Panama vs Ghana prediction for June 17, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Ghana 1.6 - Panama 1.4. Ghana is favored with a 40.7% win probability. Expected total goals: 3.0..
Ghana
1.6
Projected Goals
VS
3.0 total
Panama
1.4
Projected Goals
Match Outcome Probabilities
GhanaDrawPanama
Calibrated accuracy at this confidence: 67.2% (1,064 games)
Projected Goals Range 10th – 90th percentile
Panama
0.61.42.2
Ghana
0.81.62.4
Projected
Ghana 1.6 — Panama 1.4
Actual
Ghana 1 — Panama 0
Expected Goals (xG)
Ghana1.60
Panama1.40
18.3Shots17.4
6.5On Target6.3
6.0Corners5.8
Goal Probabilities
Over 0.5
97.0%
Over 1.5
83.3%
Over 2.5
55.3%
Over 3.5
42.5%
Under 2.5
44.7%
BTTS
59.7%
Most Likely Scores
1-1
12.2%
2-1
9.0%
1-2
7.9%
1-0
7.2%
2-0
6.4%
Match Context
WCHigh
Ghana
2.43
Draw
3.24
Panama
3.31
AI Intelligence Analysis
NEUTRALRED ZONE42.4% WR (n=51)
Nearly perfect balanced match (40.7% home, 27.3% draw, 32.0% away) with NO edge on home ML (market 41.2% implied vs model 40.7% — pick 'em), 27.3% draw risk, and completely neutral over/under (55.3% model on 2.25 market total). This is a coin flip. SKIP.
Key Factors
- Ghana xG 1.6, Panama 1.4 — only 0.2 xG difference (essentially equal teams)
- Home win probability 40.7% — LESS than pick 'em (Ghana is underdog at home)
- Draw probability 27.3% — second-highest on slate; real risk of splitting points
- Over 2.5 edge: 55.3% prob vs 2.25 line = +0.75 edge but low probability
Risk Factors
- This is a classic pick 'em/coin flip: no meaningful edge either direction
- Market is likely correct — both teams are moderately talented international squads
- Draw at 27.3% is frequent enough to remove home value even though home is favored slightly
NEUTRAL EDGEPICK EM SPREADDRAW RISKBALANCED MATCHCOIN FLIP
Edge Analysis
Moneyline
Ghana 40.7%
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Total
3.0
+29.9 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 →