Neergaard-Petersen, Rasmus vs Hojgaard, Rasmus prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Hojgaard, Rasmus 0 - Neergaard-Petersen, Rasmus 78. Neergaard-Petersen, Rasmus is favored with a 58.4% win probability. The spread is -0.17.
Hojgaard, Rasmus
+0.00
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Neergaard-Petersen, Rasmus
+0.21
Strokes Gained / Round
Head-to-Head Win Probability
Hojgaard, RasmusNeergaard-Petersen, Rasmus
-113
Best Odds
+10.1%
Edge
1.0u HIGH
Sizing
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Neergaard-Petersen, Rasmus
Strokes Gained
+0.21/round
Tour Avg
Course Fit
neutral
+0.098 SG adj
Expected Finish
78th / 132
Matchup Analysis
Neergaard-Petersen, Rasmus
+0.21 SG
EF 78th
Skill Gap
-0.17 SG/round
tight edge for Hojgaard, Rasmus
Hojgaard, Rasmus
+0.00 SG
EF 0th · Tour Avg
Edge Breakdown
Our Model
58.4%
Books Say
53.0%
Edge
+10.1%
Neergaard-Petersen, Rasmus vs Hojgaard, Rasmus: Model gives Neergaard-Petersen, Rasmus 58.4% win probability vs 53.1% implied (+10.1% edge). Skill advantage: -0.17 SG/round. Expected finish: 78.
AI Intelligence Analysis
NEUTRAL
Neergaard-Petersen's minimal advantage (−0.17 SG, actually a deficit) combined with neutral fit (+0.098) yield a 9.9% edge that is below conviction threshold and lacks thesis clarity.
Key Factors
- Model: 58.3% vs 53.1% implied (+9.9% edge)
- Skill deficit: −0.17 SG (Neergaard slightly worse)
- Course fit: +0.098 (trivial)
- Expected finish: NEP 79 (mid-field)
Risk Factors
- Skill deficit (−0.17) works against thesis
- Trivial fit advantage (+0.098)
- 9.9% edge below conviction
MARGINAL EDGE
Edge Analysis
Moneyline
Neergaard-Petersen, Rasmus 58.4%
+10.1 pts
Spread
-0.2
+10.1 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 →