Camilo Ugo Carabelli vs. Felix Auger-Aliassime prediction, odds, picks for ATP Shanghai Masters 2026

Using trusted machine learning and data, we have re-simulated the outcome of Saturday's Ugo Carabelli-Auger-Aliassime men's singles match, forming part of our tennis predictions coverage.

Our famous predictive model currently gives Auger-Aliassime a 92% chance of defeating Ugo Carabelli at the ATP Shanghai Masters tournament.

Latest betting odds

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

• Moneyline: Felix Auger-Aliassime -2500, Camilo Ugo Carabelli +1400

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 available 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: Ugo Carabelli @ +1400 via DraftKings
• First Set: Auger-Aliassime @ -150 via DraftKings

According to our tennis best bets, the top play to make on this match is Camilo Ugo Carabelli 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 ATP Shanghai Masters match between Camilo Ugo Carabelli and Felix Auger-Aliassime is scheduled to begin on Saturday at 12:00 AM ET.

• Who: Camilo Ugo Carabelli vs. Felix Auger-Aliassime
• Date: Saturday, October 10, 2026
• Approx. Time: 12:00 AM ET/9:00PM PT
• Tournament: ATP Shanghai, China Men'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 Felix Auger-Aliassime, with a 92% win probability, will likely beat Camilo Ugo Carabelli at the ATP Shanghai Masters 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.