Caty McNally vs. Dayana Yastremska prediction, odds, picks for WTA China Open 2026

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

Our famous predictive model gives McNally a 61% chance of beating Yastremska at the WTA China Open tournament. Additionally, McNally has a 59% 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: Dayana Yastremska +152, Caty McNally -179
• First Set: McNally -175, Yastremska +137

All odds are correct at the time of publication and are subject to change.

Model picks and best bet

After matching our probabilities 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: Yastremska @ +152 via DraftKings
• First Set: Yastremska @ +137 via DraftKings

According to our tennis best bets, the top play to make on this match is Dayana Yastremska 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 Caty McNally and Dayana Yastremska is scheduled to start on Tuesday at 10:00 PM ET.

• Who: Caty McNally vs. Dayana Yastremska
• 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 mentioned in this article are United States Eastern Time unless otherwise stated.

Conclusion

We predict Caty McNally, with a 61% win probability, will likely beat Dayana Yastremska 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.