MLB Baseball

WSH vs NYM Prediction

April 30, 2026

10,000 Monte Carlo simulations

WSH vs NYM prediction for April 30, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects NYM 2.7 - WSH 3.1. WSH is favored with a 53.5% win probability. The run line is -1.5 and the total is 7.5. Model projects 5.9 total runs.

NYM
2.7
Projected Score
VS O/U 7.5
WSH
3.1
Projected Score
Win Probability
46.5%
53.5%
NYMWSH
-1.5
Run Line (NYM)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.8% (2,610 games)

Projected Runs Range 10th – 90th percentile

WSH
135
NYM
135
FINALNYM 4 — WSH 5
Projected
NYM 2.7 — WSH 3.1
Actual
NYM 4 — WSH 5

Starting Pitcher Matchup

Miles Mikolas R
WSH
FF26%93 mph23% whiff
SI22%92 mph14% whiff
SL16%87 mph21% whiff
Freddy Peralta R
NYM
FF52%94 mph22% whiff
CH23%87 mph29% whiff
CU13%80 mph33% whiff

Weather Impact

Citi Field
52°F3 mph wind
HR: 0.995 Total: 0.997
neutral

Bullpen Comparison

WSH
4.90ERA
5.10FIP
7.84K/9
4.69BB/9
1.48WHIP
NYM
3.97ERA
3.49FIP
10.12K/9
3.37BB/9
1.26WHIP

Betting Edges

TOTAL OVER 7.5
-38.5% EV
-115
RUN_LINE HOME -1.5
-34.7% EV
+112
RUN_LINE AWAY +1.5
-31.3% EV
-133
TOTAL UNDER 7.5
+30.8% EV
-105
ML AWAY
+28.1% EV
+160
F5_ML AWAY
+27.0% EV
+164

First 5 Innings & NRFI

WSH F5
1.7 runs
40.2% win
NYM F5
1.5 runs
36.5% win
F5 Total
3.2
NRFI
63.0%
YRFI
37.0%
Avg 1st Inn Runs
0.67

HR Spotlight

Avg HRs
1.7
Over 0.5 HR
82%
Over 1.5 HR
51%
No HR
18%
James Wood WSH30.0%
ISO: 0.311 | Barrel: 17.9% | vs Freddy Peralta | Park: 0.96x Platoon: 1.12x
CJ Abrams WSH30.0%
ISO: 0.333 | Barrel: 13.7% | vs Freddy Peralta | Park: 0.96x Platoon: 1.12x
Juan Soto NYM30.0%
ISO: 0.264 | Barrel: 12.5% | vs Miles Mikolas | Park: 0.96x Platoon: 1.12x

Pitcher Strikeout Projections

Miles Mikolas
0.0 K projected
WSH | K/9: 0.0
Freddy Peralta
0.0 K projected
NYM | K/9: 0.0

Injury Report

WSH8 injured
Clayton Beeter RP15-DAY-IL
Tyler Baum DHDAY-TO-DAY
Josiah Gray SP60-DAY-IL
Cole Henry RP15-DAY-IL
Ken Waldichuk RP60-DAY-IL
Trevor Williams SP60-DAY-IL
+2 more
NYM8 injured
Luis Robert Jr. CF10-DAY-IL
Kodai Senga SP15-DAY-IL
Francisco Lindor SS10-DAY-IL
Jorge Polanco 1B10-DAY-IL
Kevin Herget RPDAY-TO-DAY
Jared Young 1B10-DAY-IL
+2 more

AI Intelligence Analysis

NEUTRAL -2RED ZONE41.1% WR (n=375)
Massive contradictions: model sees away value (49.3% vs 38.5% market implied = 28% edge) but market heavily favors home (65% implied) — sharp consensus opposes model. Both Away ML and Under edges >25% in RED/YELLOW zones historically underperform. Avoid entirely despite attractive numbers.

Key Factors

  • Mikolas 9.17 ERA (C+ grade, historically weak) yet market still heavily favors home (65%) — suggests WSH lineup/bullpen weakness not reflected in SP quality alone
  • Model 49.3% away (Mikolas disadvantage) vs market 38.5% = 28% edge in RED zone (41% WR historically)
  • Under 7.5 edge 30.8% also in RED zone — high edges are model failure mode
  • Peralta 4.21 ERA (B- grade) is solid but not dominant; market respects Mets home field despite roster struggles
  • Cold, damp weather (52°F, 95% humidity) should suppress runs but market still pricing high

Risk Factors

  • HIGH_EDGE_WARNING on both away ML (28%) and Under (30%) — these are zones where model has been dead wrong (38-41% WR)
  • Sharp money heavily favoring home despite Mikolas poor ERA — market clearly has information on WSH weakness model doesn't capture
  • 28-30% edges in RED zones = classic overconfidence; model is likely wrong on away value
Sharp MoneyAgainst ModelMarket has aggressively priced home (65% implied) — sharp consensus is decisively against away value model is offering.
HIGH EDGE WARNINGRED ZONESHARP OPPOSITIONDATA INTEGRITY

Edge Analysis

Moneyline
WSH 53.5%
-34.7 pts
Run Line
-1.5
-34.7 pts
Total
7.5
+30.8 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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