Using trusted machine learning and data, we have re-simulated the outcome of Monday’s Ostapenko-Mertens women’s singles match, forming part of our tennis predictions coverage.
Our famous predictive model gives Mertens a 60% chance of beating Ostapenko at the WTA China Open tournament.
Additionally, Mertens has a 58% chance of winning the first set. The simulations also show Ostapenko (+3.5) has a 53% chance of covering the games spread, while the under 22.5 games has a 55% chance of hitting, according to our model.
Latest betting odds
We have sourced the most up-to-date betting odds in America for this match, which are listed here:
Moneyline: Elise Mertens -177, Jelena Ostapenko +168
First Set: Ostapenko +137, Mertens -175
All odds are correct at the time of publication and are subject to change.
Model picks and best bet
After matching our predictions against the best available odds, we can reveal our picks for each of the major markets in this matchup. Plus, find our best bet for the game below:
Moneyline: Ostapenko @ +168 via DraftKings
First Set: Mertens @ -135 via DraftKings
According to our best tennis bets, the top play to make on this match is Jelena Ostapenko to win.
Although Ostapenko is less than likely to win, she provides the best value at the current available odds.
These picks are based on our probabilities matched against the implied probabilities from the current available odds.
Game info & first serve details
The WTA China Open match between Jelena Ostapenko and Elise Mertens is scheduled to begin on Monday at 4:30 AM ET.
Who: Jelena Ostapenko vs. Elise Mertens
Date: Monday, October 5, 2026
Approx. Time: 4:30 AM ET/1:30AM PT
Tournament: WTA Beijing, China Women’s Singles 2026
Round: Round of 32
All dates and times in this article are United States Eastern Time unless otherwise stated.
Conclusion
We predict Elise Mertens, with a 60% win probability, will likely beat Jelena Ostapenko at the WTA China Open tournament.
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This article’s predictions and probabilities are generated by Dimers’ proprietary Tennis prediction model. Our editorial team reviews the resulting content for accuracy and presentation before publication.