FC Cincinnati vs Columbus Crew SC prediction for July 25, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Columbus Crew SC 1.72 - FC Cincinnati 1.29. Columbus Crew SC is favored with a 48.1% win probability. Expected total goals: 3.0..
Columbus Crew SC
1.72
Projected Goals
VS
3.0 total
FC Cincinnati
1.29
Projected Goals
Match Outcome Probabilities
Columbus Crew SCDrawFC Cincinnati
Calibrated accuracy at this confidence: 59.5% (1,163 games)
Projected Goals Range 10th – 90th percentile
FC Cincinnati
0.51.32.1
Columbus Crew SC
0.91.72.5
Expected Goals (xG)
Columbus Crew SC1.72
FC Cincinnati1.29
19.6Shots15.6
7.1On Target5.6
6.1Corners5.6
Goal Probabilities
Over 0.5
96.7%
Over 1.5
78.4%
Over 2.5
55.7%
Over 3.5
42.8%
Under 2.5
44.3%
BTTS
59.7%
Most Likely Scores
1-1
12.0%
2-1
9.5%
1-0
7.7%
2-0
7.4%
1-2
7.1%
Match Context
MLSMedium
Columbus Crew SC
2.02
Draw
4.05
FC Cincinnati
3.42
AI Intelligence Analysis
NEUTRAL -1
Market priced ahead: home ML 48.13% model vs 49.50% market is negative edge (-1.37%), and home ML historical a supportive track record cannot overcome 23% draw rate.
Key Factors
- xG advantage home: Columbus 1.72 vs Cincinnati 1.29 = 0.43 xG gap (meaningful quality advantage)
- Negative model edge: Model 48.13% vs market 49.50% = -1.37% (market correctly priced or ahead)
- Home ML historical support weak: Historical a supportive track record — below breakeven threshold
- Draw kills value: 22.91% draw probability in model, ~25% empirical MLS rate = makes ML poor bet
- Totals flat: Model 3.01 vs line 3.25 = slight underestimate
Risk Factors
- Home ML historically weak overall (z=-2.59, n=134) — category disabled by system
- Market implied probability 49.50% requires 62.4% true win rate to break even vs -102 odds (home needs 50%+ to beat vig)
- Draw probability 22.91% is substantial portion of outcomes; Columbus ML loses if draw
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 →