Griffin, Ben vs MacIntyre, Robert prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MacIntyre, Robert 35 - Griffin, Ben 43. Griffin, Ben is favored with a 57.2% win probability. The spread is -0.1.
MacIntyre, Robert
+1.24
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Griffin, Ben
+1.13
Strokes Gained / Round
Head-to-Head Win Probability
MacIntyre, RobertGriffin, Ben
-110
Best Odds
+9.3%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Griffin, Ben
364350
MacIntyre, Robert
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
-0.10 SG/round
essentially a coin flip
MacIntyre, Robert
+1.24 SG
EF 35th · Tour Elite
Edge Breakdown
Our Model
57.2%
Books Say
52.4%
Edge
+9.3%
Griffin, Ben vs MacIntyre, Robert: Model gives Griffin, Ben 57.2% win probability vs 52.4% implied (+9.3% edge). Expected finish: 43.
AI Intelligence Analysis
NEUTRAL +0
Griffin's elite SG (1.134) + strong fit (+0.574) are offset by MacIntyre's elite baseline (1.24) + positive fit (+0.119); near-equal players yield 9.5% edge that is compressed and unreliable.
Key Factors
- Model: 57.3% vs 52.4% implied (+9.5% edge)
- Griffin SG: +1.134 vs MacIntyre +1.24 (MacIntyre slight edge)
- Course fit: Griffin +0.574 vs MacIntyre +0.119 (Griffin advantage)
- Expected finish: Griffin 43 vs MacIntyre 52 (Griffin edge)
Risk Factors
- MacIntyre's baseline (1.24) edges Griffin (1.134)
- Near-equal fit advantage (Griffin +0.574 vs +0.119)
- 9.5% edge is compressed; unreliable for betting
TIGHT MATCHUP
Edge Analysis
Moneyline
Griffin, Ben 57.2%
+9.3 pts
Spread
-0.1
+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 →