Coody, Pierceson vs Bradley, Keegan prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Bradley, Keegan 45 - Coody, Pierceson 49. Coody, Pierceson is favored with a 50.4% win probability. The spread is -0.28.
Bradley, Keegan
+0.90
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Coody, Pierceson
+0.61
Strokes Gained / Round
Head-to-Head Win Probability
Bradley, KeeganCoody, Pierceson
+111
Best Odds
+9.3%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Coody, Pierceson
424956
Bradley, Keegan
384552
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Coody, Pierceson
Strokes Gained
+0.61/round
Above Avg
Course Fit
excellent
+0.901 SG adj
Expected Finish
49th / 132
Matchup Analysis
Coody, Pierceson
+0.61 SG
EF 49th
Skill Gap
-0.28 SG/round
tight edge for Bradley, Keegan
Bradley, Keegan
+0.90 SG
EF 45th · Above Avg
Edge Breakdown
Our Model
50.4%
Books Say
47.4%
Edge
+9.3%
Coody, Pierceson vs Bradley, Keegan: Model gives Coody, Pierceson 50.4% win probability vs 47.4% implied (+6.3% edge). Skill advantage: -0.29 SG/round. Expected finish: 49. AI: strong recent form; course specialist.
AI Intelligence Analysis
NEUTRAL +0
Coody's elite fit (+0.901) is offset by Bradley's slightly better fit (+0.774), minimal skill advantage (−0.285 SG is actually negative for Coody), yields a narrow 6.5% edge that is unreliable.
Key Factors
- Model: 50.5% vs 47.4% implied (+6.5% edge)
- Coody fit: +0.901 vs Bradley +0.774 (Coody +0.127 advantage, minimal)
- Coody skill: −0.285 (NEGATIVE, working against)
- Both elite-fit players; nearly identical
Risk Factors
- Coody's skill deficit (−0.285) works against
- Fit differential (+0.127) is marginal
- 6.5% edge is too narrow
COMPRESSED EDGESKILL DEFICIT
Edge Analysis
Moneyline
Coody, Pierceson 50.4%
+9.3 pts
Spread
-0.3
+9.3 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 →