SD vs KC prediction for July 17, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects KC 4.9 - SD 6.0. SD is favored with a 54.1% win probability. The run line is 1.5 and the total is 10.0. Model projects 10.9 total runs.
KC
4.9
Projected Score
VS
O/U 10.0
SD
6.0
Projected Score
Win Probability
KCSD
+1.5
Run Line (KC)
10.0
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 55.7% (2,790 games)
Projected Runs Range 10th – 90th percentile
SD
468
KC
357
Starting Pitcher Matchup
Michael King R
SD
SI30%93 mph14% whiff
CH27%86 mph28% whiff
ST19%82 mph23% whiff
Seth Lugo R
KC
SI20%92 mph11% whiff
FF17%92 mph13% whiff
FC16%90 mph22% whiff
Weather Impact
Kauffman Stadium
92°F12 mph wind
HR: 1.026 Total: 1.011
thin air, 6mph in
Bullpen Comparison
SD
3.15ERA
3.66FIP
8.41K/9
3.44BB/9
1.23WHIP
KC
4.34ERA
5.00FIP
8.61K/9
4.51BB/9
1.45WHIP
Betting Edges
RUN_LINE HOME +1.5
-39.1% EV
-164
F5_ML HOME
-10.7% EV
-112
ML HOME
-7.6% EV
-104
TOTAL UNDER 10.0
-7.0% EV
-104
RUN_LINE AWAY -1.5
+6.9% EV
+136
TOTAL OVER 10.0
-2.8% EV
-118
First 5 Innings & NRFI
SD F5
3.3 runs
47.2% win
KC F5
2.9 runs
40.7% win
F5 Total
6.2
NRFI
51.1%
YRFI
48.9%
Avg 1st Inn Runs
1.13
HR Spotlight
Avg HRs
3.1
Over 0.5 HR
95%
Over 1.5 HR
80%
No HR
5%
Pitcher Strikeout Projections
Michael King
0.0 K projected
SD | K/9: 0.0
Seth Lugo
0.0 K projected
KC | K/9: 0.0
Injury Report
SD8 injured
Randy Vasquez SP15-DAY-IL
David Morgan RP15-DAY-IL
Wandy Peralta RPBEREAVEMENT
Luis Campusano CDAY-TO-DAY
Freddy Fermin C10-DAY-IL
Samad Taylor LF10-DAY-IL
+2 more
KC8 injured
Nick Mears RP60-DAY-IL
Kyle Isbel CF10-DAY-IL
Maikel Garcia 3B10-DAY-IL
Stephen Kolek SPBEREAVEMENT
Kris Bubic SP60-DAY-IL
Cole Ragans SP60-DAY-IL
+2 more
AI Intelligence Analysis
Edge Analysis
Moneyline
SD 54.1%
-39.1 pts
Run Line
+1.5
-39.1 pts
Total
10.0
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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 →