MLB Baseball

TEX vs BOS Prediction

June 13, 2026

10,000 Monte Carlo simulations

TEX vs BOS prediction for June 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects BOS 4.7 - TEX 3.9. BOS is favored with a 58.7% win probability. The run line is 1.5 and the total is 7.5. Model projects 8.6 total runs.

BOS
4.7
Projected Score
VS O/U 7.5
TEX
3.9
Projected Score
Win Probability
58.7%
41.3%
BOSTEX
+1.5
Run Line (BOS)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 63.2% (2,321 games)

Projected Runs Range 10th – 90th percentile

TEX
246
BOS
357
FINALBOS 6 — TEX 3
Projected
BOS 4.7 — TEX 3.9
Actual
BOS 6 — TEX 3

Starting Pitcher Matchup

Jacob deGrom R
TEX
FF42%97 mph21% whiff
SL34%91 mph40% whiff
CH15%90 mph44% whiff
Ranger Suarez L
BOS
SI26%90 mph14% whiff
FF21%92 mph17% whiff
FC20%88 mph13% whiff

Weather Impact

Fenway Park
90°F10 mph wind
HR: 1.000 Total: 0.997
thin air, 7mph in

Bullpen Comparison

TEX
3.50ERA
4.16FIP
7.56K/9
3.31BB/9
1.22WHIP
BOS
4.02ERA
4.20FIP
9.04K/9
3.56BB/9
1.32WHIP

Betting Edges

RUN_LINE HOME +1.5
-28.9% EV
-222
RUN_LINE AWAY -1.5
-20.0% EV
+180
F5_ML AWAY
-16.7% EV
-112
ML AWAY
-12.8% EV
+106
F5 OVER 4.5
+8.9% EV
+114
TOTAL UNDER 7.5
-7.0% EV
-104

First 5 Innings & NRFI

TEX F5
2.1 runs
36.2% win
BOS F5
2.7 runs
49.8% win
F5 Total
4.9
NRFI
54.0%
YRFI
46.0%
Avg 1st Inn Runs
0.99

HR Spotlight

Avg HRs
2.8
Over 0.5 HR
93%
Over 1.5 HR
76%
No HR
7%
Justin Foscue TEX30.0%
ISO: 0.429 | Barrel: 10.0% | vs Ranger Suarez | Park: 1.08x Platoon: 1.12x
Willson Contreras BOS30.0%
ISO: 0.218 | Barrel: 14.0% | vs Jacob deGrom | Park: 1.08x
Jarren Duran BOS30.0%
ISO: 0.197 | Barrel: 8.5% | vs Jacob deGrom | Park: 1.08x Platoon: 1.12x

Pitcher Strikeout Projections

Jacob deGrom
0.0 K projected
TEX | K/9: 0.0
Ranger Suarez
0.0 K projected
BOS | K/9: 0.0

Injury Report

TEX8 injured
Corey Seager SSDAY-TO-DAY
Evan Carter CF10-DAY-IL
Jalen Beeks RP15-DAY-IL
Josh Smith 2B10-DAY-IL
Danny Jansen C10-DAY-IL
Jordan Montgomery SP60-DAY-IL
+2 more
BOS8 injured
Nick Sogard 3B10-DAY-IL
Garrett Crochet SP60-DAY-IL
Roman Anthony LF10-DAY-IL
Patrick Sandoval SP60-DAY-IL
Jovani Moran RP15-DAY-IL
Trevor Story SS60-DAY-IL
+2 more

AI Intelligence Analysis

LEANGREEN ZONE56.6% WR (n=96)
Boston home ML at 4.4% edge is modest in GREEN zone (56.6% home WR), but pitcher paradox: Jacob deGrom (3.43 ERA B+ grade, 29.5% K-rate elite) away vs Ranger Suarez (3.43 ERA B- grade, 23.4% K-rate solid) home. ERA same, but deGrom elite stuff ignored by market. Home field drives edge, but pitcher quality favors away.

Key Factors

  • SP QUALITY PARADOX: deGrom (TEX away) has identical 3.43 ERA to Suarez (BOS home), but deGrom grades as B+ (stuff 71.9%, command 69.7%) elite-tier vs Suarez B- (stuff 39.8%, command 55.8%). Elite pitcher away underpriced vs home advantage.
  • Temperature 89.7F, 9.6 mph wind blowing in = slight under bias (~0.2 runs), helps elite pitcher (deGrom)
  • Model 57.7% BOS win vs market 55.2% = only 2.5% gap shows market respects deGrom's elite arm even at home
  • Fenway Park (1.0x factor HR) neutral baseline, but wind-in reduces over expectations
  • F5 BOS edge 5.7% suggests starting pitcher dominates early innings—deGrom advantage underutilized

Risk Factors

  • 4.4% edge is low; BOS home field GREEN zone (56.6% WR) is reliable but thin edge
  • deGrom elite arm (B+, 29.5% K-rate) on road typically predicts cover; market may be correctly pricing home advantage premium
  • TEX missing Evan Carter (OF, 10-day IL oblique from Friday) = lineup weakened, supporting BOS home advantage
Sharp MoneyWith ModelMarket respects deGrom (only 2.5% gap); sharp likely split between home advantage and elite arm. Line stable suggests equilibrium.
PITCHER QUALITY AWAYGREEN ZONEMODEST EDGEELITE PITCHING

Edge Analysis

Moneyline
BOS 58.7%
-28.9 pts
Run Line
+1.5
-28.9 pts
Total
7.5
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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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