MLB Baseball

BOS vs NYY Prediction

June 7, 2026

10,000 Monte Carlo simulations

BOS vs NYY prediction for June 7, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects NYY 4.8 - BOS 3.5. NYY is favored with a 63.7% win probability. The run line is -1.5 and the total is 8.0. Model projects 8.3 total runs.

NYY
4.8
Projected Score
VS O/U 8.0
BOS
3.5
Projected Score
Win Probability
63.7%
36.3%
NYYBOS
-1.5
Run Line (NYY)
8.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 64.0% (2,193 games)

Projected Runs Range 10th – 90th percentile

BOS
245
NYY
357
FINALNYY 6 — BOS 1
Projected
NYY 4.8 — BOS 3.5
Actual
NYY 6 — BOS 1

Starting Pitcher Matchup

Ranger Suarez L
BOS
SI27%90 mph14% whiff
FC21%88 mph12% whiff
FF19%92 mph15% whiff
Cam Schlittler R
NYY
FF45%98 mph31% whiff
FC26%94 mph18% whiff
SI18%97 mph15% whiff

Weather Impact

Yankee Stadium
82°F14 mph wind
HR: 0.975 Total: 0.983
thin air, 11mph in

Bullpen Comparison

BOS
3.89ERA
4.18FIP
9.07K/9
3.59BB/9
1.31WHIP
NYY
3.55ERA
3.76FIP
8.62K/9
3.45BB/9
1.28WHIP

Betting Edges

RUN_LINE AWAY +1.5
-47.4% EV
-164
TOTAL OVER 8.0
-14.7% EV
-115
ML AWAY
-13.9% EV
+130
F5_ML AWAY
-10.6% EV
+124
TOTAL UNDER 8.0
-5.8% EV
-105
RUN_LINE HOME -1.5
+4.5% EV
+136

First 5 Innings & NRFI

BOS F5
1.8 runs
32.7% win
NYY F5
2.6 runs
50.5% win
F5 Total
4.4
NRFI
55.3%
YRFI
44.7%
Avg 1st Inn Runs
0.91

HR Spotlight

Avg HRs
2.0
Over 0.5 HR
86%
Over 1.5 HR
60%
No HR
14%
Amed Rosario NYY30.0%
ISO: 0.271 | Barrel: 14.4% | vs Ranger Suarez | Park: 1.10x Platoon: 1.12x
Ben Rice NYY30.0%
ISO: 0.301 | Barrel: 20.0% | vs Ranger Suarez | Park: 1.10x
Jarren Duran BOS29.4%
ISO: 0.214 | Barrel: 9.2% | vs Cam Schlittler | Park: 1.10x Platoon: 1.12x

Pitcher Strikeout Projections

Ranger Suarez
0.0 K projected
BOS | K/9: 0.0
Cam Schlittler
0.0 K projected
NYY | K/9: 0.0

Injury Report

BOS8 injured
Jovani Moran RP15-DAY-IL
Patrick Sandoval SP60-DAY-IL
Garrett Whitlock RP15-DAY-IL
Garrett Crochet SP60-DAY-IL
Trevor Story SS60-DAY-IL
Romy Gonzalez 1B60-DAY-IL
+2 more
NYY8 injured
Austin Wells C10-DAY-IL
Payton Henry CDAY-TO-DAY
Eric Reyzelman SPDAY-TO-DAY
Aaron Judge RF10-DAY-IL
Jasson Dominguez LF10-DAY-IL
Giancarlo Stanton DH10-DAY-IL
+2 more

AI Intelligence Analysis

LEAN +1YELLOW ZONE51.1% WR (n=351)
Cam Schlittler (2.04 ERA, B grade, 27.5% K rate) is a legitimate ace-tier starter vs Ranger Suarez (3.65 ERA, B- grade, 23.4% K rate). Model correctly identifies 3.2% ML edge; NYY home field advantage (1.1 park factor, 82.4F but 11.2mph WIND IN = neutral/slight under pressure) combined with SP dominance supports home lean. Market has not fully priced Schlittler's elite command (0.767 score vs Suarez 0.538).

Key Factors

  • SP dominance: Schlittler 2.04 ERA, 27.5% K, 0.767 command vs Suarez 3.65 ERA, 23.4% K, 0.538 command — 1.6 ERA gap is 'elite' tier mismatch
  • Home field advantage: Yankee Stadium 1.1 park factor, though 11.2mph wind in partially offsets
  • Bullpen edge: NYY 3.55 ERA vs BOS 3.89 ERA — consistent advantage
  • Model ML edge 3.2% is modest but real; zone profitability for home ML in similar buckets is YELLOW but stable (~51% WR)
  • Recent performance: Schlittler likely coming off strong outings; Suarez trending weaker based on 3.65 ERA trajectory

Risk Factors

  • NYY missing Aaron Judge (ribs, 10-day IL), Giancarlo Stanton (calf, 10-day), Jasson Dominguez (shoulder, 10-day) — significant lineup depth issue. Estimated swing: -1.5 to -2.0 runs in expectancy vs healthy NYY.
  • Schlittler on short rest or high pitch count risk is unknown — confirm in pregame reports
  • BOS bullpen is only slightly weaker (0.34 ERA diff), so late-game scenarios partially mitigate SP advantage
PITCHER MISMATCHELITE SP ADVANTAGEHOME FIELDALIGNED WITH MODEL

Edge Analysis

Moneyline
NYY 63.7%
+4.5 pts
Run Line
-1.5
+4.5 pts
Total
8.0
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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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