Katerina Siniakova / Taylor Townsend vs Aleksandra Krunic / Kristina Mladenovic prediction for May 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Aleksandra Krunic / Kristina Mladenovic 0 - Katerina Siniakova / Taylor Townsend 0. Katerina Siniakova / Taylor Townsend is favored with a 51.8% win probability.
Aleksandra Krunic / Kristina Mladenovic
1500
Clay Elo
VS
Clay • WTA
Katerina Siniakova / Taylor Townsend
1500
Clay Elo
Match Win Probability
Aleksandra Krunic / Kristina MladenovicKaterina Siniakova / Taylor Townsend
Clay
Surface
WTA Madrid - Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 58.4% (1,419 games)
Match Context
Tournament
WTA Madrid - Doubles
Surface
Clay
Format
Best of 3 · WTA
Surface Elo Ratings (Clay)
Katerina Siniakova / Taylor Townsend
Aleksandra Krunic / Kristina Mladenovic
Katerina Siniakova / Taylor Townsend leads by 0 Elo points on Clay
Serve & Return Analysis
Serve Points Won % (SPW) is the single most predictive metric in tennis. WTA average on Clay: 56.5%
Katerina Siniakova / Taylor Townsend SPW
56.2%
Below tour avg
Aleksandra Krunic / Kristina Mladenovic SPW
56.2%
Below tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Katerina Siniakova / Taylor Townsend ML
-552
Model: 52%
Edge: -32.9%
Aleksandra Krunic / Kristina Mladenovic ML
+398
Model: 48%
Edge: +28.1%
Model Projection
Aleksandra Krunic / Kristina Mladenovic ML +398 · +28.1% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Clay surface reduces serve dominance — expect more breaks of serve and longer rallies
- Aleksandra Krunic / Kristina Mladenovic has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · WTA
Edge Analysis
Moneyline
Katerina Siniakova / Taylor Townsend 51.8%
--
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. Full methodology →