San Diego FC vs Colorado Rapids prediction for July 23, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Colorado Rapids 1.64 - San Diego FC 1.24. Colorado Rapids is favored with a 47.3% win probability. Expected total goals: 2.9..
Colorado Rapids
1.64
Projected Goals
VS
2.9 total
San Diego FC
1.24
Projected Goals
Match Outcome Probabilities
Colorado RapidsDrawSan Diego FC
Calibrated accuracy at this confidence: 59.9% (1,163 games)
Projected Goals Range 10th – 90th percentile
San Diego FC
0.51.22.0
Colorado Rapids
0.91.62.4
Expected Goals (xG)
Colorado Rapids1.64
San Diego FC1.24
18.8Shots15.7
6.8On Target5.6
6.0Corners5.5
Goal Probabilities
Over 0.5
96.0%
Over 1.5
77.6%
Over 2.5
52.8%
Over 3.5
41.5%
Under 2.5
47.1%
BTTS
59.5%
Most Likely Scores
1-1
12.4%
2-1
9.4%
1-0
8.3%
2-0
7.6%
1-2
7.2%
Match Context
MLSMedium
Colorado Rapids
2.00
Draw
4.06
San Diego FC
3.47
AI Intelligence Analysis
NEUTRAL -1
Colorado home has mild edge (47.35% vs 50% market) and model shows under value (2.88 vs 3.25 market, 0.37 goal lean), but historically weak home ML + high draw risk (23.50%) + weak totals category (a supportive track record) make both paths unactionable
Key Factors
- Mild home xG edge: Colorado 1.64 vs San Diego 1.24 (+0.40 advantage)
- Home ML undervalued: Model 47.35% vs market 50% (2.65% edge), but historically weak historical support
- Under value significant: Model 2.88 vs market 3.25 (+0.37 goal lean toward under)
- Home ML historical support RED: a supportive track record in SOCCER | ml | home | any | any | any
- Totals category weak: SOCCER | total shows a supportive track record despite skip calibration flags
Risk Factors
- Totals are marginal: 0.37 goal edge is within noise for soccer over/under variance
- Under is worst totals direction: SOCCER | under has a supportive track record, F grade (bad bet)
- Draw probability uncertainty: Model 23.50% vs H2H 0% makes all picks suspect
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 →