Swing is an Apple Watch app that uses machine learning to track how you hit a ball on the tennis court - giving you a chance to benefit from similar technologies to those used at Wimbledon.

The app is developed by ex-Tesla engineer Swupnil Sahai, who has used some very clever algorithms in order to track various shots as you play them on the court.

"Over time, we've added our own machine learning models to the Watch to analyse the wrist motion and your swing so it does shot tracking and swing analysis as well," explained Sahai when we caught up with him at ahead of Wimbledon.

The app, which is available for free but with the option to sign up for more advanced features, also allows you to keep score and how you won the point. That way, you know who's winning at a glance.

AppleSwing The tennis Apple Watch app that will track your shots around court image 2

It also means that, when you combine the winning point data with the swing analysis, the app can work out what your most effective, winning shots are. It will also give you detailed stats at the end of a game on whether your lob, smash or drop shots were more effective.

"Neural networks have advanced to a state where you just need to get an array of diverse data from a number of players. From there we can work out the type of shot you've played," added Sahai.

"A human could do all of this, but it would be very tedious."

The Apple Watch app is just the start. Sahai told us that he and his team are already working on the next version. It will use the iPhone to add computer vision and machine learning to be able to track the ball around the court as you play, without investing in any specialist equipment. 

That Hawkeye-like tech, which is similar to what another developer is doing with basketball app HomeCourt, is already garnering interest from tennis players and investors. Former top ranked tennis star Andy Roddick is among the backers of this new venture.

You can listen to the full interview in the latest episode (ep.9) of the Pocket-lint podcast out now.