PGA Tour Golf

Cole, Eric vs Kim, Michael Prediction

May 27, 2026

10,000 Monte Carlo simulations

Cole, Eric vs Kim, Michael prediction for May 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Kim, Michael 46 - Cole, Eric 69. Cole, Eric is favored with a 59.0% win probability. The spread is -0.02.

Kim, Michael
+0.61
Strokes Gained / Round
VS H2H • Charles Schwab Challenge
Cole, Eric
+0.59
Strokes Gained / Round
Head-to-Head Win Probability
41.0%
59.0%
Kim, MichaelCole, Eric
-110
Best Odds
+12.7%
Edge
1.5u HIGH
Sizing
FINALCole, Eric (2) def Kim, Michael (T17)

Projected Points Range 10th – 90th percentile

Cole, Eric
626976
Kim, Michael
394653

Tournament Context

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

Player Profile — Cole, Eric

Strokes Gained
+0.59/round
Above Avg
Course Fit
neutral
+0.093 SG adj
Expected Finish
69th / 132

Matchup Analysis

Cole, Eric
+0.59 SG
EF 69th
Skill Gap
-0.02 SG/round
essentially a coin flip
Kim, Michael
+0.61 SG
EF 46th · Above Avg

Edge Breakdown

Our Model
59.0%
Books Say
52.4%
Edge
+12.7%

Cole, Eric vs Kim, Michael: Model gives Cole, Eric 59.0% win probability vs 52.4% implied (+12.7% edge). Expected finish: 69.

AI Intelligence Analysis

STRONG BET +1
Cole's elite SG total (0.589, ~0.58 rank) + modest positive fit (0.093) create a 15.1% ELITE-confidence edge; expected finish (69) indicates quality match.

Key Factors

  • Model: 58.9% vs 51.2% implied (+15.1% edge, ELITE confidence marker)
  • Cole SG: +0.589 (solid baseline)
  • Course fit: +0.093 (positive, though modest)
  • Expected finish: Cole 69 vs Kim unknown

Risk Factors

  • Course fit (+0.093) is minimal; skill is primary driver
  • Kim's comp data limited (depth player)
ELITE CONFIDENCESKILL BASED

Edge Analysis

Moneyline
Cole, Eric 59.0%
+12.7 pts
Spread
-0.0
+12.7 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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