Frances Tiafoe vs. Ben Shelton prediction, odds, picks for US Open 2026

Using powerful machine learning and data, we have re-simulated the result of Friday's Tiafoe-Shelton men's singles match, forming part of our tennis predictions coverage.

Our famous predictive model currently gives Shelton a 73% chance of beating Tiafoe in the US Open 2026. Additionally, Shelton has a 65% chance of winning the first set. The simulations also show Shelton (-4.5) has a 53% chance of covering the games spread, while the under 41.5 games has a 62% chance of hitting, according to our model.

Latest betting odds

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

• Moneyline: Ben Shelton -285, Frances Tiafoe +240
• First Set: Tiafoe +162, Shelton -200

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: Shelton @ -285 via DraftKings
• First Set: Shelton @ -135 via DraftKings

According to our tennis best bets, the top play to make on this match is Ben Shelton to win the first set.

These picks are based on our probabilities matched against the implied probabilities from the current available odds.

Game info & first serve details

The US Open 2026 match between Frances Tiafoe and Ben Shelton is scheduled to commence on Friday at 7:00 PM ET.

• Who: Frances Tiafoe vs. Ben Shelton
• Date: Friday, September 11, 2026
• Approx. Time: 7:00 PM ET/4:00PM PT
• Tournament: US Open Men's Singles 2026
• Round: Semifinals

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

Conclusion

We predict Ben Shelton, with a 73% win probability, will likely beat Frances Tiafoe at the US Open 2026.

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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.