Interpreting Golf Club Data through Process Improvement
What metric is the most important to you when choosing the golf club best for your game? Interpreting golf club data is best reserved to certified club fitters, but understanding a few critical concepts about process improvement can also be beneficial to your game.
Can golf be considered a process?
Yes, to put it simply.
One of the first steps process improvement professionals take when evaluating whether a problem exists is to fully understand the process being evaluated. Where does it begin, and where does it end?
The golf swing is no different. For example, we can say the process starts at "address" and ends when the golf ball comes to rest after being hit. All the steps in between contribute to your golf ball traveling from point A to B.
What can be tested?
The variables of your individual golf swing, quality of strike, weather elements, and course condition all contribute to the actual performance of your golf shot. Since every golfer is different because of these variables, we commonly test the one factor that is consistent (so to speak) across the spectrum: the golf club.
Using a ball launch monitor -- like SkyTrak -- is helpful to clean up the rest. Annoying real life variables can be scrubbed so you can test one club against the other in a controlled environment.
How do you know if you've improved?
The first step is to get a baseline measurement of your current golf swing performance. Again, ball launch monitors can be helpful to obtain this data. Metrics like shot distance, ball speed, and shot dispersion can be accurately measured with surprising precision.
The next step is to conduct a test, or a pilot. Comparing two golf clubs against one another is an example of a very, very simple pilot test.
Each pilot test begins at the same point: the null hypothesis.
What's the null hypothesis?
Generally speaking, the null hypothesis states that there is no difference between X and Y (the two factors being compared). Entering the pilot test with this fundamental hypothesis is common in the study of process improvement.
Therefore, what you are "testing" in the pilot is whether or not to reject or fail to reject the null hypothesis. If the data shows there is a statistically significant difference between X and Y, then you can reject the null (there is a difference between the two factors being compared).
How do you know if there is a statistically significant difference?
Try to stay with me here, folks.
One way to determine whether or not a statistically significant difference exists is by running a hypothesis test to find the "p-value."
The p-value is commonly defined as the level of marginal significance within a statistical hypothesis test representing the probability of the occurrence of a given event. Don't worry if that's confusing; statisticians are still debating this definition and probably will for the rest of eternity.
For the purpose of this blog post I'll just say we will use the industry standard value of p=0.05.
If a hypothesis test yields a p-value less than 0.05, we have evidence of a significant difference between the two factors being compared. Or, in this case, golf clubs.
If the p-value is greater than 0.05, we fail to reject the null hypothesis as we have found no statistically significant difference.
OK nerd, so what did you test?
During a recent driving range session, I compared the Callaway Epic driver to the Srixon z565 driver (again) focusing on total distance, ball speed, spin rates, and other variables. For funsies, I also compared a bladed iron to a cavity back iron.
Some highlights:
Range session. @CallawayGolf Epic vs @SrixonGolf z565. Epic 5 yds longer, Srixon ball speed higher. No significant difference (p=0.5) pic.twitter.com/J6uqaIMotQ
— Golf Unfiltered (@GolfUnfiltered) August 11, 2017
Hypothesis test data. Averages close. Numbers are fun. #data pic.twitter.com/A218GQp46W
— Golf Unfiltered (@GolfUnfiltered) August 11, 2017
Also of note, @MiuraGolfInc MB-001 6-iron vs @SrixonGolf z765 iron ball speeds. Coin-flip in this session. On-course results negligible. pic.twitter.com/gvbyf8ZsnC
— Golf Unfiltered (@GolfUnfiltered) August 11, 2017
Long story short, each club performed similar to its comparison.
So are you saying all golf clubs perform the same?
Absolutely not.
There are much smarter people than me who can explain the concept of sampling better than I ever could, but the size of your sample matters. A lot.
I would argue that with a large enough sample size (think thousands of golf shots), you can eventually find a statistical difference when comparing two items. This is why stats get a bad rap.
What I am saying is 99 percent of golfers won't hit thousands of golf shots before making a club purchase. We will grab a club off the store rack, swing it a few times, perceive it to perform better or worse than our current gamers, and make a buying decision.
That's the wrong way to buy golf clubs.
The best -- and correct -- way to buy golf clubs is to visit a certified club-fitter or your local PGA professional. These individuals have been trained on how to help you make an informed decision based on your shot characteristics in a limited amount of time.
