Sascha Gueymard Wayenburg vs Roberto Bautista Agut prediction for April 29, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Roberto Bautista Agut 0 - Sascha Gueymard Wayenburg 0. Sascha Gueymard Wayenburg is favored with a 68.2% win probability.
Roberto Bautista Agut
1431
Hard Elo
VS
Hard • ATP
Sascha Gueymard Wayenburg
1515
Hard Elo
Match Win Probability
Roberto Bautista AgutSascha Gueymard Wayenburg
Hard
Surface
ATP Challenger Aix en Provence
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 59.4% (1,330 games)
Match Context
Tournament
ATP Challenger Aix en Provence
Surface
Hard
Format
Best of 3 · ATP
Surface Elo Ratings (Hard)
Sascha Gueymard Wayenburg
Roberto Bautista Agut
Sascha Gueymard Wayenburg leads by 84 Elo points on Hard
Serve & Return Analysis
Serve Points Won % (SPW) is the single most predictive metric in tennis. ATP average on Hard: 63.5%
Sascha Gueymard Wayenburg SPW
64.0%
Above tour avg
Roberto Bautista Agut SPW
60.6%
Below tour avg
● Sascha Gueymard Wayenburg has a significant serve advantage (+3.4%)
Market Odds & Model Edge
Sascha Gueymard Wayenburg ML
+162
Model: 68%
Edge: +30.0%
Roberto Bautista Agut ML
-191
Model: 32%
Edge: -33.8%
Model Projection
Sascha Gueymard Wayenburg ML +162 · +30.0% edge
Key Matchup Factors
- Sascha Gueymard Wayenburg has a moderate 84-point Elo edge on Hard
- Hard court provides a neutral surface — favors all-court players
- Sascha Gueymard Wayenburg has the stronger serve profile on this surface
- Heavy favorite (Sascha Gueymard Wayenburg at 68%) — ML value may be limited; consider live or set markets
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Sascha Gueymard Wayenburg 68.2%
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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. Full methodology →