Andrey Golubev / Aleksandr Nedovyesov vs Jesper De Jong / Valentin Royer prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Jesper De Jong / Valentin Royer 0 - Andrey Golubev / Aleksandr Nedovyesov 0. Andrey Golubev / Aleksandr Nedovyesov is favored with a 51.3% win probability.
Jesper De Jong / Valentin Royer
1500
Grass Elo
VS
Grass • ATP
Andrey Golubev / Aleksandr Nedovyesov
1500
Grass Elo
Match Win Probability
Jesper De Jong / Valentin RoyerAndrey Golubev / Aleksandr Nedovyesov
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)
Andrey Golubev / Aleksandr Nedovyesov
Jesper De Jong / Valentin Royer
Andrey Golubev / Aleksandr Nedovyesov 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%
Andrey Golubev / Aleksandr Nedovyesov SPW
66.4%
Above tour avg
Jesper De Jong / Valentin Royer SPW
66.3%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Andrey Golubev / Aleksandr Nedovyesov ML
+139
Model: 51%
Edge: +9.5%
Jesper De Jong / Valentin Royer ML
-172
Model: 49%
Edge: -14.5%
Model Projection
Andrey Golubev / Aleksandr Nedovyesov ML +139 · +9.5% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Andrey Golubev / Aleksandr Nedovyesov has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Andrey Golubev / Aleksandr Nedovyesov 51.3%
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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 →