Atlanta United FC vs Charlotte FC prediction for July 23, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Charlotte FC 1.7 - Atlanta United FC 1.31. Charlotte FC is favored with a 45.9% win probability. Expected total goals: 3.0..
Charlotte FC
1.7
Projected Goals
VS
3.0 total
Atlanta United FC
1.31
Projected Goals
Match Outcome Probabilities
Charlotte FCDrawAtlanta United FC
Calibrated accuracy at this confidence: 60.9% (1,163 games)
Projected Goals Range 10th – 90th percentile
Atlanta United FC
0.51.32.1
Charlotte FC
0.91.72.5
Expected Goals (xG)
Charlotte FC1.70
Atlanta United FC1.31
19.1Shots16.6
6.9On Target5.9
6.0Corners5.7
Goal Probabilities
Over 0.5
97.0%
Over 1.5
78.3%
Over 2.5
55.4%
Over 3.5
42.6%
Under 2.5
44.6%
BTTS
59.9%
Most Likely Scores
1-1
12.0%
2-1
9.4%
1-0
7.6%
1-2
7.3%
2-0
7.2%
Match Context
MLSMedium
Charlotte FC
2.09
Draw
3.64
Atlanta United FC
3.55
AI Intelligence Analysis
NEUTRAL -1
Draw data integrity failure (model 24.95% vs H2H 0%) makes ML unreliable; market odds already reflect reasonable calibration despite edge; ML historical support is RED (a supportive track record)
Key Factors
- xG advantage: Charlotte 1.70 xGF vs Atlanta 1.31 xGF (+0.39 edge)
- Probability gap: Model home 45.91% vs market 47.8% (market slightly favoring home, -1.89% to model)
- Draw data conflict: Model 24.95% draw vs H2H 0.0% (unexplained 24.95pt divergence)
- Home ML historical support (a supportive track record, n=68) — historically poor performance
- Totals model: 3.01 avg goals vs 2.75 market total (+0.26 over lean)
Risk Factors
- Draw probability uncertainty: If model is correct at 24.95%, home ML is poison. If H2H is correct at 0%, model xG analysis may be flawed
- Home ML trap: 43.3% historical WR in SOCCER | ml | home | any | any | any historical support means home bets systematically underperform
- Market efficiency: -110 odds suggest market has already absorbed available edge
Edge Analysis
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 →