PGA Tour Golf

Hoge, Tom vs Hughes, Mackenzie Prediction

May 27, 2026

10,000 Monte Carlo simulations

Hoge, Tom vs Hughes, Mackenzie prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Hughes, Mackenzie 0 - Hoge, Tom 82. Hoge, Tom is favored with a 59.7% win probability. The spread is -0.05.

Hughes, Mackenzie
+0.00
Strokes Gained / Round
VS H2H • Charles Schwab Challenge
Hoge, Tom
-0.07
Strokes Gained / Round
Head-to-Head Win Probability
40.3%
59.7%
Hughes, MackenzieHoge, Tom
-115
Best Odds
+11.6%
Edge
1.5u HIGH
Sizing
FINALHughes, Mackenzie (T10) def Hoge, Tom (T71)

Tournament Context

Event
Charles Schwab Challenge
Course
Colonial CC
Field
132 players
Wind
10 mph
Temp
86°F
Conditions
harder (+0.4)

Player Profile — Hoge, Tom

Strokes Gained
-0.07/round
Below Avg
Course Fit
good
+0.201 SG adj
Expected Finish
82th / 132

Matchup Analysis

Hoge, Tom
-0.07 SG
EF 82th
Skill Gap
-0.05 SG/round
essentially a coin flip
Hughes, Mackenzie
+0.00 SG
EF 0th · Tour Avg

Edge Breakdown

Our Model
59.7%
Books Say
53.5%
Edge
+11.6%

Hoge, Tom vs Hughes, Mackenzie: Model gives Hoge, Tom 59.7% win probability vs 53.5% implied (+11.6% edge). Expected finish: 82.

AI Intelligence Analysis

LEAN +0
Hoge's minimal SG (−0.065) + neutral fit (+0.201) yield an 11.8% edge that is entirely statistical without clear skill or fit support; mid-field variance dominates.

Key Factors

  • Model: 59.8% vs 53.5% implied (+11.8% edge)
  • Hoge SG: −0.065 (below-field baseline)
  • Course fit: +0.201 (positive but modest)
  • Expected finish: Hoge 82 (mid-field)

Risk Factors

  • Hoge's negative SG (−0.065) is a red flag
  • Course fit (+0.201) is marginal support
  • Mid-field variance (EF 82) is elevated
WEAK THESIS

Edge Analysis

Moneyline
Hoge, Tom 59.7%
+11.6 pts
Spread
-0.1
+11.6 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 →

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