Home / MLB Projections MLB / Giants @ Mets SF @ NYM

SAN FRANCISCO

GIANTSSF

59-84  |  Road

9
FINAL
Sat, Sep 5 • 4:10 PM ET
Citi Field
View Odds Details →

NEW YORK

METSNYM

64-78  |  Home

5
Probable Pitchers
Anthony Molina
STARTING PITCHER

Anthony Molina

#45 | Starting Pitcher
@
👤
STARTING PITCHER

Zac Thornton

#49 | Starting Pitcher
Player W-L ERA WHIP IP K
Anthony Molina 2-0 3.77 1.05 14.1 9
Zac Thornton 3-4 2.93 1.16 61.1 45
2-0 W-L 3-4
3.77 ERA Ldr 2.93
1.05 Ldr WHIP 1.16
14.1 IP (Innings Pitched) Ldr 61.1
9 K (Strikeouts) Ldr 45
AI Model Win Probability
GIANTS
51%
📊
METS
49%
▲ Edge: +10.80% (SF)
GIANTS 41% Book Odds: +147
METS 62% Book Odds: -160
Book Implied: 40.50% / 61.50%
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
-1.4
SAN FRANCISCO
Book Spread
+1.5
Book Line
▲ Edge: +2.9 points (San Francisco)
GiantsSF (+1.5) 0 MetsNYM (-1.5)
San Francisco Giants (+1.5) expected to cover
Spread Distribution
AI Model

Giants Cover

63.7%

Mets Cover

36.3%

-12-8-40+4+8+12
Distribution centered around SF -1.4 expected margin.
Margin of Victory (SF)

SF By 1-2 runs

19.7%

SF By 3-4 runs

17.2%

NYM By 1-2 runs

15.3%

NYM By 3-4 runs

9.5%

AI Model Projected Score
5.2
SAN FRANCISCO
Book Implied: 3.20
3.8
NEW YORK
Book Implied: 4.80
Simulated Score
3
SAN FRANCISCO
Book Implied: 3.20
5
NEW YORK
Book Implied: 4.80
Projections Grades
Moneyline (ML)
Divergence
AI Giants Win
51% Win Prob
D
Sim Mets Loss
69% Win Prob
C
Run Line (ATS)
Divergence
AI Giants (+1.5) Win
Proj Runs: -1.4
A
Sim Mets (-1.5) Loss
Proj Runs: -2.0
D
Over/Under (Total)
Divergence
AI o8.0 Win
Proj Total: 9.0
C
Sim o8.0 Win
Proj Total: 8.0
D
Head-to-Head History
Date Game Score
Sep 6, 2026 SF @ NYM 2 - 4
Sep 5, 2026 SF @ NYM 9 - 5
Sep 4, 2026 SF @ NYM 6 - 10
Apr 5, 2026 NYM @ SF 5 - 2
Apr 4, 2026 NYM @ SF 9 - 0
Season Statistics Comparison
San Francisco SF
vs
New York NYM
162
Games Played
162
4.13
Runs / Game
▲ 4.31
.245 ▲
Avg (AVG)
.239
.306
On-Base (OBP)
▲ .310
.400 ▲
Slugging (SLG)
.395
.706 ▲
OPS
.705
178
Home Runs
▲ 204
Key Model Drivers
Errors (L5) +32.0% more
San Francisco: 0.8 vs New York: 0.5
Walks (L5) +27.6% more
San Francisco: 4.7 vs New York: 3.4
Batting Avg (Season) +0.6%
San Francisco: 24.5% vs New York: 23.9%
Slugging Pct (Season) +0.5%
San Francisco: 40.0% vs New York: 39.5%
Post-Mortem Analysis
Metric Projected Actual
Score Game
SF Score 5.2 9
NYM Score 3.8 5
Team Stats Splits (Home / Away)
Errors 0.5 / 0.8 0.0 / 1.0
Hits 8.9 / 8.6 5.0 / 10.0
Home Runs 2.3 / 0.9 1.0 / 6.0
Strikeouts 8.6 / 6.0 9.0 / 8.0
Total Bases 17.4 / 13.5 9.0 / 29.0
Walks 3.4 / 4.7 4.0 / 2.0
Public Market Splits
Moneyline
Open: +157 / -191 → +147 / -160 ↓
15%TICKETS85%
33%MONEY67%
Spread (ATS)
Open: +1.5 / -1.5 → +1.5 / -1.5
37%TICKETS63%
32%MONEY68%
Total (O/U)
Open: 8 → 8
78%TICKETS22%
77%MONEY23%
Situational Trends
📊 Win-Loss
SF
65-97-0 (40.1%)
Margin: -0.6 ATS +/-:
NYM Advantage
74-88-0 (45.7%)
Margin: -0.2 ATS +/-:
🎯 ATS (Spread)
SF Advantage
(51.2%)
Margin: -0.6 ATS +/-:
NYM
(46.3%)
Margin: -0.2 ATS +/-:
⚖️ Over/Under
SF
76-73-13
O: 51.0% / U: 49.0% Total +/-: +0.6
NYM
77-73-12
O: 51.3% / U: 48.7% Total +/-: +0.7
Season to Date Model Accuracy
AI Model
SIM Engine
NYM
SF
NYM
SF
ML
50% 79-80
49% 79-81
48% 76-83
53% 85-75
ATS
58% 93-66
62% 100-60
49% 78-81
52% 83-77
TTL
55% 80-66-13
55% 82-68-10
51% 74-72-13
46% 68-81-11
Accuracy:
Elite (>60%)
Good (53-60%)
Neutral (48-52%)
Under (43-47%)
Danger (≤42%)
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