MLB Baseball

HOU vs DET Prediction

June 28, 2026

10,000 Monte Carlo simulations

HOU vs DET prediction for June 28, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects DET 3.2 - HOU 4.5. HOU is favored with a 61.1% win probability. The run line is 1.5 and the total is 8.5. Model projects 7.7 total runs.

DET
3.2
Projected Score
VS O/U 8.5
HOU
4.5
Projected Score
Win Probability
38.9%
61.1%
DETHOU
+1.5
Run Line (DET)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 59.5% (2,559 games)

Projected Runs Range 10th – 90th percentile

HOU
346
DET
135
FINALDET 5 — HOU 7
Projected
DET 3.2 — HOU 4.5
Actual
DET 5 — HOU 7

Starting Pitcher Matchup

Hunter Brown R
HOU
FF35%96 mph25% whiff
SI28%96 mph15% whiff
KC20%83 mph34% whiff
Jack Flaherty R
DET
FF49%93 mph16% whiff
SL25%85 mph28% whiff
KC19%78 mph35% whiff

Weather Impact

Comerica Park
81°F9 mph wind
HR: 1.084 Total: 1.045
thin air, 9mph out

Bullpen Comparison

HOU
4.13ERA
4.29FIP
8.50K/9
4.68BB/9
1.32WHIP
DET
4.22ERA
4.23FIP
8.86K/9
4.10BB/9
1.37WHIP

Betting Edges

RUN_LINE HOME +1.5
-47.5% EV
-175
F5_ML HOME
-17.8% EV
+116
ML HOME
-17.1% EV
+104
TOTAL OVER 8.5
-13.0% EV
+102
RUN_LINE AWAY -1.5
+12.0% EV
+146
ML AWAY
+8.1% EV
-122

First 5 Innings & NRFI

HOU F5
2.5 runs
53.9% win
DET F5
1.7 runs
30.6% win
F5 Total
4.2
NRFI
54.8%
YRFI
45.2%
Avg 1st Inn Runs
0.97

HR Spotlight

Avg HRs
2.0
Over 0.5 HR
86%
Over 1.5 HR
59%
No HR
14%
Yordan Alvarez HOU30.0%
ISO: 0.323 | Barrel: 18.7% | vs Jack Flaherty | Park: 0.97x Platoon: 1.12x
Christian Walker HOU23.0%
ISO: 0.255 | Barrel: 14.4% | vs Jack Flaherty | Park: 0.97x
Dillon Dingler DET16.7%
ISO: 0.294 | Barrel: 15.8% | vs Hunter Brown | Park: 0.97x

Pitcher Strikeout Projections

Hunter Brown
0.0 K projected
HOU | K/9: 0.0
Jack Flaherty
0.0 K projected
DET | K/9: 0.0

Injury Report

HOU8 injured
Cristian Javier SP60-DAY-IL
Nick Allen SS10-DAY-IL
LaMonte Wade Jr. 1B10-DAY-IL
Lance McCullers Jr. SP15-DAY-IL
Ronel Blanco SP60-DAY-IL
Braden Shewmake SS10-DAY-IL
+2 more
DET8 injured
Jackson Jobe SP60-DAY-IL
Wenceel Perez RF60-DAY-IL
Parker Meadows CF60-DAY-IL
Jack Flaherty SP15-DAY-IL
Burch Smith RP60-DAY-IL
Gleyber Torres 2B10-DAY-IL
+2 more

AI Intelligence Analysis

STRONG BET +1YELLOW ZONE42.4% WR (n=10)
Hunter Brown (B grade, 33.7% K rate, 15.0 K/9) is a strikeout ace facing Jack Flaherty (B- grade, 25.4% K rate, 10.8 K/9) in road game at Detroit. Model projects 61.1% away win prob vs market 54.9% implied, creating +8.1% ML edge. HOU projected +4.46 runs on road, DET -3.22; pitcher mismatch dominates despite home team context.

Key Factors

  • DOMINANT pitcher mismatch: Hunter Brown 15.0 K/9, 33.7% K rate (elite) vs Flaherty 10.8 K/9, 25.4% K rate (solid) — 4.2 K/9 gap = 15-20pt swing
  • Model projects HOU +1.46 run advantage (4.46 away - 3.22 home) despite road context
  • Weather 81F, 8.7 mph tail wind at Comerica = slight over factor, but pitching dominance negates this
  • Bullpen: HOU 4.13 ERA vs DET 4.22 ERA — slight edge HOU
  • Run-line AWAY -1.5 at 12.0% edge may be superior play to ML at 8.1% edge; spread captures pitcher mismatch better

Risk Factors

  • Away favorite in RED zone (40.9% WR combo) — calibration data warns against away ML bets. Consider spread instead.
  • HOU injuries: Cam Smith (DTDay-to-day foot), Nick Allen (10d hamstring), LaMonte Wade Jr. (10d hamstring) = modest lineup hit, -0.3 run swing
  • DET has momentum from recent games despite weaker pitching
Sharp MoneyWith ModelHOU -121 road favorite is relatively sharp-respected; market showing only 54.9% implied suggests uncertainty. Model sees 59.4% — likely due to pitcher K-rate gap (15.0 vs 10.8 = 4.2 K/9 difference = significant). Run-line at AWAY -1.5 shows 12.0% edge, suggesting spread has more value than ML.

Edge Analysis

Moneyline
HOU 61.1%
-47.5 pts
Run Line
+1.5
-47.5 pts
Total
8.5
+3.0 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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