Zizou Bergs / Alexander Blockx vs Kevin Krawietz / Tim Puetz prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Kevin Krawietz / Tim Puetz 0 - Zizou Bergs / Alexander Blockx 0. Kevin Krawietz / Tim Puetz is favored with a 51.4% win probability.
Kevin Krawietz / Tim Puetz
1500
Grass Elo
VS
Grass • ATP
Zizou Bergs / Alexander Blockx
1500
Grass Elo
Match Win Probability
Kevin Krawietz / Tim PuetzZizou Bergs / Alexander Blockx
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)
Zizou Bergs / Alexander Blockx
Kevin Krawietz / Tim Puetz
Zizou Bergs / Alexander Blockx 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%
Zizou Bergs / Alexander Blockx SPW
65.6%
Above tour avg
Kevin Krawietz / Tim Puetz SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Zizou Bergs / Alexander Blockx ML
+345
Model: 49%
Edge: +26.1%
Kevin Krawietz / Tim Puetz ML
-464
Model: 51%
Edge: -30.9%
Model Projection
Zizou Bergs / Alexander Blockx ML +345 · +26.1% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Kevin Krawietz / Tim Puetz has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Kevin Krawietz / Tim Puetz 51.4%
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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 →