Day, Jason vs Hovland, Viktor prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Hovland, Viktor 31 - Day, Jason 20. Day, Jason is favored with a 59.0% win probability. The spread is -0.23.
Hovland, Viktor
+1.21
Strokes Gained / Round
VS
H2H • Truist Championship
Day, Jason
+1.00
Strokes Gained / Round
Head-to-Head Win Probability
Hovland, ViktorDay, Jason
-105
Best Odds
+15.2%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Day, Jason
132027
Hovland, Viktor
243138
Tournament Context
Event
Truist Championship
Course
Quail Hollow Club
Field
72 players
Wind
11 mph
Temp
76°F
Conditions
harder (+0.5)
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
-0.23 SG/round
tight edge for Hovland, Viktor
Hovland, Viktor
+1.21 SG
EF 31th · Tour Elite
Edge Breakdown
Our Model
59.0%
Books Say
51.2%
Edge
+15.2%
Day, Jason vs Hovland, Viktor: Model gives Day, Jason 59.0% win probability vs 51.2% implied (+15.2% edge). Skill advantage: -0.23 SG/round. Expected finish: 20.
AI Intelligence Analysis
STRONG BET +1
Day 58.6% h2h vs 48.8% implied = +20.1% edge; Day's +1.21 course fit + +1.00 SG total beats Hovland's near-zero fit (0.05 SG) despite -0.23 SG skill gap.
Key Factors
- Day course fit +1.21 SG vs Hovland +0.05 SG = +1.16 SG venue edge
- Day EF 20.0 vs Hovland 30.9 = 10-spot finish gap
- Caesars +105 (48.8% implied) vs 58.6% model = +20.1% edge
- Hovland skill slight edge (-0.23 SG favors Hovland) but venue dominates
Risk Factors
- Hovland is elite player (1.21 SG total overall) despite poor fit; variance favors elite in head-to-head
- Day's weak ott (+0.08 SG) is vulnerability in windy Round 2
- Hovland may adapt better than expected in unfamiliar venue
Edge Analysis
Moneyline
Day, Jason 59.0%
+15.2 pts
Spread
-0.2
+15.2 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 →