Clara Tauson vs. Polina Kudermetova prediction, odds, picks for WTA China Open 2026

Using powerful machine learning and data, we have re-simulated the result of Thursday's Tauson-Kudermetova women's singles match, forming part of our tennis predictions coverage.

Our famous predictive model currently gives Tauson a 73% chance of defeating Kudermetova at the WTA China Open tournament. Additionally, Tauson has a 69% chance of winning the first set, according to our model.

Latest betting odds

We have researched the most up-to-date betting odds in America for this match, which are listed here:

• Moneyline: Polina Kudermetova +240, Clara Tauson -268
• First Set: Tauson -250, Kudermetova +200

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: Tauson @ -268 via DraftKings
• First Set: Tauson @ -250 via DraftKings

According to our tennis best bets, the top play to make on this match is Clara Tauson to win.

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 Clara Tauson and Polina Kudermetova is scheduled to start on Thursday at 11:00 PM ET.

• Who: Clara Tauson vs. Polina Kudermetova
• Date: Thursday, October 1, 2026
• Approx. Time: 11:00 PM ET/8:00PM PT
• Tournament: WTA Beijing, China Women's Singles 2026
• Round: Round of 64

All dates and times mentioned in this article are United States Eastern Time unless otherwise noted.

Conclusion

We predict Clara Tauson, with a 73% win probability, will likely beat Polina Kudermetova at the WTA China Open tournament.

Additional tools and information for betting safely can be found in our Responsible Gambling hub.

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.