Day, Jason vs Hojgaard, Nicolai prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Hojgaard, Nicolai 30 - Day, Jason 20. Day, Jason is favored with a 58.4% win probability. The spread is -0.06.
Hojgaard, Nicolai
+1.03
Strokes Gained / Round
VS
H2H • Truist Championship
Day, Jason
+1.00
Strokes Gained / Round
Head-to-Head Win Probability
Hojgaard, NicolaiDay, Jason
-105
Best Odds
+13.9%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Day, Jason
132027
Hojgaard, Nicolai
233037
Tournament Context
Event
Truist Championship
Course
Quail Hollow Club
Field
72 players
Wind
12 mph
Temp
76°F
Conditions
harder (+0.6)
Player Profile — Day, Jason
Strokes Gained
+1.00/round
Tour Elite
Course Fit
excellent
+1.211 SG adj
Expected Finish
20th / 72
Matchup Analysis
Day, Jason
+1.00 SG
EF 20th
Skill Gap
-0.06 SG/round
essentially a coin flip
Hojgaard, Nicolai
+1.03 SG
EF 30th · Tour Elite
Edge Breakdown
Our Model
58.4%
Books Say
51.2%
Edge
+13.9%
Day, Jason vs Hojgaard, Nicolai: Model gives Day, Jason 58.3% win probability vs 51.2% implied (+13.9% edge). Expected finish: 20.
AI Intelligence Analysis
STRONG BET +1
Day 57.7% h2h vs 46.1% implied = +25.3% edge; Day's +1.00 SG + strong +1.21 course fit (Hojgaard +0.31) = clear skill + venue advantage.
Key Factors
- Day SG Total +1.00 vs Hojgaard +1.03 (near-equal skill, slight Day edge)
- Course fit differential: Day +1.21 vs Hojgaard +0.31 = +0.90 SG advantage for Day
- Expected finishes: Day 20.0 vs Hojgaard 29.9 = 9-spot gap (major h2h edge)
- BetOnline +117 (46.1% implied) vs 57.7% model
Risk Factors
- Skill difference is minimal (-0.06 SG); edge is pure course fit
- Hojgaard is younger/rising player; may have outlier upside in windy conditions
- Day's weak ott (+0.08 SG) vulnerable vs strong ball-strikers in Round 2 wind (20.9 mph)
Edge Analysis
Moneyline
Day, Jason 58.4%
+13.9 pts
Spread
-0.1
+13.9 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 →