“Chris broke it.”

That was the statement by Alex Dee, VP of Fujikura Composites, during my recent visit to Fujikura’s R&D facility in Carlsbad, CA. The “it” to which he’s referring is ENSO, a $300,000 3D motion capture system.

Full disclosure, I didn’t really break it. But, for 10-15 minutes, plenty of nervous people considered it a real possibility.

Though this near-miss created a dash of levity bookended by several moments of panic, it revealed an important fact.

ENSO is Fujikura’s sacred cow.

It’s the heartbeat of Fujikura’s R&D process, and without it, Fujikura is likely just another middle-of-the-mall shaft manufacturer.


In (nearly) every golf shot, the ball, once struck, goes on an aerial journey, ultimately coming to rest in a fixed location. But how did it get there? And what role did each variable play in that outcome?

ENSO is a platform that measures and records large amounts of data to quantify the relationship between the shaft, swing, and ball flight. Going a layer deeper, ENSO evaluates all aspects of shaft movement to ultimately determine how shaft behavior impacts launch, spin, trajectory, peak height, and descent angle for every shot.

In other words, ENSO helps engineers like Alex Dee, VP of Fujikura, isolate the specific contributions of the shaft to ball flight. Put another way, ENSO is concerned with everything that happens with the shaft and clubhead during the swing while determining how every piece of the system impacts every other component because of individual swing characteristics.

Quick aside – the term ENSO is derived from a sacred Zen Buddhist symbol, meaning “circle.” Often, the spontaneous drawing leaves the circle incomplete, representing the perpetual quest for completion. Hopefully, this comes in handy during your next Trivial Pursuit game.


The hardware side of ENSO consists of 10 high-speed motion capture cameras and several dozen motion-sensing diodes. The diodes are affixed to specific locations on the head, shaft, and grip. Moreover, at least three cameras (recording at +/- 1,000 fps) target each diode during the swing.

To put that in context, it’s comparable to systems used by motion picture companies to create animated sequences.

The proprietary ENSO software is a joint effort between Fujikura and Vicon. More or less, Fujikura told Vicon what the software needed to do, and Vicon designed it. To be clear, that’s a broad oversimplification of a far more complex piece of the ENSO system. Effectively, it’s like mapping the human genome, but for the golf swing. And without any of the double-helix references. In fact, the software creates a data dump with 3X more information than can be expressed in video.


Unbiased. No Guesswork. All Major Brands. Matched To Your Swing. Advanced Golf Analytics matches the perfect clubs to your exact swing using connected data and machine learning.



This part might get a bit nerdy but stick with me. To understand the possible outputs of ENSO, it’s beneficial to take a glimpse into the sort of information it produces.

ENSO sees clubhead speed throughout the entire swing. Additionally, it traces the club handle and assesses every data point on all three planes of analysis. Think of it as a three-dimensional coordinate system (X, Y, Z). If that makes your eyes glaze over, just know that a shaft bends, deflects, and twists various amounts throughout a swing.


In this screengrab, we focused on Xander’s hands. Peak hand speed typically occurs during the downswing when the hands are close to hip height. This is true for most golfers, regardless of swing speed or skill.

His peak hand speed is 24.3 MPH, but at impact, it is 18.4 MPH. So, leading into impact his hands slow down almost 6 MPH. However, at the max hand speed location, the clubhead is traveling 65 MPH. At impact, it’s 119 MPH. So, as the hands decelerate, the club head accelerates by 64 MPH. Some version of a marginal decrease in hand speed coupled with a large increase in club head is typical for golfers with an efficient kinematic sequence.

Keep in mind that all of this happens in a little over 1 second. Xander’s backswing is a fraction over 0.9 seconds. His downswing takes 0.21 seconds.


The two most obvious differences when comparing an amateur golfer with an early release (casting) is the change in hand speed from max hand speed location to impact and clubhead speed at impact. Both players reach max hand speed and roughly the same time in the downswing. In this case, the amateur player had a max hand speed of 21 MPH, only 3 MPH less than Xander’s. However, the amateur’s hand speed decreased by 3 MPH leading into impact. The clubhead only gained 5-6 MPH from the max hand speed location to impact.

Amateur golfers aren’t trying to swing slow. In fact, they might be trying too hard to swing fast.


After seeing these two swings, it was my turn. It felt a little like stepping on the scale the day after Thanksgiving. You’re not expecting a lot, but you wouldn’t mind if no one else watched.

