Loko Vltavín vs Dukla Praha II Statistics & Analysis

Quick read on how the model reads this matchup.

• League: 3. liga - CFL A
• Fixture: Loko Vltavín vs Dukla Praha II
• Kickoff: 2026-05-30 15:00:00
• 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
• xG (showing): Loko Vltavín 1.45 — Dukla Praha II 1.25 (total xG ≈ 2.7)
• Best +EV line (same label as hero card when Primary thresholds are not met): Under 2.5 goals
• Model: 46.9% · Implied: 43.3% · Probability edge: +3.6 pts · Est. EV: +3.2%
• BTTS (model): Yes 58.7% · No 41.3%
• Correct score (top bin): 1-1 (11.9%)

Where EV is shown, it is estimated return per unit stake at the best tracked decimal price — not the same thing as a raw probability gap.

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

Best current value angle on the board — same leg as the “Best +EV” hero when Primary rules are not met: Under 2.5 goals.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

No pick is a guarantee; variance is especially large in scoreline markets.

Who has the edge in the match-winner market?

Use the 1X2 model percentages in the summary and the 1X2 market card: the side with the highest model % is the model lean, but check EV — a lean can still be -EV after prices.

How should I read EV versus a probability gap?

Probability edge = model probability minus implied probability (reported here in percentage points). EV ≈ model probability × best tracked decimal odds − 1, shown as return per unit stake. They are related but not interchangeable labels.

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

Why might 1X2 look unattractive while totals do not?

Tight 1X2 prices often embed a fair three-way split, so EV on match-winner can sit negative even when Over/Under or BTTS still diverges from the model — compare the 1X2 row on the market cards to O/U and BTTS.

• 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%).

• Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
• Compliance: Educational framing only; not personalised advice.

June 06, 2026 (UTC)