Pdrm vs Penang Statistics & Analysis

May 08, 2026 - 13:00
1 1.45
1 1.25
xG Accuracy: 82%
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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 Under 2.5 (2 goals) ✔ Correct
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Pdrm Draw ✖ Incorrect
  • Correct Score Insights 1-2 1-1 ✖ Incorrect

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Super League
  • Fixture: Pdrm vs Penang
  • Kickoff: 2026-05-08 13:00:00
  • 1X2 (model): Home 10.0% · Draw 45.0% · Away 45.0%
  • xG (showing): Pdrm 1.45 — Penang 1.25 (total xG ≈ 2.7)
  • Primary / headline line (Betting Primary Pick when shown): BTTS No
  • Model: 70.2% · Implied: 48.9% · Probability edge: +21.3 pts · Est. EV: +36.9%
  • BTTS (model): Yes 29.8% · No 70.2%
  • Correct score (top bin): 1-2 (9.6%)

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

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

Best Bet + Reason

The engine’s headline primary is: BTTS No.

We separate probability edge (model minus implied, in points of probability) from estimated EV (economic edge at the best price shown on the page).

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

FAQ

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.

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.

Safer market than correct score?

Markets with more liquidity and smoother prices (often 1X2 or O/U 2.5 from many books) are usually easier to reason about than long-tail correct-score prices; still read EV on each leg.

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
Super League Super LeagueStandings
# TEAM MP W D L PTS
1 Johor Darul Takzim FC 24 23 1 0 70
2 Kuching FA 23 15 5 3 50
3 Selangor 23 15 4 4 49
4 Kuala Lumpur FA 23 12 7 4 43
5 Terengganu 24 10 6 8 36
6 Imigresen 24 9 5 10 32
7 Negeri Sembilan 24 6 11 7 29
8 Penang 24 6 7 11 25
9 Sabah FA 24 5 8 11 23
10 DPMM FC Brunei 24 6 5 13 23
11 Melaka 24 4 7 13 19
12 Kelantan United 24 4 3 17 15
13 Pdrm 23 2 5 16 11
# TEAM MP GS GC +/- PTS
1 Johor Darul Takzim FC 24 117 10 +107 70
2 Selangor 23 53 20 +33 49
3 Kuching FA 23 44 14 +30 50
4 Kuala Lumpur FA 23 40 28 +12 43
5 Terengganu 24 39 34 +5 36
6 Negeri Sembilan 24 39 35 +4 29
7 Imigresen 24 38 43 -5 32
8 DPMM FC Brunei 24 30 57 -27 23
9 Sabah FA 24 29 44 -15 23
10 Penang 24 26 41 -15 25
11 Melaka 24 18 45 -27 19
12 Kelantan United 24 17 63 -46 15
13 Pdrm 23 17 73 -56 11