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

SAN FRANCISCO

GIANTSSF

57-57  |  Road

4
FINAL
Wed, Aug 6 • 12:35 PM ET
PNC Park
View Odds Details →

PITTSBURGH

PIRATESPIT

49-65  |  Home

2
Probable Pitchers
Christian Koss
STARTING PITCHER

Christian Koss

#50 | Shortstop
@
Carmen Mlodzinski
STARTING PITCHER

Carmen Mlodzinski

#50 | Relief Pitcher
Player W-L ERA WHIP IP K
Christian Koss None-None -- -- -- --
Carmen Mlodzinski None-None 4.11 -- 1.2 1
None-None W-L None-None
-- ERA 4.11
-- WHIP --
-- IP (Innings Pitched) 1.2
-- K (Strikeouts) 1
AI Model Win Probability
GIANTS
57%
📊
PIRATES
43%
GIANTS --
PIRATES --
AI Model Projected Total
AI Projection
9.8
Projected
Book Total
--
Over / Under Line
Over 0 58%
Under 0 42%
AI Model Spread Expectation
AI Spread Proj
-0.5
SAN FRANCISCO
Book Spread
--
Book Line
▲ Edge: +0.5 points (San Francisco)
GiantsSF (-3.5) 0 PiratesPIT (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Giants Cover

54.7%

Pirates Cover

45.3%

-12-8-40+4+8+12
Distribution centered around SF -0.5 expected margin.
Margin of Victory (SF)

SF By 1-2 runs

19.1%

SF By 3-4 runs

14.9%

PIT By 1-2 runs

17.5%

PIT By 3-4 runs

12.2%

AI Model Projected Score
5.13
SAN FRANCISCO
4.66
PITTSBURGH
Simulated Score
4
SAN FRANCISCO
6
PITTSBURGH
Projections Grades
Moneyline (ML)
Divergence
AI Giants Win
57% Win Prob
D
Sim Pirates Loss
50% Win Prob
D
Run Line (ATS)
Divergence
AI Giants (-0) Win
Proj Runs: -0.5
A
Sim Pirates (+0) Loss
Proj Runs: -2.0
D
Over/Under (Total)
No Consensus
AI o0 Win
Proj Total: 9.8
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
Home Runs (L5) +24.8% more
San Francisco: 1.6 vs Pittsburgh: 1.2
Total Bases (L5) +18.7% more
San Francisco: 16.2 vs Pittsburgh: 13.2
Hits (L5) +17.2% more
San Francisco: 9.7 vs Pittsburgh: 8.0
Errors (L5) +42.0% more
Pittsburgh: 1.1 vs San Francisco: 0.7
Post-Mortem Analysis
Metric Projected Actual
Score Game
SF Score 5.1 4
PIT Score 4.7 2
Team Stats Splits (Home / Away)
Errors 1.1 / 0.7 0.0 / 0.0
Hits 8.0 / 9.7 8.0 / 7.0
Home Runs 1.2 / 1.6 0.0 / 1.0
Strikeouts 9.0 / 9.8 4.0 / 11.0
Total Bases 13.2 / 16.2 9.0 / 12.0
Walks 3.7 / 2.8 2.0 / 2.0
Public Market Splits
Moneyline
Open: -- → --
79%TICKETS21%
87%MONEY13%
Spread (ATS)
Open: -- → --
69%TICKETS31%
73%MONEY27%
Total (O/U)
Open: -- → --
67%TICKETS33%
76%MONEY24%
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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