Mouw, William vs Moore, Taylor prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Moore, Taylor 92 - Mouw, William 87. Mouw, William is favored with a 59.1% win probability. The spread is 0.09.
Moore, Taylor
+0.24
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Mouw, William
+0.17
Strokes Gained / Round
Head-to-Head Win Probability
Moore, TaylorMouw, William
-112
Best Odds
+11.8%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Mouw, William
808794
Moore, Taylor
859299
Tournament Context
Event
THE CJ CUP Byron Nelson
Course
TPC Craig Ranch
Field
147 players
Wind
15 mph
Temp
82°F
Conditions
harder (+0.8)
Player Profile — Mouw, William
Strokes Gained
+0.17/round
Tour Avg
Course Fit
neutral
+0.093 SG adj
Expected Finish
87th / 147
Matchup Analysis
Mouw, William
+0.17 SG
EF 87th
Skill Gap
+0.09 SG/round
essentially a coin flip
Moore, Taylor
+0.24 SG
EF 92th · Tour Avg
Edge Breakdown
Our Model
59.1%
Books Say
52.8%
Edge
+11.8%
Mouw, William vs Moore, Taylor: Model gives Mouw, William 59.1% win probability vs 52.8% implied (+11.8% edge). Expected finish: 87.
AI Intelligence Analysis
LEAN +0YELLOW ZONE0.6% WR (n=380)
Mouw's +0.087 skill advantage and +0.093 course fit create +11.2% edge (58.75% vs 52.83%) against weaker Moore; negative-odds structure and low skill gap suggest secondary play.
Key Factors
- Skill advantage: +0.087 SG/round (Mouw slight edge)
- Course fit: +0.093 (Mouw minor advantage)
- SG total: +0.165 (Mouw)
- EF: 86.6 both (very similar finish positions)
- Edge: +11.2% at -112 Pinnacle
Risk Factors
- EF parity (86.6 both) suggests coin-flip
- Skill gap +0.087 is modest
- Negative odds (-112) reduce Kelly sizing
EF PARITYTAIL PLAYERMODEST EDGE
Edge Analysis
Moneyline
Mouw, William 59.1%
+11.8 pts
Spread
+0.1
+11.8 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 →