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

SAN FRANCISCO

GIANTSSF

56-56  |  Road

4
FINAL
Mon, Aug 4 • 6:40 PM ET
PNC Park
View Odds Details →

PITTSBURGH

PIRATESPIT

48-64  |  Home

5
Probable Pitchers
Grant McCray
STARTING PITCHER

Grant McCray

#58 | Center Field
@
Yohan Ramirez
STARTING PITCHER

Yohan Ramirez

#49 | Relief Pitcher
Player W-L ERA WHIP IP K
Grant McCray None-None -- -- -- --
Yohan Ramirez None-None 3.95 -- 2.2 2
None-None W-L None-None
-- ERA 3.95
-- WHIP --
-- IP (Innings Pitched) 2.2
-- K (Strikeouts) 2
AI Model Win Probability
GIANTS
57%
📊
PIRATES
43%
GIANTS --
PIRATES --
AI Model Projected Total
AI Projection
9.2
Projected
Book Total
--
Over / Under Line
Over 0 45%
Under 0 55%
AI Model Spread Expectation
AI Spread Proj
-0.3
SAN FRANCISCO
Book Spread
--
Book Line
▲ Edge: +0.2 points (San Francisco)
GiantsSF (-3.5) 0 PiratesPIT (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Giants Cover

52.5%

Pirates Cover

47.5%

-12-8-40+4+8+12
Distribution centered around SF -0.3 expected margin.
Margin of Victory (SF)

SF By 1-2 runs

18.8%

SF By 3-4 runs

14.3%

PIT By 1-2 runs

18.0%

PIT By 3-4 runs

12.8%

AI Model Projected Score
4.74
SAN FRANCISCO
4.49
PITTSBURGH
Simulated Score
4
SAN FRANCISCO
6
PITTSBURGH
Projections Grades
Moneyline (ML)
Divergence
AI Giants Loss
57% Win Prob
D
Sim Pirates Win
50% Win Prob
D
Run Line (ATS)
Divergence
AI Giants (-0) Loss
Proj Runs: -0.3
A
Sim Pirates (+0) Win
Proj Runs: -2.0
D
Over/Under (Total)
No Consensus
AI o0 Win
Proj Total: 9.2
D
Sim u0.0
Proj Total: 10.0
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
San Francisco SF
vs
Pittsburgh PIT
162
Games Played
162
4.13
Runs / Game
▲ 4.73
.245
Avg (AVG)
▲ .253
.306
On-Base (OBP)
▲ .330
.400
Slugging (SLG)
▲ .404
.706
OPS
▲ .734
178
Home Runs
▲ 186
Key Model Drivers
Walks (L5) +26.3% more
San Francisco: 4.0 vs Pittsburgh: 2.9
Home Runs (L5) +68.8% more
Pittsburgh: 2.2 vs San Francisco: 1.3
Errors (L5) +44.0% more
Pittsburgh: 1.0 vs San Francisco: 0.6
Runs per Game (Season) +14.5% more
Pittsburgh: 4.7 vs San Francisco: 4.1
Post-Mortem Analysis
Metric Projected Actual
Score Game
SF Score 4.7 4
PIT Score 4.5 5
Team Stats Splits (Home / Away)
Errors 1.0 / 0.6 0.0 / 2.0
Hits 9.7 / 10.0 6.0 / 7.0
Home Runs 2.2 / 1.3 1.0 / 0.0
Strikeouts 8.8 / 8.3 11.0 / 8.0
Total Bases 17.8 / 16.1 10.0 / 9.0
Walks 2.9 / 4.0 5.0 / 3.0
Public Market Splits
Moneyline
Open: -- → --
66%TICKETS34%
79%MONEY21%
Spread (ATS)
Open: -- → --
36%TICKETS64%
44%MONEY56%
Total (O/U)
Open: -- → --
52%TICKETS48%
40%MONEY60%
Situational Trends
📊 Win-Loss
SF
65-97-0 (40.1%)
Margin: -0.6 ATS +/-:
PIT Advantage
82-80-0 (50.6%)
Margin: 0.2 ATS +/-:
🎯 ATS (Spread)
SF Advantage
(51.2%)
Margin: -0.6 ATS +/-:
PIT
(49.4%)
Margin: 0.2 ATS +/-:
⚖️ Over/Under
SF
76-73-13
O: 51.0% / U: 49.0% Total +/-: +0.6
PIT
81-74-7
O: 52.3% / U: 47.7% Total +/-: +1.0
Season to Date Model Accuracy
AI Model
SIM Engine
PIT
SF
PIT
SF
ML
52% 82-77
49% 79-81
52% 85-78
53% 85-75
ATS
54% 86-73
62% 100-60
50% 81-82
52% 83-77
TTL
47% 72-81-6
55% 82-68-10
50% 79-78-6
46% 68-81-11
Accuracy:
Elite (>60%)
Good (53-60%)
Neutral (48-52%)
Under (43-47%)
Danger (≤42%)
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