Statistics / Football / Hungary. NB I / Paks vs Debreceni VSC

Paks vs Debreceni VSC Statistics & Analysis

Paks Paks
May 03, 2026 - 11:30
5 1.18
2 1.00
xG Accuracy: 27%
Debreceni VSC Debreceni VSC
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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
  • Over / Under 2.5 Under 2.5 Over 2.5 (7 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Paks Paks ✔ Correct
  • Correct Score Insights 1-0 5-2 ✖ Incorrect

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: NB I
  • Fixture: Paks vs Debreceni VSC
  • Kickoff: 2026-05-02 15:00:00
  • 1X2 (model): Home 38.2% · Draw 32.6% · Away 29.2%
  • xG (showing): Paks 1.18 — Debreceni VSC 1.0 (total xG ≈ 2.18)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 62.8% · Implied: 38.3% · Probability edge: +24.5 pts · Est. EV: +65.2%
  • BTTS (model): Yes 45.5% · No 54.5%
  • Correct score (top bin): 1-0 (13.3%)

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.

Best Bet + Reason

Primary pick from the decision engine: Under 2.5 goals.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

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

FAQ

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.

Is the most likely correct score a good bet?

Usually no as a standalone bet: the “most likely” scoreline is still a low absolute probability tail event (often single digits, sometimes low teens). Use it as context; keep any correct-score stake in the “fun / small” bucket.

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.

Risk Factors

  • 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%).

Methodology

  • 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.

Last Updated

May 17, 2026 (UTC)

How to use this
  • Focus on the Primary line when you want one actionable idea.
  • Do not parlay many thin-edge picks together; edges do not add reliably.
  • Treat longshots as optional, high-stake-sizing plays only.

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Back to Statistics
NB I NB I — Standings
# TEAM MP W D L 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
# TEAM MP GS 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
# TEAM MP 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
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