MLB Baseball

HOU vs KC Prediction

June 13, 2026

10,000 Monte Carlo simulations

HOU vs KC prediction for June 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects KC 4.4 - HOU 5.3. HOU is favored with a 55.0% win probability. The run line is -1.5 and the total is 9.5. Model projects 9.8 total runs.

KC
4.4
Projected Score
VS O/U 9.5
HOU
5.3
Projected Score
Win Probability
45.0%
55.0%
KCHOU
-1.5
Run Line (KC)
9.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 60.0% (2,321 games)

Projected Runs Range 10th – 90th percentile

HOU
357
KC
246
FINALKC 7 — HOU 8
Projected
KC 4.4 — HOU 5.3
Actual
KC 7 — HOU 8

Starting Pitcher Matchup

Mike Burrows R
HOU
FF29%95 mph12% whiff
CH27%87 mph32% whiff
SL16%90 mph29% whiff
Noah Cameron L
KC
FF29%92 mph12% whiff
CH22%81 mph28% whiff
FC19%90 mph24% whiff

Weather Impact

Kauffman Stadium
91°F12 mph wind
HR: 1.024 Total: 1.010
thin air, 7mph in

Bullpen Comparison

HOU
4.14ERA
4.30FIP
8.46K/9
4.82BB/9
1.32WHIP
KC
4.51ERA
5.05FIP
8.79K/9
4.78BB/9
1.47WHIP

Betting Edges

RUN_LINE AWAY +1.5
-25.9% EV
-192
RUN_LINE HOME -1.5
-18.3% EV
+158
ML HOME
-17.2% EV
-130
F5_ML HOME
-16.7% EV
-141
ML AWAY
+11.7% EV
+110
TOTAL OVER 9.5
-9.9% EV
-110

First 5 Innings & NRFI

HOU F5
2.8 runs
46.5% win
KC F5
2.6 runs
40.2% win
F5 Total
5.4
NRFI
50.0%
YRFI
50.0%
Avg 1st Inn Runs
1.11

HR Spotlight

Avg HRs
3.2
Over 0.5 HR
96%
Over 1.5 HR
82%
No HR
4%
Yordan Alvarez HOU30.0%
ISO: 0.356 | Barrel: 19.4% | vs Noah Cameron | Park: 0.96x
Christian Walker HOU30.0%
ISO: 0.204 | Barrel: 14.5% | vs Noah Cameron | Park: 0.96x Platoon: 1.12x
Michael Massey KC30.0%
ISO: 0.201 | Barrel: 9.7% | vs Mike Burrows | Park: 0.96x Platoon: 1.12x

Pitcher Strikeout Projections

Mike Burrows
0.0 K projected
HOU | K/9: 0.0
Noah Cameron
0.0 K projected
KC | K/9: 0.0

Injury Report

HOU8 injured
Ronel Blanco SP60-DAY-IL
Yainer Diaz C10-DAY-IL
Peter Lambert SPDAY-TO-DAY
LaMonte Wade Jr. 1B10-DAY-IL
Nick Allen SS10-DAY-IL
Hunter Brown SP60-DAY-IL
+2 more
KC8 injured
Cole Ragans SP15-DAY-IL
Kris Bubic SP15-DAY-IL
Carlos Estevez RP15-DAY-IL
Seth Lugo SP7-DAY IL
Kyle Isbel CF10-DAY-IL
Nick Mears RP15-DAY-IL
+2 more

AI Intelligence Analysis

LEANRED ZONE43.3% WR (n=96)
Houston away favorite ML at 11.7% edge is significant but in RED zone (away ML = 43.3% WR historically). However, pitcher mismatch (Mike Burrows 6.23 ERA terrible vs Noah Cameron 4.15 ERA solid) favors away strongly enough that edge justified. LEAN on pitcher quality, not on away favorite status.

Key Factors

  • SP QUALITY STRONGLY FAVORS AWAY: Mike Burrows (KC home) 6.23 ERA (C+ grade, 17.8% K-rate weak) vs Noah Cameron (HOU away) 4.15 ERA (B- grade, 22.9% K-rate). 2.08 ERA gap is massive, only seen a few times on slate.
  • Away ML RED zone (43.3% WR) is normally a blocker, but THIS pitcher gap (6.23 vs 4.15) is so large it potentially overrides systematic underperformance
  • Model 53.2% HOU prob vs market 47.6% = 5.6% gap shows market already pricing pitcher advantage
  • Temperature 90.8F hot, wind 7.1 mph in = hot weather + wind-in = neutral/slight under bias favoring Cameron (less affected by conditions)
  • 11.7% edge is substantial, but away ML zone is RED—risk-reward unfavorable despite good pitcher gap

Risk Factors

  • RED ZONE away favorite: despite 11.7% edge, historically away ML picks only 43.3% WR. This is anti-correlated with edge magnitude.
  • Burrows 6.23 ERA may be small-sample variance; Cameron 4.15 ERA also could be optimistic
  • KC has been competitive despite weak rotation; home advantage significant in AL Central
Sharp MoneyWith ModelModel backing HOU away; market respects pitcher gap but values KC home. Sharp likely split. NO clear action signal.
RED ZONEPITCHER QUALITY AWAYAWAY FAVORITEHIGH EDGE

Edge Analysis

Moneyline
HOU 55.0%
-18.3 pts
Run Line
-1.5
-18.3 pts
Total
9.5
+0.9 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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