Vilips, Karl vs Ryder, Sam prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Ryder, Sam 90 - Vilips, Karl 96. Vilips, Karl is favored with a 61.1% win probability. The spread is -0.08.
Ryder, Sam
+0.10
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Vilips, Karl
-0.07
Strokes Gained / Round
Head-to-Head Win Probability
Ryder, SamVilips, Karl
-113
Best Odds
+15.1%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Vilips, Karl
8996103
Ryder, Sam
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 — Vilips, Karl
Strokes Gained
-0.07/round
Below Avg
Course Fit
neutral
+0.025 SG adj
Expected Finish
96th / 147
Matchup Analysis
Vilips, Karl
-0.07 SG
EF 96th
Skill Gap
-0.08 SG/round
essentially a coin flip
Ryder, Sam
+0.10 SG
EF 90th · Tour Avg
Edge Breakdown
Our Model
61.1%
Books Say
53.0%
Edge
+15.1%
Vilips, Karl vs Ryder, Sam: Model gives Vilips, Karl 61.1% win probability vs 53.1% implied (+15.1% edge). Expected finish: 96.
AI Intelligence Analysis
STRONG BET +1YELLOW ZONE0.6% WR (n=380)
Vilips' +0.025 course fit and slight skill advantage (-0.083 vs Ryder) create +15.5% edge via finish position matrix; 61.26% model vs 53.05% market shows pricing inefficiency.
Key Factors
- Finish matrix: 61.26% Vilips vs 53.05% market — 8.21% edge
- Course fit: +0.025 (Vilips minor advantage)
- EF: Both ~95 (tail players; high variance)
- SG difference: -0.083 to Vilips (near-even skill)
- Odds: -113 Pinnacle — slight negative odds but edge exists
Risk Factors
- Both tail players (EF ~95); coin-flip variance high
- Negative odds (-113) work against Kelly sizing
- Ryder potentially undervalued relative to Vilips
TAIL PLAYER EDGEHIGH VARIANCE
Edge Analysis
Moneyline
Vilips, Karl 61.1%
+15.1 pts
Spread
-0.1
+15.1 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 →