MLB Baseball

SD vs STL Prediction

June 17, 2026

10,000 Monte Carlo simulations

SD vs STL prediction for June 17, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects STL 4.6 - SD 5.7. SD is favored with a 54.2% win probability. The run line is -1.5 and the total is 10.5. Model projects 10.3 total runs.

STL
4.6
Projected Score
VS O/U 10.5
SD
5.7
Projected Score
Win Probability
45.8%
54.2%
STLSD
-1.5
Run Line (STL)
10.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.8% (2,410 games)

Projected Runs Range 10th – 90th percentile

SD
468
STL
357
FINALSTL 1 — SD 6
Projected
STL 4.6 — SD 5.7
Actual
STL 1 — SD 6

Starting Pitcher Matchup

Bradgley Rodriguez R
SD
CH42%89 mph40% whiff
SI27%98 mph13% whiff
FF23%98 mph24% whiff
Kyle Leahy R
STL
FF29%94 mph12% whiff
SL16%89 mph16% whiff
CU16%82 mph26% whiff

Weather Impact

Busch Stadium
90°F25 mph wind
HR: 0.975 Total: 0.982
thin air, 15mph in

Bullpen Comparison

SD
3.15ERA
3.66FIP
8.41K/9
3.44BB/9
1.23WHIP
STL
4.30ERA
4.29FIP
8.27K/9
4.08BB/9
1.36WHIP

Betting Edges

RUN_LINE AWAY +1.5
-25.1% EV
-192
F5_ML HOME
-22.5% EV
-135
RUN_LINE HOME -1.5
-21.0% EV
+158
ML HOME
-16.1% EV
-130
F5_ML AWAY
+15.4% EV
+108
TOTAL OVER 10.5
-11.2% EV
-102

First 5 Innings & NRFI

SD F5
3.2 runs
50.9% win
STL F5
2.5 runs
35.6% win
F5 Total
5.7
NRFI
53.0%
YRFI
47.0%
Avg 1st Inn Runs
1.04

HR Spotlight

Avg HRs
3.0
Over 0.5 HR
95%
Over 1.5 HR
80%
No HR
5%
Gavin Sheets SD30.0%
ISO: 0.263 | Barrel: 12.8% | vs Kyle Leahy | Park: 0.98x Platoon: 1.12x
Rodolfo Durán SD30.0%
ISO: 0.171 | Barrel: 17.1% | vs Kyle Leahy | Park: 0.98x
Ty France SD30.0%
ISO: 0.220 | Barrel: 10.0% | vs Kyle Leahy | Park: 0.98x

Pitcher Strikeout Projections

Bradgley Rodriguez
0.0 K projected
SD | K/9: 0.0
Kyle Leahy
0.0 K projected
STL | K/9: 0.0

Injury Report

SD8 injured
Mason Miller RPBEREAVEMENT
Matt Waldron SP15-DAY-IL
Freddy Fermin C7-DAY IL
Nick Pivetta SP60-DAY-IL
Luis Campusano C10-DAY-IL
Miguel Andujar DH10-DAY-IL
+2 more
STL7 injured
Sem Robberse SPDAY-TO-DAY
Ryan Fernandez RP15-DAY-IL
Ramon Urias 3B60-DAY-IL
Packy Naughton RPDAY-TO-DAY
Victor Santos RPDAY-TO-DAY
Ixan Henderson SPDAY-TO-DAY
+1 more

AI Intelligence Analysis

LEAN +1YELLOW ZONE45.1% WR (n=106)
SD ML at +110 (47.6% market) is underpriced vs model 52.6% prob (10.4% edge). Bradgley Rodriguez (B grade, 22.97% K rate, 8.0 K/9, 2.33 ERA, 0.557 stuff) is significantly better than Kyle Leahy (C+ grade, 17.87% K rate, 7.3 K/9, weak stuff 0.23). Model sees value on SD away. Take at 0.75 units; F5 ML AWAY also shows 15.4% edge.

Key Factors

  • Pitcher mismatch significant: Bradgley Rodriguez (B grade, 2.33 ERA, 0.557 stuff, 22.97% K rate, 8.0 K/9) vs Kyle Leahy (C+ grade, 0.23 weak stuff, 17.87% K rate, 7.3 K/9). ERA gap alone (2.33 vs implied ~4.0+) is 1.5-2.0 pt swing
  • Rodriguez is elite arm relative: 2.33 Bayesian ERA is top-tier; Leahy's profile (C+ grade, weak stuff) is pedestrian middle reliever stuff
  • Market underpricing: +110 (47.6%) vs model 52.6% = 5% value to SD
  • F5 edge very strong: 15.4% edge on F5 ML AWAY (+55.5% prob) suggests Rodriguez dominates early innings especially
  • Weather confound: 89.6°F (hot) + 24.6 mph wind IN (strong = -15.1 mph tail wind, 0.982 total mult = suppression). Hot+wind-in is neutral on runs but favors low-scoring games where SD's pitcher advantage is maximized

Risk Factors

  • Away ML zone still RED (40.5% combo WR) despite pitcher advantage; historical underperformance warning
  • STL home field advantage (Busch Stadium is playoff-like environment) may offset pitcher mismatch more than model assumes
  • 10.4% edge is actionable but not massive; expect variance
Sharp MoneyWith ModelSD +110 is light for a pitcher-quality edge. Market may be overvaluing STL home field or underestimating Rodriguez quality.
PITCHER MISMATCHML VALUEF5 VALUEAWAY UNDERDOG

Edge Analysis

Moneyline
SD 54.2%
-21.0 pts
Run Line
-1.5
-21.0 pts
Total
10.5
+0.9 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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