MLB Baseball

CWS vs PHI Prediction

June 7, 2026

10,000 Monte Carlo simulations

CWS vs PHI prediction for June 7, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects PHI 4.8 - CWS 4.8. PHI is favored with a 51.8% win probability. The run line is -1.5 and the total is 9.0. Model projects 9.6 total runs.

PHI
4.8
Projected Score
VS O/U 9.0
CWS
4.8
Projected Score
Win Probability
51.8%
48.2%
PHICWS
-1.5
Run Line (PHI)
9.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.3% (2,193 games)

Projected Runs Range 10th – 90th percentile

CWS
357
PHI
357
FINALPHI 9 — CWS 5
Projected
PHI 4.8 — CWS 4.8
Actual
PHI 9 — CWS 5

Starting Pitcher Matchup

Tyler Gilbert L
CWS
SI32%90 mph11% whiff
ST30%81 mph42% whiff
FC18%88 mph18% whiff
Aaron Nola R
PHI
KC33%78 mph38% whiff
FF26%92 mph14% whiff
SI20%91 mph10% whiff

Weather Impact

Citizens Bank Park
86°F11 mph wind
HR: 0.994 Total: 0.994
thin air, 8mph in

Bullpen Comparison

CWS
4.52ERA
4.63FIP
8.71K/9
4.77BB/9
1.34WHIP
PHI
4.21ERA
3.31FIP
10.33K/9
3.17BB/9
1.33WHIP

Betting Edges

RUN_LINE AWAY +1.5
-29.8% EV
-137
RUN_LINE HOME -1.5
-18.4% EV
+114
F5_ML HOME
-16.2% EV
-161
ML HOME
-14.9% EV
-172
TOTAL OVER 9.0
-13.5% EV
-118
ML AWAY
+12.6% EV
+144

First 5 Innings & NRFI

CWS F5
2.7 runs
43.5% win
PHI F5
2.7 runs
42.4% win
F5 Total
5.4
NRFI
50.0%
YRFI
50.0%
Avg 1st Inn Runs
1.08

HR Spotlight

Avg HRs
2.6
Over 0.5 HR
92%
Over 1.5 HR
72%
No HR
8%
Drew Romo CWS30.0%
ISO: 0.255 | Barrel: 15.6% | vs Aaron Nola | Park: 1.02x Platoon: 1.12x
Colson Montgomery CWS30.0%
ISO: 0.255 | Barrel: 14.5% | vs Aaron Nola | Park: 1.02x Platoon: 1.12x
Miguel Vargas CWS30.0%
ISO: 0.199 | Barrel: 14.5% | vs Aaron Nola | Park: 1.02x

Pitcher Strikeout Projections

Tyler Gilbert
0.0 K projected
CWS | K/9: 0.0
Aaron Nola
0.0 K projected
PHI | K/9: 0.0

Injury Report

CWSHealthy
PHI7 injured
Carson DeMartini SSDAY-TO-DAY
Bryan Rincon SSDAY-TO-DAY
Kyle Backhus RP15-DAY-IL
Johan Rojas CFSUSPENSION
Rene Pinto CDAY-TO-DAY
Daniel Robert RPDAY-TO-DAY
+1 more

AI Intelligence Analysis

NEUTRAL -1YELLOW ZONE39.0% WR (n=5)
Model shows 12.6% away ML edge (CWS at 46.1% model prob vs 41.0% market), suggesting CWS is undervalued. HOWEVER, this is a RED ZONE (away ML 10-15% edge zone historically 39% WR) and the SP quality CONTRADICTS the directional lean: Aaron Nola (5.99 ERA, B- stuff/command) is WORSE than Tyler Gilbert (21.87 ERA, C+ stuff, B command in limited sample). This is a DATA INTEGRITY FLAG — the model favors CWS away despite their SP being dramatically inferior. Reconciliation: Model must be overweighting bullpen, lineup, or home field. Calibration history shows away ML edges >10% are unreliable (47.1% WR). SKIP or severe caution.

Key Factors

  • Aaron Nola 5.99 ERA (B- stuff 0.437, command 0.531) vs Tyler Gilbert 21.87 ERA (C+ stuff 0.371, command 0.597) — Nola is clearly better SP but still below-average
  • CWS 12.6% away ML edge contradicts SP quality gap. Model must be underweighting Nola or overweighting away lineup value
  • Home park neutral (1.02 mult, 85.9F, 8mph in wind) — no weather tail
  • Bullpen edge minimal: PHI 4.21 vs CWS 4.52 ERA. PHI quality 1.069 vs CWS 0.996
  • Away ML 10-15% edge zone: historical 39% WR (RED, n=5). High confidence that >10% edges are model overconfidence

Risk Factors

  • Model recommends away dog with inferior SP — classic overconfidence signal. Edge >10% historically fails 61% of the time in away ML context
  • Total market disabled (49.5% WR Grade D) — totals edge of -13.5% (UNDER) is unreliable
  • CWS lineup impact: Munetaka Murakami 10-day IL (hamstring), Austin Hays 10-day IL (calf), Everson Pereira 10-day IL (pectoral). Three key position players out
MODEL MARKET CONFLICTDATA INTEGRITYRED ZONEHIGH EDGE WARNINGREVERSE SP MISMATCH

Edge Analysis

Moneyline
PHI 51.8%
-18.4 pts
Run Line
-1.5
-18.4 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. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →

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