Bradley, Keegan vs Smalley, Alex prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Smalley, Alex 36 - Bradley, Keegan 44. Bradley, Keegan is favored with a 58.1% win probability. The spread is -0.27.
Smalley, Alex
+1.15
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Bradley, Keegan
+0.90
Strokes Gained / Round
Head-to-Head Win Probability
Smalley, AlexBradley, Keegan
-105
Best Odds
+13.5%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Bradley, Keegan
374451
Smalley, Alex
293643
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Bradley, Keegan
Strokes Gained
+0.90/round
Above Avg
Course Fit
excellent
+0.774 SG adj
Expected Finish
44th / 132
Matchup Analysis
Bradley, Keegan
+0.90 SG
EF 44th
Skill Gap
-0.27 SG/round
tight edge for Smalley, Alex
Smalley, Alex
+1.15 SG
EF 36th · Tour Elite
Edge Breakdown
Our Model
58.1%
Books Say
51.2%
Edge
+13.5%
Bradley, Keegan vs Smalley, Alex: Model gives Bradley, Keegan 58.1% win probability vs 51.2% implied (+13.5% edge). Skill advantage: -0.27 SG/round. Expected finish: 44.
AI Intelligence Analysis
STRONG BET +1
Bradley's exceptional course fit (+0.774, 2nd-best on field) + strong SG (0.895, top-10 baseline) create a 12.7% edge with dominant Colonial-specific advantage; expected finish (45) indicates quality player.
Key Factors
- Model: 57.7% vs 51.2% implied (+12.7% edge)
- Course fit: +0.774 (2nd-best fit on slate, incredible)
- SG total: +0.895 (top-10 baseline, elite)
- Expected finish: Bradley 45 (quality tier)
- Skill deficit: −0.268 (vs Smalley), but fit overwhelms
Risk Factors
- Bradley's skill deficit (−0.268 SG vs Smalley) limits upside variance
- Smalley is higher baseline player; upside risk exists
ELITE COURSE FITTOP 10 SGDOMINANT ADVANTAGE
Edge Analysis
Moneyline
Bradley, Keegan 58.1%
+13.5 pts
Spread
-0.3
+13.5 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. Full methodology →