Vit Kopriva / Filip Pieczonka vs Yannick Hanfmann / Jan-Lennard Struff prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Yannick Hanfmann / Jan-Lennard Struff 0 - Vit Kopriva / Filip Pieczonka 0. Yannick Hanfmann / Jan-Lennard Struff is favored with a 56.2% win probability.
Yannick Hanfmann / Jan-Lennard Struff
1500
Grass Elo
VS
Grass • ATP
Vit Kopriva / Filip Pieczonka
1500
Grass Elo
Match Win Probability
Yannick Hanfmann / Jan-Lennard StruffVit Kopriva / Filip Pieczonka
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.3% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Vit Kopriva / Filip Pieczonka
Yannick Hanfmann / Jan-Lennard Struff
Vit Kopriva / Filip Pieczonka 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%
Vit Kopriva / Filip Pieczonka SPW
65.6%
Above tour avg
Yannick Hanfmann / Jan-Lennard Struff SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Vit Kopriva / Filip Pieczonka ML
+290
Model: 44%
Edge: +18.2%
Yannick Hanfmann / Jan-Lennard Struff ML
-389
Model: 56%
Edge: -23.4%
Model Projection
Vit Kopriva / Filip Pieczonka ML +290 · +18.2% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Yannick Hanfmann / Jan-Lennard Struff has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Yannick Hanfmann / Jan-Lennard Struff 56.2%
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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 →