FC Dallas vs San Diego FC prediction for July 26, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects San Diego FC 1.53 - FC Dallas 1.36. San Diego FC is favored with a 42.2% win probability. Expected total goals: 2.9..
San Diego FC
1.53
Projected Goals
VS
2.9 total
FC Dallas
1.36
Projected Goals
Match Outcome Probabilities
San Diego FCDrawFC Dallas
Calibrated accuracy at this confidence: 62.9% (1,163 games)
Projected Goals Range 10th – 90th percentile
FC Dallas
0.61.42.1
San Diego FC
0.81.52.3
Expected Goals (xG)
San Diego FC1.53
FC Dallas1.36
18.6Shots15.8
6.7On Target5.6
5.9Corners5.6
Goal Probabilities
Over 0.5
96.3%
Over 1.5
77.4%
Over 2.5
54.2%
Over 3.5
41.6%
Under 2.5
45.8%
BTTS
58.5%
Most Likely Scores
1-1
12.6%
2-1
8.9%
1-2
7.9%
1-0
7.6%
0-1
6.7%
Match Context
MLSMedium
San Diego FC
2.34
Draw
3.98
FC Dallas
2.82
AI Intelligence Analysis
NEUTRAL -2
Market significantly outvalues home favorite (model 42.23% vs market 51.5%, 9.27% gap) — home ML historical support weakness (a supportive track record) combined with large market-favorable gap signals model may be offline or market has information edge (injury/lineup).
Key Factors
- Probability gap: Model 42.23% vs market 51.5% — 9.27% favoring MARKET (unusual; model lower)
- xG gap: +0.17 (SD 1.53 vs DAL 1.36) — minimal advantage, barely worth mentioning
- Home ML historical support at a supportive track record — weak despite home advantage
- Draw probability: 23.47% — significant outcome destructive to ML value
Risk Factors
- Large probability gap against model direction suggests either: (1) market is correct and model undervaluing home advantage, (2) market knows injury/lineup changes not in simulation, (3) model calibration issue
- Cannot identify specific reason to override market consensus
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 →