Polina Kudermetova vs. Janice Tjen prediction, odds, picks for WTA Guangzhou Open 2026

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

Our leading predictive model currently gives Tjen a 57% chance of winning against Kudermetova at the WTA Guangzhou Open tournament.

Latest betting odds

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

• Moneyline: Janice Tjen -172, Polina Kudermetova +146

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 current 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: Kudermetova @ +146 via DraftKings
• First Set: Tjen @ -110 via DraftKings

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

Although Kudermetova 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 Guangzhou Open match between Polina Kudermetova and Janice Tjen is scheduled to commence on Sunday at 11:00 PM ET.

• Who: Polina Kudermetova vs. Janice Tjen
• Date: Sunday, October 25, 2026
• Approx. Time: 11:00 PM ET/8:00 PM PT
• Tournament: WTA Guangzhou, China Women's Singles 2026
• Round: Round of 32

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

Conclusion

We predict Janice Tjen, with a 57% win probability, will likely beat Polina Kudermetova at the WTA Guangzhou 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.