Classical experimental methods were based upon the concept of holding all variables but one constant and then allowing that one variable to change. If an effect was related to that variable that was allowed to change, then the change in the effect was known to be caused by the change in the one variable.

There are two basic problems with classical experimental methods. First, if there were a large number of variables (also known as factors), then MANY controlled experimental runs were needed to study the effects of several factors; especially if there were multiple levels of each factor that needed to be studied. The second problem was that the interactions between those factors could not be determined, because only one factor was changing at a time.

All that changed when the use of factorial experimental designs became popular. In these experiments, multiple factors are allowed to change at the same time, while the effects of those changes are being measured. Statistical designs of experiments are well known and allow us to evaluate the cumulative effects of each factor across multiple levels. Furthermore, because several factors are allowed to change simultaneously, the number of trials is greatly reduced.

PerformaxGS is based upon the use of factorial experimental design, as applied to golf setup factors. Using teeing height and grip as an example, there is a very large number of combinations into which these two factors may be configured. The use of the factorial design concepts allow us to arrive at conclusions with regard to the cumulative effects of these factors while using only a few test runs…and so on as we evaluate the additional factors we wish to study. In the case of our five (5) factor evaluations, we are able to understand the main effects and the primary interactions between factors in 16 test runs. Compare this number to the number of runs that would be required were we to use classical experimental methods, which would be 32 chances. Not only are we able to perform the analysis in half the number of classical trials, we are also able to understand the interactions between those five factors.

To summarize, PerformaxGS applies the use of factorial experimental design to the golf setup. The engineering staff at Custom Golf Setup not only use proprietary statistical analysis tools to perform the basic analysis, they also use algorithms and proprietary factor weightings to arrive at final recommendations that optimize overall performance. The Custom Golf Setup Engineering Staff has many years’ experience in the application of statistical methods, and they are also seasoned golfers. This has allowed them to “validate what the mathematics recommends”, and in some cases, this validation process has caused them to make some modifications to the statistical results. This last point is very important. Any statistician is able to “plug and chug” the numbers; however, only Custom Golf Setup engineers have a lengthy history of validation, using hundreds of golfers to discover where modifications have been required.