Spaun, J.J. vs Matsuyama, Hideki prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Matsuyama, Hideki 34 - Spaun, J.J. 53. Spaun, J.J. is favored with a 58.5% win probability. The spread is 0.14.
Matsuyama, Hideki
+1.10
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Spaun, J.J.
+1.24
Strokes Gained / Round
Head-to-Head Win Probability
Matsuyama, HidekiSpaun, J.J.
-115
Best Odds
+6.4%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Spaun, J.J.
465360
Matsuyama, Hideki
273441
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Spaun, J.J.
Strokes Gained
+1.24/round
Tour Elite
Course Fit
neutral
+0.013 SG adj
Expected Finish
53th / 132
Matchup Analysis
Spaun, J.J.
+1.24 SG
EF 53th
Skill Gap
+0.14 SG/round
tight edge for Spaun, J.J.
Matsuyama, Hideki
+1.10 SG
EF 34th · Tour Elite
Edge Breakdown
Our Model
58.5%
Books Say
53.5%
Edge
+6.4%
Spaun, J.J. vs Matsuyama, Hideki: Model gives Spaun, J.J. 58.5% win probability vs 53.5% implied (+9.4% edge). Skill advantage: +0.14 SG/round. Expected finish: 53. AI: poor recent form; poor course history.
AI Intelligence Analysis
LEAN +0
Spaun's elite SG (1.238, near-top baseline) + skill advantage (+0.143 SG vs Matsuyama) overcome neutral course fit (+0.013) to yield 9.6% edge; but tight odds compress value.
Key Factors
- Model: 58.6% vs 53.5% implied (+9.6% edge)
- Spaun SG: +1.238 (elite baseline)
- Skill advantage: +0.143 (modest but positive)
- Course fit: +0.013 (neutral)
- Expected finish: Spaun 53 (quality tier)
Risk Factors
- Neutral fit (+0.013) provides no support
- Narrow edge (9.6%) limits unit potential
- Matsuyama is moderate-quality player
ELITE BASELINEMODEST EDGE
Edge Analysis
Moneyline
Spaun, J.J. 58.5%
+6.4 pts
Spread
+0.1
+6.4 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 →