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

PITTSBURGH

PIRATESPIT

45-62  |  Road

3
FINAL
Tue, Jul 29 • 9:45 PM ET
Oracle Park
View Odds Details →

SAN FRANCISCO

GIANTSSF

54-53  |  Home

1
Probable Pitchers
Braxton Ashcraft
STARTING PITCHER

Braxton Ashcraft

#35 | Starting Pitcher
@
Jung Hoo Lee
STARTING PITCHER

Jung Hoo Lee

#51 | Right Field
Player W-L ERA WHIP IP K
Braxton Ashcraft None-None 2.17 -- 3.0 1
Jung Hoo Lee None-None -- -- -- --
None-None W-L None-None
2.17 ERA --
-- WHIP --
3.0 IP (Innings Pitched) --
1 K (Strikeouts) --
AI Model Win Probability
PIRATES
42%
📊
GIANTS
58%
PIRATES --
GIANTS --
AI Model Projected Total
AI Projection
6.3
Projected
Book Total
--
Over / Under Line
Over 0 37%
Under 0 63%
AI Model Spread Expectation
AI Spread Proj
-0.2
SAN FRANCISCO
Book Spread
--
Book Line
▲ Edge: +0.2 points (San Francisco)
PiratesPIT (-3.5) 0 GiantsSF (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Pirates Cover

47.6%

Giants Cover

52.4%

+12+8+40-4-8-12
Distribution centered around SF -0.2 expected margin.
Margin of Victory (SF)

PIT By 1-2 runs

18.0%

PIT By 3-4 runs

12.9%

SF By 1-2 runs

18.8%

SF By 3-4 runs

14.3%

AI Model Projected Score
3.05
PITTSBURGH
3.29
SAN FRANCISCO
Simulated Score
4
PITTSBURGH
5
SAN FRANCISCO
Projections Grades
Moneyline (ML)
Agreement
AI Giants Loss
58% Win Prob
D
Sim Giants Loss
50% Win Prob
D
Run Line (ATS)
Agreement
AI Giants (+0) Loss
Proj Runs: -0.2
B
Sim Giants (+0) Loss
Proj Runs: -1.0
D
Over/Under (Total)
No Consensus
AI o0 Win
Proj Total: 6.3
D
Sim u0.0
Proj Total: 9.0
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
Walks (L5) +33.7% more
San Francisco: 3.8 vs Pittsburgh: 2.9
Errors (L5) +33.0% more
San Francisco: 0.4 vs Pittsburgh: 0.0
Hits (L5) +17.0% more
San Francisco: 9.2 vs Pittsburgh: 7.8
Total Bases (L5) +10.2% more
San Francisco: 14.3 vs Pittsburgh: 12.9
Post-Mortem Analysis
Metric Projected Actual
Score Game
PIT Score 3.1 3
SF Score 3.3 1
Team Stats Splits (Home / Away)
Errors 0.4 / 0.0 0.0 / 0.0
Hits 9.2 / 7.8 2.0 / 12.0
Home Runs 0.8 / 1.0 1.0 / 1.0
Strikeouts 8.4 / 9.9 11.0 / 4.0
Total Bases 14.3 / 12.9 6.0 / 16.0
Walks 3.8 / 2.9 1.0 / 4.0
Public Market Splits
Moneyline
Open: -- → --
30%TICKETS70%
43%MONEY57%
Spread (ATS)
Open: -- → --
59%TICKETS41%
24%MONEY76%
Total (O/U)
Open: -- → --
59%TICKETS41%
59%MONEY41%
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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