Eri Hozumi / Fang-Hsien Wu vs Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah prediction for April 29, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah 0 - Eri Hozumi / Fang-Hsien Wu 0. Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah is favored with a 52.1% win probability.
Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah
1500
Hard Elo
VS
Hard • WTA
Eri Hozumi / Fang-Hsien Wu
1500
Hard Elo
Match Win Probability
Elsa Jacquemot / Tiantsoa Rakotomanga RajaonahEri Hozumi / Fang-Hsien Wu
Hard
Surface
WTA 125k Saint Malo - Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 57.9% (1,374 games)
Match Context
Tournament
WTA 125k Saint Malo - Doubles
Surface
Hard
Format
Best of 3 · WTA
Surface Elo Ratings (Hard)
Eri Hozumi / Fang-Hsien Wu
Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah
Eri Hozumi / Fang-Hsien Wu leads by 0 Elo points on Hard
Serve & Return Analysis
Serve Points Won % (SPW) is the single most predictive metric in tennis. WTA average on Hard: 56.5%
Eri Hozumi / Fang-Hsien Wu SPW
57.7%
Above tour avg
Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah SPW
57.7%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Eri Hozumi / Fang-Hsien Wu ML
-260
Model: 48%
Edge: -24.4%
Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah ML
+188
Model: 52%
Edge: +17.4%
Model Projection
Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah ML +188 · +17.4% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Hard court provides a neutral surface — favors all-court players
- Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · WTA
Edge Analysis
Moneyline
Elsa Jacquemot / Tiantsoa Rakotomanga Rajaonah 52.1%
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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 →