Madrid CFF W vs Sevilla W Statistics & Analysis

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

• League: Primera División Femenina
• Fixture: Madrid CFF W vs Sevilla W
• Kickoff: 2026-05-10 16:00:00
• 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
• xG (showing): Madrid CFF W 1.45 — Sevilla W 1.25 (total xG ≈ 2.7)
• Primary / headline line (Betting Primary Pick when shown): BTTS No
• Model: 58.0% · Implied: 41.5% · Probability edge: +16.5 pts · Est. EV: +33.4%
• BTTS (model): Yes 42.0% · No 58.0%
• Correct score (top bin): 1-1 (11.2%)

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.

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

Primary pick from the decision engine: BTTS No.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

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.

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.

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.

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

• 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)