COL vs SF prediction for July 9, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects SF 3.6 - COL 4.0. COL is favored with a 50.8% win probability. The run line is -1.5 and the total is 9.0. Model projects 7.7 total runs.
SF
3.6
Projected Score
VS
O/U 9.0
COL
4.0
Projected Score
Win Probability
SFCOL
-1.5
Run Line (SF)
9.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 50.9% (2,789 games)
Projected Runs Range 10th – 90th percentile
COL
246
SF
246
Projected
SF 3.6 — COL 4.0
Actual
SF 8 — COL 2
Starting Pitcher Matchup
Ryan Feltner R
COL
FF26%95 mph11% whiff
SL25%89 mph28% whiff
CH15%85 mph50% whiff
Carson Whisenhunt L
SF
FF46%93 mph10% whiff
CH38%82 mph34% whiff
SL15%84 mph19% whiff
Weather Impact
Oracle Park
64°F19 mph wind
HR: 0.930 Total: 0.958
16mph in
Bullpen Comparison
COL
5.45ERA
4.65FIP
8.19K/9
4.48BB/9
1.58WHIP
SF
4.06ERA
4.43FIP
8.10K/9
4.84BB/9
1.41WHIP
Betting Edges
RUN_LINE AWAY +1.5
-30.5% EV
-192
TOTAL OVER 9.0
-24.0% EV
-106
F5_ML HOME
-20.2% EV
-135
TOTAL UNDER 9.0
+14.4% EV
-114
RUN_LINE HOME -1.5
-13.4% EV
+155
F5_ML AWAY
+12.6% EV
+108
First 5 Innings & NRFI
COL F5
2.4 runs
48.0% win
SF F5
2.0 runs
36.1% win
F5 Total
4.3
NRFI
55.1%
YRFI
44.9%
Avg 1st Inn Runs
0.96
HR Spotlight
Avg HRs
2.1
Over 0.5 HR
87%
Over 1.5 HR
62%
No HR
13%
Hunter Goodman COL30.0%
ISO: 0.250 | Barrel: 12.8% | vs Carson Whisenhunt | Park: 0.88x Platoon: 1.12x
Bryce Eldridge SF30.0%
ISO: 0.223 | Barrel: 25.0% | vs Ryan Feltner | Park: 0.88x Platoon: 1.12x
Rafael Devers SF30.0%
ISO: 0.212 | Barrel: 16.0% | vs Ryan Feltner | Park: 0.88x Platoon: 1.12x
Pitcher Strikeout Projections
Ryan Feltner
0.0 K projected
COL | K/9: 0.0
Carson Whisenhunt
0.0 K projected
SF | K/9: 0.0
Injury Report
COL8 injured
Brenton Doyle CF10-DAY-IL
Jose Quintana SP60-DAY-IL
Seth Halvorsen RP15-DAY-IL
Tomoyuki Sugano SP15-DAY-IL
Blas Castano RP15-DAY-IL
Jaden Hill RP15-DAY-IL
+2 more
SF8 injured
Keaton Winn RP15-DAY-IL
Jonah Cox CF10-DAY-IL
Matt Gage RP15-DAY-IL
Matt Chapman 3B10-DAY-IL
Daniel Susac C10-DAY-IL
Harrison Bader CF10-DAY-IL
+2 more
AI Intelligence Analysis
STRONG BET +1YELLOW ZONE50.1% WR (n=318)
Strongest UNDER candidate on slate: Oracle Park's severe run suppression (0.88 park factor, -12%), combined with extreme weather (63.8°F cold, 18.7 mph wind blowing IN at 281°, resulting in -16 mph tail wind) creates 4.4% total reduction in runs. Model projects 7.65 total vs market 9.0 (1.35 run gap, 14.4% edge). Market likely hasn't fully adjusted to 16 mph wind IN + marine layer at Oracle, which is historically one of most suppressive combinations. Pitcher matchup is neutral (Feltner B- vs Whisenhunt C), but environment is the story. 14.4% edge with 60.8% model prob + zone 50.1% WR (YELLOW acceptable for this edge magnitude) justifies BET conviction.
Key Factors
- Oracle Park factor 0.88 (severe suppression, -12%)—most suppressive park on slate besides Petco
- Weather: 63.8°F (COLD, 3rd coldest on slate), 18.7 mph wind at 281° (blowing IN), resulting in -16 mph tail wind—massive run suppression
- Density altitude 337 ft (lowest on slate)—thin air doesn't apply; coast location with marine layer present
- Model projects 7.65 runs vs market 9.0 (1.35 run gap, 14.4% edge, 60.8% prob)—meaningful value
- Pitcher matchup neutral: Feltner (B-, 6.5 K/9) vs Whisenhunt (C, no K data)—neither advantageous
Risk Factors
- Extreme weather (18.7 mph in) is unusual and could spook market; if sharp money has bet overs on the move, line could be harder than it appears
- Wind direction dependency: if wind direction shifts or forecast changes, edge could evaporate
- Pitcher quality on both sides is modest (B- and C); if either pitcher struggles early, game could turn into slugfest
WEATHER IMPACTPARK FACTOREXTREME COLDEXTREME WIND
Edge Analysis
Moneyline
COL 50.8%
-13.4 pts
Run Line
-1.5
-13.4 pts
Total
9.0
+14.4 pts
How this prediction was generated: This page shows output from the Olympus Bets MLB Baseball Monte Carlo engine. Each game is simulated 10,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →