MLB Baseball

CWS vs SEA Prediction

May 18, 2026

10,000 Monte Carlo simulations

CWS vs SEA prediction for May 18, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects SEA 2.6 - CWS 2.9. CWS is favored with a 52.2% win probability. The run line is -1.5 and the total is 7.5. Model projects 5.5 total runs.

SEA
2.6
Projected Score
VS O/U 7.5
CWS
2.9
Projected Score
Win Probability
47.8%
52.2%
SEACWS
-1.5
Run Line (SEA)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 51.7% (2,157 games)

Projected Runs Range 10th – 90th percentile

CWS
135
SEA
135
FINALSEA 6 — CWS 1
Projected
SEA 2.6 — CWS 2.9
Actual
SEA 6 — CWS 1

Starting Pitcher Matchup

Noah Schultz L
CWS
FF28%95 mph25% whiff
SI24%95 mph10% whiff
ST24%83 mph32% whiff
Bryan Woo R
SEA
FF50%96 mph21% whiff
SI19%95 mph7% whiff
ST16%84 mph37% whiff

Weather Impact

T-Mobile Park
60°F6 mph windRoof: retractable
HR: 0.998 Total: 0.999
neutral

Bullpen Comparison

CWS
4.98ERA
4.93FIP
8.16K/9
5.33BB/9
1.48WHIP
SEA
2.93ERA
3.50FIP
9.07K/9
3.44BB/9
1.31WHIP

Betting Edges

TOTAL OVER 7.5
-40.7% EV
+100
RUN_LINE AWAY +1.5
-36.5% EV
-161
F5 UNDER 3.5
+29.9% EV
+110
RUN_LINE HOME -1.5
-28.7% EV
+132
TOTAL UNDER 7.5
+28.1% EV
-122
ML HOME
-19.7% EV
-167

First 5 Innings & NRFI

CWS F5
1.4 runs
38.0% win
SEA F5
1.4 runs
36.8% win
F5 Total
2.8
NRFI
69.4%
YRFI
30.6%
Avg 1st Inn Runs
0.54

HR Spotlight

Avg HRs
1.4
Over 0.5 HR
75%
Over 1.5 HR
40%
No HR
25%
Munetaka Murakami CWS30.0%
ISO: 0.345 | Barrel: 18.8% | vs Bryan Woo | Park: 0.89x Platoon: 1.12x
Colson Montgomery CWS30.0%
ISO: 0.287 | Barrel: 16.4% | vs Bryan Woo | Park: 0.89x Platoon: 1.12x
Drew Romo CWS29.6%
ISO: 0.200 | Barrel: 20.0% | vs Bryan Woo | Park: 0.89x Platoon: 1.12x

Pitcher Strikeout Projections

Noah Schultz
0.0 K projected
CWS | K/9: 0.0
Bryan Woo
0.0 K projected
SEA | K/9: 0.0

Injury Report

CWSHealthy
SEA8 injured
Brendan Donovan 3B10-DAY-IL
J.P. Crawford SSDAY-TO-DAY
Cal Raleigh C10-DAY-IL
Matt Brash RP15-DAY-IL
Miles Mastrobuoni 3B60-DAY-IL
Teddy McGraw SPDAY-TO-DAY
+2 more

AI Intelligence Analysis

NEUTRAL -1RED ZONE45.1% WR (n=155)
EXTREME HIGH-EDGE WARNING: Bryan Woo (4.22 ERA, B grade, 22% K%) vs Noah Schultz (5.3 ERA, C+ grade, 20.8 K%). Model projects SEA 47.8% home win prob, market 62.4% (62.5 implied, heavily favoring home). Model AWAY ML 19.5% edge at 49.8% prob — this is DANGEROUSLY HIGH. Total 5.54 model vs 7.5 market = 28.1% edge on UNDER 7.5 at 70.4% prob. UNDER is DISABLED. This is a textbook case of model overconfidence. High edges (15%+) have WORST WR historically (30%). Market is likely correct in pricing SEA heavily. Cold weather (60.3F) suppresses runs. T-Mobile Park (0.89 park factor, -11% runs). The combination of suppressive factors is REAL, but 19.5% and 28.1% edges smell like model overfit. BLOCK this game.

Key Factors

  • Model 19.5% AWAY ML edge and 28.1% UNDER edge — both dangerously high (>15%)
  • Historical data: High edges (15%+) have 30% WR — worst performing zone
  • Woo (4.22 ERA, B) vs Schultz (5.3 ERA, C+) = pitcher advantage to SEA, but real edge likely 3-5%, not 19.5%
  • Cold weather (60.3F) + T-Mobile park (-11%) do suppress runs, but not -1.96 run gap
  • Market pricing SEA at 62.5% home implies market sees clear SEA advantage; model likely overfit

Risk Factors

  • AWAY ML RED ZONE (45.1% WR, n=155) combined with high edge = dangerous
  • 28.1% UNDER edge is suspiciously high; unders are disabled for good reason (45.3% WR, -40u)
  • Model overconfidence is highest risk on this slate. Market is likely correct to price SEA heavily.
Sharp MoneyAgainst ModelMarket heavily against model's AWAY lean (SEA 62.4% home). Market is telling us model is wrong.
HIGH EDGE WARNINGMODEL OVERCONFIDENCERED ZONE AWAY MLEXTREME TOTAL EDGEWEATHER IMPACTPARK FACTOR

Edge Analysis

Moneyline
CWS 52.2%
-28.7 pts
Run Line
-1.5
-28.7 pts
Total
7.5
+28.1 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. Full methodology →

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