Deportivo Llacuabamba vs UTC Cajamarca Statistics & Analysis

Quick read on how the model reads this matchup.

• League: Copa De La Liga
• Fixture: Deportivo Llacuabamba vs UTC Cajamarca
• Kickoff: 2026-06-15 18:00:00
• 1X2 (model): Home 40.5% · Draw 28.9% · Away 30.7%
• xG (showing): Deportivo Llacuabamba 1.32 — UTC Cajamarca 1.28 (total xG ≈ 2.6)
• Best +EV line (same label as hero card when Primary thresholds are not met): BTTS Yes
• Model: 54.5% · Implied: 50.6% · Probability edge: +3.9 pts · Est. EV: +4.1%
• BTTS (model): Yes 54.5% · No 45.5%
• Correct score (top bin): 1-1 (12.5%)

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

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

Top tracked +EV leg right now (hero card, non-primary grading): BTTS Yes.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

Only one modest +EV edge is highlighted here; size cautiously and re-check if odds move.

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.

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.

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.

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 16, 2026 (UTC)