How A.I.—and a good fitter—got me 19 more yards in about 10 swings

Artificial Intelligence’s impact on the world may be a source of current controversy—ChatGPT legal briefs, anyone?—but AI’s expanding role in golf, particularly when it comes to club fitting, might just be a peak into the Tomorrowland all golfers (and fitters) have dreamed of.

Even better, it’s already here.

Using computer-driven analytics to streamline fitting has been a growing part of the process of finding the right match for a golfer for more than a decade. Mizuno’s Shaft Optimizer took shaft-fitting well beyond the educated guess-and-check method from the moment it first appeared in 2010. Requiring only a handful of swings with a 7-iron, it’s continued to improve the company’s overall fitting process to where it can not only optimize shaft and head, but even lie angle and recommend a ball, too. Titleist recently developed a computer-driven method for dialing in the proper lofts and sole grinds for a wedge fitting, and Ping’s fitting matrix can show you all the possibilities for filling out the gaps in your bag just based on how you hit the 7-iron you’ve just been fit for. All of these manufacturer-driven systems, and others like them, are widely available at authorized locations.

Several independent fitters are developing brand-agnostic ways of sorting the universe of possible fits, including Dan Sueltz at D’Lance Golf outside Denver, Wade Heintzelman at the Golf Care Center in Maryland, and Chris Wycoff with his SwingFit A.I. in South Carolina. Meanwhile, Cool Clubs has been categorizing and sorting data on clubs and shafts for the last two decades, and with access to its own robot, its fitters know what a player should expect to see before she even makes a swing. GolfTec’s training centers offer clubfitting with its proprietary Tecfit system that evaluates clubs based on how closely they optimize various performance metrics, including ball speed, launch angle and spin with each player’s swing.

Now, Club Champion, the U.S.’s leading clubfitting brand with more than 110 facilities, is bringing A.I. to its methodology. The new system, called Co-Pilot, uses an artificial intelligence and machine learning system developed in conjunction with the business technology consulting firm Inspire11 (a group that included several Club Champion customers). Co-Pilot fast-tracks fitting recommendations in just 10 swings with a customer’s current driver or irons, based on the Trackman launch monitor data that the system tracks. It’s been in a beta test for the last year, but now is fully rolled out across all Club Champion locations.

It’s not about eliminating human intelligence in the fitting process, it’s about enhancing it, said Nick Sherburne, Club Champion co-founder and master fitter.

“When we first set out on this, the goal was to never take the spotlight off the fitter,” he said. “All the data people and engineers that we worked with at Inspire11 also agreed with that, and that’s why it’s called Co-Pilot. They actually came up with the name because even they don’t believe A.I. can necessarily fit somebody perfectly. What it can do is it can make the fitter smarter and quicker, faster.”

Sherburne knew that with Club Champion’s 65,000 head and shaft combinations and the equipment industry’s continuing expansion of new products, seeing perfect matches was getting beyond human capabilities. Moreover, the Co-Pilot system learns with every fit and every adjustment the human fitter makes. Sherburne estimates that Club Champion is inputting nearly a million swings a month into the system.

"We're collecting a lot of swings and then it triangulates with the Trackman Optimizer, and then it triangulates with what has been purchased in the past based off the fit," he said. "The system still wants to see how if a fitter went a different direction because there's still some art to it."

But as much as the fitter benefits from the A.I. system’s instant recommendations, it’s the customer who might be a bigger beneficiary. The ultimate truth of the new system is how it leads to fewer swings to get you closer to the perfect recommendation.

“There’s no doubt that one of the things we hear from some of our customers occasionally is that a three-and-a-half hour full bag fitting can be tiring,” said Sherburne, who believes A.I. could cut a typical 90-minute fitting session to just an hour and a three-and-half hour session to just two-and-a-half. “This just makes the whole process for the customer and the fitter much more efficient.”

The Co-Pilot fitting system builds off an initial list of five shafts based on your first 10 swings. Even fewer swings follow with any or all of the recommended shafts. All the while the system is learning and adjusting based on the Trackman data from the launch monitor and the millions of swings in Club Champion’s fitting database. In other words, if you take the highest recommended shaft and get ideal results with it in terms of ball speed, spin, launch, distance, dispersion and consistency, you might not need to make another swing to determine the proper shaft. Once the shaft is settled on, the system then recommends a series of five optimal heads for your swing.

When I experienced the system at Club Champion’s Manhattan facility last week, what was most interesting wasn’t how the computer spit out recommendations to master fitter Zach Weber. It was how the recommendations set us off on a journey that wasn’t random or even full of a rainbow of possibilities, but instead very targeted. Of course, I could see Weber looking at recommendations from the Co-Pilot system with the occasional raised eyebrow that you’d expect from any human-computer interaction.

“I recognized most of the recommendations right away, that’s where I would have gone myself, too,” said Weber, who’s been in the fitting business for more than 15 years. “But others were odd to me. It got me thinking differently about possible angles to take.”

We didn’t end up with exactly the No. 1 recommendation, but it was close, kind of a combination of the second and third initial recommended shaft and head. The difference was Weber’s golf intuition. As he said, “There’s the perfect fit by the numbers and then there’s the perfect fit you have to play golf with.” More importantly, the end result was 19 additional yards and about a 75-percent tighter dispersion. Even better: Rather than feeling like I’d just finished a triathlon, I could easily have stuck around to dial in my fairway woods and hybrids and possibly gotten a wedge or putter fitting, too. All of it took less than an hour.

It was a perfect illustration of how the explosion in average golfer data is leading not only to better fitting solutions, but a better fitting experience. Companies, fitters and retailers have been sitting on more information about optimized performance than the PGA Tour’s ShotLink system has churned out in its two decades of use. Now those numbers are going to start impacting your numbers in ways you never could have imagined. In short, fitting is working better and smarter than it ever has, thanks to how the human brain and the artificial one can now work in concert.

What we may be seeing with the increased use of analytics in fitting is how the same sort of efficiencies that have become commonplace for elite golfers are now going to be that much easier for average golfers to access. Thanks to A.I., they may be accessing that optimized performance much quicker and more effectively. And after all, it means a heck of a lot more to us average hacks than it will to any tour player trying to dial in his Trackman numbers so he’s hitting it 320 instead of 319.