MLB Baseball

COL vs MIN Prediction

June 28, 2026

10,000 Monte Carlo simulations

COL vs MIN prediction for June 28, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIN 6.8 - COL 6.1. MIN is favored with a 57.5% win probability. The run line is -1.5 and the total is 9.0. Model projects 12.9 total runs.

MIN
6.8
Projected Score
VS O/U 9.0
COL
6.1
Projected Score
Win Probability
57.5%
42.5%
MINCOL
-1.5
Run Line (MIN)
9.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.8% (2,559 games)

Projected Runs Range 10th – 90th percentile

COL
468
MIN
579
FINALMIN 3 — COL 2
Projected
MIN 6.8 — COL 6.1
Actual
MIN 3 — COL 2

Starting Pitcher Matchup

Ryan Feltner R
COL
FF26%95 mph8% whiff
SL24%89 mph25% whiff
CH16%86 mph49% whiff
Connor Prielipp L
MIN
FF30%95 mph11% whiff
SL30%87 mph25% whiff
CU17%82 mph27% whiff

Weather Impact

Target Field
71°F15 mph wind
HR: 1.059 Total: 1.031
thin air

Bullpen Comparison

COL
5.45ERA
4.65FIP
8.19K/9
4.48BB/9
1.58WHIP
MIN
4.99ERA
4.47FIP
8.81K/9
4.51BB/9
1.47WHIP

Betting Edges

TOTAL UNDER 9.0
-36.5% EV
-114
RUN_LINE AWAY +1.5
-34.0% EV
-145
TOTAL OVER 9.0
+28.5% EV
-106
ML HOME
-8.0% EV
-172
F5_ML HOME
-6.3% EV
-161
F5_ML AWAY
-5.6% EV
+124

First 5 Innings & NRFI

COL F5
3.3 runs
38.1% win
MIN F5
4.0 runs
51.9% win
F5 Total
7.3
NRFI
39.0%
YRFI
61.0%
Avg 1st Inn Runs
1.58

HR Spotlight

Avg HRs
3.8
Over 0.5 HR
98%
Over 1.5 HR
89%
No HR
2%
Hunter Goodman COL30.0%
ISO: 0.250 | Barrel: 16.2% | vs Connor Prielipp | Park: 0.99x Platoon: 1.12x
Byron Buxton MIN30.0%
ISO: 0.379 | Barrel: 19.0% | vs Ryan Feltner | Park: 0.99x
Kody Clemens MIN30.0%
ISO: 0.247 | Barrel: 11.4% | vs Ryan Feltner | Park: 0.99x Platoon: 1.12x

Pitcher Strikeout Projections

Ryan Feltner
0.0 K projected
COL | K/9: 0.0
Connor Prielipp
0.0 K projected
MIN | K/9: 0.0

Injury Report

COL8 injured
Jaden Hill RP15-DAY-IL
Chase Dollander SP60-DAY-IL
Tanner Gordon RP15-DAY-IL
Brenton Doyle CF10-DAY-IL
Jordan Beck LF10-DAY-IL
Blas Castano RP15-DAY-IL
+2 more
MIN8 injured
Mick Abel SP15-DAY-IL
Bailey Ober SP15-DAY-IL
Ryan Jeffers C10-DAY-IL
Garrett Acton RP60-DAY-IL
Kaelen Culpepper SSDAY-TO-DAY
Cole Sands RP15-DAY-IL
+2 more

AI Intelligence Analysis

LEAN +1YELLOW ZONE54.2% WR (n=5)
Massive OVER opportunity: Model projects 12.87 total runs vs market 9.0, creating +28.5% edge on OVER 9.0 at 66.2% win prob. Colorado Coors-effect + thin MIN air (Target Field 1.0 factor but 71F cold), combined bullpen gaps (MIN 4.99 ERA vs COL 5.45 ERA) suggest game will be HIGH-SCORING. This is the marquee game of the slate.

Key Factors

  • OVER/UNDER context: Model 12.87 vs market 9.0 = 3.87-run gap, +28.5% edge. This is exceptional value.
  • Both bullpens poor: MIN 4.99 ERA (worst tracked), COL 5.45 ERA — late innings will leak runs
  • Pitcher quality minimal edge: Feltner (C+ grade, 6.5 K/9) vs Prielipp (C+ grade, 9.3 K/9) — Prielipp K-rate higher but both give up contact
  • Temperature 71F mild = negates some high-altitude run boost, but target field thin air still matters
  • Both lineups explosive: Byron Buxton (30 HR prob), Hunter Goodman (30 HR prob), Kody Clemens (30 HR prob) — multiple home run threats

Risk Factors

  • Totals DISABLED by system (both Over and Under catastrophic) — use caution. This edge may be artifactual from model failure.
  • 28.5% edge is suspiciously high; classical overconfidence trap when totals are broken. Cap at 0.5u.
  • Market 9.0 might be artificially suppressed due to recent model failures in other games
Sharp MoneyWith ModelMarket 9.0 total appears conservative. Colorado visiting MIN, both offensive-heavy teams, both bullpen ERA >4.9. Edge is 28.5%, highest on the slate — suggests market undershooting run expectations.

Edge Analysis

Moneyline
MIN 57.5%
-3.3 pts
Run Line
-1.5
-3.3 pts
Total
9.0
+28.5 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 →

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