Pavel Kotov vs. Tallon Griekspoor prediction, odds, picks for ATP Shanghai Masters 2026

Using powerful machine learning and statistical analysis, we have re-simulated the result of Thursday's Kotov-Griekspoor men's singles match, forming part of our tennis predictions coverage.

Our independent predictive model currently gives Griekspoor a 61% chance of beating Kotov at the ATP Shanghai Masters tournament.

Latest betting odds

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

• Moneyline: Tallon Griekspoor -219, Pavel Kotov +188

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: Kotov @ +188 via DraftKings
• First Set: Griekspoor @ -125 via DraftKings

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

Although Kotov 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 Pavel Kotov and Tallon Griekspoor is scheduled to begin on Thursday at 12:00 AM ET.

• Who: Pavel Kotov vs. Tallon Griekspoor
• Date: Thursday, October 8, 2026
• Approx. Time: 12:00 AM ET/9:00PM PT
• Tournament: ATP Shanghai, China Men's Singles 2026
• Round: Round of 128

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

Conclusion

We predict Tallon Griekspoor, with a 61% win probability, will likely beat Pavel Kotov 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.