Keefer, Johnny vs Hubbard, Mark prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Hubbard, Mark 50 - Keefer, Johnny 87. Keefer, Johnny is favored with a 59.6% win probability. The spread is 0.12.
Hubbard, Mark
-0.04
Strokes Gained / Round
VS
H2H • Charles Schwab Challenge
Keefer, Johnny
+0.08
Strokes Gained / Round
Head-to-Head Win Probability
Hubbard, MarkKeefer, Johnny
-105
Best Odds
+16.4%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Keefer, Johnny
808794
Hubbard, Mark
435057
Tournament Context
Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)
Player Profile — Keefer, Johnny
Strokes Gained
+0.08/round
Tour Avg
Course Fit
poor
-0.132 SG adj
Expected Finish
87th / 132
Matchup Analysis
Keefer, Johnny
+0.08 SG
EF 87th
Skill Gap
+0.12 SG/round
tight edge for Keefer, Johnny
Hubbard, Mark
-0.04 SG
EF 50th · Below Avg
Edge Breakdown
Our Model
59.6%
Books Say
51.2%
Edge
+16.4%
Keefer, Johnny vs Hubbard, Mark: Model gives Keefer, Johnny 59.6% win probability vs 51.2% implied (+16.4% edge). Skill advantage: +0.12 SG/round. Expected finish: 87.
AI Intelligence Analysis
LEAN +0
Keefer's minimal skill advantage (0.123 SG) combined with negative course fit (−0.132) yields a 16.8% edge that is purely statistical without meaningful fit support.
Key Factors
- Model: 59.8% vs 51.2% implied (+16.8% edge)
- Skill advantage: +0.123 SG (marginal)
- Course fit: −0.132 (negative for Keefer)
- Expected finish: both 86+ (deep field)
Risk Factors
- Negative course fit (−0.132) contradicts edge thesis
- Skill delta (0.123) is minimal
- Deep-field players (EF 86+) = high variance
MARGINAL EDGENO FIT SUPPORT
Edge Analysis
Moneyline
Keefer, Johnny 59.6%
+16.4 pts
Spread
+0.1
+16.4 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 →