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

PITTSBURGH

PIRATESPIT

22-18  |  Road

6
FINAL
Sun, May 10 • 4:05 PM ET
Oracle Park
View Odds Details →

SAN FRANCISCO

GIANTSSF

15-24  |  Home

7
Probable Pitchers
Yohan Ramirez
STARTING PITCHER

Yohan Ramirez

#49 | Relief Pitcher
@
Dylan Smith
STARTING PITCHER

Dylan Smith

#66 | Relief Pitcher
Player W-L ERA WHIP IP K
Yohan Ramirez None-None 4.13 -- 2.0 2
Dylan Smith None-None 0.00 -- 0.2 --
None-None W-L None-None
4.13 ERA Ldr 0.00
-- WHIP --
2.0 Ldr IP (Innings Pitched) 0.2
2 Ldr K (Strikeouts) --
AI Model Win Probability
PIRATES
0%
📊
GIANTS
100%
PIRATES --
GIANTS --
AI Model Projected Total
AI Projection
10.0
Projected
Book Total
--
Over / Under Line
Over 0 0%
Under 0 100%
AI Model Spread Expectation
AI Spread Proj
-2.0
PITTSBURGH
Book Spread
--
Book Line
▲ Edge: +2.0 points (Pittsburgh)
PiratesPIT (-3.5) 0 GiantsSF (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Pirates Cover

69.1%

Giants Cover

30.9%

-12-8-40+4+8+12
Distribution centered around PIT -2.0 expected margin.
Margin of Victory (PIT)

PIT By 1-2 runs

19.6%

PIT By 3-4 runs

18.4%

SF By 1-2 runs

13.6%

SF By 3-4 runs

7.8%

AI Model Projected Score
6
PITTSBURGH
4
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) Win
Proj Runs: -2.0
D
Sim Giants (-1.5) Loss
Proj Runs: -2.0
D
Over/Under (Total)
No Consensus
AI o8.0 Win
Proj Total: 10.0
D
Sim u8.0 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) +31.8% more
Pittsburgh: 11.0 vs San Francisco: 7.5
Total Bases (L5) +29.1% more
Pittsburgh: 15.9 vs San Francisco: 11.3
Runs per Game (Season) +12.7% more
Pittsburgh: 4.7 vs San Francisco: 4.1
Home Runs (Season) +4.3% more
Pittsburgh: 186.0 vs San Francisco: 178.0
Post-Mortem Analysis
Metric Projected Actual
Score Game
PIT Score 6.0 6
SF Score 4.0 7
Team Stats Splits (Home / Away)
Errors 0.8 / 0.1 2.0 / 1.0
Hits 7.5 / 11.0 13.0 / 7.0
Home Runs 1.0 / 0.7 1.0 / 2.0
Strikeouts 8.1 / 6.7 9.0 / 8.0
Total Bases 11.3 / 15.9 23.0 / 15.0
Walks 4.5 / 2.5 9.0 / 5.0
Public Market Splits
Moneyline
Open: -- → --
66%TICKETS34%
67%MONEY33%
Spread (ATS)
Open: -- → --
43%TICKETS57%
32%MONEY68%
Total (O/U)
Open: -- → --
63%TICKETS37%
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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