Ghana vs Croatia prediction for June 27, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Croatia 1.91 - Ghana 1.12. Croatia is favored with a 50.0% win probability. Expected total goals: 3.0..
Croatia
1.91
Projected Goals
VS
3.0 total
Ghana
1.12
Projected Goals
Match Outcome Probabilities
CroatiaDrawGhana
Calibrated accuracy at this confidence: 63.3% (1,110 games)
Projected Goals Range 10th – 90th percentile
Ghana
0.31.11.9
Croatia
1.11.92.7
Projected
Croatia 1.91 — Ghana 1.12
Actual
Croatia 2 — Ghana 1
Expected Goals (xG)
Croatia1.91
Ghana1.12
20.2Shots16.3
7.4On Target5.8
6.2Corners5.8
Goal Probabilities
Over 0.5
97.0%
Over 1.5
83.1%
Over 2.5
56.1%
Over 3.5
43.1%
Under 2.5
43.9%
BTTS
60.4%
Most Likely Scores
1-1
11.3%
2-1
10.0%
2-0
8.9%
1-0
8.5%
3-1
6.4%
Match Context
WCHigh
Croatia
1.93
Draw
3.09
Ghana
5.47
AI Intelligence Analysis
NEUTRALRED ZONE45.8% WR (n=None)
Croatia home favorite with xG edge (1.91 vs 1.12, +0.79) but market priced 2% MORE confident (51.8% vs model 50%); negligible edge and home ML RED zone risk makes this a pass.
Key Factors
- Croatia xG 1.91 > Ghana 1.12 (+0.79 edge, meaningful quality gap)
- Market home 1.93 = 51.8% vs model 50.0% (market -1.8 pts sharper—home SLIGHTLY overpriced)
- Draw probability 30.7% (model) significantly reduces home win outright probability, making ML risky
- Home ML RED zone 45.8% WR + marginal edge = poor risk-reward
Risk Factors
- Draw risk 30.7% means home only wins 50% of outcomes; at -1.8% edge, home ML is a losing bet
- High-stakes group match context suggests both teams may play conservatively; model 3.03 total optimistic
- Market respects draw risk better than model on home side
HOME ML TRAPRED ZONEMARGINAL EDGEDRAW RISKHIGH STAKES
Edge Analysis
Moneyline
Croatia 50.0%
--
Total
3.0
+26.7 pts
How this prediction was generated: This page shows output from the Olympus Bets Soccer Monte Carlo engine. Each game is simulated 5,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →