MLB Baseball

SD vs LAD Prediction

July 5, 2026

10,000 Monte Carlo simulations

FINAL: LAD 2 — SD 5. Our Monte Carlo simulation projected LAD 3.6 - SD 3.9 (SD at 51.1% win probability). The run line is -1.5 and the total is 9.5. Model projects 7.5 total runs.

LAD
3.6
Projected Score
VS O/U 9.5
SD
3.9
Projected Score
Win Probability
48.9%
51.1%
LADSD
-1.5
Run Line (LAD)
9.5
Total Line
10,000
Simulations
SD L4LAD L5
Calibrated accuracy at this confidence: 50.7% (2,777 games)

Projected Runs Range 10th – 90th percentile

SD
246
LAD
246
FINALLAD 2 — SD 5
Projected
LAD 3.6 — SD 3.9
Actual
LAD 2 — SD 5

Pick Results

SD @ LAD F5 UNDER 5.5f5WIN+0.43u
SD @ LAD NRFInrfiWIN+0.59u

Starting Pitcher Matchup

JP Sears L
SD
FF40%92 mph17% whiff
ST27%79 mph24% whiff
CH16%84 mph17% whiff
Emmet Sheehan R
LAD
FF43%94 mph26% whiff
SL31%87 mph39% whiff
CH15%85 mph22% whiff

Weather Impact

Dodger Stadium
79°F10 mph wind
HR: 0.984 Total: 0.988
thin air, 10mph in

Bullpen Comparison

SD
3.15ERA
3.66FIP
8.41K/9
3.44BB/9
1.23WHIP
LAD
3.58ERA
3.45FIP
10.00K/9
3.64BB/9
1.19WHIP

Betting Edges

TOTAL OVER 9.5
-34.9% EV
-120
F5_ML AWAY
+28.3% EV
+164
RUN_LINE HOME -1.5
-28.3% EV
-110
TOTAL UNDER 9.5
+27.5% EV
-102
ML AWAY
+24.5% EV
+184
F5_ML HOME
-23.9% EV
-208

First 5 Innings & NRFI

SD F5
2.3 runs
45.0% win
LAD F5
2.1 runs
39.8% win
F5 Total
4.4
NRFI
54.1%
YRFI
45.9%
Avg 1st Inn Runs
1.03

HR Spotlight

Avg HRs
2.5
Over 0.5 HR
91%
Over 1.5 HR
70%
No HR
9%
Gavin Sheets SD30.0%
ISO: 0.263 | Barrel: 9.5% | vs Emmet Sheehan | Park: 0.92x Platoon: 1.12x
Manny Machado SD30.0%
ISO: 0.167 | Barrel: 12.9% | vs Emmet Sheehan | Park: 0.92x
Shohei Ohtani LAD30.0%
ISO: 0.250 | Barrel: 23.4% | vs JP Sears | Park: 0.92x

Pitcher Strikeout Projections

JP Sears
0.0 K projected
SD | K/9: 0.0
Emmet Sheehan
0.0 K projected
LAD | K/9: 0.0

Injury Report

SD8 injured
Randy Vasquez SP15-DAY-IL
Freddy Fermin C10-DAY-IL
Jeremiah Estrada RP15-DAY-IL
Lucas Giolito SP15-DAY-IL
Jason Adam RP15-DAY-IL
David Morgan RP15-DAY-IL
+2 more
LAD8 injured
Tommy Edman LFDAY-TO-DAY
Edwin Diaz RP60-DAY-IL
Will Smith C10-DAY-IL
Tyler Glasnow SP60-DAY-IL
Blake Treinen RP15-DAY-IL
Blake Snell SP60-DAY-IL
+2 more

AI Intelligence Analysis

NEUTRAL -2RED ZONE43.9% WR (n=156)
STRONG AVOID (-2). Model shows AWAY ML (SD) at 24.5% edge with MARKET implying 35.2% win prob (away only gets 35.2%!). This is a RED ZONE away underdog situation (43.9% historical WR on away ML). The market is MASSIVELY overvaluing LAD at -222 (68.9% implied). However, the away ML edge is SO HIGH (24.5%) that it triggers our HIGH_EDGE_WARNING: when models see 20%+ edges, they are WRONG 60-70% of the time in back-test. The underlying pitcher matchup is clear: Emmet Sheehan (B-grade, 5.49 ERA at home) vs JP Sears (B- grade, 7.53 ERA away) suggests LAD is inferior, yet Sears is at LAD. Wait, no — SHEEHAN is home pitcher (SD home), SEARS is away (LAD away). So Sheehan (5.49) at home vs Sears (7.53) away = Sheehan better. This supports SD lean... but SD is in RED ZONE away underdog! This is precisely where the model FAILS. Red zone + high edge suggests market is RIGHT and model is overconfident. AVOID away ML entirely. SKIP or recommend SKIP.

Key Factors

  • Away ML at 24.5% edge is RED ZONE (43.9% historical WR, z=-1.6, sample 156) — structural weakness
  • Historical data shows high-edge plays (>20%) underperform by 8-15% in real execution
  • Market odds -222 LAD reflects genuine team quality gap (LAD roster >> SD)

Risk Factors

  • Model sees 24.5% away ML edge; this is a KNOWN failure point in calibration. Avoid entirely.
Sharp MoneyAgainst ModelMarket heavily favoring LAD at -222; sharp money likely on LAD. Model lean is against sharp consensus.
RED ZONEHIGH EDGE WARNINGHIGH EDGE AWAY UNDERDOG FAILUREMODEL OVERCONFIDENCE

Edge Analysis

Moneyline
SD 51.1%
-28.3 pts
Run Line
-1.5
-28.3 pts
Total
9.5
+27.5 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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