Metalist 1925 Kharkiv vs Karpaty Statistics & Analysis

Below is a compact, numbers-first snapshot aligned with the same engine as the cards above.

• League: Premier League
• Fixture: Metalist 1925 Kharkiv vs Karpaty
• Kickoff: 2026-05-16 15:00:00
• 1X2 (model): Home 39.3% · Draw 32.5% · Away 28.2%
• xG (showing): Metalist 1925 Kharkiv 1.2 — Karpaty 0.98 (total xG ≈ 2.18)
• Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
• Model: 62.8% · Implied: 54.0% · Probability edge: +8.8 pts · Est. EV: +13.7%
• BTTS (model): Yes 45.4% · No 54.6%
• Correct score (top bin): 1-0 (13.6%)

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.

Primary pick from the decision engine: Under 2.5 goals.

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.

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 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.

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.

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.

• 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 18, 2026 (UTC)