Thiago Agustin Tirante vs. Rafael Jodar prediction, odds, picks for ATP Shanghai Masters 2026

Using innovative machine learning and statistical analysis, we have re-simulated the outcome of Saturday's Tirante-Jodar men's singles match, forming part of our tennis predictions coverage.

Our famous predictive model gives Jodar a 67% chance of winning against Tirante 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: Rafael Jodar -250, Thiago Tirante +221

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: Tirante @ +221 via DraftKings
• First Set: Jodar @ -120 via DraftKings

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

Although Tirante 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 Thiago Agustin Tirante and Rafael Jodar is scheduled to commence on Saturday at 12:00 AM ET.

• Who: Thiago Agustin Tirante vs. Rafael Jodar
• 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 Rafael Jodar, with a 67% win probability, will likely beat Thiago Tirante 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.