Katarzyna Kawa vs. Maja Chwalinska prediction, odds, picks for WTA Ningbo Open 2026

Using trusted machine learning and data analysis, we have re-simulated the outcome of Monday's Kawa-Chwalinska women's singles match, forming part of our tennis predictions coverage.

Our famous predictive model gives Chwalinska a 76% chance of beating Kawa at the WTA Ningbo Open tournament. Additionally, Chwalinska has a 72% chance of winning the first set, 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: Maja Chwalinska -357, Katarzyna Kawa +275
• First Set: Kawa +240, Chwalinska -303

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: Chwalinska @ -357 via DraftKings
• First Set: Kawa @ +240 via DraftKings

According to our tennis best bets, the top play to make on this match is Maja Chwalinska 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 Ningbo Open match between Katarzyna Kawa and Maja Chwalinska is scheduled to start on Monday at 10:00 PM ET.

• Who: Katarzyna Kawa vs. Maja Chwalinska
• Date: Monday, October 19, 2026
• Approx. Time: 10:00 PM ET/7:00PM PT
• Tournament: WTA Ningbo, China Women's Singles 2026
• Round: Round of 32

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

Conclusion

We predict Maja Chwalinska, with a 76% win probability, will likely beat Katarzyna Kawa at the WTA Ningbo 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.