Francisco Cabral / Lucas Miedler vs Diego Hidalgo / Matej Vocel prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Diego Hidalgo / Matej Vocel 0 - Francisco Cabral / Lucas Miedler 0. Diego Hidalgo / Matej Vocel is favored with a 50.6% win probability.
Diego Hidalgo / Matej Vocel
1500
Grass Elo
VS
Grass • ATP
Francisco Cabral / Lucas Miedler
1500
Grass Elo
Match Win Probability
Diego Hidalgo / Matej VocelFrancisco Cabral / Lucas Miedler
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)
Francisco Cabral / Lucas Miedler
Diego Hidalgo / Matej Vocel
Francisco Cabral / Lucas Miedler 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%
Francisco Cabral / Lucas Miedler SPW
65.6%
Above tour avg
Diego Hidalgo / Matej Vocel SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Francisco Cabral / Lucas Miedler ML
-189
Model: 49%
Edge: -16.0%
Diego Hidalgo / Matej Vocel ML
+154
Model: 51%
Edge: +11.3%
Model Projection
Diego Hidalgo / Matej Vocel ML +154 · +11.3% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Diego Hidalgo / Matej Vocel has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Diego Hidalgo / Matej Vocel 50.6%
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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 →