As mentioned, a shaft has three states of deformation. In techy terms, droop (or drift) is the vertical bend of the shaft. Lead/lag is horizontal bend, and twisting manifests in the clubhead opening/closing at impact.

With the first shaft, I had a max handle speed of 27.7 MPH. This decreased to 13.3 MPH at impact with a total shaft deflection of 5.46.” The droop/drift was 0.9, but the face twisted 5.6° closed.

The shaft had 1.45″ of lead and a negative kick of 3.2 MPH. This means this shaft robbed the clubhead of at least 3 MPH of swing speed.


With the second shaft, the max handle speed dropped to 20.8 MPH and 15.1 MPH at impact. I didn’t deflect this shaft quite as much (4.68″), but the kick speed went from -3.2 MPH to +1.3 MPH. In general, 3 “-3.5” of shaft deflection is reasonably optimal.

The bounce speed on the second shaft indicated that the clubhead twisted shut but also shot away from me approaching impact. This is what caused the droop/drift numbers to sit close to zero, which “we’ve never seen before…and I didn’t think was even possible,” according to Alex Dee. This was the point at which it seemed more likely that I ruined Fujikura’s prized possession than actually had a swing with 0.2° drift.

While neither shaft I tested produced optimal results, the quick experiment unveiled a couple of crucial points of differentiation. The second shaft launched 2° lower with a start direction 1.5° closer to my target line. It might not sound like much, but 1°, 1 MPH or 100 RPM can be a big deal, particularly when trying to fine-tune performance. Moreover, think of each individual degree of launch or start direction as a zip code. From 30,000 feet, it might look insignificant. That is until your Amazon package arrives in the wrong part of town.


It would be easy to skim this article and reach the conclusion ENSO-provided information disproportionately benefits better players. And when it comes to very specific use-case scenarios, that line of thinking largely holds up. For example, ENSO helped Fujikura TOUR staff find an additional 15-yards of carry distance while retaining the desired feel in the driver of 3X PGA Tour winner, Jhonattan Vegas. Vegas’ Fujikura Motore 70X shaft is a little softer profile and probably not the obvious choice for a guy with a 120 MPH swing and quick tempo. But, tipping the shaft 2″,  increased the total stiffness while creating more lead into impact. Again, more lead increases dynamic loft. The net result is more distance without a sacrifice in feel for this specific player.

For the rest of us mere mortals, the implications are likely as impactful, though perhaps not as targeted. ENSO can identify and quantify the relationship between specific swing characteristics and shaft profiles that best fit certain golfers. With that in mind, why couldn’t ENSO data be leveraged to optimally fit any golfer in 3-5 swings? Or perhaps identify the most impactful variables that fitters should assess during a shaft fitting? If ENSO can articulate the levers that most directly impact ball flight, why couldn’t Fujikura develop fitting protocols based chiefly around those elements? It’s my understanding that we’re much closer to tangible answers than some might think.


ENSO is roughly 10 years old and “we’re just scratching the surface” according to Dee. The primary upside to big data is always the main drawback – there’s a lot of it.  It took engineers several years to get a framework of what ENSO could measure. From there, the primary objective centered around finding palpable applications for the mountain of data. And now that Fujikura has a reasonable handle on what that means, what’s next?

In the short term, it’s examining the best way to arrange materials to best serve the needs of the majority of golfers. That’s bland marketing speak for let’s find dominant swing characteristics of average golfers and build shafts based on those needs. It might read something like a prescription drug commercial. “Have an early release and steep swing? Try this.” “Want to hit that majestic tight draw, but struggle to shallow the club in transition. We got you.” Or it could be more simplistic scenarios where ENSO findings allow engineers to construct a shaft with a positive kick (adding MPH of swing speed) on top of previous EI profiles. Basically, taking an existing design and tweaking it to be faster.

The “you don’t know what you don’t know” category is always viable, particularly in R&D applications. Fujikura’s Ventus shaft emanated from ENSO data that illuminated how bending and torsional stiffness operate together. In practice, Ventus increased the golfer’s access to driver MOI (forgiveness). If a shaft can make a driver more forgiving, what could it do for irons or wedges? It’s a blue ocean of potential answers.

And while the nebulous world of composite shafts will evolve, at least one thing is abundantly clear.

Does the shaft matter? Damn straight, it does.

*This content is backed by the MyGolfSpy Integrity in Advertising Promise.

*We may earn a commission when you buy through links on our site.