MLB Baseball

CHC vs SF Prediction

June 13, 2026

10,000 Monte Carlo simulations

CHC vs SF prediction for June 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects SF 3.1 - CHC 3.9. CHC is favored with a 55.4% win probability. The run line is 1.5 and the total is 7.5. Model projects 7.0 total runs.

SF
3.1
Projected Score
VS O/U 7.5
CHC
3.9
Projected Score
Win Probability
44.6%
55.4%
SFCHC
+1.5
Run Line (SF)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 60.0% (2,321 games)

Projected Runs Range 10th – 90th percentile

CHC
246
SF
135
FINALSF 1 — CHC 6
Projected
SF 3.1 — CHC 3.9
Actual
SF 1 — CHC 6

Starting Pitcher Matchup

Ben Brown R
CHC
KC37%87 mph43% whiff
FF36%96 mph12% whiff
SI20%97 mph10% whiff
Trevor McDonald R
SF
SI57%94 mph14% whiff
SL29%86 mph42% whiff
CH13%84 mph40% whiff

Weather Impact

Oracle Park
70°F10 mph wind
HR: 0.967 Total: 0.979
10mph in

Bullpen Comparison

CHC
4.09ERA
5.13FIP
8.16K/9
4.04BB/9
1.37WHIP
SF
4.14ERA
4.38FIP
7.98K/9
5.04BB/9
1.44WHIP

Betting Edges

RUN_LINE HOME +1.5
-42.6% EV
-161
TOTAL OVER 7.5
-25.7% EV
-122
TOTAL UNDER 7.5
+18.4% EV
+100
F5 UNDER 3.5
+12.3% EV
+116
F5_ML AWAY
-7.3% EV
-135
ML HOME
-6.7% EV
+108

First 5 Innings & NRFI

CHC F5
1.8 runs
42.0% win
SF F5
1.6 runs
37.5% win
F5 Total
3.4
NRFI
60.9%
YRFI
39.1%
Avg 1st Inn Runs
0.75

HR Spotlight

Avg HRs
1.6
Over 0.5 HR
77%
Over 1.5 HR
45%
No HR
23%

Pitcher Strikeout Projections

Ben Brown
0.0 K projected
CHC | K/9: 0.0
Trevor McDonald
0.0 K projected
SF | K/9: 0.0

Injury Report

CHC8 injured
Matthew Boyd SP15-DAY-IL
Jameson Taillon SP15-DAY-IL
Hunter Harvey RP60-DAY-IL
Brandon Birdsell RPDAY-TO-DAY
Jeff Brigham RPDAY-TO-DAY
Justin Steele SP60-DAY-IL
+2 more
SF8 injured
Jason Foley RP60-DAY-IL
Matt Gage RP15-DAY-IL
Heliot Ramos LF10-DAY-IL
Joel Peguero RP60-DAY-IL
Jared Oliva CF60-DAY-IL
Harrison Bader CF10-DAY-IL
+2 more

AI Intelligence Analysis

LEANYELLOW ZONE50.1% WR (n=184)
UNDER 7.5 at 18.4% edge is supported by Ben Brown 1.88 ERA (elite pitcher) vs Trevor McDonald 4.48 ERA (mid-tier) and cold-wind Oracle Park (69.6F, 10 mph wind in = run suppression). Market pricing high-edge total in YELLOW zone; lean respects pitcher quality but caps units to defend overconfidence.

Key Factors

  • SP QUALITY STRONGLY FAVORS AWAY: Ben Brown (CHC away) 1.88 ERA (elite pitcher, 26.2% K-rate, B-grade) vs Trevor McDonald (SF home) 4.48 ERA (mid-tier, 22.7% K-rate, B-grade). 2.60 ERA gap is massive.
  • Oracle Park environment EXTREME: cold 69.6F (cool), 10 mph wind blowing in (marine layer effect) = suppresses runs 0.8-1.2 runs vs baseline. Park factor 0.88 (12% run suppression) already baked in.
  • Model 6.96 total vs market 7.5 = 0.54 run gap (moderate for baseball), edge 18.4% shows model forecasts tighter game
  • NRFI edge 2.9% (model 58.4%) suggests tight first inning, early run scoring suppressed by elite Brown arm
  • F5 UNDER 3.5 at 12.3% edge (model 52%) confirms early-inning suppression expected

Risk Factors

  • 18.4% edge in YELLOW zone (50.1% WR baseline) is moderately high-confidence alert. Market pricing reflects pitcher advantage and weather already.
  • Ben Brown 1.88 ERA could be small-sample variance (2026 rookie/recent call-up likely); McDonald 4.48 ERA could improve
  • Oracle Park is notoriously difficult to predict; wind direction/intensity varies game-to-game and hour-to-hour
Sharp MoneyWith ModelModel and sharp likely aligned on under given elite pitcher (Brown 1.88 ERA) and cold oracle environment. Line 7.5 suggests market respecting these factors but pricing typical low-total game. Sharp likely on under.
PITCHER QUALITYWEATHER IMPACTTOTALS VALUEHIGH EDGE WARNING

Edge Analysis

Moneyline
CHC 55.4%
-42.6 pts
Run Line
+1.5
-42.6 pts
Total
7.5
+18.4 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 →

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