Marcos Giron / Alejandro Tabilo vs Adam Pavlasek / Patrik Rikl prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Adam Pavlasek / Patrik Rikl 0 - Marcos Giron / Alejandro Tabilo 0. Adam Pavlasek / Patrik Rikl is favored with a 56.8% win probability.
Adam Pavlasek / Patrik Rikl
1500
Grass Elo
VS
Grass • ATP
Marcos Giron / Alejandro Tabilo
1500
Grass Elo
Match Win Probability
Adam Pavlasek / Patrik RiklMarcos Giron / Alejandro Tabilo
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)
Marcos Giron / Alejandro Tabilo
Adam Pavlasek / Patrik Rikl
Marcos Giron / Alejandro Tabilo 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%
Marcos Giron / Alejandro Tabilo SPW
65.6%
Above tour avg
Adam Pavlasek / Patrik Rikl SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Marcos Giron / Alejandro Tabilo ML
+376
Model: 43%
Edge: +22.2%
Adam Pavlasek / Patrik Rikl ML
-516
Model: 57%
Edge: -27.0%
Model Projection
Marcos Giron / Alejandro Tabilo ML +376 · +22.2% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Adam Pavlasek / Patrik Rikl has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Adam Pavlasek / Patrik Rikl 56.8%
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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 →