SAN FRANCISCO
GIANTSSF
57-82 | Road
12
PITTSBURGH
PIRATESPIT
68-71 | Home
13
Probable Pitchers
STARTING PITCHER
Logan Webb
#62 | Starting Pitcher@
STARTING PITCHER
Paul Skenes
#30 | SP| Player | W-L | ERA | WHIP | IP | K |
|---|---|---|---|---|---|
| Logan Webb | 8-8 | 3.72 | 1.10 | 147.2 | 118 |
| Paul Skenes | 9-11 | 3.79 | 1.12 | 145.0 | 175 |
8-8
W-L
9-11
3.72 Ldr
ERA
3.79
1.10 Ldr
WHIP
1.12
147.2 Ldr
IP (Innings Pitched)
145.0
118
K (Strikeouts)
Ldr 175
AI Model Win Probability
GIANTS
65%
PIRATES
35%
▲ Edge: +23.80% (SF)
GIANTS
41%
Book Odds: +144
PIRATES
64%
Book Odds: -175
AI Model Projected Total
AI Projection
9.5
Projected
Book Total
7.5
Over / Under Line
▲
Edge: +2.0 (Over)
Over 7.5
69%
Under 7.5
31%
AI Model Spread Expectation
AI Spread Proj
-1.7
SAN FRANCISCO
Book Spread
+1.5
Book Line
▲ Edge: +3.2 points (San Francisco)
Spread Distribution
AI Model
-12-8-40+4+8+12
Distribution centered around SF -1.7 expected margin.
Margin of Victory (SF)
SF By 1-2 runs
19.7%
SF By 3-4 runs
17.9%
PIT By 1-2 runs
14.4%
PIT By 3-4 runs
8.6%
AI Model Projected Score
San Francisco
5.6
SAN FRANCISCO
Book Implied: 3
Pittsburgh
3.9
PITTSBURGH
Book Implied: 4.50
Simulated Score
San Francisco
2
SAN FRANCISCO
Book Implied: 3
Pittsburgh
4
PITTSBURGH
Book Implied: 4.50
Projections Grades
Moneyline (ML)
Divergence
AI
Giants
Loss
65% Win Prob
B
Sim
Pirates
Win
68% Win Prob
D
Run Line (ATS)
Divergence
AI
Giants (+1.5)
Win
Proj Runs: -1.7
A
Sim
Pirates (-1.5)
Loss
Proj Runs: -2.0
D
Over/Under (Total)
Divergence
👑 Top Selection
AI
o7.5
Win
Proj Total: 9.5
B
Sim
u7.5
Loss
Proj Total: 6.0
A
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
143
▲
Games Played
142
4.20
Runs / Game
▲
4.87
.248
Avg (AVG)
▲
.253
.309
On-Base (OBP)
▲
.331
.409
▲
Slugging (SLG)
.402
.718
OPS
▲
.733
164
▲
Home Runs
163
Key Model Drivers
Home Runs (L5)
+27.0% more
San Francisco: 1.7
vs
Pittsburgh: 1.3
Total Bases (L5)
+13.7% more
San Francisco: 14.7
vs
Pittsburgh: 12.7
Slugging Pct (Season)
+0.7%
San Francisco: 40.9%
vs
Pittsburgh: 40.2%
Errors (L5)
+41.0% more
Pittsburgh: 1.3
vs
San Francisco: 0.9
Post-Mortem Analysis
Metric
Projected
Actual
Score Game
SF Score
5.6
12
PIT Score
3.9
13
Team Stats Splits (Home / Away)
Errors
1.3 / 0.9
0.0 / 1.0
Hits
7.8 / 7.9
19.0 / 16.0
Home Runs
1.3 / 1.7
1.0 / 1.0
Strikeouts
9.0 / 8.5
8.0 / 8.0
Total Bases
12.7 / 14.7
23.0 / 22.0
Walks
3.2 / 2.4
4.0 / 8.0
Public Market Splits
Moneyline
Open: +129 / -156
→
+144 / -175
↑
Spread (ATS)
Open: +1.5 / -1.5
→
+1.5 / -1.5
Total (O/U)
Open: 7.5
→
7.5
Situational Trends
📊 Win-Loss
SF
59-84-0
(41.3%)
Margin: -0.5
ATS +/-:
PIT
Advantage
70-73-0
(48.9%)
Margin: 0.2
ATS +/-:
🎯 ATS (Spread)
SF
Advantage
(51.7%)
Margin: -0.5
ATS +/-:
PIT
(47.6%)
Margin: 0.2
ATS +/-:
⚖️ Over/Under
SF
67-64-12
O: 51.2% / U: 48.9%
Total +/-: +0.6
PIT
74-63-6
O: 54.0% / U: 46.0%
Total +/-: +1.2
Season to Date Model Accuracy
AI Model
SIM Engine
PIT
SF
PIT
SF
ML
50%
70-70
48%
68-73
51%
73-71
56%
79-62
ATS
54%
76-64
62%
87-54
49%
70-74
55%
78-63
TTL
47%
63-72-5
55%
72-60-9
86%
122-20-2
86%
120-19-2
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