Smith, Jordan vs Jaeger, Stephan prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Jaeger, Stephan 77 - Smith, Jordan 79. Smith, Jordan is favored with a 60.6% win probability. The spread is 0.11.
Jaeger, Stephan
+0.41
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Smith, Jordan
+0.34
Strokes Gained / Round
Head-to-Head Win Probability
Jaeger, StephanSmith, Jordan
-118
Best Odds
+11.9%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Smith, Jordan
727986
Jaeger, Stephan
707784
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 — Smith, Jordan
Strokes Gained
+0.34/round
Tour Avg
Course Fit
good
+0.225 SG adj
Expected Finish
79th / 147
Matchup Analysis
Smith, Jordan
+0.34 SG
EF 79th
Skill Gap
+0.11 SG/round
tight edge for Smith, Jordan
Jaeger, Stephan
+0.41 SG
EF 77th · Tour Avg
Edge Breakdown
Our Model
60.6%
Books Say
54.1%
Edge
+11.9%
Smith, Jordan vs Jaeger, Stephan: Model gives Smith, Jordan 60.6% win probability vs 54.1% implied (+11.9% edge). Skill advantage: +0.11 SG/round. Expected finish: 79.
AI Intelligence Analysis
STRONG BET +1GREEN ZONE0.6% WR (n=380)
Smith's +0.114 skill advantage and +0.225 course fit create solid +11.5% edge (60.36% vs 54.13%); mid-field matchup with positive finish differential supports play.
Key Factors
- Skill advantage: +0.114 SG/round (Smith better)
- Course fit: +0.225 (Smith advantage at TPC Craig Ranch)
- SG total: +0.341 (Smith advantage)
- EF: 78.8 vs Jaeger ~86, shows finish gap
- Edge: +11.5% at -118 DraftKings
Risk Factors
- Negative odds (-118) reduce Kelly sizing
- Moderate edge magnitude (6.23%)
- Mid-field finisher; moderate variance
DUAL ADVANTAGECOURSE FIT
Edge Analysis
Moneyline
Smith, Jordan 60.6%
+11.9 pts
Spread
+0.1
+11.9 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 →