Hodges, Lee vs Hubbard, Mark prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Hubbard, Mark 90 - Hodges, Lee 93. Hodges, Lee is favored with a 58.0% win probability. The spread is 0.14.
Hubbard, Mark
-0.04
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Hodges, Lee
-0.00
Strokes Gained / Round
Head-to-Head Win Probability
Hubbard, MarkHodges, Lee
-117
Best Odds
+7.7%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Hodges, Lee
8693100
Hubbard, Mark
839097
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 — Hodges, Lee
Strokes Gained
-0.00/round
Below Avg
Course Fit
neutral
+0.031 SG adj
Expected Finish
93th / 147
Matchup Analysis
Hodges, Lee
-0.00 SG
EF 93th
Skill Gap
+0.14 SG/round
tight edge for Hodges, Lee
Hubbard, Mark
-0.04 SG
EF 90th · Below Avg
Edge Breakdown
Our Model
58.0%
Books Say
53.9%
Edge
+7.7%
Hodges, Lee vs Hubbard, Mark: Model gives Hodges, Lee 58.0% win probability vs 53.9% implied (+7.7% edge). Skill advantage: +0.14 SG/round. Expected finish: 93.
AI Intelligence Analysis
NEUTRAL +0RED ZONE0.6% WR (n=380)
Model 58.25% vs market 54.13% creates only +7.6% edge on tail players (EF 93.5 both); near-zero SG differences (-0.001) make this too noisy for -118 odds.
Key Factors
- SG parity: -0.001 (near-zero difference)
- Course fit: +0.031 (minimal advantage)
- Skill advantage: +0.142 (Hodges slight edge)
- EF: 93.5 both (tail players, high variance)
- Edge: +7.6% at -118 Pinnacle (expensive for tail)
Risk Factors
- Tail player matchup (EF ~93+) means high variance
- SG parity (-0.001) near-zero
- Steep negative odds (-118) unfavorable
TAIL PLAYERPARITYEXPENSIVE ODDS
Edge Analysis
Moneyline
Hodges, Lee 58.0%
+7.7 pts
Spread
+0.1
+7.7 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 →