Spaun, J.J. vs Kitayama, Kurt prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Kitayama, Kurt 39 - Spaun, J.J. 29. Spaun, J.J. is favored with a 59.1% win probability. The spread is 0.23.
Kitayama, Kurt
+0.96
Strokes Gained / Round
VS
H2H • Truist Championship
Spaun, J.J.
+1.19
Strokes Gained / Round
Head-to-Head Win Probability
Kitayama, KurtSpaun, J.J.
-110
Best Odds
+12.8%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Spaun, J.J.
222936
Kitayama, Kurt
323946
Tournament Context
Event
Truist Championship
Course
Quail Hollow Club
Field
72 players
Wind
11 mph
Temp
76°F
Conditions
harder (+0.5)
Player Profile — Spaun, J.J.
Strokes Gained
+1.19/round
Tour Elite
Course Fit
good
+0.190 SG adj
Expected Finish
29th / 72
Matchup Analysis
Spaun, J.J.
+1.19 SG
EF 29th
Skill Gap
+0.23 SG/round
tight edge for Spaun, J.J.
Kitayama, Kurt
+0.96 SG
EF 39th · Above Avg
Edge Breakdown
Our Model
59.1%
Books Say
52.4%
Edge
+12.8%
Spaun, J.J. vs Kitayama, Kurt: Model gives Spaun, J.J. 59.1% win probability vs 52.4% implied (+12.8% edge). Skill advantage: +0.23 SG/round. Expected finish: 29.
AI Intelligence Analysis
LEAN +0
Spaun 59.1% h2h vs 52.8% implied = +11.8% edge; Spaun's +1.19 SG total + +0.23 SG skill edge + modest +0.191 course fit = balanced edge but mid-tier player variance.
Key Factors
- Spaun SG +1.19 (solid mid-tier, EF 29.5)
- Skill diff +0.23 SG (clear edge vs Kitayama)
- Course fit +0.191 SG (modest venue help)
- Pinnacle -112 (52.8% implied) vs 59.1% model = +11.8% edge
Risk Factors
- Mid-tier matchup with higher volatility than elite-player edges
- Kitayama's profile unknown; could be stronger than expected
- Skill edge +0.23 SG is modest in absolute terms
Edge Analysis
Moneyline
Spaun, J.J. 59.1%
+12.8 pts
Spread
+0.2
+12.8 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 →