Luca Van Assche vs. Yunchaokete Bu prediction, odds, picks for ATP Shanghai Masters 2026

Using innovative machine learning and data, we have re-simulated the outcome of Wednesday's Van Assche-Bu men's singles match, forming part of our tennis predictions coverage.

Our leading predictive model currently gives Bu a 63% chance of winning against Van Assche 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: Yunchaokete Bu -172, Luca Van Assche +175

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: Van Assche @ +175 via DraftKings
• First Set: Bu @ -125 via DraftKings

According to our tennis best bets, the top play to make on this match is Luca Van Assche to win.

Although Van Assche 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 Luca Van Assche and Yunchaokete Bu is scheduled to start on Wednesday at 12:00 AM ET.

• Who: Luca Van Assche vs. Yunchaokete Bu
• Date: Wednesday, October 7, 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 Yunchaokete Bu, with a 63% win probability, will likely beat Luca Van Assche 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.