MLB Baseball

HOU vs DET Prediction

June 27, 2026

10,000 Monte Carlo simulations

HOU vs DET prediction for June 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects DET 3.9 - HOU 3.1. DET is favored with a 60.1% win probability. The run line is -1.5 and the total is 8.5. Model projects 7.0 total runs.

DET
3.9
Projected Score
VS O/U 8.5
HOU
3.1
Projected Score
Win Probability
60.1%
39.9%
DETHOU
-1.5
Run Line (DET)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 58.3% (2,559 games)

Projected Runs Range 10th – 90th percentile

HOU
135
DET
246
FINALDET 6 — HOU 8
Projected
DET 3.9 — HOU 3.1
Actual
DET 6 — HOU 8

Starting Pitcher Matchup

Kai-Wei Teng R
HOU
ST34%84 mph29% whiff
FF27%94 mph19% whiff
SI16%93 mph18% whiff
Framber Valdez L
DET
SI45%94 mph10% whiff
CU28%78 mph30% whiff
CH21%89 mph22% whiff

Weather Impact

Comerica Park
78°F11 mph wind
HR: 1.090 Total: 1.049
thin air, 11mph 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 AWAY +1.5
-45.4% EV
-189
TOTAL OVER 8.5
-27.0% EV
-110
TOTAL UNDER 8.5
+18.0% EV
-110
F5_ML AWAY
-15.7% EV
+112
ML AWAY
-12.5% EV
+114
RUN_LINE HOME -1.5
+7.0% EV
+155

First 5 Innings & NRFI

HOU F5
1.5 runs
31.6% win
DET F5
2.2 runs
51.0% win
F5 Total
3.7
NRFI
59.3%
YRFI
40.7%
Avg 1st Inn Runs
0.85

HR Spotlight

Avg HRs
1.9
Over 0.5 HR
85%
Over 1.5 HR
56%
No HR
15%
Christian Walker HOU23.2%
ISO: 0.241 | Barrel: 14.4% | vs Framber Valdez | Park: 0.97x Platoon: 1.12x
Kerry Carpenter DET16.3%
ISO: 0.270 | Barrel: 14.3% | vs Kai-Wei Teng | Park: 0.97x Platoon: 1.12x
Spencer Torkelson DET15.0%
ISO: 0.227 | Barrel: 12.1% | vs Kai-Wei Teng | Park: 0.97x

Pitcher Strikeout Projections

Kai-Wei Teng
0.0 K projected
HOU | K/9: 0.0
Framber Valdez
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

LEANYELLOW ZONE50.1% WR (n=253)
Model projects 7.0 total runs vs market 8.5 → 18% UNDER edge. DET generated runs (mean 3.85) suggests both teams underscored; DET bullpen (4.22 ERA) is average, HOU bullpen (4.13 ERA) near-identical. Valdez (no Bayesian ERA available, 19.0% K) outmatches Teng's 23.6% K by pure rate, but Teng has superior stuff grade (B- vs C). Wind factor: 10.6 mph out at Comerica = +1.049 multiplier on runs. UNDER 8.5 still justified by SP misalignment.

Key Factors

  • Valdez vs Teng: Framber 4.13 ERA (SP comparison data missing) with 19.0% K vs Teng 23.6% K — Teng has higher strikeout potential
  • Bullpen parity: HOU bullpen 4.13 ERA, DET bullpen 4.22 ERA — neither provides run suppression
  • Weather multiplier: +1.049 on totals (10.6 mph out + thin air) — adds ~0.5 runs model didn't fully price
  • Market total 8.5 vs model 7.0: 1.5-run gap suggests market saw environmental factors model missed
  • Game result: 14 total runs, confirming market's +0.5 edge over model's conservative projection

Risk Factors

  • Game already final (14 runs scored) — model's UNDER play completely wrong
  • Bullpen fatigue: Both teams' bullpens unreliable; high-leverage situations converted to offense
  • High-edge zone (18% UNDER): Historical zone performance YELLOW but recent picks 50% WR — marginal edge unreliable
WEATHER IMPACTPARK FACTORGAME COMPLETEDTOTALS VALUEMODEL MARKET CONFLICT

Edge Analysis

Moneyline
DET 60.1%
+7.0 pts
Run Line
-1.5
+7.0 pts
Total
8.5
+18.0 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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