Justina Mikulskyte vs. Paula Badosa prediction, odds, picks for WTA Sao Paulo 2026

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

Our independent predictive model gives Badosa a 92% chance of winning against Mikulskyte at the WTA Sao Paulo tournament. Additionally, Badosa has an 86% chance of winning the first set, while the **over/under of 17.5 games also has an equal 50-50 chance of hitting, 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: Paula Badosa -2500, Justina Mikulskyte +1200
• First Set: Mikulskyte +700, Badosa -1250

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: Mikulskyte @ +1200 via DraftKings
• First Set: Badosa @ -115 via DraftKings

According to our tennis best bets, the top play to make on this match is over 17.5 total games.

These picks are based on our probabilities matched against the implied probabilities from the current available odds.

Game info & first serve details

The WTA Sao Paulo match between Justina Mikulskyte and Paula Badosa is scheduled to begin on Tuesday at 4:30 PM ET.

• Who: Justina Mikulskyte vs. Paula Badosa
• Date: Tuesday, September 15, 2026
• Approx. Time: 4:30 PM ET/1:30PM PT
• Tournament: WTA Sao Paulo, Brazil Women's, Singles 2026
• Round: Round of 32

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

Conclusion

We predict Paula Badosa, with a 92% win probability, will likely beat Justina Mikulskyte at the WTA Sao Paulo 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.