FINAL: MIN 2 — CLE 5. Our Monte Carlo simulation projected MIN 4.9 - CLE 3.2 (MIN at 59.8% win probability). The run line is 1.5 and the total is 8.5. Model projects 8.1 total runs.
MIN
4.9
Projected Score
VS
O/U 8.5
CLE
3.2
Projected Score
Win Probability
MINCLE
+1.5
Run Line (MIN)
8.5
Total Line
10,000
Simulations
CLE W4MIN L5
Calibrated accuracy at this confidence: 60.7% (2,789 games)
Projected Runs Range 10th – 90th percentile
CLE
135
MIN
357
Projected
MIN 4.9 — CLE 3.2
Actual
MIN 2 — CLE 5
Pick Results
MIN F5 MLf5_mlPUSH+0.00u
Starting Pitcher Matchup
Gavin Williams R
CLE
ST26%87 mph41% whiff
FF22%97 mph26% whiff
CU22%83 mph27% whiff
Bailey Ober R
MIN
CH36%83 mph23% whiff
FF31%89 mph15% whiff
SL16%83 mph12% whiff
Weather Impact
Target Field
82°F7 mph wind
HR: 1.077 Total: 1.041
thin air, 6mph out
Bullpen Comparison
CLE
3.52ERA
3.55FIP
10.51K/9
3.50BB/9
1.23WHIP
MIN
4.99ERA
4.47FIP
8.81K/9
4.51BB/9
1.47WHIP
Betting Edges
RUN_LINE AWAY -1.5
-40.4% EV
+132
F5_ML AWAY
-36.7% EV
-132
F5_ML HOME
+31.9% EV
+106
TOTAL OVER 8.5
-20.7% EV
-115
ML AWAY
-17.8% EV
-123
ML HOME
+12.5% EV
+106
First 5 Innings & NRFI
CLE F5
1.6 runs
25.7% win
MIN F5
3.0 runs
60.4% win
F5 Total
4.6
NRFI
49.7%
YRFI
50.3%
Avg 1st Inn Runs
1.07
HR Spotlight
Avg HRs
3.0
Over 0.5 HR
95%
Over 1.5 HR
79%
No HR
5%
Kody Clemens MIN30.0%
ISO: 0.247 | Barrel: 12.0% | vs Gavin Williams | Park: 0.99x Platoon: 1.12x
Brooks Lee MIN24.7%
ISO: 0.194 | Barrel: 5.8% | vs Gavin Williams | Park: 0.99x Platoon: 1.12x
Gabriel Arias CLE18.9%
ISO: 0.113 | Barrel: 11.3% | vs Bailey Ober | Park: 0.99x
Pitcher Strikeout Projections
Gavin Williams
0.0 K projected
CLE | K/9: 0.0
Bailey Ober
0.0 K projected
MIN | K/9: 0.0
Injury Report
CLE3 injured
Tim Herrin RP15-DAY-IL
Jose Ramirez 3B10-DAY-IL
Angel Martinez LF10-DAY-IL
MIN8 injured
Connor Prielipp SP15-DAY-IL
Marco Raya RP15-DAY-IL
Cole Sands RP15-DAY-IL
Ryan Jeffers C10-DAY-IL
Byron Buxton CF10-DAY-IL
Garrett Acton RP60-DAY-IL
+2 more
AI Intelligence Analysis
STRONG BET +1YELLOW ZONE57.9% WR (n=8)
Market severely underpricing MIN home field advantage: model 59.8% win prob vs market 48.5% implied (11.3% gap!)—largest discrepancy on slate. Gavin Williams (B, 10.3 K/9) is elite pitcher but AWAY; Bailey Ober (C+, 6.2 K/9) is weaker but HOME. Home field at Target Field (thin air, 6.1 mph wind OUT, 1.077 HR mult, 1.041 runs mult) + MIN's strong lineup projection (4.87 runs vs CLE 3.22) create legitimate 12.5% ML edge. F5_ML HOME even stronger at 31.9% edge (64.1% prob)—prefer full game ML at +106 over F5 for value.
Key Factors
- Pitcher mismatch AGAINST MIN: Williams (B, 10.3 K/9, elite stuff) vs Ober (C+, 6.2 K/9)—CLE has clear SP advantage. Model's MIN lean DESPITE pitcher disadvantage = lineup-driven thesis
- MIN projected 4.87 runs (home) vs CLE 3.22 (away)—1.65 run offensive advantage; this gap exceeds pitcher mismatch magnitude (Williams elite but not +2 runs swing alone)
- Target Field thin air (1.077 HR mult, 1.041 runs mult) + 6.1 mph wind OUT favor home offensive production—environmental boost to MIN advantage
- MIN bullpen 4.99 ERA (weak) vs CLE 3.52 ERA (strong)—BP gap favors CLE, but starting pitching + home field override
- ML zone: 57.9% WR on 10-15% edge home teams (n=8)—better than YELLOW average, suggesting 12.5% edge is actionable
Risk Factors
- Gavin Williams' elite 10.3 K/9 + B stuff could suppress MIN scoring despite home advantage; if game becomes pitching-heavy, edge compresses
- MIN bullpen weakness (4.99 ERA, 0.902 quality rating) could blow lead in late innings vs CLE's elite BP (3.52 ERA, 1.278 quality)
- Weather: 81.5°F moderate, 6.1 mph wind assists but not extreme; not a slam-dunk environmental edge
PITCHER MISMATCHHOME FIELD ADVANTAGESHARP SUPPORTMODEL MARKET CONFLICT
Edge Analysis
Moneyline
MIN 59.8%
-9.8 pts
Run Line
+1.5
-9.8 pts
Total
8.5
+12.3 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 →