FINAL: KC 4 — SF 3. Our Monte Carlo simulation projected KC 4.3 - SF 6.2 (SF at 56.4% win probability). The run line is 1.5 and the total is 10.5. Model projects 10.5 total runs.
KC
4.3
Projected Score
VS
O/U 10.5
SF
6.2
Projected Score
Win Probability
KCSF
+1.5
Run Line (KC)
10.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 56.2% (2,956 games)
Projected Runs Range 10th – 90th percentile
SF
468
KC
246
Projected
KC 4.3 — SF 6.2
Actual
KC 4 — SF 3
Pick Results
SF @ KC NRFInrfiWIN+1.12u
Starting Pitcher Matchup
Trevor McDonald R
SF
usagewhiffbar labels show usage · mph
SI57%94 mph9% whiff
SL26%86 mph38% whiff
CH15%84 mph33% whiff
Michael Wacha R
KC
usagewhiffbar labels show usage · mph
FF27%93 mph18% whiff
CH23%81 mph27% whiff
FC16%89 mph12% whiff
Weather Impact
Kauffman Stadium
103°F8 mph wind
HR: 1.110 Total: 1.057
thin air, 8mph out
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
RUN_LINE HOME +1.5
-46.0% EV
-192
RUN_LINE AWAY -1.5
+23.7% EV
+158
F5_ML HOME
-23.6% EV
-120
F5_ML AWAY
+14.2% EV
-104
ML HOME
-13.9% EV
-120
ML AWAY
+7.0% EV
+102
First 5 Innings & NRFI
SF F5
3.2 runs
53.2% win
KC F5
2.3 runs
33.2% win
F5 Total
5.5
NRFI
53.4%
YRFI
46.6%
Avg 1st Inn Runs
1.07
HR Spotlight
Avg HRs
2.5
Over 0.5 HR
91%
Over 1.5 HR
70%
No HR
9%
Bryce Eldridge SF30.0%
ISO: 0.223 | Barrel: 25.0% | vs Michael Wacha | Park: 0.96x Platoon: 1.12x
Rafael Devers SF25.8%
ISO: 0.212 | Barrel: 16.0% | vs Michael Wacha | Park: 0.96x Platoon: 1.12x
Casey Schmitt SF25.2%
ISO: 0.254 | Barrel: 9.0% | vs Michael Wacha | Park: 0.96x
Pitcher Strikeout Projections
Trevor McDonald
0.0 K projected
SF | K/9: 0.0
Michael Wacha
0.0 K projected
KC | K/9: 0.0
Injury Report
SF8 injured
Matt Chapman 3B10-DAY-IL
Victor Bericoto RF10-DAY-IL
Jonah Cox CF10-DAY-IL
Matt Gage RP15-DAY-IL
Harrison Bader CF10-DAY-IL
Joel Peguero RP60-DAY-IL
+2 more
KC8 injured
Stephen Kolek SP15-DAY-IL
Maikel Garcia 3B10-DAY-IL
Carlos Estevez RP60-DAY-IL
Kris Bubic SP60-DAY-IL
Kyle Isbel CF10-DAY-IL
Nick Mears RP60-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1
Model favors KC away at 56.4% win prob (7.0% ML edge, +102 money) but market prices SF home at -120 (54.5% implied). This is sharp conflict: market trusts Wacha (0.454 overall, C-stuff) more than model credits. Trevor McDonald (5.42 ERA, C-grade) is significantly worse. Extreme heat (102.9F, 8mph out wind, 1.057x multiplier) should boost scoring, not suppress. Lean AWAY on model edge, but CAUTION: combo zone away ML at RED 44.3% WR. F5 ML AWAY at 14.2% edge more attractive signal.
Key Factors
- Pitcher ERA: McDonald 5.42 ERA (C-grade, 0.447 overall) vs Wacha unclear ERA (C-grade, 0.454 overall). Both mediocre, effectively matched despite McDonald worse K-rate (7.1 vs 8.0)
- Market-model signal: Model 53% away win, market 49.5% implies (SF home -120). Market is MORE bullish on home than model. This suggests market sees Wacha > sim credit OR SD quality < model estimate
- Extreme heat (102.9F) + 8mph out wind (1.057x total multiplier) + high altitude (3282 ft) should inflate scoring significantly. Model total 10.48 is near market 10.5 — both underpricing run environment
- F5 ML AWAY shows 14.2% edge at 58.2% prob — superior risk/reward to full-game 7% edge. This is the cleaner signal
- Combo zone away ML is RED (44.3% WR) — historical trap
Risk Factors
- Away ML in positive-edge bucket consistently underperforms (44.3% combo WR across 107 tracked bets)
- Market pricing suggests Wacha is sharper arm than model assigns. Trust market over model on this SP matchup
- Extreme heat should help BOTH teams equally; doesn't tilt game direction significantly
MODEL MARKET CONFLICTF5 VALUEWEATHER IMPACTEXTREME HEAT
Edge Analysis
Moneyline
SF 56.4%
-46.0 pts
Run Line
+1.5
-46.0 pts
Total
10.5
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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 →