Feignies-Aulnoye vs Haguenau Statistics & Analysis

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

• League: National 2 - Group B
• Fixture: Feignies-Aulnoye vs Haguenau
• Kickoff: 2026-05-16 16:00:00
• 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
• xG (showing): Feignies-Aulnoye 1.45 — Haguenau 1.25 (total xG ≈ 2.7)
• Primary / headline line (Betting Primary Pick when shown): BTTS Yes
• Model: 54.9% · Implied: 47.3% · Probability edge: +7.6 pts · Est. EV: +7.0%
• BTTS (model): Yes 54.9% · No 45.1%
• Correct score (top bin): 1-1 (13.2%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

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

Primary pick from the decision engine: BTTS Yes.

We separate probability edge (model minus implied, in points of probability) from estimated EV (economic edge at the best price shown on the page).

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

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.

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.

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.

• 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)