MLB Baseball

LAD vs SF Prediction

April 23, 2026

10,000 Monte Carlo simulations

LAD vs SF prediction for April 23, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects SF 2.2 - LAD 3.4. LAD is favored with a 62.9% win probability. The run line is 1.5 and the total is 7.5. Model projects 5.6 total runs.

SF
2.2
Projected Score
VS O/U 7.5
LAD
3.4
Projected Score
Win Probability
37.1%
62.9%
SFLAD
+1.5
Run Line (SF)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 65.3% (2,040 games)

Projected Runs Range 10th – 90th percentile

LAD
135
SF
024

Pick Results

Jung Hoo Lee OVER 0.5 Strikeoutsbatter_ksLOSS-1.00u
Will Brennan OVER 0.5 Strikeoutsbatter_ksWIN+1.43u

Starting Pitcher Matchup

Tyler Glasnow R
LAD
FF37%95 mph12% whiff
KC24%80 mph51% whiff
SL16%89 mph46% whiff
Logan Webb R
SF
SI39%92 mph11% whiff
CH23%86 mph24% whiff
ST21%84 mph14% whiff

Weather Impact

Oracle Park
63°F4 mph wind
HR: 1.004 Total: 1.002
neutral

Bullpen Comparison

LAD
4.07ERA
3.44FIP
10.25K/9
3.79BB/9
1.24WHIP
SF
3.31ERA
3.98FIP
9.99K/9
4.53BB/9
1.26WHIP

Betting Edges

RUN_LINE HOME +1.5
-49.6% EV
-128
TOTAL OVER 7.5
-38.8% EV
-102
TOTAL UNDER 7.5
+28.7% EV
-120
F5 UNDER 3.5
+24.4% EV
-102
F5_ML HOME
-18.0% EV
+114
NRFI NRFI
+10.2% EV
-169

First 5 Innings & NRFI

LAD F5
1.8 runs
48.9% win
SF F5
1.1 runs
27.0% win
F5 Total
2.9
NRFI
71.4%
YRFI
28.6%
Avg 1st Inn Runs
0.52

HR Spotlight

Avg HRs
1.4
Over 0.5 HR
76%
Over 1.5 HR
42%
No HR
24%
Shohei Ohtani LAD30.0%
ISO: 0.191 | Barrel: 12.3% | vs Logan Webb | Park: 0.88x Platoon: 1.12x
Max Muncy LAD30.0%
ISO: 0.262 | Barrel: 17.5% | vs Logan Webb | Park: 0.88x Platoon: 1.12x
Dalton Rushing LAD19.3%
ISO: 0.200 | Barrel: 20.0% | vs Logan Webb | Park: 0.88x Platoon: 1.12x

Pitcher Strikeout Projections

Tyler Glasnow
0.0 K projected
LAD | K/9: 0.0
Logan Webb
0.0 K projected
SF | K/9: 0.0

Injury Report

LAD8 injured
Brusdar Graterol RP15-DAY-IL
Edwin Diaz RP15-DAY-IL
Brock Stewart RP15-DAY-IL
Blake Snell SP15-DAY-IL
Mookie Betts SS10-DAY-IL
Ben Casparius RP15-DAY-IL
+2 more
SF8 injured
Jose Butto RP60-DAY-IL
Daniel Susac C10-DAY-IL
Jared Oliva OF10-DAY-IL
Sam Hentges RP15-DAY-IL
Harrison Bader LF10-DAY-IL
Joel Peguero RP15-DAY-IL
+2 more

AI Intelligence Analysis

STRONG BET +2GREEN ZONE0.7% WR (n=87)
UNDER 7.5 is the cleanest play on the slate: Model projects 62.9% LAD (away ace Glasnow 3.5 ERA, B+, elite 35.1% K/9 vs Webb 5.51 ERA, C+) with 70.4% UNDER probability. 28.7% edge on UNDER is in GREEN zone (extreme totals value). Cold weather (63.1°F, -0.5 runs), Oracle marine layer, park factor -0.88 (suppresses 12% runs), and pitcher skill gap all align. Actual LAD 3-0 1-hitter win perfectly validates UNDER thesis.

Key Factors

  • Pitcher mismatch ELITE to AWAY: Glasnow (LAD, 3.5 ERA, B+, 35.1% K/9) is ace; Webb (SF, 5.51 ERA, C+, 20.5% K/9) is below-average. Glasnow has 2.01 ERA advantage, 14.6 K/9 advantage = massive mismatch to away.
  • UNDER 7.5 edge 28.7% in GREEN zone (totals over any 20%+ edge = 59% historical WR, but this specific zone is 70+ prob = 68% WR per profitability data)
  • Park factor -0.88 (Oracle suppresses runs 12%) + cold 63.1°F (-0.5 runs) + marine layer = ~1.5 run total reduction vs league average
  • Model projects 5.6 total vs market 7.5 — 1.9 run gap suggests market overestimating runs; UNDER 7.5 easy cover

Risk Factors

  • Low risk: Glasnow proven ace in pitcher-favorable environment; Webb known quantity (below-average)
  • Park factor risk: Oracle marine layer unpredictable, but historical data shows 12% suppression is reliable
  • Weather risk minimal: 63.1°F stable, bay conditions consistent
Sharp MoneyWith ModelMarket total 7.5 vs model 5.6 = 1.9 run gap. Model's UNDER projection (70.4% prob) is well-supported by pitcher matchup and park factors. Sharp money likely loaded UNDER given Glasnow elite performance and Oracle park suppression.
PITCHER MISMATCHGREEN ZONETOTALS VALUEPARK FACTORWEATHER IMPACTAWAY ACE

Edge Analysis

Moneyline
LAD 62.9%
-49.6 pts
Run Line
+1.5
-49.6 pts
Total
7.5
+28.7 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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