Eva Lys vs. Nadia Podoroska prediction, odds, picks for WTA Sao Paulo 2026

Using powerful machine learning and data, we have re-simulated the result of Thursday's Lys-Podoroska women's singles match, forming part of our tennis predictions coverage.

Our famous predictive model gives Lys a 63% chance of beating Podoroska at the WTA Sao Paulo tournament. Additionally, Lys has a 61% chance of winning the first set, while the over 20.5 games has a 57% 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: Nadia Podoroska +191, Eva Lys -222
• First Set: Lys -200, Podoroska +162

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: Podoroska @ +191 via DraftKings
• First Set: Podoroska @ -115 via DraftKings

According to our tennis best bets, the top play to make on this match is Nadia Podoroska 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 Sao Paulo match between Eva Lys and Nadia Podoroska is scheduled to begin on Thursday at 4:30 PM ET.

• Who: Eva Lys vs. Nadia Podoroska
• Date: Thursday, September 17, 2026
• Approx. Time: 4:30 PM ET/1:30PM PT
• Tournament: WTA Sao Paulo, Brazil Women's, Singles 2026
• Round: Round of 16

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

Conclusion

We predict Eva Lys, with a 63% win probability, will likely beat Nadia Podoroska 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.