Benjamin Bonzi / Arthur Rinderknech vs James Duckworth / Miomir Kecmanovic prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects James Duckworth / Miomir Kecmanovic 0 - Benjamin Bonzi / Arthur Rinderknech 0. James Duckworth / Miomir Kecmanovic is favored with a 50.5% win probability.
James Duckworth / Miomir Kecmanovic
1500
Grass Elo
VS
Grass • ATP
Benjamin Bonzi / Arthur Rinderknech
1500
Grass Elo
Match Win Probability
James Duckworth / Miomir KecmanovicBenjamin Bonzi / Arthur Rinderknech
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.0% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Benjamin Bonzi / Arthur Rinderknech
James Duckworth / Miomir Kecmanovic
Benjamin Bonzi / Arthur Rinderknech leads by 0 Elo points on Grass
Serve & Return Analysis
Serve Points Won % (SPW) is the single most predictive metric in tennis. ATP average on Grass: 63.5%
Benjamin Bonzi / Arthur Rinderknech SPW
65.6%
Above tour avg
James Duckworth / Miomir Kecmanovic SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Benjamin Bonzi / Arthur Rinderknech ML
-189
Model: 50%
Edge: -15.9%
James Duckworth / Miomir Kecmanovic ML
+151
Model: 50%
Edge: +10.7%
Model Projection
James Duckworth / Miomir Kecmanovic ML +151 · +10.7% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- James Duckworth / Miomir Kecmanovic has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
James Duckworth / Miomir Kecmanovic 50.5%
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More Projections Today
How this prediction was generated: This page shows output from the Olympus Bets ATP/WTA Tennis 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 →