Saint-Priest vs Toulon Statistics & Analysis

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

• League: National 2 - Group C
• Fixture: Saint-Priest vs Toulon
• Kickoff: 2026-05-09 16:00:00
• 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
• xG (showing): Saint-Priest 1.45 — Toulon 1.25 (total xG ≈ 2.7)
• Primary / headline line (Betting Primary Pick when shown): Over 2.5 goals
• Model: 53.1% · Implied: 46.2% · Probability edge: +6.9 pts · Est. EV: +8.8%
• BTTS (model): Yes 53.3% · No 46.7%
• Correct score (top bin): 1-1 (11.9%)

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.

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

Primary pick from the decision engine: Over 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).

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.

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 17, 2026 (UTC)