Dinamo Makhachkala vs Spartak Moscow Statistics & Analysis

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

• League: Premier League
• Fixture: Dinamo Makhachkala vs Spartak Moscow
• Kickoff: 2026-05-17 15:00:00
• 1X2 (model): Home 32.2% · Draw 35.0% · Away 32.7%
• xG (showing): Dinamo Makhachkala 0.96 — Spartak Moscow 0.97 (total xG ≈ 1.93)
• Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
• Model: 69.6% · Implied: 56.6% · Probability edge: +13.0 pts · Est. EV: +21.1%
• BTTS (model): Yes 40.1% · No 59.9%
• Correct score (top bin): 0-0 (14.5%)

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

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

The engine’s headline primary is: Under 2.5 goals.

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.

No pick is a guarantee; variance is especially large in scoreline markets.

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.

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.

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.

• 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 19, 2026 (UTC)