Home / MLB Projections MLB / Pirates @ Giants PIT @ SF

PITTSBURGH

PIRATESPIT

46-62  |  Road

2
FINAL
Wed, Jul 30 • 3:45 PM ET
Oracle Park
View Odds Details →

SAN FRANCISCO

GIANTSSF

54-54  |  Home

1
Probable Pitchers
Carmen Mlodzinski
STARTING PITCHER

Carmen Mlodzinski

#50 | Relief Pitcher
@
Camilo Doval
STARTING PITCHER

Camilo Doval

#57 | Relief Pitcher
Player W-L ERA WHIP IP K
Carmen Mlodzinski None-None 4.34 -- 2.0 --
Camilo Doval None-None 3.09 -- 1.0 3
None-None W-L None-None
4.34 ERA Ldr 3.09
-- WHIP --
2.0 Ldr IP (Innings Pitched) 1.0
-- K (Strikeouts) Ldr 3
AI Model Win Probability
PIRATES
43%
📊
GIANTS
57%
PIRATES --
GIANTS --
AI Model Projected Total
AI Projection
6.0
Projected
Book Total
--
Over / Under Line
Over 0 37%
Under 0 63%
AI Model Spread Expectation
AI Spread Proj
-0.1
PITTSBURGH
Book Spread
--
Book Line
▲ Edge: +0.1 points (Pittsburgh)
PiratesPIT (-3.5) 0 GiantsSF (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Pirates Cover

51.4%

Giants Cover

48.6%

-12-8-40+4+8+12
Distribution centered around PIT -0.1 expected margin.
Margin of Victory (PIT)

PIT By 1-2 runs

18.7%

PIT By 3-4 runs

14.0%

SF By 1-2 runs

18.2%

SF By 3-4 runs

13.2%

AI Model Projected Score
3.07
PITTSBURGH
2.93
SAN FRANCISCO
Simulated Score
3
PITTSBURGH
5
SAN FRANCISCO
Projections Grades
Moneyline (ML)
Divergence
AI Pirates Win
43% Win Prob
D
Sim Giants Loss
50% Win Prob
D
Run Line (ATS)
Divergence
AI Pirates (-0) Win
Proj Runs: -0.1
A
Sim Giants (+0) Loss
Proj Runs: -2.0
D
Over/Under (Total)
No Consensus
AI o0 Win
Proj Total: 6.0
D
Sim u0.0
Proj Total: 8.0
D
Head-to-Head History
Date Game Score
Sep 3, 2026 SF @ PIT 2 - 5
Sep 2, 2026 SF @ PIT 5 - 4
Sep 1, 2026 SF @ PIT 12 - 13
May 10, 2026 PIT @ SF 6 - 7
May 9, 2026 PIT @ SF 13 - 3
Season Statistics Comparison
Pittsburgh PIT
vs
San Francisco SF
162
Games Played
162
4.73 ▲
Runs / Game
4.13
.253 ▲
Avg (AVG)
.245
.330 ▲
On-Base (OBP)
.306
.404 ▲
Slugging (SLG)
.400
.734 ▲
OPS
.706
186 ▲
Home Runs
178
Key Model Drivers
Hits (L5) +26.5% more
Pittsburgh: 9.2 vs San Francisco: 6.8
Total Bases (L5) +17.6% more
Pittsburgh: 14.0 vs San Francisco: 11.5
Runs per Game (Season) +12.7% more
Pittsburgh: 4.7 vs San Francisco: 4.1
Walks (L5) +11.1% more
Pittsburgh: 3.2 vs San Francisco: 2.9
Post-Mortem Analysis
Metric Projected Actual
Score Game
PIT Score 3.1 2
SF Score 2.9 1
Team Stats Splits (Home / Away)
Errors 0.3 / 0.0 0.0 / 0.0
Hits 6.8 / 9.2 6.0 / 7.0
Home Runs 0.9 / 1.0 0.0 / 0.0
Strikeouts 9.3 / 7.9 14.0 / 10.0
Total Bases 11.5 / 14.0 6.0 / 8.0
Walks 2.9 / 3.2 7.0 / 1.0
Public Market Splits
Moneyline
Open: -- → --
20%TICKETS80%
24%MONEY76%
Spread (ATS)
Open: -- → --
38%TICKETS62%
19%MONEY81%
Total (O/U)
Open: -- → --
61%TICKETS39%
27%MONEY73%
Situational Trends
📊 Win-Loss
PIT Advantage
82-80-0 (50.6%)
Margin: 0.2 ATS +/-:
SF
65-97-0 (40.1%)
Margin: -0.6 ATS +/-:
🎯 ATS (Spread)
PIT
(49.4%)
Margin: 0.2 ATS +/-:
SF Advantage
(51.2%)
Margin: -0.6 ATS +/-:
⚖️ Over/Under
PIT
81-74-7
O: 52.3% / U: 47.7% Total +/-: +1.0
SF
76-73-13
O: 51.0% / U: 49.0% Total +/-: +0.6
Season to Date Model Accuracy
AI Model
SIM Engine
SF
PIT
SF
PIT
ML
49% 79-81
52% 82-77
53% 85-75
52% 85-78
ATS
62% 100-60
54% 86-73
52% 83-77
50% 81-82
TTL
55% 82-68-10
47% 72-81-6
46% 68-81-11
50% 79-78-6
Accuracy:
Elite (>60%)
Good (53-60%)
Neutral (48-52%)
Under (43-47%)
Danger (≤42%)
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