Jaeger, Stephan vs Li, Haotong prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Li, Haotong 80 - Jaeger, Stephan 86. Jaeger, Stephan is favored with a 55.8% win probability. The spread is 0.01.
Li, Haotong
+0.20
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Jaeger, Stephan
+0.23
Strokes Gained / Round
Head-to-Head Win Probability
Li, HaotongJaeger, Stephan
-107
Best Odds
+7.9%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Jaeger, Stephan
798693
Li, Haotong
738087
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 — Jaeger, Stephan
Strokes Gained
+0.23/round
Tour Avg
Course Fit
neutral
+0.075 SG adj
Expected Finish
86th / 147
Matchup Analysis
Jaeger, Stephan
+0.23 SG
EF 86th
Skill Gap
+0.01 SG/round
essentially a coin flip
Li, Haotong
+0.20 SG
EF 80th · Tour Avg
Edge Breakdown
Our Model
55.8%
Books Say
51.7%
Edge
+7.9%
Jaeger, Stephan vs Li, Haotong: Model gives Jaeger, Stephan 55.8% win probability vs 51.7% implied (+7.9% edge). Expected finish: 86.
AI Intelligence Analysis
NEUTRAL +0YELLOW ZONE0.6% WR (n=380)
Model 56.19% vs market 51.69% creates +8.7% edge, but near-zero skill gap (+0.010), modest course fit (+0.075), and negative odds (-107) on modest edge suggest too much uncertainty; skip.
Key Factors
- Skill parity: +0.010 (near-zero gap)
- Course fit: +0.075 (Jaeger minimal advantage)
- SG total: +0.231 (Jaeger slight advantage)
- EF: 85.9 (both similar finish positions)
- Edge: +8.7% at -107 Pinnacle (modest, negative odds)
Risk Factors
- Skill parity (+0.010) means no clear winner
- Course fit (+0.075) is minimal
- Negative odds (-107) unfavorable for modest edge
SKILL PARITYMODEST EDGENEGATIVE ODDS
Edge Analysis
Moneyline
Jaeger, Stephan 55.8%
+7.9 pts
Spread
+0.0
+7.9 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 →