Burns, Sam vs Fowler, Rickie prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Fowler, Rickie 37 - Burns, Sam 25. Burns, Sam is favored with a 60.8% win probability. The spread is 0.17.
Fowler, Rickie
+1.20
Strokes Gained / Round
VS
H2H • Truist Championship
Burns, Sam
+1.38
Strokes Gained / Round
Head-to-Head Win Probability
Fowler, RickieBurns, Sam
-120
Best Odds
+11.5%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Burns, Sam
182532
Fowler, Rickie
303744
Tournament Context
Event
Truist Championship
Course
Quail Hollow Club
Field
72 players
Wind
12 mph
Temp
76°F
Conditions
harder (+0.6)
Player Profile — Burns, Sam
Strokes Gained
+1.38/round
Tour Elite
Course Fit
excellent
+0.333 SG adj
Expected Finish
25th / 72
Matchup Analysis
Burns, Sam
+1.38 SG
EF 25th
Skill Gap
+0.17 SG/round
tight edge for Burns, Sam
Fowler, Rickie
+1.20 SG
EF 37th · Tour Elite
Edge Breakdown
Our Model
60.8%
Books Say
54.5%
Edge
+11.5%
Burns, Sam vs Fowler, Rickie: Model gives Burns, Sam 60.8% win probability vs 54.5% implied (+11.5% edge). Skill advantage: +0.17 SG/round. Expected finish: 25.
AI Intelligence Analysis
STRONG BET +1
Burns 60.3% h2h vs 52.4% implied = +15.2% edge; Burns' exceptional +1.38 SG total + +0.17 SG skill edge + modest +0.33 course fit = strong multi-layered advantage vs veteran Fowler.
Key Factors
- Burns SG +1.38 total (strong mid-tier, EF 25.4)
- Skill diff +0.17 SG (clear edge vs Fowler)
- Course fit +0.334 SG (modest venue help)
- BetOnline -110 (52.4% implied) vs 60.3% model = +15.2% edge
Risk Factors
- Fowler is veteran with experience; may outperform expected in high-pressure moments
- Burns' strength is approach (+0.34 SG) but putting (+0.63 SG) is elite; condition-dependent
- Skill edge +0.17 SG is real but not dominant vs experienced player
Edge Analysis
Moneyline
Burns, Sam 60.8%
+11.5 pts
Spread
+0.2
+11.5 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 →