Griffin, Ben vs Spaun, J.J. prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Spaun, J.J. 35 - Griffin, Ben 43. Griffin, Ben is favored with a 57.9% win probability. The spread is -0.13.
Spaun, J.J.
+1.24
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Griffin, Ben
+1.13
Strokes Gained / Round
Head-to-Head Win Probability
Spaun, J.J.Griffin, Ben
-118
Best Odds
+7.0%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Griffin, Ben
364350
Spaun, J.J.
283542
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Griffin, Ben
Strokes Gained
+1.13/round
Tour Elite
Course Fit
excellent
+0.574 SG adj
Expected Finish
43th / 132
Matchup Analysis
Griffin, Ben
+1.13 SG
EF 43th
Skill Gap
-0.13 SG/round
tight edge for Spaun, J.J.
Spaun, J.J.
+1.24 SG
EF 35th · Tour Elite
Edge Breakdown
Our Model
57.9%
Books Say
54.1%
Edge
+7.0%
Griffin, Ben vs Spaun, J.J.: Model gives Griffin, Ben 57.9% win probability vs 54.1% implied (+7.0% edge). Skill advantage: -0.13 SG/round. Expected finish: 43.
AI Intelligence Analysis
NEUTRAL +0
Griffin's strong fit (+0.574) + elite SG (1.134) vs Spaun's elite SG (1.238) + neutral fit yield a compressed 7.1% edge; two elite players = high variance and margin compression.
Key Factors
- Model: 58.0% vs 54.1% implied (+7.1% edge)
- Griffin SG: +1.134 vs Spaun +1.238 (Spaun slight edge)
- Course fit: Griffin +0.574 vs Spaun +0.013 (Griffin advantage)
- Expected finish: both 42-53 range (elite tier)
Risk Factors
- Two elite players (1.134 vs 1.238 SG)
- 7.1% edge is narrow for elite matchup
- High variance tournament
COMPRESSED EDGEELITE MATCHUP
Edge Analysis
Moneyline
Griffin, Ben 57.9%
+7.0 pts
Spread
-0.1
+7.0 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 →