FINAL: CHC 11 — DET 2. Our Monte Carlo simulation projected CHC 5.3 - DET 5.0 (CHC at 53.7% win probability). The run line is 1.5 and the total is 8.0. Model projects 10.3 total runs.
CHC
5.3
Projected Score
VS
O/U 8.0
DET
5.0
Projected Score
Win Probability
CHCDET
+1.5
Run Line (CHC)
8.0
Total Line
10,000
Simulations
DET L4CHC W4
Calibrated accuracy at this confidence: 53.9% (2,971 games)
Projected Runs Range 10th – 90th percentile
DET
357
CHC
357
Projected
CHC 5.3 — DET 5.0
Actual
CHC 11 — DET 2
Pick Results
DET @ CHC F5 OVER 4.5f5WIN+0.83u
Starting Pitcher Matchup
Framber Valdez L
DET
usagewhiffbar labels show usage · mph
SI46%94 mph10% whiff
CU28%78 mph29% whiff
CH20%89 mph22% whiff
David Peterson L
CHC
usagewhiffbar labels show usage · mph
SI29%92 mph12% whiff
SL24%86 mph28% whiff
FF23%92 mph19% whiff
Weather Impact
Wrigley Field
76°F18 mph wind
HR: 0.947 Total: 0.966
thin air, 18mph in
Bullpen Comparison
DET
4.22ERA
4.23FIP
8.86K/9
4.10BB/9
1.37WHIP
CHC
4.04ERA
5.13FIP
8.17K/9
4.04BB/9
1.34WHIP
Betting Edges
RUN_LINE HOME +1.5
-28.7% EV
-200
TOTAL UNDER 8.0
-26.0% EV
-118
TOTAL OVER 8.0
+17.6% EV
-104
F5 OVER 4.5
+16.1% EV
+114
RUN_LINE AWAY -1.5
-9.6% EV
+164
F5_ML AWAY
-6.8% EV
-108
First 5 Innings & NRFI
DET F5
2.7 runs
41.5% win
CHC F5
2.9 runs
44.9% win
F5 Total
5.6
NRFI
51.5%
YRFI
48.5%
Avg 1st Inn Runs
1.09
HR Spotlight
Avg HRs
2.1
Over 0.5 HR
86%
Over 1.5 HR
60%
No HR
14%
Spencer Torkelson DET23.0%
ISO: 0.151 | Barrel: 13.5% | vs David Peterson | Park: 1.03x Platoon: 1.12x
Dillon Dingler DET21.7%
ISO: 0.233 | Barrel: 9.2% | vs David Peterson | Park: 1.03x Platoon: 1.12x
Riley Greene DET20.2%
ISO: 0.151 | Barrel: 17.1% | vs David Peterson | Park: 1.03x
Pitcher Strikeout Projections
Framber Valdez
0.0 K projected
DET | K/9: 0.0
David Peterson
0.0 K projected
CHC | K/9: 0.0
Injury Report
DET8 injured
Kerry Carpenter RFDAY-TO-DAY
Javier Baez SS60-DAY-IL
Jackson Jobe SP60-DAY-IL
Casey Mize SPDAY-TO-DAY
Will Vest RP60-DAY-IL
Bailey Horn RP60-DAY-IL
+2 more
CHC8 injured
Trent Thornton RPDAY-TO-DAY
Phil Maton RP15-DAY-IL
Shelby Miller RP60-DAY-IL
Hunter Harvey RP60-DAY-IL
Ben Brown RP15-DAY-IL
Ethan Roberts RP15-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1
Model 60.0% OVER 8.0 (10.34 total, +17.6% edge) despite massive headwind (18.1 mph blowing into Wrigley = 0.966 multiplier, suppress ~0.4-0.5 runs). SP: Peterson (C+, TBD ERA, 8.2 K/9, command 0.55) vs Valdez (C+, TBD ERA, 7.1 K/9, command 0.592 better) — both mediocre but Valdez slight edge command. Weather 75.5F cool + 78% humidity + vicious 18mph headwind = net suppression of 0.5-0.6 runs from baseline. Model total 10.34 with 0.966 multiplier = adjusted ~10.0 vs market 8.0 = +2.0 edge reasonable but headwind more severe than typical. 17.6% edge is good but weather makes it tougher.
Key Factors
- SP comparison: Peterson (C+, 8.2 K/9, command 0.55) vs Valdez (C+, 7.1 K/9, command 0.592 better) — Valdez slightly superior command; both mediocre starters limiting runs
- EXTREME WEATHER: 18.1 mph wind blowing INTO Wrigley (North 18°) = 0.966 park multiplier, suppressing runs by ~0.5 runs vs baseline — model may underestimate this
- Temperature 75.5F cool + humidity 78% = neutral baseline, but wind dominates, net suppression
- Bullpen: CHC 4.04 ERA (weak) vs DET 4.22 ERA (weak) — essentially equal
- F5 OVER 4.5 edge 16.1% (54.3% prob) supports early-game lean, but wind will suppress throughout
Risk Factors
- 18mph headwind at Wrigley is SEVERE weather, may suppress more than model accounts for (model may have dated wind data)
- Both pitchers TBD (no established ERA) — high uncertainty
- CHC bullpen weak (4.04 ERA) but DET equally weak (4.22 ERA) — no late-game advantage either team
Edge Analysis
Moneyline
CHC 53.7%
-28.7 pts
Run Line
+1.5
-28.7 pts
Total
8.0
+17.6 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 →