Hossler, Beau vs Putnam, Andrew prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Putnam, Andrew 82 - Hossler, Beau 90. Hossler, Beau is favored with a 53.6% win probability. The spread is -0.37.
Putnam, Andrew
+0.43
Strokes Gained / Round
VS
H2H • John Deere Classic
Hossler, Beau
+0.06
Strokes Gained / Round
Head-to-Head Win Probability
Putnam, AndrewHossler, Beau
+105
Best Odds
+9.9%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Hossler, Beau
839097
Putnam, Andrew
758289
Tournament Context
Event
John Deere Classic
Course
TPC Deere Run
Field
144 players
Wind
14 mph
Temp
92°F
Conditions
harder (+0.8)
Player Profile — Hossler, Beau
Strokes Gained
+0.06/round
Tour Avg
Course Fit
neutral
+0.066 SG adj
Expected Finish
90th / 144
Matchup Analysis
Hossler, Beau
+0.06 SG
EF 90th
Skill Gap
-0.37 SG/round
tight edge for Putnam, Andrew
Putnam, Andrew
+0.43 SG
EF 82th · Tour Avg
Edge Breakdown
Our Model
53.6%
Books Say
48.8%
Edge
+9.9%
Hossler, Beau vs Putnam, Andrew: Model gives Hossler, Beau 53.6% win probability vs 48.8% implied (+9.9% edge). Skill advantage: -0.37 SG/round. Expected finish: 90.
AI Intelligence Analysis
LEAN +0
Hossler's course fit (+0.066) and near-parity skill (-0.371 gap but Hossler +0.062 SG total) generate 53.8% edge; +10.2% is marginal after calibration.
Key Factors
- Model prob 53.8% vs market 48.8% (+10.2% edge)
- Odds: +105 (Bovada) generates value at 2.05 decimal
- Skill gap -0.371 is massive disadvantage (Hossler SG 0.062, Putnam SG 0.43)
- Expected finish: Hossler 90.4 places him in contention
Risk Factors
- HUGE skill gap (-0.371) works against Hossler; course fit (+0.066) may not compensate
- Putnam's skill (0.43 SG) is significantly higher; market may be correctly priced
- 10.2% edge with skill headwind = lean conviction, not bet
Edge Analysis
Moneyline
Hossler, Beau 53.6%
+9.9 pts
Spread
-0.4
+9.9 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. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →