MLB Baseball

PHI vs MIL Prediction

June 12, 2026

10,000 Monte Carlo simulations

PHI vs MIL prediction for June 12, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIL 4.7 - PHI 3.8. MIL is favored with a 57.8% win probability. The run line is -1.5 and the total is 7.5. Model projects 8.5 total runs.

MIL
4.7
Projected Score
VS O/U 7.5
PHI
3.8
Projected Score
Win Probability
57.8%
42.2%
MILPHI
-1.5
Run Line (MIL)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 61.3% (2,305 games)

Projected Runs Range 10th – 90th percentile

PHI
246
MIL
357
FINALMIL 6 — PHI 0
Projected
MIL 4.7 — PHI 3.8
Actual
MIL 6 — PHI 0

Starting Pitcher Matchup

Tanner Banks L
PHI
SL35%87 mph23% whiff
ST23%82 mph28% whiff
FF20%92 mph27% whiff
Jacob Misiorowski R
MIL
FF62%100 mph38% whiff
SL14%93 mph28% whiff
CU11%87 mph45% whiff

Weather Impact

American Family Field
77°F12 mph windRoof: retractable
HR: 0.977 Total: 0.984
thin air, 12mph in

Bullpen Comparison

PHI
4.19ERA
3.25FIP
10.34K/9
3.20BB/9
1.34WHIP
MIL
3.72ERA
3.58FIP
9.23K/9
3.92BB/9
1.32WHIP

Betting Edges

RUN_LINE AWAY +1.5
-35.9% EV
-108
F5_ML AWAY
+25.8% EV
+215
ML AWAY
+20.4% EV
+200
F5_ML HOME
-18.3% EV
-278
ML HOME
-15.6% EV
-244
RUN_LINE HOME -1.5
-15.2% EV
-114

First 5 Innings & NRFI

PHI F5
1.7 runs
35.9% win
MIL F5
2.2 runs
46.5% win
F5 Total
3.9
NRFI
60.8%
YRFI
39.2%
Avg 1st Inn Runs
0.79

HR Spotlight

Avg HRs
1.7
Over 0.5 HR
80%
Over 1.5 HR
49%
No HR
20%
Kyle Schwarber PHI21.5%
ISO: 0.336 | Barrel: 19.3% | vs Jacob Misiorowski | Platoon: 1.12x
Bryce Harper PHI19.3%
ISO: 0.312 | Barrel: 14.4% | vs Jacob Misiorowski | Platoon: 1.12x
Jake Bauers MIL14.7%
ISO: 0.176 | Barrel: 13.7% | vs Tanner Banks

Pitcher Strikeout Projections

Tanner Banks
0.0 K projected
PHI | K/9: 0.0
Jacob Misiorowski
0.0 K projected
MIL | K/9: 0.0

Injury Report

PHI8 injured
Steward Berroa RFPATERNITY
Adolis Garcia RF60-DAY-IL
Johan Rojas CFOUT
Kyle Backhus RP15-DAY-IL
Andrew Walling RPDAY-TO-DAY
Carson DeMartini SSDAY-TO-DAY
+2 more
MIL8 injured
Quinn Priester SP60-DAY-IL
Jared Koenig RP15-DAY-IL
Brandon Woodruff SP15-DAY-IL
Trevor Megill RPDAY-TO-DAY
Brian Fitzpatrick RP60-DAY-IL
DL Hall RP15-DAY-IL
+2 more

AI Intelligence Analysis

NEUTRAL -2RED ZONE43.3% WR (n=98)
Dominant home favorite MIL massively underpriced by market: Model 57.8% vs market 70.9% (!!) — MIL 20.4% edge away PHI bets. Jacob Misiorowski elite ace (1.62 ERA, A- 0.824 grade, 36.96% K-rate) completely overwhelms Tanner Banks (6.57 ERA, B- 0.505, weaker). NRFI edge (+8%, 56.8% model) confirms low-scoring setup. But market pricing MIL at -243 odds (70.9%) is extreme overvalue. Model flags DATA_INTEGRITY concern: Is market right that MIL is untouchable (>70%)? Or is model correctly sizing at 58%?

Key Factors

  • Misiorowski elite ace: 1.62 ERA, A- 0.824 overall, 36.96% K-rate — ace-tier dominant, should suppress runs
  • Banks weak: 6.57 ERA, B- 0.505, 23.4% K-rate — back-end starter, vulnerable to home team
  • Market extreme: -243 implies 70.9% MIL win prob — 13.1pt more confident than model's 57.8%; suggests market has edge intelligence PHI is injured/depleted beyond public knowledge

Risk Factors

  • PHI away favorite in RED zone (43.3% WR, 39.5% combo WR) — avoid betting PHI side at all
  • Data integrity concern: If market is right (70.9% > 57.8%), model is missing information. If model right, odds are value, but RED zone away suggests market consensus, not model error.
Sharp MoneyAgainst ModelMarket has moved sharply vs model (implied 70.9% at -243 vs model 57.8%), suggesting strong consensus that MIL is dominant or PHI is broken.
DATA INTEGRITY CONCERNMARKET EXTREME CHALKELITE PITCHERNRFI STRONGRED ZONE AWAY FAVORITEMODEL MARKET CONFLICT

Edge Analysis

Moneyline
MIL 57.8%
-15.2 pts
Run Line
-1.5
-15.2 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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