Statistics / Football / Slovenia. 2. SNL / Brinje-Grosuplje vs Nafta

Brinje-Grosuplje vs Nafta Statistics & Analysis

May 17, 2026 - 17:30
2 1.45
1 1.25
xG Accuracy: 80%
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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 Over 2.5 Over 2.5 (3 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Brinje-Grosuplje Brinje-Grosuplje ✔ Correct
  • Correct Score Insights 1-1 2-1 ✖ Incorrect

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: 2. SNL
  • Fixture: Brinje-Grosuplje vs Nafta
  • Kickoff: 2026-05-17 15:00:00
  • 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
  • xG (showing): Brinje-Grosuplje 1.45 — Nafta 1.25 (total xG ≈ 2.7)
  • Primary / headline line (Betting Primary Pick when shown): BTTS Yes
  • Model: 92.9% · Implied: 52.3% · Probability edge: +40.6 pts · Est. EV: +62.6%
  • BTTS (model): Yes 92.9% · No 7.1%
  • Correct score (top bin): 1-1 (10.6%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

1X2 can look balanced even when side markets show clearer structure.

Best Bet + Reason

Primary pick from the decision engine: BTTS Yes.

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.

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

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.

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.

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.

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.

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 19, 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
2. SNL 2. SNLStandings
# TEAM MP W D L PTS
1 Nafta 29 23 5 1 74
2 Brinje-Grosuplje 29 22 6 1 72
3 Triglav 29 20 3 6 63
4 Beltinci 29 17 6 6 57
5 Tabor Sežana 29 14 5 10 47
6 Bistrica 29 12 6 11 42
7 Rudar 29 9 10 10 37
8 Dravinja 29 9 7 13 34
9 Bilje 29 9 7 13 34
10 Slovan Ljubljana 29 9 5 15 32
11 Dekani 29 7 9 13 30
12 Krka 29 6 11 12 29
13 Ilirija 29 6 9 14 27
14 Krško 29 6 5 18 23
15 Gorica 29 4 10 15 22
16 Jesenice 29 4 6 19 18
# TEAM MP GS GC +/- PTS
1 Triglav 29 71 27 +44 63
2 Brinje-Grosuplje 29 69 26 +43 72
3 Nafta 29 64 22 +42 74
4 Beltinci 29 52 24 +28 57
5 Bistrica 29 49 36 +13 42
6 Rudar 29 46 37 +9 37
7 Ilirija 29 37 49 -12 27
8 Dekani 29 36 43 -7 30
9 Tabor Sežana 29 35 33 +2 47
10 Slovan Ljubljana 29 32 51 -19 32
11 Gorica 29 31 43 -12 22
12 Dravinja 29 31 44 -13 34
13 Krka 29 29 47 -18 29
14 Bilje 29 27 46 -19 34
15 Jesenice 29 26 67 -41 18
16 Krško 29 22 62 -40 23