Soccer

Mallorca vs Getafe Prediction

May 13, 2026

15,000 Monte Carlo simulations

Mallorca vs Getafe prediction for May 13, 2026: Our Monte Carlo simulation ran 15,000 game iterations and projects Getafe 1.29 - Mallorca 1.26. Getafe is favored with a 31.8% win probability. Expected total goals: 2.5..

Getafe
1.29
Projected Goals
VS 2.5 total
Mallorca
1.26
Projected Goals
Match Outcome Probabilities
31.8%
38%
30.2%
GetafeDrawMallorca
Calibrated accuracy at this confidence: 88.9% (1,051 games)

Projected Goals Range 10th – 90th percentile

Mallorca
0.51.32.0
Getafe
0.51.32.1
FINALGetafe 3 — Mallorca 1
Projected
Getafe 1.29 — Mallorca 1.26
Actual
Getafe 3 — Mallorca 1

Expected Goals (xG)

Getafe0.62
Mallorca0.59
15.7Shots15.1

Goal Probabilities

Over 0.5
92.4%
Over 1.5
73.4%
Over 2.5
47.4%
Over 3.5
24.7%
Under 2.5
52.6%
BTTS
55.4%

Match Context

LALMedium
Getafe
2.37
Draw
3.05
Mallorca
3.60

AI Intelligence Analysis

NEUTRALYELLOW ZONE56.2% WR (n=7)
Market correctly prices this as a coin-flip between two weak teams. High draw probability (38%) kills all ML value. UNDER 2.5 (52.65% model) offers slight edge but confidence is marginal. Skip ML, skip totals — neither offers sufficient value.

Key Factors

  • Both weak-tier teams with poor attacking: Getafe 0.6, Mallorca 0.69 — virtually identical
  • xG gap negligible (0.03) — coin flip match fundamentals
  • Draw probability 38% — well above league avg (23-25%), indicates tight, defensive battle

Risk Factors

  • High draw rate (38%) suppresses all ML value — outright win odds overpriced at current lines
  • Both teams weak in attack (0.6-0.69) — low BTTS likelihood (55.35%), supports UNDER 2.5
  • UNDER 2.5 at 52.65% is only slight edge — not sufficient for HIGH confidence recommendation
WEAK TIER MISMATCHCOIN FLIPDRAW RISKLOW SCORINGBOTH WEAK TEAMS

Edge Analysis

Moneyline
Getafe 31.8%
--
Total
2.5
+25.1 pts
How this prediction was generated: This page shows output from the Olympus Bets Soccer Monte Carlo engine. Each game is simulated 15,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →

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