MIN vs CWS prediction for May 25, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects CWS 5.2 - MIN 6.5. MIN is favored with a 58.2% win probability. The run line is 1.5 and the total is 8.5. Model projects 11.6 total runs.
CWS
5.2
Projected Score
VS
O/U 8.5
MIN
6.5
Projected Score
Win Probability
CWSMIN
+1.5
Run Line (CWS)
8.5
Total Line
10,000
Simulations
MINCWS W5
Calibrated accuracy at this confidence: 58.0% (2,300 games)
Projected Runs Range 10th – 90th percentile
MIN
568
CWS
357
Projected
CWS 5.2 — MIN 6.5
Actual
CWS 3 — MIN 1
Pick Results
Kody Clemens OVER 0.5 RBIsbatter_rbiLOSS-1.50u
Starting Pitcher Matchup
Zebby Matthews R
MIN
FF42%96 mph19% whiff
SL24%88 mph36% whiff
FC13%92 mph23% whiff
Anthony Kay L
CWS
FF28%96 mph16% whiff
FC20%91 mph17% whiff
ST19%82 mph26% whiff
Weather Impact
Guaranteed Rate Field
79°F9 mph wind
HR: 1.068 Total: 1.037
thin air, 7mph out
Bullpen Comparison
MIN
4.63ERA
4.10FIP
7.56K/9
4.14BB/9
1.40WHIP
CWS
5.22ERA
5.13FIP
8.73K/9
5.24BB/9
1.47WHIP
Betting Edges
RUN_LINE HOME +1.5
-43.3% EV
-185
TOTAL UNDER 8.5
-36.8% EV
-120
TOTAL OVER 8.5
+29.6% EV
-102
F5_ML HOME
-19.8% EV
-102
ML HOME
-16.1% EV
-106
F5 OVER 4.5
+13.5% EV
-114
First 5 Innings & NRFI
MIN F5
3.6 runs
54.5% win
CWS F5
2.7 runs
33.5% win
F5 Total
6.3
NRFI
50.4%
YRFI
49.6%
Avg 1st Inn Runs
1.11
HR Spotlight
Avg HRs
3.0
Over 0.5 HR
95%
Over 1.5 HR
80%
No HR
5%
Byron Buxton MIN30.0%
ISO: 0.089 | Barrel: 18.2% | vs Anthony Kay | Park: 1.01x Platoon: 1.12x
Ryan Kreidler MIN30.0%
ISO: 0.158 | Barrel: 15.8% | vs Anthony Kay | Park: 1.01x Platoon: 1.12x
Colson Montgomery CWS30.0%
ISO: 0.252 | Barrel: 14.5% | vs Zebby Matthews | Park: 1.01x Platoon: 1.12x
Pitcher Strikeout Projections
Zebby Matthews
0.0 K projected
MIN | K/9: 0.0
Anthony Kay
0.0 K projected
CWS | K/9: 0.0
Injury Report
MIN8 injured
Ryan Jeffers C10-DAY-IL
Garrett Acton RP60-DAY-IL
Walker Jenkins CFDAY-TO-DAY
David Festa SP60-DAY-IL
Cole Sands RP15-DAY-IL
Mick Abel SP15-DAY-IL
+2 more
CWSHealthy
AI Intelligence Analysis
LEAN +1YELLOW ZONE50.0% WR (n=285)
Model projects 11.61 total runs (OVER 8.5, +29.6% edge, 65.5% model prob) — massive edge driven by warm weather (79F), thin-air Kansas City effect (1.028x run multiplier, 1.037x total mult), and two mediocre starters (Anthony Kay no-data ERA vs Zebby Matthews no-data; Kay quality bottom-20 percentile per grade C-, Matthews B grade). Wind 7mph out amplifies HR potential.
Key Factors
- Massive total gap: +3.11 runs (model 11.61 vs market 8.5) — 37% underpricing by market if model correct
- Park/weather multiplier: 1.037x total multiplier (thin air, warm temp 79F, wind 7mph out, HR mult 1.068x) = cumulative ~1.04x boost = ~0.35 additional runs baseline
- Pitcher profile uncertainty: Anthony Kay with 0 ERA (literally no 2026 starts?) and Zebby Matthews also new/no-ERA. Grade shows Kay C- (worst in slate), Matthews B — significant skill gap (+15-20% win prob swing)
- CWS bullpen (ERA 5.22, quality 0.86) is WORST in MLB per bullpen stats; MIN bullpen (ERA 4.63, quality 0.97) still weak. Late innings = runs likely scoring.
- NRFI probability 50.4% (almost coin flip) — elevated first-inning risk suggests scoring starts early, contradicts under lean.
Risk Factors
- HIGH_EDGE_WARNING: 29.6% edge is EXTREME and beyond calibration comfort zone. Totals are grade F (44.9% WR) — this massive edge likely represents model overconfidence in environment multipliers.
- TBD pitcher (Kay) with zero documented ERA — cannot verify actual control. If Kay pitches effectively despite C- grade, unders could hit hard.
- Calibration signal: Totals >20% edge historically show DEGRADED win rates. This 29.6% edge is likely false signal.
WEATHER IMPACTPARK FACTORPITCHER MISMATCHHIGH EDGE WARNINGTBD PITCHER
Edge Analysis
Moneyline
MIN 58.2%
-43.3 pts
Run Line
+1.5
-43.3 pts
Total
8.5
+29.6 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. Full methodology →