MLB Baseball

SF vs LAD Prediction

May 12, 2026

10,000 Monte Carlo simulations

SF vs LAD prediction for May 12, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects LAD 2.8 - SF 3.0. SF is favored with a 51.1% win probability. The run line is -1.5 and the total is 8.5. Model projects 5.7 total runs.

LAD
2.8
Projected Score
VS O/U 8.5
SF
3.0
Projected Score
Win Probability
48.9%
51.1%
LADSF
-1.5
Run Line (LAD)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.6% (2,040 games)

Projected Runs Range 10th – 90th percentile

SF
135
LAD
135
FINALLAD 2 — SF 6
Projected
LAD 2.8 — SF 3.0
Actual
LAD 2 — SF 6

Starting Pitcher Matchup

Adrian Houser R
SF
SI46%95 mph8% whiff
CH19%86 mph35% whiff
SL14%88 mph15% whiff
Yoshinobu Yamamoto R
LAD
FS30%91 mph37% whiff
FF24%96 mph19% whiff
FC17%91 mph26% whiff

Weather Impact

Dodger Stadium
64°F9 mph wind
HR: 0.977 Total: 0.985
9mph in

Bullpen Comparison

SF
3.52ERA
4.10FIP
7.87K/9
4.84BB/9
1.37WHIP
LAD
3.61ERA
3.21FIP
9.60K/9
3.63BB/9
1.22WHIP

Betting Edges

ML AWAY
+57.2% EV
+250
TOTAL OVER 8.5
-47.5% EV
-106
RUN_LINE HOME -1.5
-40.5% EV
-147
F5 UNDER 4.5
+39.0% EV
-104
F5_ML AWAY
+38.1% EV
+205
TOTAL UNDER 8.5
+37.1% EV
-114

First 5 Innings & NRFI

SF F5
1.4 runs
37.0% win
LAD F5
1.4 runs
36.5% win
F5 Total
2.8
NRFI
68.8%
YRFI
31.2%
Avg 1st Inn Runs
0.57

HR Spotlight

Avg HRs
1.3
Over 0.5 HR
72%
Over 1.5 HR
36%
No HR
28%

Pitcher Strikeout Projections

Adrian Houser
0.0 K projected
SF | K/9: 0.0
Yoshinobu Yamamoto
0.0 K projected
LAD | K/9: 0.0

Injury Report

SF8 injured
Jason Foley RP60-DAY-IL
Erik Miller RP15-DAY-IL
Daniel Susac C10-DAY-IL
Logan Webb SP15-DAY-IL
Jared Oliva CF60-DAY-IL
Rowan Wick RP60-DAY-IL
+2 more
LAD8 injured
Ben Casparius RP15-DAY-IL
Edwin Diaz RP60-DAY-IL
Brock Stewart RP15-DAY-IL
Tommy Edman 2B60-DAY-IL
Tyler Glasnow SP15-DAY-IL
Landon Knack SP60-DAY-IL
+2 more

Edge Analysis

Moneyline
SF 51.1%
-40.5 pts
Run Line
-1.5
-40.5 pts
Total
8.5
+37.1 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. Full methodology →

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