Statistics / Football / Slovenia. 2. SNL / Dravinja vs Krško

Dravinja vs Krško Statistics & Analysis

May 16, 2026 - 14:00
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 Under 2.5 Over 2.5 (3 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Dravinja Dravinja ✔ Correct
  • Correct Score Insights 1-1 2-1 ✖ Incorrect

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: 2. SNL
  • Fixture: Dravinja vs Krško
  • Kickoff: 2026-05-17 15:00:00
  • 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
  • xG (showing): Dravinja 1.45 — Krško 1.25 (total xG ≈ 2.7)
  • Primary / headline line (Betting Primary Pick when shown): BTTS No
  • Model: 66.1% · Implied: 44.1% · Probability edge: +22.0 pts · Est. EV: +38.8%
  • BTTS (model): Yes 33.9% · No 66.1%
  • Correct score (top bin): 1-1 (12.0%)

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

Primary angle highlighted on the page: 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).

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

FAQ

What is the best-supported line in this snapshot?

Match the hero card above: if it says “Betting Primary Pick”, that leg cleared primary rules; if it says “Best +EV (tracked markets)”, it is the strongest +EV line that did not meet stricter Primary thresholds. The bullets below repeat the same model %, implied %, edge (pts), and EV % as that card.

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.

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.

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.

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