DeChambeau, Bryson vs Koepka, Brooks prediction for May 13, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Koepka, Brooks 83 - DeChambeau, Bryson 61. DeChambeau, Bryson is favored with a 63.9% win probability. The spread is 0.85.
Koepka, Brooks
+0.48
Strokes Gained / Round
VS
H2H • PGA Championship
DeChambeau, Bryson
+1.32
Strokes Gained / Round
Head-to-Head Win Probability
Koepka, BrooksDeChambeau, Bryson
-145
Best Odds
+8.0%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
DeChambeau, Bryson
546168
Koepka, Brooks
768390
Tournament Context
Event
PGA Championship
Course
Aronimink Golf Club
Field
156 players
Player Profile — DeChambeau, Bryson
Strokes Gained
+1.32/round
Tour Elite
Course Fit
excellent
+0.790 SG adj
Expected Finish
61th / 156
Matchup Analysis
DeChambeau, Bryson
+1.32 SG
EF 61th
Skill Gap
+0.85 SG/round
large edge for DeChambeau, Bryson
Koepka, Brooks
+0.48 SG
EF 83th · Tour Avg
Edge Breakdown
Our Model
63.9%
Books Say
59.2%
Edge
+8.0%
DeChambeau, Bryson vs Koepka, Brooks: Model gives DeChambeau, Bryson 63.9% win probability vs 59.2% implied (+8.0% edge). Skill advantage: +0.85 SG/round. Expected finish: 61.
AI Intelligence Analysis
STRONG BET +1YELLOW ZONE0.5% WR (n=200)
DeChambeau's massive +0.845 SG skill advantage overwhelms course fit parity; drives +9.3% edge despite negative odds.
Key Factors
- Skill advantage: +0.845 SG/round (DeChambeau +1.325 vs Koepka +0.480) = DOMINANT
- Course fit parity: Both ~+1.15 SG, minimal differential
- Finish position: DeChambeau EF 50.4 vs Koepka EF 75.8 = 25-position gap
- Edge: +9.3% (64.7% model vs 59.2% implied) = value even at -145
Risk Factors
- Negative odds (-145) require high conviction
- Koepka's major championship pedigree allows for elevation
- Single-event variance higher with negative odds
LINE VALUESKILL EDGE DOMINANTNEGATIVE ODDS
Edge Analysis
Moneyline
DeChambeau, Bryson 63.9%
+8.0 pts
Spread
+0.8
+8.0 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 →