FC Dallas vs Portland Timbers prediction for July 23, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Portland Timbers 1.53 - FC Dallas 1.34. Portland Timbers is favored with a 42.3% win probability. Expected total goals: 2.9..
Portland Timbers
1.53
Projected Goals
VS
2.9 total
FC Dallas
1.34
Projected Goals
Match Outcome Probabilities
Portland TimbersDrawFC Dallas
Calibrated accuracy at this confidence: 62.8% (1,163 games)
Projected Goals Range 10th – 90th percentile
FC Dallas
0.61.32.1
Portland Timbers
0.81.52.3
Expected Goals (xG)
Portland Timbers1.53
FC Dallas1.34
18.5Shots16.0
6.7On Target5.7
5.9Corners5.6
Goal Probabilities
Over 0.5
96.2%
Over 1.5
77.2%
Over 2.5
53.4%
Over 3.5
41.5%
Under 2.5
46.6%
BTTS
58.9%
Most Likely Scores
1-1
12.6%
2-1
9.0%
1-2
7.8%
1-0
7.8%
0-1
6.7%
Match Context
MLSMedium
Portland Timbers
2.34
Draw
3.82
FC Dallas
2.90
AI Intelligence Analysis
NEUTRAL
Market has priced Portland home nearly perfectly (42.29% model vs 42.7% market, 0.41% gap negligible). Model shows under value (2.87 vs 3.25, 0.38 goal lean), but totals category underperforms (a supportive track record) and under direction is historically worst (a supportive track record)
Key Factors
- Market efficiency: Model 42.29% home win vs market 42.7% (0.41% gap — essentially perfect)
- Minimal xG edge: Portland 1.53 vs Dallas 1.34 (+0.19 — very slight)
- Under value marginal: Model 2.87 vs market 3.25 (+0.38 lean, within variance noise)
- Under direction broken: SOCCER | under a supportive track record (F grade)
- Draw risk high: Model 24.32% draw vs H2H 0%
Risk Factors
- Under bets systematically fail: a supportive track record across 23 under picks in soccer
- Totals category weak overall: a supportive track record despite skip calibration flags
- Market may have superior data: 3.25 total reflects recent MLS scoring trends
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 →