Zalaegerszegi TE vs Puskas Academy Statistics & Analysis

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

• League: NB I
• Fixture: Zalaegerszegi TE vs Puskas Academy
• Kickoff: 2026-05-02 15:00:00
• 1X2 (model): Home 47.4% · Draw 32.3% · Away 20.3%
• xG (showing): Zalaegerszegi TE 1.29 — Puskas Academy 0.76 (total xG ≈ 2.05)
• Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
• Model: 66.3% · Implied: 52.0% · Probability edge: +14.3 pts · Est. EV: +22.7%
• BTTS (model): Yes 40.2% · No 59.8%
• Correct score (top bin): 1-0 (16.6%)

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

Correct score remains high-variance even when a line is most likely on paper.

Primary pick from the decision engine: Under 2.5 goals.

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

Edges shrink quickly if prices move; always re-check the number on your book.

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.

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.

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.

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).
• Narrative: Template sentence library with fixture-stable selection (no per-request LLM for this block).
• Compliance: Educational framing only; not personalised advice.

May 01, 2026 (UTC)