Estadísticas / Fútbol / Hungary. NB I / Zalaegerszegi TE vs Puskas Academy

Zalaegerszegi TE vs Puskas Academy Statistics & Analysis

Zalaegerszegi TE Zalaegerszegi TE
May 01, 2026 - 18:00
1 1.29
3 0.76
xG Accuracy: 50%
Puskas Academy Puskas Academy
Casa de apuestas premium 1xBet: los nuevos usuarios pueden usar el código promocional 1x_3342271. Registrarse

Tracked markets vs full-time result

Each row compares the model’s highlighted side (or lean) to what happened at full time.

  • Market Prediction Result Outcome
  • Más / Menos de 2.5 Menos de 2.5 Más de 2.5 (4 goals) ✖ Incorrect
  • Ambos Equipos Marcarán BTTS No Sí ✖ Incorrect
  • 1X2 Zalaegerszegi TE Puskas Academy ✖ Incorrect
  • Perspectivas de marcador exacto 1-0 1-3 ✖ Incorrect

Informe IA del partido

Resumen del partido IA

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.

Mejor apuesta y razones

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.

FAQ

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.

Factores de riesgo

  • 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%).

Metodología

  • 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.

Última actualización

May 01, 2026 (UTC)

¿Cómo usar esto?
  • Concéntrese en la línea principal cuando desee una idea viable.
  • No combine muchas púas de punta fina;Los bordes no se suman de manera confiable.
  • Trate las apuestas arriesgadas como jugadas opcionales y de alto riesgo únicamente.

Obtén predicciones premium para Zalaegerszegi TE & Puskas Academy!

Desbloquea análisis en profundidad, consejos de apuestas exclusivos y pronósticos de partidos con nuestro servicio de suscripción premium.

Suscríbete ahora
Volver a Estadísticas
NB I NB I — Clasificación
# EQUIPO PJ G E P Pts
1 Gyori ETO FC 32 19 9 4 66
2 Ferencvarosi TC 32 20 5 7 65
3 Paks 32 14 8 10 50
4 Debreceni VSC 32 13 11 8 50
5 Zalaegerszegi TE 32 13 9 10 48
6 Puskas Academy 32 13 6 13 45
7 Ujpest 32 11 7 14 40
8 Kisvarda FC 32 11 7 14 40
9 Nyiregyhaza 32 10 9 13 39
10 MTK Budapest 32 9 10 13 37
11 Diosgyori VTK 32 6 10 16 28
12 Kazincbarcikai 32 6 3 23 21
# EQUIPO PJ GF GC +/- Pts
1 Gyori ETO FC 32 64 30 +34 66
2 Ferencvarosi TC 32 64 31 +33 65
3 Paks 32 60 45 +15 50
4 MTK Budapest 32 53 60 -7 37
5 Debreceni VSC 32 49 40 +9 50
6 Zalaegerszegi TE 32 49 40 +9 48
7 Ujpest 32 47 55 -8 40
8 Nyiregyhaza 32 45 55 -10 39
9 Puskas Academy 32 41 41 0 45
10 Diosgyori VTK 32 38 62 -24 28
11 Kisvarda FC 32 36 48 -12 40
12 Kazincbarcikai 32 29 68 -39 21
# EQUIPO PJ xG xGC +/- Pts
1 Ferencvarosi TC 32 20.8 11.2 +9.6 65
2 Zalaegerszegi TE 32 15.9 15.2 +0.7 48
3 Ujpest 32 15.3 14.8 +0.5 40
4 Gyori ETO FC 32 2.3 2.2 +0.1 66
5 Paks 32 16.4 16.4 0.0 50
6 Puskas Academy 32 14.3 15.0 -0.7 45
7 MTK Budapest 32 18.3 19.1 -0.8 37
8 Kisvarda FC 32 13.9 15.8 -1.9 40
9 Debreceni VSC 32 15.3 18.8 -3.5 50
10 Diosgyori VTK 32 14.2 18.1 -3.9 28
11 Nyiregyhaza 32 — — — 39
12 Kazincbarcikai 32 — — — 21
Ligas Populares