Espoir Savalou vs Hodio Statistics & Analysis

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

• League: Championnat National
• Fixture: Espoir Savalou vs Hodio
• Kickoff: 2026-06-13 15:00:00
• 1X2 (model): Home 31.5% · Draw 38.0% · Away 30.5%
• xG (showing): Espoir Savalou 0.84 — Hodio 0.82 (total xG ≈ 1.66)
• Value headline: None — no positive EV could be estimated on tracked lines at current best prices (missing odds or thin book depth).
• Structural leans (not bets): Structural lean (model): O/U 2.5 Under 2.5 (Under 2.5 76.8% · Over 2.5 23.2%); BTTS No (Yes 33.5% · No 66.5%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS No
• BTTS (model): Yes 33.5% · No 66.5%
• Correct score (top bin): 0-0 (19.0%)

When book depth is thin or odds are missing, EV may be unavailable even though the model still prefers one side on totals or BTTS — wait for cleaner prices or skip.

Most likely correct score stays a low-probability tail: use it for context, not as a must-bet story.

No bankroll-sized bet is implied here.

Treat this page as a read-only diagnostic: totals/BTTS structure can be informative even when the honest answer is to wait.

Correct-score markets remain high-variance even when one scoreline leads the table.

Why is there no “best bet” on this page?

The headline engine uses a minimum +EV threshold (e.g. 2%) for a default pick. A line can still show tiny +EV that fails that bar — we still call it no default bet so readers do not over-size thin edges.

Is the most likely correct score still relevant?

As context only: it is still a low absolute probability tail outcome (often in the single digits, sometimes low teens). It does not override the “no headline +EV” stance — treat score bets as fun-sized if you play them at all.

Should I still read the 1X2 card?

Yes — it shows whether any winner price clears value. Here it often explains why there is no headline: probabilities can be clustered while prices already embed that uncertainty.

What do the grey “lean” labels mean then?

They summarise where the model tilts (e.g. Under 2.5 or BTTS No) without claiming a positive economic edge. Use them as context; size to zero unless you deliberately accept discretionary risk.

• 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.

June 15, 2026 (UTC)