PGA Tour Golf

McCarty, Matt vs Thompson, Davis Prediction

May 27, 2026

10,000 Monte Carlo simulations

McCarty, Matt vs Thompson, Davis prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Thompson, Davis 0 - McCarty, Matt 70. McCarty, Matt is favored with a 56.8% win probability. The spread is 0.32.

Thompson, Davis
+0.00
Strokes Gained / Round
VS H2H • Charles Schwab Challenge
McCarty, Matt
+0.74
Strokes Gained / Round
Head-to-Head Win Probability
43.2%
56.8%
Thompson, DavisMcCarty, Matt
-105
Best Odds
+7.9%
Edge
1.0u MEDIUM
Sizing
FINALThompson, Davis (T35) def McCarty, Matt (T60)

Tournament Context

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

Player Profile — McCarty, Matt

Strokes Gained
+0.74/round
Above Avg
Course Fit
poor
-0.105 SG adj
Expected Finish
70th / 132

Matchup Analysis

McCarty, Matt
+0.74 SG
EF 70th
Skill Gap
+0.32 SG/round
tight edge for McCarty, Matt
Thompson, Davis
+0.00 SG
EF 0th · Tour Avg

Edge Breakdown

Our Model
56.8%
Books Say
51.2%
Edge
+7.9%

McCarty, Matt vs Thompson, Davis: Model gives McCarty, Matt 56.8% win probability vs 51.2% implied (+10.9% edge). Skill advantage: +0.32 SG/round. Expected finish: 70. AI: poor recent form; poor course history.

AI Intelligence Analysis

LEAN +0
McCarty's solid SG (0.745) + skill advantage (+0.321 SG vs Thompson) overcome negative fit (−0.105) to yield 11.6% edge; similar to MCCARTY_HOEY but tighter odds.

Key Factors

  • Model: 57.2% vs 51.2% implied (+11.6% edge)
  • McCarty SG: +0.745 (solid)
  • Skill advantage: +0.321 (significant)
  • Course fit: −0.105 (negative)

Risk Factors

  • Negative fit (−0.105) is headwind
  • Thompson's comp data limited (depth player)
  • Narrower edge (11.6%) vs Hoey matchup
SKILL DRIVEN

Edge Analysis

Moneyline
McCarty, Matt 56.8%
+7.9 pts
Spread
+0.3
+7.9 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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