MLB Baseball

LAD vs CWS Prediction

June 13, 2026

10,000 Monte Carlo simulations

LAD vs CWS prediction for June 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects CWS 4.4 - LAD 5.2. LAD is favored with a 53.8% win probability. The run line is 1.5 and the total is 8.5. Model projects 9.6 total runs.

CWS
4.4
Projected Score
VS O/U 8.5
LAD
5.2
Projected Score
Win Probability
46.2%
53.8%
CWSLAD
+1.5
Run Line (CWS)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 57.5% (2,321 games)

Projected Runs Range 10th – 90th percentile

LAD
357
CWS
246
FINALCWS 1 — LAD 7
Projected
CWS 4.4 — LAD 5.2
Actual
CWS 1 — LAD 7

Starting Pitcher Matchup

Yoshinobu Yamamoto R
LAD
FS28%91 mph34% whiff
FF27%96 mph25% whiff
FC13%91 mph24% whiff
Sean Burke R
CWS
FF38%94 mph20% whiff
KC21%80 mph21% whiff
SL16%87 mph32% whiff

Weather Impact

Guaranteed Rate Field
85°F15 mph wind
HR: 0.969 Total: 0.979
thin air, 15mph in

Bullpen Comparison

LAD
3.57ERA
3.39FIP
10.08K/9
3.75BB/9
1.20WHIP
CWS
4.34ERA
4.65FIP
8.47K/9
4.72BB/9
1.33WHIP

Betting Edges

RUN_LINE HOME +1.5
-27.9% EV
+104
ML HOME
+21.4% EV
+176
RUN_LINE AWAY -1.5
-19.1% EV
-125
ML AWAY
-17.1% EV
-208
F5_ML AWAY
-16.5% EV
-222
F5_ML HOME
+15.4% EV
+172

First 5 Innings & NRFI

LAD F5
2.7 runs
47.1% win
CWS F5
2.4 runs
38.9% win
F5 Total
5.1
NRFI
50.2%
YRFI
49.8%
Avg 1st Inn Runs
1.08

HR Spotlight

Avg HRs
2.7
Over 0.5 HR
93%
Over 1.5 HR
75%
No HR
7%
Shohei Ohtani LAD30.0%
ISO: 0.229 | Barrel: 13.7% | vs Sean Burke | Park: 1.01x Platoon: 1.12x
Max Muncy LAD30.0%
ISO: 0.253 | Barrel: 14.0% | vs Sean Burke | Park: 1.01x Platoon: 1.12x
Freddie Freeman LAD30.0%
ISO: 0.224 | Barrel: 9.7% | vs Sean Burke | Park: 1.01x Platoon: 1.12x

Pitcher Strikeout Projections

Yoshinobu Yamamoto
0.0 K projected
LAD | K/9: 0.0
Sean Burke
0.0 K projected
CWS | K/9: 0.0

Injury Report

LAD8 injured
Tommy Edman 2B60-DAY-IL
Brock Stewart RP15-DAY-IL
Justin Wrobleski SPDAY-TO-DAY
Will Smith C10-DAY-IL
Kendall George CFOUT
Chris Campos SPDAY-TO-DAY
+2 more
CWSHealthy

AI Intelligence Analysis

NEUTRAL -1RED ZONE43.3% WR (n=96)
CWS +175 underdog ML at 21.4% edge is EXTREMELY HIGH and model-market conflict (model 44% prob vs market 36.2% prob gap of 8 percentage points) screams overconfidence. Away ML is RED zone (43.3% WR, n=96). Skip despite model favoring CWS; sharp likely fading.

Key Factors

  • HIGH_EDGE_WARNING: 21.4% edge on away underdog is absurd. Our 30-day data: 10-15% ML edges = 44.4% WR. This is AWAY ML zone which is RED (43.3% WR baseline, n=96).
  • SP mismatch favors LAD NOT CWS: Yoshinobu Yamamoto (LAD away SP) 2.89 ERA (B-grade, 25.6% K-rate) vs Sean Burke (CWS home) 4.19 ERA (B- grade, 23.5% K-rate). LAD's better starter is being ignored by model somehow.
  • Model 44% CWS win prob vs market 36.2% = 7.8% gap is too large. Market is probably correct; Yamamoto elite arm should dominate.
  • Temperature 85.4F, 15.1 mph wind BLOWING IN heavily (14.6 mph tail-in) = depresses home runs, helps elite pitcher (Yamamoto) even more

Risk Factors

  • Model sees path to CWS underdog win (44% is playable), but away underdog ML zone is RED historically (43.3% WR). Betting against own system's RED zone data is mistake.
  • Yamamoto elite arm (2.89 ERA, 25.6% K) should dominate road game vs mid-tier Burke. Market pricing this correctly.
  • White Sox have been weak team all season (implied by high underdog odds); no injury edge changes assessment
Sharp MoneyAgainst ModelCWS +175 is heavy underdog pricing. Sharp money likely on LAD side (Yamamoto elite arm, away favorite in weak CWS market). Model favoring CWS despite RED zone flag is overconfident.
HIGH EDGE WARNINGRED ZONEMODEL MARKET CONFLICTAWAY UNDERDOG

Edge Analysis

Moneyline
LAD 53.8%
-27.9 pts
Run Line
+1.5
-27.9 pts
Total
8.5
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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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