Talia Gibson vs. Fiona Ferro prediction, odds, picks for WTA Singapore 2026

Using powerful machine learning and data, we have re-simulated the outcome of Monday's Gibson-Ferro women's singles match, forming part of our tennis predictions coverage.

Our famous predictive model gives Gibson a 55% chance of defeating Ferro at the WTA Singapore tournament. Additionally, Gibson has a 54% 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: Fiona Ferro +110, Talia Gibson -118
• First Set: Gibson -139, Ferro +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: Gibson @ -118 via DraftKings
• First Set: Ferro @ -110 via DraftKings

According to our best tennis bets, the top play to make on this match is Talia Gibson 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 Singapore match between Talia Gibson and Fiona Ferro is scheduled to start on Monday at 2:00 AM ET.

• Who: Talia Gibson vs. Fiona Ferro
• Date: Monday, September 21, 2026
• Approx. Time: 2:00 AM ET/11:00PM PT
• Tournament: WTA Singapore, Singapore 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 Talia Gibson, with a 55% win probability, will likely beat Fiona Ferro at the WTA Singapore 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.