Lorenzo Sonego vs. Moise Kouame prediction, odds, picks for ATP Lyon Open 2026

Using trusted machine learning and data, we have re-simulated the outcome of Monday's Sonego-Kouame men's singles match, forming part of our tennis predictions coverage.

Our famous predictive model gives Sonego a 65% chance of winning against Kouame at the ATP Lyon Open tournament. Additionally, Sonego has a 62% chance of winning the first set, while the under 23.5 games has a 58% chance of hitting, according to our model.

Latest betting odds

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

• Moneyline: Moise Kouame +154, Lorenzo Sonego -158
• First Set: Sonego -152, Kouame +120

All odds are correct at the time of publication and are subject to change.

Model picks and best bet

After matching our probabilities 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: Sonego @ -158 via DraftKings
• First Set: Kouame @ -135 via DraftKings

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

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 Lorenzo Sonego and Moise Kouame is scheduled to commence on Monday at 5:00 AM ET.

• Who: Lorenzo Sonego vs. Moise Kouame
• Date: Monday, October 19, 2026
• Approx. Time: 5:00 AM ET/2:00AM PT
• Tournament: ATP Lyon, France Men's Singles 2026
• Round: Round of 32

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

Conclusion

We predict Lorenzo Sonego, with a 65% win probability, will likely beat Moise Kouame 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.