PGA Tour Golf

Putnam, Andrew vs Glover, Lucas Prediction

July 21, 2026

10,000 Monte Carlo simulations

Putnam, Andrew vs Glover, Lucas prediction for July 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Glover, Lucas 101 - Putnam, Andrew 82. Putnam, Andrew is favored with a 62.8% win probability. The spread is 0.22.

Glover, Lucas
+0.05
Strokes Gained / Round
VS H2H • 3M Open
Putnam, Andrew
+0.36
Strokes Gained / Round
Head-to-Head Win Probability
37.2%
62.8%
Glover, LucasPutnam, Andrew
-125
Best Odds
FINALGlover, Lucas (T5) def Putnam, Andrew (T41)

Projected Points Range 10th – 90th percentile

Putnam, Andrew
758289
Glover, Lucas
94101108

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 +1
Putnam's +0.221 SG/round skill advantage should generate roughly 1-2 strokes over 72 holes; market odds (-125) don't fully capture this advantage, giving us a clean 12.9% edge.

Key Factors

  • Model edge: +12.9% probability (62.7% vs 55.6%) — third-largest edge
  • Skill advantage: +0.221 SG/round is meaningful (translates to ~0.7-0.9 stroke edge per round)
  • Course fit: Putnam +0.053 vs Glover neutral = small structural advantage
  • Odds -125 (fanduel): implies 55.56%, our model 62.70% = +714 bps edge
  • Expected finish: Putnam 81.9 (10-stroke better) validates skill difference

Risk Factors

  • Glover is streaky (high variance player); could play above form on any given week
  • Both expected finishes in 80-82 range suggests mid-pack, higher-variance matchup
  • Putnam's weak OTT (-0.59 SG) could be liability if course rewards bombers
Sharp MoneyWith ModelSkill gap is real; market has not fully priced Putnam's advantage

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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