Day, Jason vs Smalley, Alex prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Smalley, Alex 33 - Day, Jason 20. Day, Jason is favored with a 62.4% win probability. The spread is 0.09.
Smalley, Alex
+0.89
Strokes Gained / Round
VS
H2H • Truist Championship
Day, Jason
+1.00
Strokes Gained / Round
Head-to-Head Win Probability
Smalley, AlexDay, Jason
-111
Best Odds
+18.5%
Edge
1.5u ELITE
Sizing
Projected Points Range 10th – 90th percentile
Day, Jason
132027
Smalley, Alex
263340
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
+0.09 SG/round
essentially a coin flip
Smalley, Alex
+0.89 SG
EF 33th · Above Avg
Edge Breakdown
Our Model
62.4%
Books Say
52.6%
Edge
+18.5%
Day, Jason vs Smalley, Alex: Model gives Day, Jason 62.4% win probability vs 52.6% implied (+18.5% edge). Expected finish: 20.
AI Intelligence Analysis
STRONG BET +1
Day 62.4% h2h vs 53.5% implied = +16.6% edge; Day's +1.00 SG + +1.21 course fit (Smalley likely weaker fit) + +0.089 SG skill edge = multi-layered advantage.
Key Factors
- Day SG +1.00 total + +1.21 course fit = strong venue specialist
- Skill diff +0.089 SG (Day slight edge over Smalley)
- Day EF 20.0 (strong finish position)
- Pinnacle -115 (53.5% implied) vs 62.4% model = +16.6% edge
Risk Factors
- Skill edge minimal (+0.089 SG); matchup heavily venue-dependent
- Smalley's profile unknown; could be stronger player than data suggests
- Day's weak ott (+0.08 SG) is vulnerability in Round 2 wind
Edge Analysis
Moneyline
Day, Jason 62.4%
+18.5 pts
Spread
+0.1
+18.5 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 →