Real Salt Lake vs Portland Timbers prediction for July 26, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Portland Timbers 1.51 - Real Salt Lake 1.42. Portland Timbers is favored with a 40.5% win probability. Expected total goals: 2.9..
Portland Timbers
1.51
Projected Goals
VS
2.9 total
Real Salt Lake
1.42
Projected Goals
Match Outcome Probabilities
Portland TimbersDrawReal Salt Lake
Calibrated accuracy at this confidence: 63.4% (1,163 games)
Projected Goals Range 10th – 90th percentile
Real Salt Lake
0.61.42.2
Portland Timbers
0.71.52.3
Expected Goals (xG)
Portland Timbers1.51
Real Salt Lake1.42
18.6Shots16.4
6.7On Target5.9
6.0Corners5.6
Goal Probabilities
Over 0.5
96.6%
Over 1.5
77.6%
Over 2.5
54.8%
Over 3.5
42.2%
Under 2.5
45.2%
BTTS
60.1%
Most Likely Scores
1-1
12.4%
2-1
8.7%
1-2
8.2%
1-0
7.2%
0-1
6.7%
Match Context
MLSMedium
Portland Timbers
2.49
Draw
3.96
Real Salt Lake
2.64
AI Intelligence Analysis
NEUTRAL
Model-market probability gap (11.1% favoring market) suggests market is correctly pricing home advantage; minimal xG gap (0.09) and weak home ML historical support provide no defensible edge.
Key Factors
- xG gap: +0.09 (POR 1.51 vs RSL 1.42) — essentially tied, suggesting teams are evenly matched on quality
- Probability gap: Model 40.5% vs market 51.6% — 11.1% favoring MARKET on home favorite
- Home ML historical support at a supportive track record — weak despite home advantage
- Draw probability: 23.32% — high, material risk to moneyline
Risk Factors
- Large probability gap against model (11.1%) suggests market correctly valuing home advantage; model may be undervaluing home field
- xG gap trivial (0.09); market confidence is driven by home advantage rather than quality gap
- Draw risk at 23.32% is above slate average — particularly destructive to home ML value
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 →