ATP/WTA Tennis

Sadio Doumbia / Fabien Reboul vs Benjamin Winter Lopez / Pablo Llamas Ruiz Prediction

April 30, 2026

10,000 Monte Carlo simulations

Sadio Doumbia / Fabien Reboul vs Benjamin Winter Lopez / Pablo Llamas Ruiz prediction for April 30, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Benjamin Winter Lopez / Pablo Llamas Ruiz 0 - Sadio Doumbia / Fabien Reboul 0. Benjamin Winter Lopez / Pablo Llamas Ruiz is favored with a 52.0% win probability.

Benjamin Winter Lopez / Pablo Llamas Ruiz
1500
Clay Elo
VS Clay • ATP
Sadio Doumbia / Fabien Reboul
1500
Clay Elo
Match Win Probability
52.0%
48.0%
Benjamin Winter Lopez / Pablo Llamas RuizSadio Doumbia / Fabien Reboul
Clay
Surface
ATP Madrid - Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 58.4% (1,363 games)

Match Context

Tournament
ATP Madrid - Doubles
Surface
Clay
Format
Best of 3 · ATP

Surface Elo Ratings (Clay)

Sadio Doumbia / Fabien Reboul
1500
Benjamin Winter Lopez / Pablo Llamas Ruiz
1500
Sadio Doumbia / Fabien Reboul leads by 0 Elo points on Clay

Serve & Return Analysis

Serve Points Won % (SPW) is the single most predictive metric in tennis. ATP average on Clay: 63.5%

Sadio Doumbia / Fabien Reboul SPW
60.3%
Below tour avg
Benjamin Winter Lopez / Pablo Llamas Ruiz SPW
60.3%
Below tour avg
● Serve statistics are nearly identical — expect a close match

Market Odds & Model Edge

Sadio Doumbia / Fabien Reboul ML
-555
Model: 48%
Edge: -36.8%
Benjamin Winter Lopez / Pablo Llamas Ruiz ML
+388
Model: 52%
Edge: +31.5%
Model Projection
Benjamin Winter Lopez / Pablo Llamas Ruiz ML +388 · +31.5% edge

Key Matchup Factors

Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP

Edge Analysis

Moneyline
Benjamin Winter Lopez / Pablo Llamas Ruiz 52.0%
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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 →

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