MLB Baseball

MIL vs STL Prediction

July 9, 2026

10,000 Monte Carlo simulations

MIL vs STL prediction for July 9, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects STL 3.2 - MIL 3.3. STL is favored with a 51.0% win probability. The run line is 1.5 and the total is 8.5. Model projects 6.5 total runs.

STL
3.2
Projected Score
VS O/U 8.5
MIL
3.3
Projected Score
Win Probability
51.0%
49.0%
STLMIL
+1.5
Run Line (STL)
8.5
Total Line
10,000
Simulations
MIL W5STL W4
Calibrated accuracy at this confidence: 50.9% (2,789 games)

Projected Runs Range 10th – 90th percentile

MIL
135
STL
135
FINALSTL 4 — MIL 8
Projected
STL 3.2 — MIL 3.3
Actual
STL 4 — MIL 8

Pick Results

Logan Henderson OVER 4.5 Ksk_propsLOSS-0.50u

Starting Pitcher Matchup

Logan Henderson R
MIL
FF45%93 mph26% whiff
CH31%83 mph30% whiff
FC20%87 mph11% whiff
Andre Pallante R
STL
FF30%95 mph12% whiff
SL30%88 mph29% whiff
SI19%95 mph11% whiff

Weather Impact

Busch Stadium
82°F2 mph wind
HR: 1.032 Total: 1.016
thin air

Bullpen Comparison

MIL
3.66ERA
3.52FIP
9.38K/9
3.94BB/9
1.32WHIP
STL
4.30ERA
4.29FIP
8.27K/9
4.08BB/9
1.36WHIP

Betting Edges

TOTAL OVER 8.5
-38.9% EV
-106
RUN_LINE HOME +1.5
-31.0% EV
-149
TOTAL UNDER 8.5
+28.8% EV
-114
F5_ML AWAY
-19.3% EV
-132
RUN_LINE AWAY -1.5
-18.5% EV
+125
F5 UNDER 4.5
+12.6% EV
-118

First 5 Innings & NRFI

MIL F5
1.6 runs
34.9% win
STL F5
2.0 runs
46.2% win
F5 Total
3.5
NRFI
59.8%
YRFI
40.2%
Avg 1st Inn Runs
0.79

HR Spotlight

Avg HRs
1.7
Over 0.5 HR
82%
Over 1.5 HR
50%
No HR
18%
Jake Bauers MIL30.0%
ISO: 0.268 | Barrel: 13.6% | vs Andre Pallante | Park: 0.98x Platoon: 1.12x
Jordan Walker STL26.1%
ISO: 0.236 | Barrel: 10.9% | vs Logan Henderson | Park: 0.98x
Alec Burleson STL25.4%
ISO: 0.278 | Barrel: 9.4% | vs Logan Henderson | Park: 0.98x Platoon: 1.12x

Pitcher Strikeout Projections

Logan Henderson
0.0 K projected
MIL | K/9: 0.0
Andre Pallante
0.0 K projected
STL | K/9: 0.0

Injury Report

MIL8 injured
Kyle Harrison SPDAY-TO-DAY
Brandon Woodruff SP15-DAY-IL
Brandon Lockridge LF60-DAY-IL
David Hamilton 3B10-DAY-IL
Brian Fitzpatrick RP60-DAY-IL
Joel Kuhnel RP15-DAY-IL
+2 more
STL3 injured
Ryne Stanek RPDAY-TO-DAY
Max Rajcic RP60-DAY-IL
Ramon Urias 3B60-DAY-IL

AI Intelligence Analysis

LEANYELLOW ZONE48.9% WR (n=7)
Model favors STL home (51% vs 49% away) despite Logan Henderson (B+, 2.96 ERA) being elite away pitcher with clear advantage over Andre Pallante (B-, no ERA stat). Home field overcomes pitcher disadvantage; F5_ML HOME (11.6% edge, 54.2% prob) is cleaner than full-game ML (3.9% edge, 48.6% prob). UNDER 8.5 shows 28.8% edge (YELLOW zone red flag, 50.1% WR). Prefer F5_ML HOME to exploit first 5 innings home advantage; avoid full-game ML due to weak edge.

Key Factors

  • Logan Henderson (MIL away): B+ grade, 2.96 Bayesian ERA, 33.7% K rate, elite stuff (0.723 overall score)—truly excellent pitcher
  • Andre Pallante (STL home): B-, no ERA stat available, 7.3 K/9, B+ command (0.678)—solid but clearly inferior to Henderson
  • Pitcher advantage AGAINST STL: Henderson elite (2.96 ERA) vs Pallante mediocre—yet model favors home 51%, indicating home field + lineup overcome pitcher gap
  • F5_ML HOME 11.6% edge (54.2% prob)—superior to full-game 3.9% edge; first 5 innings more favorable for home
  • UNDER 8.5 shows 28.8% edge but falls into YELLOW zone (50.1% WR, n=318)—pattern risk like other high-edge games

Risk Factors

  • Full-game HOME ML edge only 3.9% with 48.9% zone WR (n=7)—weak signal, small sample
  • Henderson's elite pitching could suppress STL scoring despite home advantage; pitcher advantage might be underestimated in model
  • UNDER edge 28.8% is massive and part of 5-game pattern—model inflation warning
PITCHER MISMATCHF5 ADVANTAGEYELLOW ZONE

Edge Analysis

Moneyline
STL 51.0%
-31.0 pts
Run Line
+1.5
-31.0 pts
Total
8.5
+28.8 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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