Zizou Bergs vs. Carlos Alcaraz prediction, odds, picks for ATP Shanghai Masters 2026

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

Our independent predictive model gives Alcaraz an 88% chance of winning against Bergs 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: Carlos Alcaraz -1408, Zizou Bergs +980

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: Bergs @ +980 via DraftKings
• First Set: Alcaraz @ -111 via DraftKings

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

Although Bergs is less than likely to win, he 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 ATP Shanghai Masters match between Zizou Bergs and Carlos Alcaraz is scheduled to begin on Monday at 12:00 AM ET.

• Who: Zizou Bergs vs. Carlos Alcaraz
• Date: Monday, October 12, 2026
• Approx. Time: 12:00 AM ET/9:00PM PT
• Tournament: ATP Shanghai, China Men'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 Carlos Alcaraz, with an 88% win probability, will likely beat Zizou Bergs 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.