Katerina Siniakova vs. Peyton Stearns prediction, odds, picks for WTA Japan Open 2026

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

Our famous predictive model gives Siniakova a 54% chance of defeating Stearns at the WTA Japan Open tournament. Additionally, Siniakova has a 53% 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: Peyton Stearns +110, Katerina Siniakova -139
• First Set: Siniakova -139, Stearns +110

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: Stearns @ +110 via DraftKings
• First Set: Stearns @ +110 via DraftKings

According to our tennis best bets, the top play to make on this match is Peyton Stearns 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 Japan Open match between Katerina Siniakova and Peyton Stearns is scheduled to start on Sunday at 10:30 PM ET.

• Who: Katerina Siniakova vs. Peyton Stearns
• 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 Katerina Siniakova, with a 54% win probability, will likely beat Peyton Stearns 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.