Austria Klagenfurt vs FC Liefering Statistics & Analysis

Quick read on how the model reads this matchup.

• League: 2. Liga
• Fixture: Austria Klagenfurt vs FC Liefering
• Kickoff: 2026-05-14 16:00:00
• 1X2 (model): Home 16.6% · Draw 27.4% · Away 56.0%
• xG (showing): Austria Klagenfurt 0.63 — FC Liefering 1.41 (total xG ≈ 2.04)
• Primary / headline line (Betting Primary Pick when shown): BTTS No
• Model: 54.0% · Implied: 40.9% · Probability edge: +13.1 pts · Est. EV: +24.2%
• BTTS (model): Yes 46.0% · No 54.0%
• Correct score (top bin): 1-1 (11.8%)

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

Correct score remains high-variance even when a line is most likely on paper.

Primary pick from the decision engine: BTTS No.

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.

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.

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.

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.

• 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%).

• 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.

May 17, 2026 (UTC)