Toronto FC vs D.C. United prediction for July 25, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects D.C. United 1.64 - Toronto FC 1.33. D.C. United is favored with a 44.7% win probability. Expected total goals: 3.0..
D.C. United
1.64
Projected Goals
VS
3.0 total
Toronto FC
1.33
Projected Goals
Match Outcome Probabilities
D.C. UnitedDrawToronto FC
Calibrated accuracy at this confidence: 62.4% (1,163 games)
Projected Goals Range 10th – 90th percentile
Toronto FC
0.61.32.1
D.C. United
0.91.62.4
Projected
D.C. United 1.64 — Toronto FC 1.33
Actual
D.C. United 2 — Toronto FC 1
Expected Goals (xG)
D.C. United1.64
Toronto FC1.33
19.1Shots16.4
6.9On Target5.9
6.0Corners5.7
Goal Probabilities
Over 0.5
96.7%
Over 1.5
77.8%
Over 2.5
55.6%
Over 3.5
42.7%
Under 2.5
44.4%
BTTS
59.4%
Most Likely Scores
1-1
12.2%
2-1
9.3%
1-0
7.6%
1-2
7.5%
2-0
7.0%
Match Context
MLSMedium
D.C. United
2.18
Draw
3.68
Toronto FC
3.30
AI Intelligence Analysis
NEUTRAL -1
Market correctly priced: D.C. home ML 44.66% model vs 45.87% market = -1.21% edge. Home ML historical support weak YELLOW (a supportive track record); draw risk 24.54% kills value.
Key Factors
- xG advantage home: D.C. 1.64 vs Toronto 1.33 = 0.31 xG edge (reasonable quality gap)
- Negative edge on home ML: Model 44.66% vs market 45.87% = -1.21% (market ahead)
- Home ML weak: historically mixed a supportive track record below breakeven
- Draw risk significant: 24.54% draw probability, ~25% empirical MLS = substantial outcomes loss
- Totals suggest value: Model 2.97 vs 2.75 = +0.22 edge on OVER (but totals D grade overall)
Risk Factors
- Home ML historically weak overall (a supportive track record) — disabled category
- Away ML historically weak — cannot recommend Toronto
- Market -110 requires 55% true prob to break even; model only 44.66%
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 →