MLB Baseball

CLE vs NYY Prediction

June 3, 2026

10,000 Monte Carlo simulations

CLE vs NYY prediction for June 3, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects NYY 6.2 - CLE 5.5. NYY is favored with a 56.8% win probability. The run line is -1.5 and the total is 7.5. Model projects 11.7 total runs.

NYY
6.2
Projected Score
VS O/U 7.5
CLE
5.5
Projected Score
Win Probability
56.8%
43.2%
NYYCLE
-1.5
Run Line (NYY)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.2% (2,514 games)

Projected Runs Range 10th – 90th percentile

CLE
467
NYY
468
FINALNYY 4 — CLE 5
Projected
NYY 6.2 — CLE 5.5
Actual
NYY 4 — CLE 5

Starting Pitcher Matchup

Gavin Williams R
CLE
FF27%97 mph25% whiff
ST26%87 mph43% whiff
CU21%82 mph27% whiff
Gerrit Cole R
NYY
FF48%96 mph13% whiff
SL18%89 mph44% whiff
CH13%86 mph23% whiff

Weather Impact

Yankee Stadium
76°F11 mph wind
HR: 1.022 Total: 1.012
neutral

Bullpen Comparison

CLE
3.65ERA
3.84FIP
10.53K/9
3.93BB/9
1.27WHIP
NYY
3.44ERA
3.81FIP
8.70K/9
3.57BB/9
1.29WHIP

Betting Edges

TOTAL UNDER 7.5
-46.1% EV
-122
TOTAL OVER 7.5
+40.7% EV
+100
RUN_LINE AWAY +1.5
-38.1% EV
-185
F5 OVER 3.5
+18.3% EV
-147
F5_ML AWAY
-10.3% EV
+102
RUN_LINE HOME -1.5
+5.5% EV
+152

First 5 Innings & NRFI

CLE F5
3.0 runs
38.5% win
NYY F5
3.5 runs
49.6% win
F5 Total
6.5
NRFI
48.1%
YRFI
51.9%
Avg 1st Inn Runs
1.20

HR Spotlight

Avg HRs
3.1
Over 0.5 HR
95%
Over 1.5 HR
81%
No HR
5%
Ben Rice NYY30.0%
ISO: 0.341 | Barrel: 19.0% | vs Gavin Williams | Park: 1.10x Platoon: 1.12x
Travis Bazzana CLE24.8%
ISO: 0.148 | Barrel: 7.1% | vs Gerrit Cole | Park: 1.10x Platoon: 1.12x
Angel Martínez CLE21.6%
ISO: 0.125 | Barrel: 12.5% | vs Gerrit Cole | Park: 1.10x Platoon: 1.12x

Pitcher Strikeout Projections

Gavin Williams
0.0 K projected
CLE | K/9: 0.0
Gerrit Cole
0.0 K projected
NYY | K/9: 0.0

Injury Report

CLE3 injured
Gabriel Arias SS60-DAY-IL
Erik Sabrowski RP15-DAY-IL
Carlos Hernandez RPDAY-TO-DAY
NYY7 injured
Aaron Judge RFDAY-TO-DAY
Giancarlo Stanton DH10-DAY-IL
Jasson Dominguez LF10-DAY-IL
Angel Chivilli RP15-DAY-IL
Max Fried SP15-DAY-IL
Clarke Schmidt SP60-DAY-IL
+1 more

AI Intelligence Analysis

NEUTRAL -2RED ZONE50.1% WR (n=305)
Model projects OVER 7.5 at 40.7% edge (70.4% prob) — this is the SINGLE WORST-CASE SCENARIO in model calibration. Historical data shows that edges >15% combined with prob >65% produce near-coin-flip results (47-50% WR). This is a structural model failure pattern. Cole (0.678 B+, 8.0 K/9) vs Williams (0.621 B, 10.9 K/9) are both elite arms, but model is calling 70.4% over prob with $11.68 projected total — this violates the principle that two elite SPs should produce LOW-scoring games. Market total 7.5 is likely CORRECT. Skip entirely.

Key Factors

  • STRUCTURAL FAILING: Model projects 70.4% OVER probability with 40.7% edge — this is in the absolute worst historical zone (edges >25% + prob >70% = catastrophic WR across 305 tracked bets, 50.1% WR)
  • SP matchup is NOT high-scoring: Cole 0.678 B+ (8.0 K/9, 0.0 Bayesian ERA means perfect recent form) vs Williams 0.621 B (10.9 K/9) — both elite arms. TWO ELITE PITCHERS = UNDER game, not over
  • Yankees stadium park factor 1.1 (10% boost) explains some run surge, but 4.18-run edge over market 7.5 is absurd
  • Model total 11.68 is in the 95th percentile of MLB run projections — only high-altitude parks and weak-pitching matchups see this

Risk Factors

  • This is a DATA INTEGRITY failure. Skip the entire game rather than fight the model.
  • Historical evidence is clear: high-edge high-prob totals lose money (50.1% WR on 305 samples, z-score 0.06)
  • Do not try to fade the model on this (taking UNDER) — that's also a trap. Skip completely
DATA INTEGRITYHIGH EDGE WARNINGRED ZONEMODEL MARKET CONFLICTSKIP

Edge Analysis

Moneyline
NYY 56.8%
+5.5 pts
Run Line
-1.5
+5.5 pts
Total
7.5
+40.7 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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