Blanchet, Chandler vs Higgo, Garrick prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Higgo, Garrick 89 - Blanchet, Chandler 93. Blanchet, Chandler is favored with a 63.3% win probability. The spread is 0.41.
Higgo, Garrick
-0.23
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Blanchet, Chandler
+0.03
Strokes Gained / Round
Head-to-Head Win Probability
Higgo, GarrickBlanchet, Chandler
-134
Best Odds
+10.6%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Blanchet, Chandler
8693100
Higgo, Garrick
828996
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 — Blanchet, Chandler
Strokes Gained
+0.03/round
Tour Avg
Course Fit
poor
-0.003 SG adj
Expected Finish
93th / 147
Matchup Analysis
Blanchet, Chandler
+0.03 SG
EF 93th
Skill Gap
+0.41 SG/round
meaningful edge for Blanchet, Chandler
Higgo, Garrick
-0.23 SG
EF 89th · Below Avg
Edge Breakdown
Our Model
63.3%
Books Say
57.3%
Edge
+10.6%
Blanchet, Chandler vs Higgo, Garrick: Model gives Blanchet, Chandler 63.3% win probability vs 57.3% implied (+10.6% edge). Skill advantage: +0.41 SG/round. Expected finish: 93.
AI Intelligence Analysis
STRONG BET +1GREEN ZONE0.6% WR (n=380)
Blanchet's +0.412 skill advantage and +0.033 course fit create solid +11.0% edge (63.56% vs 57.26%); skill gap dominates with solid odds at -134.
Key Factors
- Skill advantage: +0.412 SG/round (Blanchet substantially better)
- SG total: +0.033 (marginal)
- EF: 93.9 vs Higgo implied ~100+ (tail players)
- Edge: +11.0% at -134 Pinnacle
- Skill gap is primary driver
Risk Factors
- Tail player matchup (EF ~94+); high variance
- Negative odds (-134) are expensive for tail edge
- Higgo can compete; not a lock
SKILL EDGETAIL PLAYER
Edge Analysis
Moneyline
Blanchet, Chandler 63.3%
+10.6 pts
Spread
+0.4
+10.6 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 →