Soccer

Colorado Rapids vs St. Louis City SC Prediction

July 26, 2026

5,000 Monte Carlo simulations

Colorado Rapids vs St. Louis City SC prediction for July 26, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects St. Louis City SC 1.71 - Colorado Rapids 1.28. St. Louis City SC is favored with a 47.8% win probability. Expected total goals: 3.0..

St. Louis City SC
1.71
Projected Goals
VS 3.0 total
Colorado Rapids
1.28
Projected Goals
Match Outcome Probabilities
47.8%
24%
28.5%
St. Louis City SCDrawColorado Rapids
Calibrated accuracy at this confidence: 59.5% (1,163 games)

Projected Goals Range 10th – 90th percentile

Colorado Rapids
0.51.32.1
St. Louis City SC
0.91.72.5

Expected Goals (xG)

St. Louis City SC1.71
Colorado Rapids1.28
19.7Shots15.9
7.3On Target5.6
6.1Corners5.7

Goal Probabilities

Over 0.5
96.6%
Over 1.5
78.0%
Over 2.5
55.6%
Over 3.5
42.9%
Under 2.5
44.4%
BTTS
60.2%

Most Likely Scores

1-1
12.0%
2-1
9.5%
1-0
7.8%
2-0
7.4%
1-2
7.1%

Match Context

MLSMedium
St. Louis City SC
2.02
Draw
3.89
Colorado Rapids
3.55

AI Intelligence Analysis

NEUTRAL
Model and market are tightly aligned with only 2.46% probability divergence; home ML historical support weakness (a supportive track record) and moderate xG gap (0.43) offer no defensible edge.

Key Factors

  • xG gap: +0.43 (STL 1.71 vs COL 1.28) — minor advantage insufficient to overcome historical support weakness
  • Model prob 47.84% vs market 50.3% — alignment within 2.46%, suggesting efficient pricing
  • Home ML profitability historical support at a supportive track record — structural weakness evident
  • Draw probability: 23.71% — material risk to moneyline outcomes

Risk Factors

  • Essentially pick-em matchup at -101 — coin flip with draw risk destroying value
  • No quantifiable reason to believe model beats market here

Edge Analysis

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Edge Analysis
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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 →

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