Castillo, Ricky vs Smotherman, Austin prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Smotherman, Austin 0 - Castillo, Ricky 86. Castillo, Ricky is favored with a 61.6% win probability. The spread is 0.14.
Smotherman, Austin
+0.00
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Castillo, Ricky
+0.25
Strokes Gained / Round
Head-to-Head Win Probability
Smotherman, AustinCastillo, Ricky
-110
Best Odds
+17.6%
Edge
1.5u HIGH
Sizing
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Castillo, Ricky
Strokes Gained
+0.25/round
Tour Avg
Course Fit
poor
-0.231 SG adj
Expected Finish
86th / 132
Matchup Analysis
Castillo, Ricky
+0.25 SG
EF 86th
Skill Gap
+0.14 SG/round
tight edge for Castillo, Ricky
Smotherman, Austin
+0.00 SG
EF 0th · Tour Avg
Edge Breakdown
Our Model
61.6%
Books Say
52.4%
Edge
+17.6%
Castillo, Ricky vs Smotherman, Austin: Model gives Castillo, Ricky 61.6% win probability vs 52.4% implied (+17.6% edge). Skill advantage: +0.14 SG/round. Expected finish: 86.
AI Intelligence Analysis
LEAN +0
Castillo's modest SG advantage (0.249) + negative course fit (−0.231) create a 17.9% edge that is skill-based rather than fit-based; mid-tier variance is high.
Key Factors
- Model: 61.8% vs 52.4% implied (+17.9% edge)
- Skill differential: +0.138 SG (minimal)
- Course fit: −0.231 (working against Castillo)
- Expected finish: Castillo 86 (deep field)
Risk Factors
- Castillo's negative course fit (−0.231) weakens thesis
- Both players deep-field (EF 86+); high variance
- Skill edge (0.138 SG) is marginal
MODERATE EDGESKILL NOT FIT
Edge Analysis
Moneyline
Castillo, Ricky 61.6%
+17.6 pts
Spread
+0.1
+17.6 pts
How this prediction was generated: This page shows output from the Olympus Bets PGA Tour Golf 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 →