MLB Baseball

LAD vs NYM Prediction

July 24, 2026

10,000 Monte Carlo simulations

FINAL: NYM 2 — LAD 4. Our Monte Carlo simulation projected NYM 4.4 - LAD 4.5 (LAD at 50.2% win probability). The run line is 1.5 and the total is 9.0. Model projects 9.0 total runs.

NYM
4.4
Projected Score
VS O/U 9.0
LAD
4.5
Projected Score
Win Probability
49.8%
50.2%
NYMLAD
+1.5
Run Line (NYM)
9.0
Total Line
10,000
Simulations
LAD W4NYM
Calibrated accuracy at this confidence: 48.2% (2,978 games)

Projected Runs Range 10th – 90th percentile

LAD
346
NYM
246
FINALNYM 2 — LAD 4
Projected
NYM 4.4 — LAD 4.5
Actual
NYM 2 — LAD 4

Pick Results

LAD @ NYM NRFInrfiWIN+0.52u

Starting Pitcher Matchup

Roki Sasaki R
LAD
FF43% · 98
FS25% · 90
SL21% · 87
usagewhiffbar labels show usage · mph
FF43%98 mph16% whiff
FS25%90 mph32% whiff
SL21%87 mph38% whiff
Sean Manaea L
NYM
FF34% · 91
ST32% · 76
SI22% · 90
usagewhiffbar labels show usage · mph
FF34%91 mph18% whiff
ST32%76 mph29% whiff
SI22%90 mph7% whiff

Weather Impact

Citi Field
79°F8 mph wind
HR: 1.024 Total: 1.012
neutral

Bullpen Comparison

LAD
3.58ERA
3.45FIP
10.00K/9
3.64BB/9
1.19WHIP
NYM
3.44ERA
3.69FIP
9.32K/9
3.58BB/9
1.21WHIP

Betting Edges

RUN_LINE HOME +1.5
-32.1% EV
-143
TOTAL OVER 9.0
-15.4% EV
-114
F5_ML AWAY
-14.1% EV
-132
RUN_LINE AWAY -1.5
-13.6% EV
+118
ML AWAY
-9.0% EV
-133
TOTAL UNDER 9.0
+6.7% EV
-106

First 5 Innings & NRFI

LAD F5
2.5 runs
39.6% win
NYM F5
2.8 runs
46.6% win
F5 Total
5.3
NRFI
52.4%
YRFI
47.6%
Avg 1st Inn Runs
1.04

HR Spotlight

Avg HRs
2.4
Over 0.5 HR
90%
Over 1.5 HR
69%
No HR
10%
Shohei Ohtani LAD30.0%
ISO: 0.250 | Barrel: 23.4% | vs Sean Manaea | Park: 0.96x
Juan Soto NYM30.0%
ISO: 0.287 | Barrel: 18.1% | vs Roki Sasaki | Park: 0.96x Platoon: 1.12x
Francisco Alvarez NYM28.7%
ISO: 0.165 | Barrel: 12.1% | vs Roki Sasaki | Park: 0.96x

Pitcher Strikeout Projections

Roki Sasaki
0.0 K projected
LAD | K/9: 0.0
Sean Manaea
0.0 K projected
NYM | K/9: 0.0

Injury Report

LAD8 injured
Blake Snell SP60-DAY-IL
Edwin Diaz RP60-DAY-IL
Will Smith C60-DAY-IL
Enrique Hernandez 1B10-DAY-IL
Tyler Glasnow SP60-DAY-IL
Blake Treinen RP15-DAY-IL
+2 more
NYM7 injured
Clay Holmes SP60-DAY-IL
Justin Hagenman RP60-DAY-IL
Mark Vientos 1B10-DAY-IL
Austin Warren RP15-DAY-IL
Dedniel Nunez RP60-DAY-IL
Reed Garrett RP60-DAY-IL
+1 more

AI Intelligence Analysis

LEAN
NYM UNDER 9.0 at 55.0% model probability (6.7% edge) is weak value in the 5-10% sweet historical support. Manaea (B-, 5.1 Bayesian ERA, 8.0 K/9, 0.467 grade) vs Sasaki (B-, 5.38 ERA, 8.0 K/9, 0.486 grade) creates pitcher washout — both mid-tier arms with concerning ERAs (>5.0 = weakness signal). Model projects 8.97 total (55.0% under 9.0); market at 9.0 is fair. The 6.7% edge is marginal and historically mixed offers no confidence. Citi Field park factor 1.0 neutral. Recent 30d under performance: a supportive track record. Slight lean on empirical under edge but restrained confidence and unit sizing (0.75 instead of 1.0).

Key Factors

  • SP weakness: Both Manaea (5.1 ERA) and Sasaki (5.38 ERA) underperform baseline — weak matchup quality overall
  • Lineup parity: LAD (.260+ EBA) vs NYM (.250+ EBA) suggests balanced run environment; no clear advantage
  • Park factor 1.0 (Citi neutral) — no ballpark edge
  • Edge 6.7% falls in sweet 5-10% historical support but historically mixed total (50.1%) shows no historical advantage
  • Weather neutral (78.8F, minimal wind 7.7 mph crosswind)

Risk Factors

  • SP quality concern: Both pitchers carrying 5.1-5.38 ERAs suggest market underestimating scoring risk
  • Model-market gap near zero (8.97 vs 9.0 total) — market is likely calibrated
  • historically mixed = no statistical edge despite claimed 6.7%

Edge Analysis

Moneyline
LAD 50.2%
-32.1 pts
Run Line
+1.5
-32.1 pts
Total
9.0
+6.7 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. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →

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