MLB Baseball

NYY vs TOR Prediction

June 13, 2026

10,000 Monte Carlo simulations

NYY vs TOR prediction for June 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects TOR 4.8 - NYY 4.6. TOR is favored with a 52.9% win probability. The run line is 1.5 and the total is 7.5. Model projects 9.3 total runs.

TOR
4.8
Projected Score
VS O/U 7.5
NYY
4.6
Projected Score
Win Probability
52.9%
47.1%
TORNYY
+1.5
Run Line (TOR)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 57.4% (2,321 games)

Projected Runs Range 10th – 90th percentile

NYY
357
TOR
357
FINALTOR 1 — NYY 3
Projected
TOR 4.8 — NYY 4.6
Actual
TOR 1 — NYY 3

Starting Pitcher Matchup

Cam Schlittler R
NYY
FF44%98 mph30% whiff
FC27%94 mph17% whiff
SI19%97 mph16% whiff
Kevin Gausman R
TOR
FF53%94 mph14% whiff
FS38%84 mph36% whiff
SL9%84 mph32% whiff

Weather Impact

Rogers Centre
80°F13 mph windRoof: retractable
HR: 1.035 Total: 1.018
thin air

Bullpen Comparison

NYY
3.43ERA
3.71FIP
8.71K/9
3.55BB/9
1.29WHIP
TOR
3.87ERA
3.61FIP
8.96K/9
3.46BB/9
1.31WHIP

Betting Edges

RUN_LINE HOME +1.5
-30.8% EV
-175
TOTAL UNDER 7.5
-16.8% EV
-108
RUN_LINE AWAY -1.5
-14.2% EV
+146
F5_ML AWAY
-11.8% EV
-130
ML AWAY
-11.1% EV
-118
TOTAL OVER 7.5
+7.5% EV
-112

First 5 Innings & NRFI

NYY F5
2.5 runs
41.6% win
TOR F5
2.6 runs
44.2% win
F5 Total
5.0
NRFI
52.5%
YRFI
47.5%
Avg 1st Inn Runs
1.02

HR Spotlight

Avg HRs
2.9
Over 0.5 HR
94%
Over 1.5 HR
78%
No HR
6%
Ben Rice NYY30.0%
ISO: 0.342 | Barrel: 18.3% | vs Kevin Gausman | Park: 1.01x Platoon: 1.12x
Cody Bellinger NYY28.2%
ISO: 0.199 | Barrel: 10.2% | vs Kevin Gausman | Park: 1.01x Platoon: 1.12x
Paul Goldschmidt NYY25.5%
ISO: 0.135 | Barrel: 13.3% | vs Kevin Gausman | Park: 1.01x

Pitcher Strikeout Projections

Cam Schlittler
0.0 K projected
NYY | K/9: 0.0
Kevin Gausman
0.0 K projected
TOR | K/9: 0.0

Injury Report

NYY8 injured
Trent Grisham CF10-DAY-IL
Max Fried SP15-DAY-IL
Austin Wells C10-DAY-IL
Clarke Schmidt SP60-DAY-IL
Payton Henry CDAY-TO-DAY
Eric Reyzelman SPDAY-TO-DAY
+2 more
TOR8 injured
Addison Barger RF10-DAY-IL
Yimi Garcia RP60-DAY-IL
Shane Bieber SP60-DAY-IL
Daulton Varsho CF10-DAY-IL
Lazaro Estrada RP60-DAY-IL
Joe Mantiply RP60-DAY-IL
+2 more

AI Intelligence Analysis

STRONG BET +1YELLOW ZONE50.1% WR (n=184)
OVER 7.5 at 7.5% edge exploits massive pitcher quality mismatch (Cam Schlittler 2.02 ERA elite ace vs Kevin Gausman 3.89 ERA solid #2 starter). Elite starter pitching *reduces* total in market pricing, but Toronto lineup is competent; Toronto parks run-suppressing. High-confidence OVER play.

Key Factors

  • SP mismatch inverted: Schlittler (2.02 ERA, 27.2% K-rate, 0.051 BB-rate) is ace-tier; Gausman (3.89 ERA, 23.2% K-rate, 0.049 BB) is solid #2. Market mispriced this 1.87 ERA gap at 2.5 run line move only.
  • Temperature 80.3F + retractable roof (neutral baseline) + weather multiplier 1.018 = +~0.15 run boost
  • Model total 9.34 vs market 7.5 = 1.84 run gap (huge); edge calc shows 7.5% pure value
  • Home Run environment: Rogers Centre 1.035x HR factor + slight air density = runs play into LAD's favor moderately
  • F5 OVER 3.5 at 6.5% edge is a secondary edge confirming full-game over bias

Risk Factors

  • Schlittler 2.02 ERA is elite-tier but small sample (likely <100 IP); Toronto parks have run-suppression history Citi Field style
  • Edge >7% is historically risky (44.4% WR on 10-15% edges in 30-day data), but this is TOTAL not ML; OVER zone performs better (60.6% WR in 30 days)
  • If Schlittler's ERA inflates to 2.5+ over full season, this edge evaporates
Sharp MoneyWith ModelModel heavily favors over; market likely adjusted post-injury uncertainty around Guerrero. Sharp money likely already in overs; line may have tightened.
PITCHER MISMATCHTOTALS VALUEWEATHER IMPACTDIRECTION CONFIRMED

Edge Analysis

Moneyline
TOR 52.9%
-30.8 pts
Run Line
+1.5
-30.8 pts
Total
7.5
+7.5 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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