MLB Baseball

ARI vs SD Prediction

July 9, 2026

10,000 Monte Carlo simulations

FINAL: SD 1 — ARI 3. Our Monte Carlo simulation projected SD 4.0 - ARI 3.5 (SD at 54.1% win probability). The run line is -1.5 and the total is 8.5. Model projects 7.5 total runs.

SD
4.0
Projected Score
VS O/U 8.5
ARI
3.5
Projected Score
Win Probability
54.1%
45.9%
SDARI
-1.5
Run Line (SD)
8.5
Total Line
10,000
Simulations
ARI W5SD
Calibrated accuracy at this confidence: 52.9% (2,789 games)

Projected Runs Range 10th – 90th percentile

ARI
245
SD
246
FINALSD 1 — ARI 3
Projected
SD 4.0 — ARI 3.5
Actual
SD 1 — ARI 3

Pick Results

ARI @ SD NRFInrfiWIN+1.00u

Starting Pitcher Matchup

Merrill Kelly R
ARI
FF26%92 mph12% whiff
CH25%88 mph28% whiff
FC16%91 mph11% whiff
Griffin Canning R
SD
FF27%94 mph15% whiff
SL25%87 mph38% whiff
CH23%90 mph22% whiff

Weather Impact

PETCO Park
68°F6 mph wind
HR: 0.996 Total: 0.996
neutral

Bullpen Comparison

ARI
3.28ERA
3.68FIP
8.28K/9
3.10BB/9
1.10WHIP
SD
3.15ERA
3.66FIP
8.41K/9
3.44BB/9
1.23WHIP

Betting Edges

RUN_LINE AWAY +1.5
-43.2% EV
-200
TOTAL OVER 8.5
-29.7% EV
-123
TOTAL UNDER 8.5
+23.5% EV
+102
F5_ML AWAY
-13.0% EV
-104
RUN_LINE HOME -1.5
+7.4% EV
+164
NRFI NRFI
+6.0% EV
+100

First 5 Innings & NRFI

ARI F5
2.1 runs
36.7% win
SD F5
2.5 runs
48.4% win
F5 Total
4.6
NRFI
56.7%
YRFI
43.3%
Avg 1st Inn Runs
0.95

HR Spotlight

Avg HRs
2.3
Over 0.5 HR
90%
Over 1.5 HR
67%
No HR
10%
Corbin Carroll ARI30.0%
ISO: 0.264 | Barrel: 14.5% | vs Griffin Canning | Park: 0.90x Platoon: 1.12x
Ketel Marte ARI30.0%
ISO: 0.162 | Barrel: 13.5% | vs Griffin Canning | Park: 0.90x Platoon: 1.12x
Gavin Sheets SD30.0%
ISO: 0.263 | Barrel: 9.5% | vs Merrill Kelly | Park: 0.90x Platoon: 1.12x

Pitcher Strikeout Projections

Merrill Kelly
0.0 K projected
ARI | K/9: 0.0
Griffin Canning
0.0 K projected
SD | K/9: 0.0

Injury Report

ARI8 injured
Jordan Lawlar LF10-DAY-IL
Corbin Burnes SP60-DAY-IL
Michael Soroka SP15-DAY-IL
Ryne Nelson SP60-DAY-IL
A.J. Puk RP60-DAY-IL
Cristian Mena RP60-DAY-IL
+2 more
SD8 injured
Randy Vasquez SP15-DAY-IL
Freddy Fermin C10-DAY-IL
Samad Taylor LF10-DAY-IL
Jason Adam RP15-DAY-IL
Matt Waldron RP15-DAY-IL
Jeremiah Estrada RP15-DAY-IL
+2 more

AI Intelligence Analysis

LEAN +1YELLOW ZONE50.1% WR (n=318)
Model projects 7.47 total runs vs market 8.5, yielding 23.5% UNDER edge (61.1% prob)—large but not extreme compared to 30%+ games and part of slate pattern. However, qualitative context supports under: Petco Park naturally suppresses runs (0.9 park factor, -12%), cold weather (68.2°F), and both pitchers have high ERAs (Kelly 6.17, Canning 7.25)—suggesting a low-scoring, pitcher-disadvantaged game despite poor arsenals. Zone WR 50.1% (YELLOW) requires caution, but moderate edge (23.5%) is acceptable. Prefer UNDER 8.5.

Key Factors

  • Merrill Kelly (ARI away): C grade, 8.0 K/9, 6.17 ERA (high)—below-average pitcher
  • Griffin Canning (SD home): C+ grade, 8.0 K/9, 7.25 ERA (terrible!!!)—worst pitcher on slate by ERA
  • Both pitchers are high-ERA, poor-quality starts; poor command from both suggests walks, hits, but weak strikeout upside
  • Petco Park (0.9 factor) naturally suppresses runs by 10-12%—most suppressive park on slate besides Oracle
  • Temperature 68.2°F (cool) + 5.7 mph neutral wind—cool suppresses scoring slightly

Risk Factors

  • UNDER edge 23.5% is large and part of 5-game pattern (all 23%+), suggesting potential model inflation on totals
  • Both pitchers have terrible ERAs (6.17, 7.25) but model still projects low scoring—could suggest weak offenses or Petco suppression overestimated
  • Zone WR 50.1% on totals doesn't provide confidence boost; YELLOW zone suggests market is tougher than edge implies
WEATHER IMPACTPARK FACTORYELLOW ZONEPITCHER QUALITY

Edge Analysis

Moneyline
SD 54.1%
+7.4 pts
Run Line
-1.5
+7.4 pts
Total
8.5
+23.5 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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