SAN FRANCISCO
GIANTSSF
64-90 | Road
2
LOS ANGELES
DODGERSLAD
94-60 | Home
8
Probable Pitchers
👤
STARTING PITCHER
Cesar Perdomo
#83 | Relief Pitcher@
STARTING PITCHER
Tyler Glasnow
#31 | Starting Pitcher| Player | W-L | ERA | WHIP | IP | K |
|---|---|---|---|---|---|
| Cesar Perdomo | 0-1 | 3.68 | 1.36 | 14.2 | 11 |
| Tyler Glasnow | 4-1 | 3.50 | 0.97 | 61.2 | 77 |
0-1
W-L
4-1
3.68
ERA
Ldr 3.50
1.36
WHIP
Ldr 0.97
14.2
IP (Innings Pitched)
Ldr 61.2
11
K (Strikeouts)
Ldr 77
AI Model Win Probability
GIANTS
34%
DODGERS
66%
▲ Edge: +5.40% (SF)
GIANTS
28%
Book Odds: +254
DODGERS
76%
Book Odds: -322
AI Model Projected Total
AI Projection
9.0
Projected
Book Total
8.0
Over / Under Line
▲
Edge: +1.0 (Over)
Over 8.0
60%
Under 8.0
40%
AI Model Spread Expectation
AI Spread Proj
-2.0
LOS ANGELES
Book Spread
-1.5
Book Line
▲ Edge: +0.5 points (Los Angeles)
Spread Distribution
AI Model
+12+8+40-4-8-12
Distribution centered around LAD -2.0 expected margin.
Margin of Victory (LAD)
SF By 1-2 runs
13.6%
SF By 3-4 runs
7.8%
LAD By 1-2 runs
19.6%
LAD By 3-4 runs
18.4%
AI Model Projected Score
San Francisco
3.5
SAN FRANCISCO
Book Implied: 3.20
Los Angeles
5.5
LOS ANGELES
Book Implied: 4.80
Simulated Score
San Francisco
6
SAN FRANCISCO
Book Implied: 3.20
Los Angeles
5
LOS ANGELES
Book Implied: 4.80
Projections Grades
Moneyline (ML)
Divergence
AI
Dodgers
Win
66% Win Prob
B
Sim
Giants
Loss
61% Win Prob
C
Run Line (ATS)
Divergence
AI
Dodgers (-1.5)
Win
Proj Runs: -2.0
D
Sim
Giants (+1.5)
Loss
Proj Runs: -1.0
A
Over/Under (Total)
Agreement
👑 Top Selection
AI
o8.0
Win
Proj Total: 9.0
B
Sim
o8.0
Win
Proj Total: 11.0
A
Head-to-Head History
Date
Game
Score
Sep 27, 2026
LAD @ SF
5 - 1
Sep 26, 2026
LAD @ SF
4 - 3
Sep 25, 2026
LAD @ SF
2 - 0
Sep 20, 2026
SF @ LAD
1 - 3
Sep 19, 2026
SF @ LAD
4 - 10
Season Statistics Comparison
San Francisco
SF
vs
Los Angeles
LAD
162
Games Played
162
4.13
Runs / Game
▲
4.94
.245
Avg (AVG)
▲
.257
.306
On-Base (OBP)
▲
.338
.400
Slugging (SLG)
▲
.424
.706
OPS
▲
.762
178
Home Runs
▲
204
Key Model Drivers
Runs per Game (Season)
+19.6% more
Los Angeles: 4.9
vs
San Francisco: 4.1
Home Runs (L5)
+15.0% more
Los Angeles: 1.5
vs
San Francisco: 1.3
Home Runs (Season)
+14.6% more
Los Angeles: 204.0
vs
San Francisco: 178.0
OPS (Season)
+5.6%
Los Angeles: 76.2%
vs
San Francisco: 70.6%
Post-Mortem Analysis
Metric
Projected
Actual
Score Game
SF Score
3.5
2
LAD Score
5.5
8
Team Stats Splits (Home / Away)
Errors
0.1 / 0.9
0.0 / 0.0
Hits
8.6 / 10.3
11.0 / 5.0
Home Runs
1.5 / 1.3
5.0 / 1.0
Strikeouts
5.9 / 7.1
6.0 / 14.0
Total Bases
14.7 / 15.8
27.0 / 8.0
Walks
4.1 / 4.2
5.0 / 2.0
Public Market Splits
Moneyline
Open: +235 / -295
→
+254 / -322
↑
Spread (ATS)
Open: +1.5 / -1.5
→
+1.5 / -1.5
Total (O/U)
Open: 8
→
8
Situational Trends
📊 Win-Loss
SF
65-97-0
(40.1%)
Margin: -0.6
ATS +/-:
LAD
Advantage
103-63-0
(62.1%)
Margin: 1.2
ATS +/-:
🎯 ATS (Spread)
SF
Advantage
(51.2%)
Margin: -0.6
ATS +/-:
LAD
(43.4%)
Margin: 1.2
ATS +/-:
⚖️ Over/Under
SF
76-73-13
O: 51.0% / U: 49.0%
Total +/-: +0.6
LAD
72-89-5
O: 44.7% / U: 55.3%
Total +/-: 0.0
Season to Date Model Accuracy
AI Model
SIM Engine
LAD
SF
LAD
SF
ML
56%
92-73
49%
79-81
48%
79-87
53%
85-75
ATS
59%
98-67
62%
100-60
43%
72-94
52%
83-77
TTL
48%
77-82-6
55%
82-68-10
44%
70-90-6
46%
68-81-11
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