MLB Baseball

SEA vs HOU Prediction

May 14, 2026

10,000 Monte Carlo simulations

SEA vs HOU prediction for May 14, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects HOU 4.3 - SEA 4.6. SEA is favored with a 51.4% win probability. The run line is 1.5 and the total is 9.0. Model projects 8.9 total runs.

HOU
4.3
Projected Score
VS O/U 9.0
SEA
4.6
Projected Score
Win Probability
48.6%
51.4%
HOUSEA
+1.5
Run Line (HOU)
9.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.5% (2,063 games)

Projected Runs Range 10th – 90th percentile

SEA
357
HOU
246
FINALHOU 3 — SEA 8
Projected
HOU 4.3 — SEA 4.6
Actual
HOU 3 — SEA 8

Starting Pitcher Matchup

Luis Castillo R
SEA
FF44%95 mph24% whiff
SL26%85 mph33% whiff
SI18%95 mph7% whiff
Mike Burrows R
HOU
FF28%95 mph9% whiff
CH26%87 mph36% whiff
SL17%90 mph27% whiff

Weather Impact

Minute Maid Park
86°F1 mph windRoof: retractable
HR: 1.027 Total: 1.013
thin air

Bullpen Comparison

SEA
3.40ERA
3.45FIP
9.64K/9
3.78BB/9
1.40WHIP
HOU
5.42ERA
4.86FIP
8.35K/9
5.22BB/9
1.51WHIP

Betting Edges

RUN_LINE HOME +1.5
-36.1% EV
-161
TOTAL OVER 9.0
-19.0% EV
-108
RUN_LINE AWAY -1.5
-11.6% EV
+134
F5_ML AWAY
-6.3% EV
-116
ML AWAY
-5.7% EV
-122
TOTAL UNDER 9.0
-5.1% EV
-112

First 5 Innings & NRFI

SEA F5
2.5 runs
42.3% win
HOU F5
2.4 runs
42.0% win
F5 Total
4.9
NRFI
54.8%
YRFI
45.2%
Avg 1st Inn Runs
0.92

HR Spotlight

Avg HRs
3.3
Over 0.5 HR
96%
Over 1.5 HR
84%
No HR
4%
Luke Raley SEA30.0%
ISO: 0.321 | Barrel: 17.7% | vs Mike Burrows | Park: 0.99x Platoon: 1.12x
Brendan Donovan SEA30.0%
ISO: 0.235 | Barrel: 8.1% | vs Mike Burrows | Park: 0.99x Platoon: 1.12x
Dominic Canzone SEA30.0%
ISO: 0.229 | Barrel: 10.9% | vs Mike Burrows | Park: 0.99x Platoon: 1.12x

Pitcher Strikeout Projections

Luis Castillo
0.0 K projected
SEA | K/9: 0.0
Mike Burrows
0.0 K projected
HOU | K/9: 0.0

Injury Report

SEA8 injured
Cal Raleigh CDAY-TO-DAY
Matt Brash RP15-DAY-IL
Jose Ferrer RPPATERNITY
Miles Mastrobuoni 3B60-DAY-IL
Teddy McGraw SPDAY-TO-DAY
Patrick Wisdom 1B10-DAY-IL
+2 more
HOU8 injured
Jeremy Pena SS10-DAY-IL
Nate Pearson RP15-DAY-IL
Jake Meyers CF10-DAY-IL
Carlos Correa SS10-DAY-IL
Hunter Brown SP60-DAY-IL
Bennett Sousa RP15-DAY-IL
+2 more

AI Intelligence Analysis

NEUTRALYELLOW ZONE50.4% WR (n=250)
Near-even matchup (SEA 51.4% vs HOU 48.6%) with no ML edge; totals neutral (-5.1% UNDER). Game environment favors overs (86.4F hot dome + thin air 1.013x boost) but two mediocre SPs (Burrows 5.44, Castillo 7.10) with similar K-rates offset. Model correctly shows no edge; weather suggests overs but zones don't support high confidence. Game resolved SEA 8-3, total 11 (OVER 9.0).

Key Factors

  • Pitcher comparison: Burrows 5.44 ERA vs Castillo 7.10 ERA (slight HOU advantage but both weak). K-rates nearly identical (20.4% vs 20.5%).
  • Weather dominance: 86.4F hot, domed, thin air (density 1772) = 1.013x totals boost + 1.027x HR multiplier. Should add 0.5-1.0 runs.
  • Model projects 8.92 total vs market 9.0 — essentially even, missing weather tailwind. Model conservative on hot-weather scenarios.
  • SEA offensive explosion (Raley 3-run HR, Garver HR) suggests lineup strength not fully captured in pregame sims.
  • No ML edge appropriate given pitcher quality matching.

Risk Factors

  • Model significantly underestimated run projection (8.92 vs actual 11, -2.08 gap) — hot ballpark weather not weighted sufficiently.
  • Raley's 3-run HR typical of hot-dome power, but unpredictable pregame; model's HR multiplier (1.027x) may not translate to actual home run frequency.
  • CWS's mediocre bullpen likely allowed run accumulation late — not predictable from starting pitcher data alone.
RESOLVED GAMEWEATHER IMPACT UNDERESTIMATEDHOT DOMENO ML EDGEMEDIOCRE PITCHERS

Edge Analysis

Moneyline
SEA 51.4%
-36.1 pts
Run Line
+1.5
-36.1 pts
Total
9.0
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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. Full methodology →

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