Predictions / Football / Czech-Republic. FNL / Dukla Praha vs Vlašim

Prediction Audit: Dukla Praha vs Vlašim Prediction, Odds & AI Betting Tips

Sep 11, 2026 - 16:00
3 1.27
1 1.33
xG Accuracy: 57%

The model missed the final outcome (Dukla Praha win 3–1).

The model had projected Vlašim at 36.6%, 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 (4 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Vlašim Dukla Praha ✖ 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 Dukla Praha higher than the statistical model.

Largest probability gap: Dukla Praha -18.6 pp

Outcome Model Closing Market Difference Signal
Dukla Praha 33.8% 52.3% -18.6 pp Market Higher
Draw 29.6% 24.4% +5.2 pp Model Higher
Vlašim 36.6% 23.2% +13.3 pp Model Edge

The closing market estimates Dukla Praha's win probability at 52.3%, compared with the model's estimate of 33.8%, a difference of 18.6 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 Dukla Praha win 3–1.

Market Assessment

The market is materially more optimistic about Dukla Praha 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

  • Expected goals projected a high-scoring match (ΣxG 2.60) — 4 goals materialised

What failed

  • Both Teams To Score: model leaned BTTS No; match finished BTTS Yes
  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (4 goals)

Market lesson

The closing market differed from the model on Vlašim by 18.6 percentage points (55.2% vs model 36.6%) — 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 11, 2026 · 16:00 UTC Forecast generated
    • Model 1X2 · Dukla Praha 33.8% · Draw 29.6% · Vlašim 36.6%
    • xG · Dukla Praha 1.27 — Vlašim 1.33
  2. Sep 11, 2026 · 15:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Dukla Praha 1.75 · Draw 3.75 · Vlašim 3.94
    • Implied 1X2 · Dukla Praha 52.3% · Draw 24.4% · Vlašim 23.2%
    • Bookmaker · Pinnacle
  3. Sep 11, 2026 · 15:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Dukla Praha 1.75 · Draw 3.75 · Vlašim 3.94
    • Implied 1X2 · Dukla Praha 52.3% · Draw 24.4% · Vlašim 23.2%
    • Bookmaker · Pinnacle
  4. Sep 11, 2026 · 16:00 UTC Kickoff
  5. FT Full-time result Dukla Praha 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: Monitor
Historical Decision Monitor
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 54/100 · Moderate
  • Validation: Warning
  • Large market gap (19 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Pricing remains divergent
  • Validation warning
Pricing proximity (inverse gap) 2/100
Monitoring Confidence 19/100

Validation Report

Immutable Snapshot

Prediction Time: Sep 05, 2026 · 01:36 UTC Snapshot ID: dp-7030474

Closing Odds 1.75
AI Fair Odds —
CLV Pending
Final Result Dukla Praha win · Dukla Praha 3–1 Vlašim
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

Quick read on how the model reads this matchup.

  • League: FNL
  • Fixture: Dukla Praha vs Vlašim
  • Kickoff: 2026-09-11 16:00:00
  • 1X2 (model): Home 33.8% · Draw 29.6% · Away 36.6%
  • xG (showing): Dukla Praha 1.27 — Vlašim 1.33 (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: 44.1% · Probability edge: +7.7 pts · Est. EV: +15.0%
  • BTTS (model): Yes 54.5% · No 45.5%
  • Correct score (top bin): 1-1 (12.5%)

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: 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

October 01, 2026 (UTC)

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