Jan-Lennard Struff vs. Thiago Agustin Tirante prediction, odds, picks for ATP China Open 2026

Using powerful machine learning and data analysis, we have re-simulated the outcome of Tuesday's Struff-Tirante men's singles match, forming part of our tennis predictions coverage.

Our independent predictive model gives Tirante a 54% chance of defeating Struff at the ATP China Open tournament.

Latest betting odds

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

• Moneyline: Thiago Tirante -146, Jan-Lennard Struff +122

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 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: Struff @ +122 via DraftKings
• First Set: Tirante @ -135 via DraftKings

According to our best tennis bets, the top play to make on this match is Jan-Lennard Struff to win.

Although Struff 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 China Open match between Jan-Lennard Struff and Thiago Agustin Tirante is scheduled to commence on Tuesday at 10:00 PM ET.

• Who: Jan-Lennard Struff vs. Thiago Agustin Tirante
• Date: Tuesday, September 29, 2026
• Approx. Time: 10:00 PM ET/7:00PM PT
• Tournament: ATP Beijing, 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 Thiago Tirante, with a 54% win probability, will likely beat Jan-Lennard Struff at the ATP China Open 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.