Paula Badosa vs. Antonia Ruzic prediction, odds, picks for WTA Japan Open 2026

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

Our famous predictive model gives Badosa a 72% chance of winning against Ruzic at the WTA Japan Open tournament. Additionally, Badosa has a 68% 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: Antonia Ruzic +220, Paula Badosa -278
• First Set: Badosa -227, Ruzic +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 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: Badosa @ -278 via DraftKings
• First Set: Badosa @ -227 via DraftKings

According to our tennis best bets, the top play to make on this match is Paula Badosa 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 Japan Open match between Paula Badosa and Antonia Ruzic is scheduled to commence on Sunday at 10:30 PM ET.

• Who: Paula Badosa vs. Antonia Ruzic
• Date: Sunday, October 18, 2026
• Approx. Time: 10:30 PM ET/7:30PM PT
• Tournament: WTA Osaka, Japan 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 Paula Badosa, with a 72% win probability, will likely beat Antonia Ruzic at the WTA Japan 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.