PGA Tour Golf

Thompson, Davis vs Greyserman, Max Prediction

July 21, 2026

10,000 Monte Carlo simulations

Thompson, Davis vs Greyserman, Max prediction for July 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Greyserman, Max 87 - Thompson, Davis 84. Thompson, Davis is favored with a 57.2% win probability. The spread is 0.04.

Greyserman, Max
+0.33
Strokes Gained / Round
VS H2H • 3M Open
Thompson, Davis
+0.30
Strokes Gained / Round
Head-to-Head Win Probability
42.8%
57.2%
Greyserman, MaxThompson, Davis
-105
Best Odds
FINALThompson, Davis (T28) def Greyserman, Max (T41)

Projected Points Range 10th – 90th percentile

Thompson, Davis
778491
Greyserman, Max
808794

Tournament Context

Event
3M Open
Course
TPC Twin Cities
Field
144 players
Wind
12 mph
Temp
81°F
Conditions
harder (+0.6)

AI Intelligence Analysis

STRONG BET +0
Thompson's modest +0.035 SG advantage and +0.023 course fit provide 11.9% edge at -105 odds; edge is real but smaller, reflecting two similar-quality players.

Key Factors

  • Model edge: +11.9% probability (57.3% vs 51.2%)
  • Skill advantage: +0.035 SG/round (small but consistent advantage)
  • Course fit: Thompson +0.023 vs Greyserman appears neutral = marginal benefit
  • Odds -105 (caesars): implies 51.22%, our model 57.30% = +608 bps edge
  • Expected finish: Thompson 84.1 (within same tier as Greyserman)

Risk Factors

  • Skill and course fit advantages are both small; variance in matchup outcomes higher
  • Both mid-field quality players (84-85 expected finish) suggests competitive matchup
  • Thompson's SG profile (0.305 total) is mid-pack; no standout strengths
Sharp MoneyWith ModelBalanced edge; both players comparable quality but Thompson has slight edge

Edge Analysis

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Edge Analysis
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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. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →

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