Temperley vs Club Atlético Güemes Statistics & Analysis

Quick read on how the model reads this matchup.

• League: Primera Nacional
• Fixture: Temperley vs Club Atlético Güemes
• Kickoff: 2026-06-14 19:00:00
• 1X2 (model): Home 41.0% · Draw 31.0% · Away 28.0%
• xG (showing): Temperley 1.36 — Club Atlético Güemes 1.24 (total xG ≈ 2.6)
• Primary / headline line (Betting Primary Pick when shown): BTTS Yes
• Model: 54.4% · Implied: 35.7% · Probability edge: +18.7 pts · Est. EV: +49.6%
• BTTS (model): Yes 54.4% · No 45.6%
• Correct score (top bin): 1-1 (12.5%)

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.

The engine’s headline primary is: BTTS Yes.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

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

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.

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.

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.

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.

• 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)