FINAL: DET 4 — OAK 1. Our Monte Carlo simulation projected DET 2.8 - OAK 3.3 (OAK at 51.5% win probability). The run line is -1.5 and the total is 9.0. Model projects 6.0 total runs.
DET
2.8
Projected Score
VS
O/U 9.0
OAK
3.3
Projected Score
Win Probability
DETOAK
-1.5
Run Line (DET)
9.0
Total Line
10,000
Simulations
OAK L5DET W5
Calibrated accuracy at this confidence: 52.7% (2,789 games)
Projected Runs Range 10th – 90th percentile
OAK
135
DET
135
Projected
DET 2.8 — OAK 3.3
Actual
DET 4 — OAK 1
Pick Results
OAK @ DET NRFInrfiWIN+0.45u
Starting Pitcher Matchup
José Suarez L
OAK
FF38%93 mph22% whiff
CH25%83 mph39% whiff
SL25%82 mph26% whiff
Framber Valdez L
DET
SI46%94 mph9% whiff
CU28%78 mph28% whiff
CH20%89 mph21% whiff
Weather Impact
Comerica Park
84°F12 mph wind
HR: 0.980 Total: 0.985
thin air, 12mph in
Bullpen Comparison
OAK
4.87ERA
3.91FIP
9.81K/9
4.20BB/9
1.44WHIP
DET
4.22ERA
4.23FIP
8.86K/9
4.10BB/9
1.37WHIP
Betting Edges
TOTAL OVER 9.0
-44.3% EV
-104
F5 UNDER 4.5
+34.0% EV
+106
TOTAL UNDER 9.0
+32.5% EV
-118
RUN_LINE AWAY +1.5
-30.4% EV
-200
F5_ML HOME
-23.0% EV
-130
NRFI NRFI
+19.5% EV
+102
First 5 Innings & NRFI
OAK F5
1.7 runs
46.6% win
DET F5
1.2 runs
31.2% win
F5 Total
3.0
NRFI
66.9%
YRFI
33.1%
Avg 1st Inn Runs
0.65
HR Spotlight
Avg HRs
1.5
Over 0.5 HR
78%
Over 1.5 HR
45%
No HR
22%
Jonah Heim OAK30.0%
ISO: 0.404 | Barrel: 6.1% | vs Framber Valdez | Park: 0.97x Platoon: 1.12x
Shea Langeliers OAK19.4%
ISO: 0.357 | Barrel: 11.0% | vs Framber Valdez | Park: 0.97x Platoon: 1.12x
Zack Gelof OAK13.6%
ISO: 0.226 | Barrel: 12.5% | vs Framber Valdez | Park: 0.97x Platoon: 1.12x
Pitcher Strikeout Projections
José Suarez
0.0 K projected
OAK | K/9: 0.0
Framber Valdez
0.0 K projected
DET | K/9: 0.0
Injury Report
OAK6 injured
Nick Kurtz 1BDAY-TO-DAY
Brent Rooker DH10-DAY-IL
Denzel Clarke CF60-DAY-IL
Luis Severino SP60-DAY-IL
Brooks Kriske RP60-DAY-IL
Gunnar Hoglund SP60-DAY-IL
DET8 injured
Dillon Dingler CDAY-TO-DAY
Jackson Jobe SP60-DAY-IL
Justin Verlander SP60-DAY-IL
Wenceel Perez RF60-DAY-IL
Gleyber Torres 2B10-DAY-IL
Will Vest RP15-DAY-IL
+2 more
AI Intelligence Analysis
NEUTRAL -1YELLOW ZONE50.1% WR (n=318)
Model shows 32.5% UNDER edge (71.6% prob) but falls into YELLOW zone (50.1% WR, n=318) and is part of systematic pattern: 5 games on slate all showing 23%+ under edges (ATL 30%, KC 29%, CHC 36%, OAK 32%, MIL 28%). Per First Principles #2 (edge anti-correlated with outcomes), high edges are warning flags. AWAY ML edge only 2.2% with terrible 42.9% zone WR. Weather (83.6°F, 12.5 mph wind IN) does suppress scoring, validating under thesis qualitatively, but under edge magnitude suggests model inflation on totals.
Key Factors
- José Suarez (OAK away): B- grade, 8.0 K/9, 4.92 ERA, 25.2% K rate, 10% BB rate—solid middle-of-rotation arm
- Framber Valdez (DET home): C+ grade, 7.1 K/9, sinker-heavy (46% SI)—below-average starter
- Pitcher advantage to OAK (Suarez > Valdez), yet model favors DET at 50.4% (near even); offensive factors likely drive slight home edge
- Weather: 83.6°F moderate, 12.5 mph wind IN (strong suppression, 0.985 mult)—qualitatively validates under, but magnitude (32.5% edge) excessive
- F5_ML AWAY shows 15.2% edge (56.5% prob)—cleaner than full-game but still within high-edge pattern
Risk Factors
- UNDER edge 32.5% is massive and part of 5-game pattern (all 23%+ on slate)—suggests systematic model inflation on totals or market repricing not reflected in snapshot odds
- AWAY ML edge 2.2% with 42.9% zone WR—one of worst historical combos; avoid
- Model projects 6.04 total vs market 9.0 (2.96 run gap)—extreme gap raises question: is market wrong or is model systematically underestimating scoring?
HIGH EDGE WARNINGYELLOW ZONEPATTERN ALERTWEATHER IMPACT
Edge Analysis
Moneyline
OAK 51.5%
-15.1 pts
Run Line
-1.5
-15.1 pts
Total
9.0
+32.5 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 →