St. Anna vs Deutschlandsberger SC Statistics & Analysis

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

• League: Regionalliga - Mitte
• Fixture: St. Anna vs Deutschlandsberger SC
• Kickoff: 2026-05-09 17:00:00
• 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
• xG (showing): St. Anna 1.45 — Deutschlandsberger SC 1.25 (total xG ≈ 2.7)
• Primary / headline line (Betting Primary Pick when shown): BTTS No
• Model: 71.2% · Implied: 39.2% · Probability edge: +32.0 pts · Est. EV: +68.0%
• BTTS (model): Yes 28.8% · No 71.2%
• Correct score (top bin): 0-1 (10.2%)

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

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

Primary pick from the decision engine: BTTS No.

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

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

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.

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.

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 17, 2026 (UTC)