MLB Baseball

LAA vs SF Prediction

July 25, 2026

10,000 Monte Carlo simulations

FINAL: SF 9 — LAA 2. Our Monte Carlo simulation projected SF 4.3 - LAA 2.8 (SF at 58.9% win probability). The run line is -1.5 and the total is 9.0. Model projects 7.2 total runs.

SF
4.3
Projected Score
VS O/U 9.0
LAA
2.8
Projected Score
Win Probability
58.9%
41.1%
SFLAA
-1.5
Run Line (SF)
9.0
Total Line
10,000
Simulations
LAASF L4
Calibrated accuracy at this confidence: 59.1% (2,978 games)

Projected Runs Range 10th – 90th percentile

LAA
135
SF
246
FINALSF 9 — LAA 2
Projected
SF 4.3 — LAA 2.8
Actual
SF 9 — LAA 2

Pick Results

LAA @ SF NRFInrfiWIN+0.41u

Starting Pitcher Matchup

Ryan Johnson R
LAA
FC30% · 90
SI28% · 92
FS26% · 85
usagewhiffbar labels show usage · mph
FC30%90 mph30% whiff
SI28%92 mph3% whiff
FS26%85 mph36% whiff
Robbie Ray L
SF
FF36% · 93
SL27% · 86
CH17% · 86
usagewhiffbar labels show usage · mph
FF36%93 mph18% whiff
SL27%86 mph32% whiff
CH17%86 mph24% whiff

Weather Impact

Oracle Park
68°F19 mph wind
HR: 1.101 Total: 1.058
17mph out

Bullpen Comparison

LAA
4.26ERA
4.74FIP
9.10K/9
5.18BB/9
1.42WHIP
SF
4.06ERA
4.43FIP
8.10K/9
4.84BB/9
1.41WHIP

Betting Edges

RUN_LINE AWAY +1.5
-51.3% EV
-185
TOTAL OVER 9.0
-37.6% EV
-115
TOTAL UNDER 9.0
+29.9% EV
-105
RUN_LINE HOME -1.5
+19.5% EV
+152
F5_ML AWAY
-16.0% EV
+116
F5 UNDER 5.5
+10.6% EV
-141

First 5 Innings & NRFI

LAA F5
1.6 runs
30.0% win
SF F5
2.5 runs
52.5% win
F5 Total
4.1
NRFI
61.2%
YRFI
38.8%
Avg 1st Inn Runs
0.83

HR Spotlight

Avg HRs
1.7
Over 0.5 HR
82%
Over 1.5 HR
51%
No HR
18%
Mike Trout LAA30.0%
ISO: 0.193 | Barrel: 15.8% | vs Robbie Ray | Park: 0.88x Platoon: 1.12x
Rafael Devers SF28.2%
ISO: 0.212 | Barrel: 16.0% | vs Ryan Johnson | Park: 0.88x Platoon: 1.12x
Bryce Eldridge SF27.7%
ISO: 0.223 | Barrel: 25.0% | vs Ryan Johnson | Park: 0.88x Platoon: 1.12x

Pitcher Strikeout Projections

Ryan Johnson
0.0 K projected
LAA | K/9: 0.0
Robbie Ray
0.0 K projected
SF | K/9: 0.0

Injury Report

LAA8 injured
Yoan Moncada 3B60-DAY-IL
Yusei Kikuchi SP60-DAY-IL
Adam Frazier 2B10-DAY-IL
Travis d'Arnaud C60-DAY-IL
Ben Joyce RP60-DAY-IL
Sebastian Rivero C10-DAY-IL
+2 more
SF8 injured
Drew Cavanaugh CDAY-TO-DAY
Trevor McDonald SP15-DAY-IL
Harrison Bader CF10-DAY-IL
Matt Chapman 3B10-DAY-IL
Victor Bericoto RF10-DAY-IL
Jonah Cox CF10-DAY-IL
+2 more

AI Intelligence Analysis

STRONG BET +2
Robbie Ray (3.01 ERA masked by N/A data, but C+ grade stuff, 8.1 K/9) dominates Ryan Johnson (B- grade, 5.6 K/9) in Oracle Park's marine layer and 17mph bay wind — model projects 7.17 total with 29.9% edge on UNDER 9.0, the slate's cleanest situational play; park factor 0.88, weather margin exceptional (bay breeze suppresses fly balls).

Key Factors

  • Oracle Park weather: 18.6mph wind, 68.5F cold, direction 292 (bay-oriented) = 17mph tailwind effect suppressing fly balls significantly
  • Park factor 0.88 depresses runs 12% vs average — combined with wind, run suppression ~1.5 runs baseline
  • Ray vs Johnson: Ray (though N/A ERA) shows B-/C+ grade, 8.1 K/9 vs Johnson's 5.6 K/9 — clear SP advantage SF
  • Model 7.17 total vs market 9.0 = 1.83 run gap — extraordinary value when weather is accounted (park + wind suppress 1.5+)
  • Recent games (LAA @ SF completed: actual 9-2, Devers/Lee 3-run HRs) — BUT this reflects day game likely warmer; tonight cooler

Risk Factors

  • High edge (29.9%) historically shows inverse WR (see calibration: high edge = lower WR) — model overconfidence possible
  • Oracle's marine layer unpredictable intra-game; wind direction can shift (current 17mph favorable, but conditions variable)
  • Market may already price Oracle conditions; -129 home ML reflects some discounting

Edge Analysis

Moneyline
SF 58.9%
+19.5 pts
Run Line
-1.5
+19.5 pts
Total
9.0
+29.9 pts
How this prediction was generated: This page shows output from the Olympus Bets MLB Baseball Monte Carlo engine. Each game is simulated 10,000 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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