FINAL: KC 3 — SF 2. Our Monte Carlo simulation projected KC 4.5 - SF 6.0 (SF at 55.1% win probability). The run line is 1.5 and the total is 9.5. Model projects 10.6 total runs.
KC
4.5
Projected Score
VS
O/U 9.5
SF
6.0
Projected Score
Win Probability
KCSF
+1.5
Run Line (KC)
9.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 54.2% (2,971 games)
Projected Runs Range 10th – 90th percentile
SF
468
KC
346
Projected
KC 4.5 — SF 6.0
Actual
KC 3 — SF 2
Pick Results
SF F5 MLf5_mlWIN+0.47u
Starting Pitcher Matchup
Tyler Mahle R
SF
usagewhiffbar labels show usage · mph
FF48%92 mph17% whiff
FS25%86 mph26% whiff
FC15%88 mph8% whiff
Luinder Avila R
KC
usagewhiffbar labels show usage · mph
FF27%96 mph14% whiff
SI26%96 mph17% whiff
SL22%88 mph31% whiff
Weather Impact
Kauffman Stadium
93°F14 mph wind
HR: 1.016 Total: 1.005
thin air, 7mph 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
RUN_LINE HOME +1.5
-43.2% EV
-179
TOTAL UNDER 9.5
-17.6% EV
-123
F5_ML HOME
-14.6% EV
-110
RUN_LINE AWAY -1.5
+14.3% EV
+146
TOTAL OVER 9.5
+10.0% EV
+102
ML HOME
-9.6% EV
-108
First 5 Innings & NRFI
SF F5
3.3 runs
49.9% win
KC F5
2.7 runs
37.9% win
F5 Total
6.0
NRFI
47.6%
YRFI
52.4%
Avg 1st Inn Runs
1.21
HR Spotlight
Avg HRs
2.4
Over 0.5 HR
90%
Over 1.5 HR
68%
No HR
10%
Bryce Eldridge SF30.0%
ISO: 0.223 | Barrel: 25.0% | vs Luinder Avila | Park: 0.96x Platoon: 1.12x
Jac Caglianone KC28.6%
ISO: 0.164 | Barrel: 12.0% | vs Tyler Mahle | Park: 0.96x Platoon: 1.12x
Heliot Ramos SF28.6%
ISO: 0.135 | Barrel: 8.8% | vs Luinder Avila | Park: 0.96x
Pitcher Strikeout Projections
Tyler Mahle
0.0 K projected
SF | K/9: 0.0
Luinder Avila
0.0 K projected
KC | K/9: 0.0
Injury Report
SF8 injured
Trevor McDonald SPDAY-TO-DAY
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
Bobby Witt Jr. SSDAY-TO-DAY
Kyle Isbel CF10-DAY-IL
Stephen Kolek SP15-DAY-IL
Maikel Garcia 3B10-DAY-IL
Carlos Estevez RP60-DAY-IL
Kris Bubic SP60-DAY-IL
+2 more
AI Intelligence Analysis
LEAN +1
Model 55.1% SF (away) with +2.6% ML edge (53.2% vs 51.8% market) — narrow edge but confirmed by RUN LINE +14.3% edge (46.5% prob for SF -1.5). SP: Tyler Mahle (C+, 5.73 ERA, 8.0 K/9, lower command 0.523) vs Luinder Avila (C+, 5.49 ERA, 8.0 K/9, similar command 0.346 even lower) — nearly identical mediocre starters, but SF is away team with better bullpen (4.06 ERA) vs KC (4.34 ERA). Weather hot (92.7F) + 7mph into wind (suppresses) = neutral. Model projects SF 6.05 runs vs KC 4.52 runs (1.53 run edge). 2.6% ML edge modest but run-line at +14.3% suggests model overconfident on spread.
Key Factors
- SP comparison: Mahle (5.73 ERA, C+ grade, command 0.523) vs Avila (5.49 ERA, C+ grade, command 0.346 even lower) — extremely similar mediocre profile; slight edge Mahle with better command
- Bullpen edge SF: 4.06 ERA vs KC 4.34 ERA (+0.28 ERA advantage away team) — small but measurable
- Weather heat (92.7F) + 7mph headwind = modest neutral (+0.1 runs added despite headwind due to heat)
- Run-line edge +14.3% for SF -1.5 (46.5% prob) suggests model overconfident on spread; ML edge only +2.6% more realistic
- SF lineup quality (Eldridge, Ramos power hitters 30% HR prob) slightly stronger than KC middle-tier hitters
Risk Factors
- Both starters poor quality (5.49-5.73 ERA, both C+) = high volatility, unpredictable outcomes
- ML edge only 2.6% (model 53.2% vs market 51.8%) — within margin of error given SP uncertainty
- KC home park Kauffman typically neutral to slight hitter's park (1.0x) — should favor home team slightly
Edge Analysis
Moneyline
SF 55.1%
-43.2 pts
Run Line
+1.5
-43.2 pts
Total
9.5
+10.0 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 →