PGA Tour Golf

Kim, Michael vs Harman, Brian Prediction

July 21, 2026

10,000 Monte Carlo simulations

Kim, Michael vs Harman, Brian prediction for July 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Harman, Brian 0 - Kim, Michael 80. Kim, Michael is favored with a 54.3% win probability. The spread is -0.13.

Harman, Brian
+0.00
Strokes Gained / Round
VS H2H • 3M Open
Kim, Michael
+0.41
Strokes Gained / Round
Head-to-Head Win Probability
45.7%
54.3%
Harman, BrianKim, Michael
+105
Best Odds

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
Kim's -0.132 SG/round skill deficit is balanced by his +0.088 course fit advantage; net edge of 10.9% at +105 odds represents decent value in a close matchup.

Key Factors

  • Model edge: +10.9% probability (54.1% vs 48.8%) — narrower edge reflecting close matchup
  • Skill disadvantage: -0.132 SG/round (Kim is worse player), but offset by course fit
  • Course fit: Kim +0.088 vs Harman +0.082 = marginal (0.006) Kim benefit
  • Odds +105 (caesars): implies 48.78%, our model 54.09% = +531 bps edge
  • Expected finish: Kim 79.3 vs Harman 75.7 suggests Harman is stronger player, but matchup is close

Risk Factors

  • Skill deficit means Kim is inherently weaker; course fit alone may not overcome this
  • Harman is more experienced and consistent (higher quality player); could overpower Kim
  • Both players mid-tier quality (75-79 expected finish) — matchup outcome genuinely uncertain
Sharp MoneyWith ModelClose matchup; edge reflects course fit neutralizing Kim's skill deficit

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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