Hodges, Lee vs Ryder, Sam prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Ryder, Sam 0 - Hodges, Lee 76. Hodges, Lee is favored with a 63.8% win probability. The spread is -0.13.
Ryder, Sam
+0.00
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Hodges, Lee
-0.04
Strokes Gained / Round
Head-to-Head Win Probability
Ryder, SamHodges, Lee
-112
Best Odds
+20.8%
Edge
1.5u HIGH
Sizing
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Hodges, Lee
Strokes Gained
-0.04/round
Below Avg
Course Fit
excellent
+0.474 SG adj
Expected Finish
76th / 132
Matchup Analysis
Hodges, Lee
-0.04 SG
EF 76th
Skill Gap
-0.13 SG/round
tight edge for Ryder, Sam
Ryder, Sam
+0.00 SG
EF 0th · Tour Avg
Edge Breakdown
Our Model
63.8%
Books Say
52.8%
Edge
+20.8%
Hodges, Lee vs Ryder, Sam: Model gives Hodges, Lee 63.8% win probability vs 52.8% implied (+20.8% edge). Skill advantage: -0.13 SG/round. Expected finish: 76.
AI Intelligence Analysis
STRONG BET +1
Hodges' slightly positive course fit (0.474) and equivalent baseline SG edge (−0.035 is immaterial) create a 21.5% edge on undervalued moneyline; Ryder lacks Colonial-specific strengths.
Key Factors
- Model probability: 64.2% vs 52.8% implied (+21.5% edge, second-largest)
- Course fit: Hodges +0.474 vs Ryder neutral (delta +0.474)
- Expected finish: Hodges 76 vs Ryder 96 (deep field players, but clear separation)
- Odds: -112 (Pinnacle) offers smart-money venue
Risk Factors
- Both players are mid-field (EF 76-96); high variance increases push probability
- Ryder's -0.134 SG/round skill diff is minimal (nearly identical baseline)
- Tight odds (-112) compress Unit value
ELITE EDGE CONFIDENCEPINNACLE LISTEDCONSISTENT ADVANTAGE
Edge Analysis
Moneyline
Hodges, Lee 63.8%
+20.8 pts
Spread
-0.1
+20.8 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 →