Soccer

LA Galaxy vs San Jose Earthquakes Prediction

July 26, 2026

5,000 Monte Carlo simulations

LA Galaxy vs San Jose Earthquakes prediction for July 26, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects San Jose Earthquakes 1.73 - LA Galaxy 1.28. San Jose Earthquakes is favored with a 48.9% win probability. Expected total goals: 3.0..

San Jose Earthquakes
1.73
Projected Goals
VS 3.0 total
LA Galaxy
1.28
Projected Goals
Match Outcome Probabilities
48.9%
22%
28.6%
San Jose EarthquakesDrawLA Galaxy
Calibrated accuracy at this confidence: 59.5% (1,163 games)

Projected Goals Range 10th – 90th percentile

LA Galaxy
0.51.32.1
San Jose Earthquakes
1.01.72.5

Expected Goals (xG)

San Jose Earthquakes1.73
LA Galaxy1.28
19.0Shots16.5
6.8On Target5.9
6.0Corners5.7

Goal Probabilities

Over 0.5
97.0%
Over 1.5
78.1%
Over 2.5
55.1%
Over 3.5
42.9%
Under 2.5
44.9%
BTTS
60.3%

Most Likely Scores

1-1
11.9%
2-1
9.5%
1-0
7.8%
2-0
7.5%
1-2
7.1%

Match Context

MLSMedium
San Jose Earthquakes
1.89
Draw
4.18
LA Galaxy
3.72

AI Intelligence Analysis

NEUTRAL
Model and market closely aligned (2.42% gap); home ML historical support weakness (a supportive track record) and modest xG advantage (0.45) offer no edge over market consensus.

Key Factors

  • xG gap: +0.45 (SJ 1.73 vs LAG 1.28) — minor advantage insufficient to overcome historical support weakness
  • Model prob 48.88% vs market 51.3% — alignment within 2.42%, efficient pricing
  • Home ML historical support at a supportive track record — weak category
  • Draw probability: 22.52% — moderate risk, lower than slate average

Risk Factors

  • Home ML historical support consistently underperforms historical average (43.8% vs ~50% expected)
  • xG gap modest; home advantage is primary driver, which market has already reflected

Edge Analysis

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Edge Analysis
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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 →

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