Sevilla vs Villarreal prediction for May 13, 2026: Our Monte Carlo simulation ran 15,000 game iterations and projects Villarreal 2.18 - Sevilla 1.24. Villarreal is favored with a 65.2% win probability. Expected total goals: 3.4..
Villarreal
2.18
Projected Goals
VS
3.4 total
Sevilla
1.24
Projected Goals
Match Outcome Probabilities
VillarrealDrawSevilla
Calibrated accuracy at this confidence: 75.5% (1,051 games)
Projected Goals Range 10th – 90th percentile
Sevilla
0.51.22.0
Villarreal
1.42.23.0
Projected
Villarreal 2.18 — Sevilla 1.24
Actual
Villarreal 2 — Sevilla 3
Expected Goals (xG)
Villarreal1.46
Sevilla0.57
39.7Shots12.2
Goal Probabilities
Over 0.5
97.1%
Over 1.5
86.4%
Over 2.5
53.0%
Over 3.5
44.8%
Under 2.5
47.0%
BTTS
65.9%
Match Context
LALMedium
Villarreal
2.19
Draw
3.42
Sevilla
3.58
AI Intelligence Analysis
NEUTRAL -1RED ZONE42.9% WR (n=75)
While model correctly identifies Villarreal dominance (elite vs weak, 0.89 xG gap, 5-game streak), home ML is a RED ZONE category (42.9% WR) that historically loses money. Draw outcomes (real ~22-25%) will suppress value. Do not take ML bet.
Key Factors
- Villarreal elite tier (attack 3.07) vs Sevilla weak tier (attack 1.05): 2.02-point quality gap → 0.89 xG advantage confirmed
- Form disparity extreme: Villarreal WWWWW (form_mult 1.2) vs Sevilla LLWLD (form_mult 0.9)
- Model edge 19.55% is largest on slate, but home ML in RED zone with 42.9% WR (n=75, z=-1.27)
Risk Factors
- Home ML zone is RED zone (42.9% historical WR) — largest losing category in soccer betting
- Draw probability model-estimated at 19.86% is likely LOW — actual draw rate 22-25% would suppress outright win value significantly
- Calibration: 65-70% prob bucket has only 63.6% actual hit rate per shrinkage tables — overconfident
RED ZONEHOME ML TRAPLARGEST EDGE SLOTDRAW RISK UNDERESTIMATEDTIER MISMATCHFORM GAP
Edge Analysis
Moneyline
Villarreal 65.2%
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Total
3.4
+10.8 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 →