Understanding a Product

 

When we buy a product I think it important we understand why it exists. This understanding is what can unify the company and the customer. Remember, the company exists for the customer and without the customer’s wants and needs being at the core of the company, a strong disconnect can manifest itself in a  poor relationship. For this reason, I think it is extremely important for the company to highlight why they believe a product was made and then go out into the world and hear how the customer actually uses it!

What is the G-Flight

The G-Flight is portable contact grid used to measure jump height, ground contact time and reactive strength index. The why from my end (the company side) may appear quite obvious at first “we want to help coaches measure these variables”, but that is only part of the picture. To get a better understanding of our goals, I sat down and asked my self “five whys” and then trekked into the real world to see what I was missing.

 

Why did you build the G-Flight?

 

To provide customers with a portable and affordable device that can measure performance metrics.

Why does something affordable and portable need to exist?

  

The affordability makes its accessible to all athletes, coaches and practitioners who may be interested. The portability makes it easier to move around and carry  (coach friendly). The cheaper and easier it is to use means the less collecting data gets in the way of the coach doing their job

Why do you want to collect data? 

 

Using the old adage, “you cannot improve what you cannot measure”. Data is critical for both the coach and the athlete.

 

Why is data critical for coaches and athletes?

 

Data allows coaches to objectify the athlete’s progress or lack of (change form baseline) through a program. Simple performance metrics can help determine whether or not a coach is getting the changes they want. In short, data provides performance insights.

Why do coaches want to know performance insights?

 

Hopefully this answer is quite obvious… The performance insights allow you to better understand your own program. Did the changes that you expected to occur take place? If not, what went wrong. Objective data can be coupled with the qualitative understanding of your program. Combining these, you can start to piece together important training related information. However, this isn’t the only side of the coin…

 

After speaking with a couple of coaches from the U.K. they mentioned something I never thought of. They commented how the G-Flight has brought value to their gym. To be honest, at first I was a little confused. You see, I only looked at this device from my side of the coin, but it took a better businessman than me to point out what I was missing. Like all of you know, we live in a highly competitive world and being able to show your worth is of great significance. The G-Flight allows coaches in all fields (physios, athletic trainers, coaches etc…) to apply performance metrics to show that what they are doing is working. Whether it is client retention or acquisition, having data to back you up can give you something to highlight! Not only does it show the client that what you are doing is working, but also it shows that you are willing to invest the time to objectively prove it! This was an eye opener for me, because now I realized we are not just providing the coach with tools to better their training, but we are providing them with the tools to increase their own value!

Visit the G-Flight Shop Here http://www.exsurgo.us/gshop/

Because value can come in the form of information, I wanted to provide a short little run down on some simple stats one can use to better understand training. Below, I go over how to calculate Cohen’s Effect Size in a simple, quick to do way, that can hopefully provide value to both the coach and the athlete. 

Making stats simple (Cohen’s Effect Size)

 

This is a small little add on section for those who are unsure where to turn when it comes to stats. For a long time, I didn’t truly appreciate the importance of formal statics. However, overtime I have come to realize that they can bring more good than harm.

 

Below is an example of how to use Cohen’s effect size to understand changes in your data.

 

Effective size = Mean 2 – Mean 1 / Standard deviation (either from mean 1 or average from mean 1 and mean 2)

 

F or example, say you have two weeks of testing data. In week 1, the mean jump height was 25 inches (mean 1) and had a standard deviation of 2.5. In week 2, the mean jump height was 27 inches (mean 2) and the standard deviation was 2.1.

 

Plug this into the equation and you get

 

Effective size = Mean 2 (27inches) – Mean 1 (25inches) / Standard deviation average of two means ((2.5+2.1) / 2  = 2.3)

 

Effective size = 2 inches / 2.3

 

Effect size = 0.87

 

To determine what the magnitude of change was (effect size), we can look at a scale developed by Hopkins

 

Trivial                         < 0.2

Small                           0.2-0.6

Moderate                   0.6-1.2

Large                          1.2-2.0

Very Large                 2.0-4.0

Extremely large         >4.0

 

As we can see, the magnitude of change was in the moderate zone (0.6-1.2), effect size of data = 0.87.

 

This can inform a coach whether or not the training is causing a statistical change in performance. Combine this with subjective data and you can start to paint a pretty good picture of how one’s program is progressing.

 

Here is an example of how I used it to track bar velocity over a 6 week sampling period. Each sampling period consisted of 6 days of testing.

Here is a simple graph showing effect size of changes in average bar velocity of standard weight over a 6 week sampling period. . . Week 1 was used as a baseline with all of the following weeks compared against week one. . . You can also use a rolling comparison, which takes the average of the previous week compared to the average of the current week. . . Why is this important? . . Using stats is a simple way to paint a picture as to how effective your training is. Not all weeks need to have a linear increase, but using longitudinal data in conjunction with training context can help you better understand why these fluctuations are occurring. . . In the graph above, you can see that from week 1-4 there is pretty much a linear increase, then a reduction at week 5 and a sole at week 6. . Yes, you can see some of these differences just by looking at the mean, but the effect size lets you have a standard quantification of change.

A post shared by Max Schmarzo (ATC/CSCS/MS) (@strong_by_science) on

 

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