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

PITTSBURGH

PIRATESPIT

21-17  |  Road

2
FINAL
Fri, May 8 • 10:15 PM ET
Oracle Park
View Odds Details →

SAN FRANCISCO

GIANTSSF

14-23  |  Home

5
Probable Pitchers
Carmen Mlodzinski
STARTING PITCHER

Carmen Mlodzinski

#50 | Relief Pitcher
@
👤
STARTING PITCHER

Keaton Winn

#67 | Relief Pitcher
Player W-L ERA WHIP IP K
Carmen Mlodzinski None-None 4.50 -- 6.0 1
Keaton Winn None-None 2.76 -- 1.0 1
None-None W-L None-None
4.50 ERA Ldr 2.76
-- WHIP --
6.0 Ldr IP (Innings Pitched) 1.0
1 K (Strikeouts) 1
AI Model Win Probability
PIRATES
0%
📊
GIANTS
100%
PIRATES --
GIANTS --
AI Model Projected Total
AI Projection
9.0
Projected
Book Total
--
Over / Under Line
Over 0 0%
Under 0 100%
AI Model Spread Expectation
AI Spread Proj
-3.0
PITTSBURGH
Book Spread
--
Book Line
▲ Edge: +3.0 points (Pittsburgh)
PiratesPIT (-3.5) 0 GiantsSF (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Pirates Cover

77.3%

Giants Cover

22.7%

-12-8-40+4+8+12
Distribution centered around PIT -3.0 expected margin.
Margin of Victory (PIT)

PIT By 1-2 runs

18.4%

PIT By 3-4 runs

19.6%

SF By 1-2 runs

10.6%

SF By 3-4 runs

5.4%

AI Model Projected Score
6
PITTSBURGH
3
SAN FRANCISCO
Simulated Score
3
PITTSBURGH
5
SAN FRANCISCO
Projections Grades
Moneyline (ML)
Divergence
AI Pirates Loss
0% Win Prob
D
Sim Giants Win
50% Win Prob
D
Run Line (ATS)
Divergence
AI Pirates (-1.5) Loss
Proj Runs: -3.0
D
Sim Giants (+1.5) Win
Proj Runs: -2.0
D
Over/Under (Total)
No Consensus
AI o7.5 Loss
Proj Total: 9.0
D
Sim o7.5 Loss
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) +22.1% more
Pittsburgh: 7.2 vs San Francisco: 5.6
Total Bases (L5) +19.7% more
Pittsburgh: 12.3 vs San Francisco: 9.9
Strikeouts (L5) +18.4% more
Pittsburgh: 7.3 vs San Francisco: 5.9
Home Runs (L5) +15.5% more
Pittsburgh: 1.2 vs San Francisco: 1.0
Post-Mortem Analysis
Metric Projected Actual
Score Game
PIT Score 6.0 2
SF Score 3.0 5
Team Stats Splits (Home / Away)
Errors 1.0 / 0.1 0.0 / 0.0
Hits 5.6 / 7.2 12.0 / 5.0
Home Runs 1.0 / 1.2 1.0 / 1.0
Strikeouts 5.9 / 7.3 8.0 / 5.0
Total Bases 9.9 / 12.3 16.0 / 8.0
Walks 2.8 / 3.3 7.0 / 0.0
Public Market Splits
Moneyline
Open: -- → --
61%TICKETS39%
56%MONEY44%
Spread (ATS)
Open: -- → --
36%TICKETS64%
33%MONEY67%
Total (O/U)
Open: -- → --
66%TICKETS34%
41%MONEY59%
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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