Valentin Vacherot vs. Luca Van Assche prediction, odds, picks for ATP Lyon Open 2026

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

Our famous predictive model gives Vacherot a 62% chance of defeating Van Assche at the ATP Lyon 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: Luca Van Assche +176, Valentin Vacherot -200

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 @ +176 via DraftKings
• First Set: Van Assche @ -135 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 Lyon Open match between Valentin Vacherot and Luca Van Assche is scheduled to commence on Thursday at 6:00 AM ET.

• Who: Valentin Vacherot vs. Luca Van Assche
• Date: Thursday, October 22, 2026
• Approx. Time: 6:00 AM ET/3:00 AM PT
• Tournament: ATP Lyon, France Men's Singles 2026
• Round: Round of 16

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

Conclusion

We predict Valentin Vacherot, with a 62% win probability, will likely beat Luca Van Assche at the ATP Lyon 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.