Home / MLB Projections MLB / Blue Jays @ Giants TOR @ SF

TORONTO

BLUE JAYSTOR

43-49  |  Road

9
FINAL
Tue, Jul 7 • 9:45 PM ET
Oracle Park
View Odds Details →

SAN FRANCISCO

GIANTSSF

38-53  |  Home

3
Probable Pitchers
Patrick Corbin
STARTING PITCHER

Patrick Corbin

#46 | SP
@
Trevor McDonald
STARTING PITCHER

Trevor McDonald

#72 | Starting Pitcher
Player W-L ERA WHIP IP K
Patrick Corbin None-None 5.07 -- 2.1 2
Trevor McDonald None-None 5.46 -- 2.1 --
None-None W-L None-None
5.07 Ldr ERA 5.46
-- WHIP --
2.1 IP (Innings Pitched) 2.1
2 Ldr K (Strikeouts) --
AI Model Win Probability
BLUE JAYS
69%
📊
GIANTS
31%
BLUE JAYS --
GIANTS --
AI Model Projected Total
AI Projection
10.7
Projected
Book Total
--
Over / Under Line
Over 0 77%
Under 0 23%
AI Model Spread Expectation
AI Spread Proj
-2.1
TORONTO
Book Spread
--
Book Line
▲ Edge: +2.1 points (Toronto)
Blue JaysTOR (-3.5) 0 GiantsSF (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Blue Jays Cover

70.0%

Giants Cover

30.0%

-12-8-40+4+8+12
Distribution centered around TOR -2.1 expected margin.
Margin of Victory (TOR)

TOR By 1-2 runs

19.5%

TOR By 3-4 runs

18.6%

SF By 1-2 runs

13.3%

SF By 3-4 runs

7.6%

AI Model Projected Score
6.4
TORONTO
4.3
SAN FRANCISCO
Simulated Score
4
TORONTO
6
SAN FRANCISCO
Projections Grades
Moneyline (ML)
Divergence 👑 Top Selection
AI Blue Jays Win
69% Win Prob
A
Sim Giants Loss
69% Win Prob
A
Run Line (ATS)
Divergence
AI Blue Jays (+1.5) Win
Proj Runs: -2.1
C
Sim Giants (-1.5) Loss
Proj Runs: -2.0
A
Over/Under (Total)
No Consensus 👑 Top Selection
AI o7.5 Win
Proj Total: 10.7
A
Sim o7.5 Win
Proj Total: 10.0
A
Head-to-Head History
Date Game Score
Jul 8, 2026 TOR @ SF 10 - 0
Jul 7, 2026 TOR @ SF 9 - 3
Jul 6, 2026 TOR @ SF 1 - 10
Jul 20, 2025 SF @ TOR 6 - 8
Jul 19, 2025 SF @ TOR 3 - 6
Season Statistics Comparison
Toronto TOR
vs
San Francisco SF
162
Games Played
162
4.00
Runs / Game
▲ 4.13
.247 ▲
Avg (AVG)
.245
.310 ▲
On-Base (OBP)
.306
.381
Slugging (SLG)
▲ .400
.692
OPS
▲ .706
152
Home Runs
▲ 178
Key Model Drivers
Errors (L5) +49.3% more
Toronto: 1.5 vs San Francisco: 0.8
Strikeouts (L5) +15.7% more
Toronto: 9.0 vs San Francisco: 7.6
On-Base Pct (Season) +0.4%
Toronto: 31.0% vs San Francisco: 30.6%
Batting Avg (Season) +0.2%
Toronto: 24.7% vs San Francisco: 24.5%
Post-Mortem Analysis
Metric Projected Actual
Score Game
TOR Score 6.4 9
SF Score 4.3 3
Team Stats Splits (Home / Away)
Errors 0.8 / 1.5 0.0 / 0.0
Hits 9.4 / 4.0 9.0 / 13.0
Home Runs 2.1 / 0.5 0.0 / 1.0
Strikeouts 7.6 / 9.0 6.0 / 5.0
Total Bases 18.1 / 6.4 10.0 / 17.0
Walks 4.0 / 2.4 0.0 / 2.0
Public Market Splits
Moneyline
Open: -- → --
55%TICKETS45%
56%MONEY44%
Spread (ATS)
Open: -- → --
57%TICKETS43%
74%MONEY26%
Total (O/U)
Open: -- → --
93%TICKETS7%
92%MONEY8%
Situational Trends
📊 Win-Loss
TOR Advantage
79-83-0 (48.8%)
Margin: -0.3 ATS +/-:
SF
65-97-0 (40.1%)
Margin: -0.6 ATS +/-:
🎯 ATS (Spread)
TOR
(46.9%)
Margin: -0.3 ATS +/-:
SF Advantage
(51.2%)
Margin: -0.6 ATS +/-:
⚖️ Over/Under
TOR
81-76-5
O: 51.6% / U: 48.4% Total +/-: +0.3
SF
76-73-13
O: 51.0% / U: 49.0% Total +/-: +0.6
Season to Date Model Accuracy
AI Model
SIM Engine
SF
TOR
SF
TOR
ML
49% 79-81
46% 74-87
53% 85-75
50% 80-80
ATS
62% 100-60
56% 90-71
52% 83-77
49% 79-81
TTL
55% 82-68-10
51% 80-76-5
46% 68-81-11
45% 70-85-5
Accuracy:
Elite (>60%)
Good (53-60%)
Neutral (48-52%)
Under (43-47%)
Danger (≤42%)
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