LAD vs OAK prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects OAK 4.6 - LAD 6.3. LAD is favored with a 62.4% win probability. The run line is 1.5 and the total is 9.5. Model projects 10.9 total runs.
OAK
4.6
Projected Score
VS
O/U 9.5
LAD
6.3
Projected Score
Win Probability
OAKLAD
+1.5
Run Line (OAK)
9.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 60.7% (2,559 games)
Projected Runs Range 10th – 90th percentile
LAD
468
OAK
357
Starting Pitcher Matchup
R TBD
LAD
J.T. Ginn R
OAK
SI34%94 mph21% whiff
FC19%91 mph20% whiff
CH18%88 mph31% whiff
Weather Impact
Oakland Coliseum
66°F10 mph wind
HR: 0.976 Total: 0.984
8mph in
Bullpen Comparison
LAD
3.58ERA
3.45FIP
10.00K/9
3.64BB/9
1.19WHIP
OAK
4.87ERA
3.91FIP
9.81K/9
4.20BB/9
1.44WHIP
Betting Edges
RUN_LINE HOME +1.5
-41.3% EV
-118
TOTAL UNDER 9.5
-10.7% EV
-102
ML HOME
-9.7% EV
+136
RUN_LINE AWAY -1.5
-0.5% EV
+100
TOTAL OVER 9.5
+0.4% EV
-120
First 5 Innings & NRFI
LAD F5
3.3 runs
51.1% win
OAK F5
2.6 runs
36.1% win
F5 Total
5.9
NRFI
51.3%
YRFI
48.7%
Avg 1st Inn Runs
1.07
HR Spotlight
Avg HRs
2.7
Over 0.5 HR
92%
Over 1.5 HR
74%
No HR
8%
Pitcher Strikeout Projections
0.0 K projected
LAD | K/9: 0.0
J.T. Ginn
0.0 K projected
OAK | K/9: 0.0
Injury Report
LAD8 injured
Tyler Glasnow SP60-DAY-IL
Will Smith C10-DAY-IL
Blake Treinen RP15-DAY-IL
Blake Snell SP60-DAY-IL
Landon Knack SP60-DAY-IL
Kendall George CFOUT
+2 more
OAK8 injured
Jose Suarez RPPATERNITY
Tyler Soderstrom LF10-DAY-IL
Jacob Wilson SS10-DAY-IL
Wei-En Lin POUT
Zack Gelof 3B10-DAY-IL
Mark Leiter Jr. RP15-DAY-IL
+2 more
AI Intelligence Analysis
Edge Analysis
Moneyline
LAD 62.4%
-41.3 pts
Run Line
+1.5
-41.3 pts
Total
9.5
+0.4 pts
More Projections Today
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 →