MLB Baseball

SF vs KC Prediction

July 22, 2026

10,000 Monte Carlo simulations

SF vs KC prediction for July 22, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects KC 4.0 - SF 5.4. SF is favored with a 54.2% win probability. The run line is 1.5 and the total is 8.5. Model projects 9.4 total runs.

KC
4.0
Projected Score
VS O/U 8.5
SF
5.4
Projected Score
Win Probability
45.8%
54.2%
KCSF
+1.5
Run Line (KC)
8.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.7% (2,978 games)

Projected Runs Range 10th – 90th percentile

SF
357
KC
246
FINALKC 5 — SF 4
Projected
KC 4.0 — SF 5.4
Actual
KC 5 — SF 4

Starting Pitcher Matchup

Landen Roupp R
SF
SI36% · 93
CU26% · 77
CH20% · 87
usagewhiffbar labels show usage · mph
SI36%93 mph9% whiff
CU26%77 mph33% whiff
CH20%87 mph29% whiff
Seth Lugo R
KC
SI20% · 92
FF17% · 92
FC16% · 90
usagewhiffbar labels show usage · mph
SI20%92 mph10% whiff
FF17%92 mph13% whiff
FC16%90 mph22% whiff

Weather Impact

Kauffman Stadium
85°F12 mph wind
HR: 0.998 Total: 0.996
thin air, 9mph in

Bullpen Comparison

SF
4.06ERA
4.43FIP
8.10K/9
4.84BB/9
1.41WHIP
KC
4.34ERA
5.00FIP
8.61K/9
4.51BB/9
1.45WHIP

Betting Edges

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Edge Analysis
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First 5 Innings & NRFI

SF F5
3.0 runs
50.9% win
KC F5
2.2 runs
35.4% win
F5 Total
5.2
NRFI
55.5%
YRFI
44.5%
Avg 1st Inn Runs
1.01

HR Spotlight

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Home Run Analysis
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Injury Report

SF8 injured
Trevor McDonald SP15-DAY-IL
Harrison Bader CF10-DAY-IL
Matt Chapman 3B10-DAY-IL
Victor Bericoto RF10-DAY-IL
Jonah Cox CF10-DAY-IL
Matt Gage RP15-DAY-IL
+2 more
KC8 injured
Stephen Kolek SP60-DAY-IL
Jac Caglianone RFDAY-TO-DAY
Bobby Witt Jr. SS10-DAY-IL
Kyle Isbel CF10-DAY-IL
Maikel Garcia 3B10-DAY-IL
Carlos Estevez RP60-DAY-IL
+2 more

Model Edge & Sizing

🔒The model's edge against the market, and the stake it sizes for this matchup, are premium.Unlock

Edge Analysis

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Edge Analysis
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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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