Taylah Preston vs. Tamara Korpatsch prediction, odds, picks for WTA China Open 2026

Using powerful machine learning and data, we have re-simulated the outcome of Tuesday's Preston-Korpatsch women's singles match, forming part of our tennis predictions coverage.

Our famous predictive model gives Preston a 68% chance of winning against Korpatsch at the WTA China Open tournament. Additionally, Preston has a 65% 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: Tamara Korpatsch +200, Taylah Preston -225
• First Set: Preston -227, Korpatsch +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: Preston @ -225 via DraftKings
• First Set: Korpatsch @ +175 via DraftKings

According to our tennis best bets, the top play to make on this match is Taylah Preston 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 Taylah Preston and Tamara Korpatsch is scheduled to commence on Tuesday at 10:00 PM ET.

• Who: Taylah Preston vs. Tamara Korpatsch
• Date: Tuesday, September 29, 2026
• Approx. Time: 10:00 PM ET/7:00PM PT
• Tournament: WTA Beijing, China Women's Singles 2026
• Round: Round of 128

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

Conclusion

We predict Taylah Preston, with a 68% win probability, will likely beat Tamara Korpatsch 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.