MLB Baseball

ARI vs PIT Prediction

July 30, 2026

10,000 Monte Carlo simulations

ARI vs PIT prediction for July 30, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects PIT 3.4 - ARI 4.1. ARI is favored with a 52.9% win probability. The run line is -1.5 and the total is 8.5. Model projects 7.5 total runs.

PIT
3.4
Projected Score
VS O/U 8.5
ARI
4.1
Projected Score
Win Probability
47.1%
52.9%
PITARI
-1.5
Run Line (PIT)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 54.6% (2,978 games)

Projected Runs Range 10th – 90th percentile

ARI
246
PIT
135

Starting Pitcher Matchup

Eduardo Rodriguez L
ARI
FF39% · 92
CH27% · 86
CU13% · 80
usagewhiffbar labels show usage · mph
FF39%92 mph19% whiff
CH27%86 mph20% whiff
CU13%80 mph22% whiff
Jared Jones R
PIT
FF45% · 98
SL32% · 90
CH14% · 93
usagewhiffbar labels show usage · mph
FF45%98 mph23% whiff
SL32%90 mph34% whiff
CH14%93 mph34% whiff

Weather Impact

PNC Park
76°F11 mph wind
HR: 1.089 Total: 1.048
thin air, 9mph out

Bullpen Comparison

ARI
3.51ERA
3.72FIP
7.95K/9
2.82BB/9
1.15WHIP
PIT
4.43ERA
4.56FIP
9.09K/9
4.27BB/9
1.40WHIP

Betting Edges

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Edge Analysis
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First 5 Innings & NRFI

ARI F5
2.0 runs
42.6% win
PIT F5
1.9 runs
38.7% win
F5 Total
4.0
NRFI
57.9%
YRFI
42.1%
Avg 1st Inn Runs
0.87

HR Spotlight

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Home Run Analysis
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Injury Report

ARI8 injured
Michael Soroka SP15-DAY-IL
Zac Gallen SP15-DAY-IL
Justin Martinez RP60-DAY-IL
Jordan Lawlar LF10-DAY-IL
Drey Jameson RP15-DAY-IL
Lourdes Gurriel Jr. LF10-DAY-IL
+2 more
PIT6 injured
Spencer Horwitz 1B10-DAY-IL
Oneil Cruz CF60-DAY-IL
Evan Sisk RP15-DAY-IL
Rafael Flores Jr. C7-DAY IL
Chris Devenski RP60-DAY-IL
Konnor Griffin SS60-DAY-IL

AI Intelligence Analysis

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Edge Analysis

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Edge Analysis
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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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