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MINNESOTA

TWINSMIN

75-84  |  Road

4
FINAL
Wed, Sep 23 • 3:45 PM ET
Oracle Park
View Odds Details →

SAN FRANCISCO

GIANTSSF

65-94  |  Home

2
Probable Pitchers
Connor Prielipp
STARTING PITCHER

Connor Prielipp

#61 | Starting Pitcher
@
👤
STARTING PITCHER

Cesar Perdomo

#83 | Relief Pitcher
Player W-L ERA WHIP IP K
Connor Prielipp 6-7 4.94 1.30 120.1 125
Cesar Perdomo 0-1 3.72 1.50 19.1 12
6-7 W-L 0-1
4.94 ERA Ldr 3.72
1.30 Ldr WHIP 1.50
120.1 Ldr IP (Innings Pitched) 19.1
125 Ldr K (Strikeouts) 12
AI Model Win Probability
TWINS
36%
📊
GIANTS
64%
▲ Edge: +20.10% (SF)
TWINS 61% Book Odds: -154
GIANTS 44% Book Odds: +128
Book Implied: 60.60% / 43.90%
AI Model Projected Total
AI Projection
9.0
Projected
Book Total
7.5
Over / Under Line
▲ Edge: +1.5 (Over)
Over 7.5 60%
Under 7.5 40%
AI Model Spread Expectation
AI Spread Proj
-1.4
SAN FRANCISCO
Book Spread
+1.5
Book Line
▲ Edge: +2.9 points (San Francisco)
TwinsMIN (-1.5) 0 GiantsSF (+1.5)
San Francisco Giants (+1.5) expected to cover
Spread Distribution
AI Model

Twins Cover

36.3%

Giants Cover

63.7%

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

MIN By 1-2 runs

15.3%

MIN By 3-4 runs

9.5%

SF By 1-2 runs

19.7%

SF By 3-4 runs

17.2%

AI Model Projected Score
3.8
MINNESOTA
Book Implied: 4.50
5.2
SAN FRANCISCO
Book Implied: 3
Simulated Score
4
MINNESOTA
Book Implied: 4.50
5
SAN FRANCISCO
Book Implied: 3
Projections Grades
Moneyline (ML)
Agreement
AI Giants Loss
64% Win Prob
B
Sim Giants Loss
59% Win Prob
C
Run Line (ATS)
Agreement 👑 Top Selection
AI Giants (+1.5) Loss
Proj Runs: -1.4
A
Sim Giants (+1.5) Loss
Proj Runs: -1.0
A
Over/Under (Total)
Agreement 👑 Top Selection
AI o7.5 Loss
Proj Total: 9.0
B
Sim o7.5 Loss
Proj Total: 9.0
B
Head-to-Head History
Date Game Score
Sep 23, 2026 MIN @ SF 4 - 2
Sep 22, 2026 MIN @ SF 3 - 2
Sep 21, 2026 MIN @ SF 2 - 5
May 11, 2025 SF @ MIN 6 - 7
May 10, 2025 SF @ MIN 1 - 2
Season Statistics Comparison
Minnesota MIN
vs
San Francisco SF
162
Games Played
162
4.56 ▲
Runs / Game
4.13
.245
Avg (AVG)
.245
.317 ▲
On-Base (OBP)
.306
.403 ▲
Slugging (SLG)
.400
.720 ▲
OPS
.706
187 ▲
Home Runs
178
Key Model Drivers
Walks (L5) +130.2% more
San Francisco: 3.4 vs Minnesota: 1.5
Strikeouts (L5) +34.0% more
San Francisco: 11.2 vs Minnesota: 8.4
Errors (L5) +19.0% more
San Francisco: 0.7 vs Minnesota: 0.5
Home Runs (L5) +54.2% more
Minnesota: 1.2 vs San Francisco: 0.6
Post-Mortem Analysis
Metric Projected Actual
Score Game
MIN Score 3.8 4
SF Score 5.2 2
Team Stats Splits (Home / Away)
Errors 0.7 / 0.5 1.0 / 0.0
Hits 7.0 / 7.6 5.0 / 9.0
Home Runs 0.6 / 1.2 0.0 / 1.0
Strikeouts 11.2 / 8.4 7.0 / 6.0
Total Bases 9.6 / 12.5 6.0 / 14.0
Walks 3.4 / 1.5 3.0 / 3.0
Public Market Splits
Moneyline
Open: -131 / +109 → -154 / +128 ↓
66%TICKETS34%
75%MONEY25%
Spread (ATS)
Open: -1.5 / +1.5 → -1.5 / +1.5
64%TICKETS36%
61%MONEY39%
Total (O/U)
Open: 7.5 → 7.5
83%TICKETS17%
88%MONEY12%
Situational Trends
📊 Win-Loss
MIN Advantage
77-85-0 (47.5%)
Margin: -0.4 ATS +/-:
SF
65-97-0 (40.1%)
Margin: -0.6 ATS +/-:
🎯 ATS (Spread)
MIN Advantage
(51.8%)
Margin: -0.4 ATS +/-:
SF
(51.2%)
Margin: -0.6 ATS +/-:
⚖️ Over/Under
MIN
88-64-10
O: 57.9% / U: 42.1% Total +/-: +1.0
SF
76-73-13
O: 51.0% / U: 49.0% Total +/-: +0.6
Season to Date Model Accuracy
AI Model
SIM Engine
SF
MIN
SF
MIN
ML
49% 79-81
52% 84-78
53% 85-75
50% 80-81
ATS
62% 100-60
59% 95-67
52% 83-77
59% 95-66
TTL
55% 82-68-10
51% 78-75-9
46% 68-81-11
53% 80-72-9
Accuracy:
Elite (>60%)
Good (53-60%)
Neutral (48-52%)
Under (43-47%)
Danger (≤42%)
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