Ben Jones / Joshua Paris vs Simone Bolelli / Andrea Vavassori prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Simone Bolelli / Andrea Vavassori 0 - Ben Jones / Joshua Paris 0. Simone Bolelli / Andrea Vavassori is favored with a 57.0% win probability.
Simone Bolelli / Andrea Vavassori
1500
Grass Elo
VS
Grass • ATP
Ben Jones / Joshua Paris
1500
Grass Elo
Match Win Probability
Simone Bolelli / Andrea VavassoriBen Jones / Joshua Paris
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.4% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Ben Jones / Joshua Paris
Simone Bolelli / Andrea Vavassori
Ben Jones / Joshua Paris 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%
Ben Jones / Joshua Paris SPW
65.6%
Above tour avg
Simone Bolelli / Andrea Vavassori SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Ben Jones / Joshua Paris ML
+405
Model: 43%
Edge: +23.2%
Simone Bolelli / Andrea Vavassori ML
-565
Model: 57%
Edge: -27.9%
Model Projection
Ben Jones / Joshua Paris ML +405 · +23.2% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Simone Bolelli / Andrea Vavassori has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Simone Bolelli / Andrea Vavassori 57.0%
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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 →