MLB Baseball

SD vs MIA Prediction

July 27, 2026

10,000 Monte Carlo simulations

SD vs MIA prediction for July 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIA 4.6 - SD 3.9. MIA is favored with a 54.0% win probability. The run line is 1.5 and the total is 8.5. Model projects 8.5 total runs.

MIA
4.6
Projected Score
VS O/U 8.5
SD
3.9
Projected Score
Win Probability
54.0%
46.0%
MIASD
+1.5
Run Line (MIA)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 54.5% (2,930 games)

Projected Runs Range 10th – 90th percentile

SD
246
MIA
357

Starting Pitcher Matchup

Walker Buehler R
SD
FC23% · 90
FF21% · 94
SI17% · 94
usagewhiffbar labels show usage · mph
FC23%90 mph19% whiff
FF21%94 mph10% whiff
SI17%94 mph9% whiff
Janson Junk R
MIA
FF35% · 94
SL22% · 86
CH19% · 87
usagewhiffbar labels show usage · mph
FF35%94 mph10% whiff
SL22%86 mph13% whiff
CH19%87 mph26% whiff

Weather Impact

loanDepot park
85°F8 mph windRoof: retractable
HR: 0.993 Total: 0.993
thin air, 8mph in

Bullpen Comparison

SD
3.15ERA
3.66FIP
8.41K/9
3.44BB/9
1.23WHIP
MIA
3.85ERA
3.56FIP
9.97K/9
4.48BB/9
1.21WHIP

Betting Edges

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Edge Analysis
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First 5 Innings & NRFI

SD F5
2.0 runs
33.9% win
MIA F5
2.7 runs
50.5% win
F5 Total
4.7
NRFI
56.3%
YRFI
43.7%
Avg 1st Inn Runs
0.94

HR Spotlight

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Home Run Analysis
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Injury Report

SD8 injured
Joe Musgrove SP60-DAY-IL
Lucas Giolito SP15-DAY-IL
Jeremiah Estrada RP15-DAY-IL
Nick Pivetta SP60-DAY-IL
Miguel Andujar DH10-DAY-IL
David Morgan RP15-DAY-IL
+2 more
MIA8 injured
Max Meyer SP15-DAY-IL
Owen Caissie RF10-DAY-IL
Josh Ekness RP60-DAY-IL
Ronny Henriquez RP60-DAY-IL
Anthony Bender RP15-DAY-IL
William Kempner RP15-DAY-IL
+2 more

Edge Analysis

Moneyline
MIA 54.0%
-29.6 pts
Run Line
+1.5
-29.6 pts
Total
8.5
+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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