Ghim, Doug vs Smith, Jordan prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Smith, Jordan 79 - Ghim, Doug 84. Ghim, Doug is favored with a 54.7% win probability. The spread is -0.15.
Smith, Jordan
+0.28
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Ghim, Doug
+0.19
Strokes Gained / Round
Head-to-Head Win Probability
Smith, JordanGhim, Doug
-107
Best Odds
+5.9%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Ghim, Doug
778491
Smith, Jordan
727986
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 — Ghim, Doug
Strokes Gained
+0.19/round
Tour Avg
Course Fit
good
+0.212 SG adj
Expected Finish
84th / 147
Matchup Analysis
Ghim, Doug
+0.19 SG
EF 84th
Skill Gap
-0.15 SG/round
tight edge for Smith, Jordan
Smith, Jordan
+0.28 SG
EF 79th · Tour Avg
Edge Breakdown
Our Model
54.7%
Books Say
51.7%
Edge
+5.9%
Ghim, Doug vs Smith, Jordan: Model gives Ghim, Doug 54.7% win probability vs 51.7% implied (+5.9% edge). Skill advantage: -0.15 SG/round. Expected finish: 84.
AI Intelligence Analysis
NEUTRAL +0RED ZONE0.5% WR (n=380)
Model 54.66% vs market 50.25% creates +8.8% edge, but -0.151 skill gap to Smith and EF parity (83.4 vs 78.8) with negative odds (-101) make this unreliable; skip for better edges.
Key Factors
- Skill gap: -0.151 to Smith (Smith much better)
- EF: 83.4 vs Smith 78.8 (Smith positioned better)
- SG total: +0.193 (Ghim advantage, contradicts skill gap)
- Edge: +8.8% at -101 Pinnacle (modest, negative odds)
- Conflicting signals
Risk Factors
- Smith skill advantage (-0.151) is meaningful
- EF gap shows Smith in better position
- Negative odds (-101) unfavorable
CONFLICTING SIGNALSSKILL GAP CONCERNEF MISMATCH
Edge Analysis
Moneyline
Ghim, Doug 54.7%
+5.9 pts
Spread
-0.1
+5.9 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 →