Daniil Medvedev vs. Dalibor Svrcina prediction, odds, picks for ATP Shanghai Masters 2026

Using trusted machine learning and statistical analysis, we have re-simulated the outcome of Monday's Medvedev-Svrcina men's singles match, forming part of our tennis predictions coverage.

Our independent predictive model currently gives Medvedev an 82% chance of beating Svrcina 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: Dalibor Svrcina +500, Daniil Medvedev -641

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: Svrcina @ +500 via DraftKings
• First Set: Svrcina @ -115 via DraftKings

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

Although Svrcina 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 Daniil Medvedev and Dalibor Svrcina is scheduled to begin on Monday at 12:00 AM ET.

• Who: Daniil Medvedev vs. Dalibor Svrcina
• 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 Daniil Medvedev, with an 82% win probability, will likely beat Dalibor Svrcina 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.