Hanyu Guo vs. Sorana Cirstea prediction, odds, picks for WTA Wuhan Open 2026

Using innovative machine learning and statistical analysis, we have re-simulated the outcome of Sunday's Guo-Cirstea women's singles match, forming part of our tennis predictions coverage.

Our famous predictive model currently gives Cirstea an 83% chance of defeating Guo at the WTA Wuhan Open tournament. Additionally, Cirstea has a 77% chance of winning the first set, while the over 18.5 games has a 54% 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: Sorana Cirstea -714, Hanyu Guo +540
• First Set: Guo +350, Cirstea -500

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: Guo @ +540 via DraftKings
• First Set: Cirstea @ -115 via DraftKings

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

Although Guo 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 Wuhan Open match between Hanyu Guo and Sorana Cirstea is scheduled to begin on Sunday at 10:00 PM ET.

• Who: Hanyu Guo vs. Sorana Cirstea
• Date: Sunday, October 11, 2026
• Approx. Time: 10:00 PM ET/7:00PM PT
• Tournament: WTA Wuhan, China Women's Singles 2026
• Round: Round of 64

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

Conclusion

We predict Sorana Cirstea, with an 83% win probability, will likely beat Hanyu Guo at the WTA Wuhan Open tournament.

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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.