IBM launched limb tracking at this year’s U.S. Open – and it’s scoring every tennis player’s serve for fans to see

When world number three and top-ranked American tennis player Jessica Pegula stepped up to the baseline to serve against Belarus’ Aryna Sabalenka in the semifinals at the U.S.

Open on Thursday night, it wasn’t only cameras from fans, broadcasters, or journalists capturing her every move.

That’s because at the start of this year’s tournament, IBM launched a new feature in the U.S.

Open’s app with the United States Tennis Association (USTA) as part of their ongoing partnership.

Called “serve quality,” it tracks over 20 points on Pegula’s body (and every other singles athlete competing at the tournament on both the men’s and women’s side) using technology powered by cameras.

Serve quality is measured out of 100 and considers knee, wrist, and elbow movements, among other factors.

IBM’s WatsonX then processes the data to put together the score shown in the app.

Although limb tracking, or skeletal tracking, has been used in pro sports events like soccer, this marks the first time a Grand Slam tennis tournament has offered a feature for fans.

And, unsurprisingly, AI is powering it.

This launch came as part of a suite of other app additions for the 2026 event, including an AI chat feature and highlighting “key moments” during a match.

According to IBM, the recently unveiled feature began private testing over the last couple of years and estimates 1.2 billion joints will be analyzed by the end of this year’s tournament.

Additionally, the company anticipates that the app will generate 7 million serve quality insights.

Here’s how the serve quality score works: People can find the match listed on the tournament’s app, click on “match recap,” and use IBM’s “match chat feature” to find a readily available suggestion: “How did serve quality affect the match?” Courtesy of IBM From there, the AI response noted: “Jessica Pegula outperformed Aryna Sabalenka on serve quality, posting a 72.32% serve quality score compared to Aryna Sabalenka’s 71.95%.” Even though Pegula lost the match, her serve quality score was higher, according to IBM’s logic, which includes ball, racquet, and player-movement tracking data.

“Jessica Pegula was sharp with their placement, landing 75.95% of 79 total serves in the box, with an average placement of 1.555 feet away from the optimal serve zone,” it continued.