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

SAN FRANCISCO

GIANTSSF

52-45  |  Road

0
FINAL
Fri, Jul 18 • 7:07 PM ET
Rogers Centre
View Odds Details →

TORONTO

BLUE JAYSTOR

55-41  |  Home

4
Probable Pitchers
Jung Hoo Lee
STARTING PITCHER

Jung Hoo Lee

#51 | Right Field
@
Brendon Little
STARTING PITCHER

Brendon Little

#54 | Relief Pitcher
Player W-L ERA WHIP IP K
Jung Hoo Lee None-None -- -- -- --
Brendon Little None-None 2.00 -- 0.2 2
None-None W-L None-None
-- ERA 2.00
-- WHIP --
-- IP (Innings Pitched) 0.2
-- K (Strikeouts) 2
AI Model Win Probability
GIANTS
46%
📊
BLUE JAYS
54%
GIANTS --
BLUE JAYS --
AI Model Projected Total
AI Projection
10.0
Projected
Book Total
--
Over / Under Line
Over 0 50%
Under 0 50%
AI Model Spread Expectation
AI Spread Proj
-1.8
TORONTO
Book Spread
--
Book Line
▲ Edge: +1.8 points (Toronto)
GiantsSF (-3.5) 0 Blue JaysTOR (+3.5)
Model Expected Spread
Spread Distribution
AI Model

Giants Cover

32.9%

Blue Jays Cover

67.1%

+12+8+40-4-8-12
Distribution centered around TOR -1.8 expected margin.
Margin of Victory (TOR)

SF By 1-2 runs

14.2%

SF By 3-4 runs

8.4%

TOR By 1-2 runs

19.7%

TOR By 3-4 runs

18.0%

AI Model Projected Score
4.12
SAN FRANCISCO
5.89
TORONTO
Simulated Score
4
SAN FRANCISCO
5
TORONTO
Projections Grades
Moneyline (ML)
Agreement
AI Blue Jays Win
54% Win Prob
D
Sim Blue Jays Win
50% Win Prob
D
Run Line (ATS)
Agreement
AI Blue Jays (+0) Win
Proj Runs: -1.8
D
Sim Blue Jays (+0) Win
Proj Runs: -1.0
D
Over/Under (Total)
No Consensus
AI o0 Win
Proj Total: 10.0
D
Sim u0.0
Proj Total: 9.0
D
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
San Francisco SF
vs
Toronto TOR
162
Games Played
162
4.13 ▲
Runs / Game
4.00
.245
Avg (AVG)
▲ .247
.306
On-Base (OBP)
▲ .310
.400 ▲
Slugging (SLG)
.381
.706 ▲
OPS
.692
178 ▲
Home Runs
152
Key Model Drivers
Errors (L5) +98.0% more
Toronto: 1.8 vs San Francisco: 0.8
Home Runs (L5) +39.0% more
Toronto: 1.2 vs San Francisco: 0.8
Total Bases (L5) +34.4% more
Toronto: 12.9 vs San Francisco: 9.6
Hits (L5) +26.3% more
Toronto: 7.3 vs San Francisco: 5.8
Post-Mortem Analysis
Metric Projected Actual
Score Game
SF Score 4.1 0
TOR Score 5.9 4
Team Stats Splits (Home / Away)
Errors 1.8 / 0.8 0.0 / 0.0
Hits 7.3 / 5.8 10.0 / 11.0
Home Runs 1.2 / 0.8 0.0 / 0.0
Strikeouts 8.4 / 9.4 12.0 / 2.0
Total Bases 12.9 / 9.6 12.0 / 12.0
Walks 3.4 / 2.9 0.0 / 3.0
Public Market Splits
Moneyline
Open: -- → --
21%TICKETS79%
17%MONEY83%
Spread (ATS)
Open: -- → --
44%TICKETS56%
16%MONEY84%
Total (O/U)
Open: -- → --
58%TICKETS42%
67%MONEY33%
Situational Trends
📊 Win-Loss
SF
65-97-0 (40.1%)
Margin: -0.6 ATS +/-:
TOR Advantage
79-83-0 (48.8%)
Margin: -0.3 ATS +/-:
🎯 ATS (Spread)
SF Advantage
(51.2%)
Margin: -0.6 ATS +/-:
TOR
(46.9%)
Margin: -0.3 ATS +/-:
⚖️ Over/Under
SF
76-73-13
O: 51.0% / U: 49.0% Total +/-: +0.6
TOR
81-76-5
O: 51.6% / U: 48.4% Total +/-: +0.3
Season to Date Model Accuracy
AI Model
SIM Engine
TOR
SF
TOR
SF
ML
46% 74-87
49% 79-81
50% 80-80
53% 85-75
ATS
56% 90-71
62% 100-60
49% 79-81
52% 83-77
TTL
51% 80-76-5
55% 82-68-10
45% 70-85-5
46% 68-81-11
Accuracy:
Elite (>60%)
Good (53-60%)
Neutral (48-52%)
Under (43-47%)
Danger (≤42%)
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