Hoey, Rico vs Greyserman, Max prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Greyserman, Max 78 - Hoey, Rico 91. Hoey, Rico is favored with a 55.0% win probability. The spread is -0.2.
Greyserman, Max
+0.56
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Hoey, Rico
+0.20
Strokes Gained / Round
Head-to-Head Win Probability
Greyserman, MaxHoey, Rico
-105
Best Odds
+7.4%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Hoey, Rico
849198
Greyserman, Max
717885
Tournament Context
Event
THE CJ CUP Byron Nelson
Course
TPC Craig Ranch
Field
147 players
Wind
15 mph
Temp
82°F
Conditions
harder (+0.8)
Player Profile — Hoey, Rico
Strokes Gained
+0.20/round
Tour Avg
Course Fit
poor
-0.049 SG adj
Expected Finish
91th / 147
Matchup Analysis
Hoey, Rico
+0.20 SG
EF 91th
Skill Gap
-0.20 SG/round
tight edge for Greyserman, Max
Greyserman, Max
+0.56 SG
EF 78th · Above Avg
Edge Breakdown
Our Model
55.0%
Books Say
51.2%
Edge
+7.4%
Hoey, Rico vs Greyserman, Max: Model gives Hoey, Rico 55.0% win probability vs 51.2% implied (+7.4% edge). Skill advantage: -0.20 SG/round. Expected finish: 91.
AI Intelligence Analysis
NEUTRAL +0RED ZONE0.6% WR (n=380)
Model 55.04% vs market 51.22% creates +7.5% edge, but -0.200 skill gap to Greyserman, tail EF (90.5), and mid-range odds (-105) make this uncertain; skip for clarity.
Key Factors
- Skill gap: -0.200 to Greyserman (Greyserman better)
- Course fit: -0.049 (Hoey slight disadvantage)
- SG total: +0.195 (Hoey advantage, contradicts skill)
- EF: 90.5 (tail player, high variance)
- Edge: +7.5% at -105 Caesars (modest)
Risk Factors
- Greyserman -0.200 skill advantage is meaningful
- Negative course fit (-0.049) works against Hoey
- Tail EF (90.5) means high variance
CONFLICTING SIGNALSTAIL PLAYERSKILL GAP CONCERN
Edge Analysis
Moneyline
Hoey, Rico 55.0%
+7.4 pts
Spread
-0.2
+7.4 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 →