Karolina Muchova vs. Katie Boulter prediction, odds, picks for WTA China Open 2026

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

Our independent predictive model gives Muchova an 85% chance of defeating Boulter at the WTA China Open tournament. Additionally, Muchova has an 80% 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: Katie Boulter +580, Karolina Muchova -800
• First Set: Muchova -625, Boulter +400

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: Boulter @ +580 via DraftKings
• First Set: Boulter @ +400 via DraftKings

According to our best tennis bets, the top play to make on this match is Katie Boulter 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 China Open match between Karolina Muchova and Katie Boulter is scheduled to start on Thursday at 11:00 PM ET.

• Who: Karolina Muchova vs. Katie Boulter
• 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 stated.

Conclusion

We predict Karolina Muchova, with an 85% win probability, will likely beat Katie Boulter 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.