PGA Tour Golf

Thompson, Davis vs Jaeger, Stephan Prediction

May 27, 2026

10,000 Monte Carlo simulations

Thompson, Davis vs Jaeger, Stephan prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Jaeger, Stephan 0 - Thompson, Davis 75. Thompson, Davis is favored with a 59.8% win probability. The spread is 0.03.

Jaeger, Stephan
+0.00
Strokes Gained / Round
VS H2H • Charles Schwab Challenge
Thompson, Davis
+0.43
Strokes Gained / Round
Head-to-Head Win Probability
40.2%
59.8%
Jaeger, StephanThompson, Davis
-128
Best Odds
+6.4%
Edge
1.0u MEDIUM
Sizing
FINALThompson, Davis (T35) def Jaeger, Stephan (WD)

Tournament Context

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

Player Profile — Thompson, Davis

Strokes Gained
+0.43/round
Tour Avg
Course Fit
neutral
+0.020 SG adj
Expected Finish
75th / 132

Matchup Analysis

Thompson, Davis
+0.43 SG
EF 75th
Skill Gap
+0.03 SG/round
essentially a coin flip
Jaeger, Stephan
+0.00 SG
EF 0th · Tour Avg

Edge Breakdown

Our Model
59.8%
Books Say
56.1%
Edge
+6.4%

Thompson, Davis vs Jaeger, Stephan: Model gives Thompson, Davis 59.7% win probability vs 56.1% implied (+6.4% edge). Expected finish: 75.

AI Intelligence Analysis

NEUTRAL +0
Thompson's modest advantage (0.43 SG, 0.025 skill delta) combined with minimal fit (+0.02) yield a compressed 6.6% edge; mid-field variance limits conviction.

Key Factors

  • Model: 59.8% vs 56.1% implied (+6.6% edge)
  • Thompson SG: +0.43 (modest)
  • Skill delta: +0.025 (minimal)
  • Course fit: +0.02 (trivial)

Risk Factors

  • Minimal skill delta (+0.025)
  • Trivial fit (+0.02)
  • 6.6% edge is narrow
COMPRESSED EDGE

Edge Analysis

Moneyline
Thompson, Davis 59.8%
+6.4 pts
Spread
+0.0
+6.4 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 →

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