Belinda Bencic vs. Shuai Zhang prediction, odds, picks for WTA Wuhan Open 2026

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

Our famous predictive model currently gives Bencic a 79% chance of defeating Zhang at the WTA Wuhan Open tournament. Additionally, Bencic has a 74% chance of winning the first set, while the over 19.5 games has a 56% 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: Shuai Zhang +365, Belinda Bencic -415
• First Set: Bencic -400, Zhang +300

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: Zhang @ +365 via DraftKings
• First Set: Zhang @ -105 via DraftKings

According to our tennis best bets, the top play to make on this match is Shuai Zhang to win the first set.

These picks are based on our probabilities matched against the implied probabilities from the current available odds.

Game info & first serve details

The WTA Wuhan Open match between Belinda Bencic and Shuai Zhang is scheduled to begin on Sunday at 10:00 PM ET.

• Who: Belinda Bencic vs. Shuai Zhang
• Date: Sunday, October 11, 2026
• Approx. Time: 10:00 PM ET/7:00PM PT
• Tournament: WTA Wuhan, China Women's Singles 2026
• Round: Round of 64

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

Conclusion

We predict Belinda Bencic, with a 79% win probability, will likely beat Shuai Zhang at the WTA Wuhan 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.