Iva Jovic vs. Iga Swiatek prediction, odds, picks for WTA China Open 2026

Using innovative machine learning and data analysis, we have re-simulated the result of Tuesday's Jovic-Swiatek women's singles match, forming part of our tennis predictions coverage.

Our independent predictive model gives Swiatek a 65% chance of defeating Jovic at the WTA China Open tournament. Additionally, Swiatek has a 61% chance of winning the first set, while the over 20.5 games has a 53% chance of hitting, 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: Iga Swiatek -208, Iva Jovic +175
• First Set: Jovic +150, Swiatek -189

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: Jovic @ +175 via DraftKings
• First Set: Swiatek @ -118 via DraftKings

According to our tennis best bets, the top play to make on this match is Iva Jovic 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 Iva Jovic and Iga Swiatek is scheduled to start on Tuesday at 11:00 PM ET.

• Who: Iva Jovic vs. Iga Swiatek
• Date: Tuesday, October 6, 2026
• Approx. Time: 11:00 PM ET/8:00PM PT
• Tournament: WTA Beijing, China Women's Singles 2026
• Round: Round of 16

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

Conclusion

We predict Iga Swiatek, with a 65% win probability, will likely beat Iva Jovic 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.