D.C. United vs Houston Dynamo prediction for July 23, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Houston Dynamo 1.82 - D.C. United 1.2. Houston Dynamo is favored with a 51.6% win probability. Expected total goals: 3.0..
Houston Dynamo
1.82
Projected Goals
VS
3.0 total
D.C. United
1.2
Projected Goals
Match Outcome Probabilities
Houston DynamoDrawD.C. United
Calibrated accuracy at this confidence: 59.3% (1,163 games)
Projected Goals Range 10th – 90th percentile
D.C. United
0.41.22.0
Houston Dynamo
1.01.82.6
Expected Goals (xG)
Houston Dynamo1.82
D.C. United1.20
19.8Shots16.1
7.2On Target5.7
6.1Corners5.6
Goal Probabilities
Over 0.5
97.0%
Over 1.5
78.3%
Over 2.5
56.0%
Over 3.5
43.2%
Under 2.5
44.0%
BTTS
60.9%
Most Likely Scores
1-1
11.6%
2-1
9.8%
2-0
8.2%
1-0
8.1%
1-2
6.5%
Match Context
MLSMedium
Houston Dynamo
1.79
Draw
3.89
D.C. United
4.52
AI Intelligence Analysis
NEUTRAL -1
Houston shows meaningful xG edge (1.82 vs 1.20, +0.62) and model suggests market slightly favors home, but historically weak home ML + 23.83% draw probability eliminate actionability
Key Factors
- xG advantage: Houston 1.82 vs DC 1.20 (+0.62 — second-largest on slate)
- Market undervalues home slightly: Model 51.59% win vs market 55.9% (4.31% edge to model)
- Draw risk: Model 23.83% draw vs H2H 0% (data mismatch continues)
- Home ML historical support RED: a supportive track record, n=68
- Odds reasonable: -124 is not aggressively priced; market respecting some home advantage
Risk Factors
- Home ML historical failure: a supportive track record despite xG signals suggests model may be overestimating home value
- Draw probability unresolved: If 23.83% is accurate, draw risk is massive; if 0%, model structure is suspect
- Quality mismatch not captured: xG advantage may not translate to wins if DC's defense is better-organized than shot quality implies
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 →