Peyton Stearns vs. Sloane Stephens prediction, odds, picks for WTA Guadalajara Open 2026

Using innovative machine learning and data, we have re-simulated the outcome of Thursday's Stearns-Stephens women's singles match, forming part of our tennis predictions coverage.

Our independent predictive model currently gives Stearns a 59% chance of defeating Stephens at the WTA Guadalajara Open tournament.

Latest betting odds

We have sourced the most up-to-date betting odds in America for this match, which are listed here:

• Moneyline: Sloane Stephens +148, Peyton Stearns -174

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: Stephens @ +148 via DraftKings
• First Set: Stephens @ -115 via DraftKings

According to our best tennis bets, the top play to make on this match is Sloane Stephens to win.

Although Stephens is less than likely to win, she provides the best value at the current available odds.

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

Game info & first serve details

The WTA Guadalajara Open match between Peyton Stearns and Sloane Stephens is scheduled to commence on Thursday at 2:00 PM ET.

• Who: Peyton Stearns vs. Sloane Stephens
• Date: Thursday, September 17, 2026
• Approx. Time: 2:00 PM ET/11:00AM PT
• Tournament: WTA Guadalajara, Mexico Women's Singles 2026
• Round: Quarterfinals

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

Conclusion

We predict Peyton Stearns, with a 59% win probability, will likely beat Sloane Stephens at the WTA Guadalajara 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.