Nicolas Barrientos / Nuno Borges vs Robert Cash / James Tracy prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Robert Cash / James Tracy 0 - Nicolas Barrientos / Nuno Borges 0. Nicolas Barrientos / Nuno Borges is favored with a 50.8% win probability.
Robert Cash / James Tracy
1500
Grass Elo
VS
Grass • ATP
Nicolas Barrientos / Nuno Borges
1500
Grass Elo
Match Win Probability
Robert Cash / James TracyNicolas Barrientos / Nuno Borges
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)
Nicolas Barrientos / Nuno Borges
Robert Cash / James Tracy
Nicolas Barrientos / Nuno Borges 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%
Nicolas Barrientos / Nuno Borges SPW
65.6%
Above tour avg
Robert Cash / James Tracy SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Nicolas Barrientos / Nuno Borges ML
+164
Model: 51%
Edge: +12.9%
Robert Cash / James Tracy ML
-205
Model: 49%
Edge: -18.0%
Model Projection
Nicolas Barrientos / Nuno Borges ML +164 · +12.9% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Robert Cash / James Tracy has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Nicolas Barrientos / Nuno Borges 50.8%
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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 →