Krško vs Krka Prediction, Odds & AI Betting Tips

May 10, 2026 - 15:00
0 1.45
4 1.25
xG Accuracy: 32%
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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 (4 goals) ✔ Correct
  • Both Teams To Score BTTS No No ✔ Correct
  • 1X2 Krško Krka ✖ Incorrect
  • Correct Score Insights 1-1, 0-1, 1-2, 1-0, 2-1 0-4 ✖ Incorrect

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: 2. SNL
  • Fixture: Krško vs Krka
  • Kickoff: 2026-05-10 15:00:00
  • 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
  • xG (showing): Krško 1.45 — Krka 1.25 (total xG ≈ 2.7)
  • Primary / headline line (Betting Primary Pick when shown): BTTS No
  • Model: 75.0% · Implied: 46.1% · Probability edge: +28.9 pts · Est. EV: +50.0%
  • BTTS (model): Yes 25.0% · No 75.0%
  • Correct score (top bin): 1-1 (12.2%)

Where EV is shown, it is estimated return per unit stake at the best tracked decimal price — not the same thing as a raw probability gap.

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

Best Bet + Reason

The engine’s headline primary is: BTTS No.

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

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.

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.

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.

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 23, 2026 (UTC)

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Back to Predictions
2. SNL 2. SNLStandings
# TEAM MP W D L PTS
1 Brinje-Grosuplje 30 23 6 1 75
2 Nafta 30 23 6 1 75
3 Triglav 30 20 4 6 64
4 Beltinci 30 18 6 6 60
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 30 9 7 14 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 30 6 9 15 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 30 72 28 +44 64
2 Brinje-Grosuplje 30 70 26 +44 75
3 Nafta 30 65 23 +42 75
4 Beltinci 30 55 25 +30 60
5 Bistrica 29 49 36 +13 42
6 Rudar 29 46 37 +9 37
7 Ilirija 30 38 52 -14 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 30 27 47 -20 34
15 Jesenice 29 26 67 -41 18
16 Krško 29 22 62 -40 23