 2.60) — 4 goals materialised
  • Both Teams To Score (Yes) matched the full-time result

What failed

  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (4 goals)
  • 1X2: model leaned Telstar; match finished VfL Wolfsburg

Market lesson

The closing market differed from the model on Telstar by 25.0 percentage points (62.1% vs model 37.1%) — 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. Jul 31, 2026 · 15:02 UTC Forecast generated
    • Model 1X2 · VfL Wolfsburg 33.4% · Draw 29.6% · Telstar 37.0%
    • xG · VfL Wolfsburg 1.26 — Telstar 1.34
  2. Jul 31, 2026 · 14:32 UTC Opening odds snapshot PRE30
    • 1X2 odds · VfL Wolfsburg 1.51 · Draw 4.04 · Telstar 4.44
    • Implied 1X2 · VfL Wolfsburg 58.4% · Draw 21.8% · Telstar 19.8%
    • Bookmaker · Pinnacle
  3. Jul 31, 2026 · 15:02 UTC Closing snapshot recorded PRE1
    • 1X2 odds · VfL Wolfsburg 1.51 · Draw 4.04 · Telstar 4.44
    • Implied 1X2 · VfL Wolfsburg 58.4% · Draw 21.8% · Telstar 19.8%
    • Bookmaker · Pinnacle
  4. Jul 31, 2026 · 15:00 UTC Kickoff
  5. FT Full-time result VfL Wolfsburg win · 3–1
  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: Cautious / Wait

Historical label: Originally displayed as "Wait for validation".

Historical Decision Wait
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 50/100 · Moderate
  • Validation: Warning
  • Large market gap (25 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Market strongly disagrees on price
  • Validation warning
Pricing proximity (inverse gap) 0/100
Monitoring Confidence 14/100

Validation Report

Immutable Snapshot

Prediction Time: Jul 31, 2026 · 01:19 UTC Snapshot ID: dp-2553912

Closing Odds 1.51
AI Fair Odds —
CLV Pending
Final Result VfL Wolfsburg win · VfL Wolfsburg 3–1 Telstar
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

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

  • League: Friendlies Clubs
  • Fixture: VfL Wolfsburg vs Telstar
  • Kickoff: 2026-07-31 15:00:00
  • 1X2 (model): Home 33.4% · Draw 29.6% · Away 37.0%
  • xG (showing): VfL Wolfsburg 1.26 — Telstar 1.34 (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 (12.5%)

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.

Prefer skipping to over-staking when the engine is honest about missing edge.

Historical Recommendation

Historical Decision: Wait

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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