LAD vs ARI prediction for June 4, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects ARI 6.2 - LAD 6.9. LAD is favored with a 53.1% win probability. The run line is 1.5 and the total is 9.0. Model projects 13.1 total runs.
ARI
6.2
Projected Score
VS
O/U 9.0
LAD
6.9
Projected Score
Win Probability
ARILAD
+1.5
Run Line (ARI)
9.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.5% (1,970 games)
Projected Runs Range 10th – 90th percentile
LAD
579
ARI
468
Starting Pitcher Matchup
Justin Wrobleski L
LAD
FF51%94 mph20% whiff
SL34%86 mph19% whiff
SI5%93 mph0% whiff
Ryne Nelson R
ARI
FF58%96 mph16% whiff
SL19%88 mph31% whiff
CU10%80 mph19% whiff
Weather Impact
Chase Field
105°F12 mph windRoof: retractable
HR: 1.015 Total: 1.004
thin air, 11mph in
Bullpen Comparison
LAD
2.93ERA
3.15FIP
9.52K/9
3.60BB/9
1.10WHIP
ARI
3.11ERA
3.50FIP
8.21K/9
2.70BB/9
1.05WHIP
Betting Edges
TOTAL UNDER 9.0
-49.0% EV
-112
RUN_LINE HOME +1.5
-33.8% EV
-143
TOTAL OVER 9.0
+29.0% EV
-108
F5 OVER 4.5
+20.2% EV
-145
F5_ML HOME
-12.3% EV
-106
RUN_LINE AWAY -1.5
-8.1% EV
+116
First 5 Innings & NRFI
LAD F5
4.2 runs
50.2% win
ARI F5
3.6 runs
39.6% win
F5 Total
7.8
NRFI
37.6%
YRFI
62.4%
Avg 1st Inn Runs
1.58
HR Spotlight
Avg HRs
3.6
Over 0.5 HR
97%
Over 1.5 HR
87%
No HR
3%
Dalton Rushing LAD30.0%
ISO: 0.282 | Barrel: 15.1% | vs Ryne Nelson | Park: 1.06x Platoon: 1.12x
Max Muncy LAD30.0%
ISO: 0.267 | Barrel: 15.1% | vs Ryne Nelson | Park: 1.06x Platoon: 1.12x
Mookie Betts LAD30.0%
ISO: 0.163 | Barrel: 10.9% | vs Ryne Nelson | Park: 1.06x
Pitcher Strikeout Projections
Justin Wrobleski
0.0 K projected
LAD | K/9: 0.0
Ryne Nelson
0.0 K projected
ARI | K/9: 0.0
Injury Report
LAD8 injured
Bobby Miller SP60-DAY-IL
Gavin Stone SP60-DAY-IL
Brusdar Graterol RP60-DAY-IL
Brock Stewart RP15-DAY-IL
Tommy Edman 2B60-DAY-IL
Tyler Glasnow SP15-DAY-IL
+2 more
ARI8 injured
Taylor Clarke RPBEREAVEMENT
Corbin Burnes SP60-DAY-IL
James McCann C10-DAY-IL
Jordan Lawlar LF60-DAY-IL
A.J. Puk RP60-DAY-IL
Spencer Giesting SPDAY-TO-DAY
+2 more
AI Intelligence Analysis
NEUTRAL -2YELLOW ZONE50.1% WR (n=303)
Extreme high edge warning: 29.0% over edge (model 66.9% vs market 37.0% implied) is largest on slate. Model overfits to extreme heat (104.7F) and ignores elite pitcher advantage (Wrobleski 3.10 ERA vs Nelson 5.21 ERA) + wind-in suppression (-11.2 mph). Historical pattern: high edges (>20%) + high probs (>65%) = worst WR. Market is sharp at 9.0 total; model is chasing heat angle.
Key Factors
- EXTREME OVER EDGE: 29.0% edge (model 66.9% vs market 37.0% implied from -49.0 under edge) is the LARGEST edge on entire slate. Historical performance: high edges produce WORST WR.
- Pitcher advantage LAD: Wrobleski 3.10 ERA vs Nelson 5.21 ERA (2.11 ERA gap, significant); Wrobleski elite A- command (0.819) vs Nelson B+ command (0.716)
- Heat effect: 104.7F is extreme and does add ~1-1.5 runs per team. Model captures this.
- Wind-in SUPPRESSION: 11.7 mph wind BLOWING IN (-11.2 tail wind) is massive suppressor. Market total 9.0 reflects both heat AND wind effect. Model 13.09 underweights wind.
- Market 4.09 run gap: Market 9.0 vs model 13.09 is HUGE (4.09 run difference). Market is saying: elite pitcher + wind-in override heat.
Risk Factors
- Model overconfidence: 66.9% over probability at high edge is textbook overfit. Historical data: >20% edges + >65% prob = worst outcomes.
- Calibration failure: TOTAL threshold disabled due to poor performance; this game exceeds edge caps.
- Pitcher ignored: Elite LAD pitcher (Wrobleski 3.10 ERA, A- command) is being underweighted by model's run projection.
HIGH EDGE WARNINGWEATHER IMPACTPITCHER MISMATCHMODEL MARKET CONFLICTCALIBRATION FAILURE
Edge Analysis
Moneyline
LAD 53.1%
-33.8 pts
Run Line
+1.5
-33.8 pts
Total
9.0
+29.0 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 →