Mentorship Week 1: Interpreting Results & Finding “Point A”

Parisi Mentorship! What’s up?! Happy Tuesday.

Yesterday we talked about the Parisi Evaluation, the tests we use, and unearthed the concept of testing validity.

If you haven’t read that one, please, go check it out, it’s a good one. In summary, if you’re going to test it, make sure the test tells you what you need to know. It also helps if the test can be repeated at will, whenever you need to do it.

Today I wanted to dive in to interpreting data, specifically, interpreting the results of our athlete evaluation.

It’s great to measure, but explaining what it means is a part of the secret sauce. In Parisi-Land we explain the results for athlete and parent buy-in, which helps us bring new athletes in to our program, keeps existing athletes in our program, and for us Performance Coaches, helps us find our athlete’s “point A”

And that is where we will start today. Answering the question of “Where do I start with my athletes?”

 

Step 1: Interpret the results…

At Parisi, we have 25+ years of normative data.

*for those keeping score at home, “normative data” is data that characterizes what is deemed ‘usual’ in a defined population at a specific point or period of time* 

In Parisi-Land, this means we have a collection of results from youth athletes ages 7-18. This helps us answer the inevitable question from athletes and parents…

“Is that number good?”

You saw it in your Monday morning care package, but you can find out data points below…

 

Parisi Mentorship- Evaluation Standardized Results

 

Now, before you look at these numbers and think they are the holy grail of all things ‘performance’, please consider the following…

1. The Parisi population is non-sports specific. Meaning, these numbers are based off youth athletes that run track, play baseball, softball, football, lacrosse, etc. Clearly athletes of certain sports AND positions in those sports will have better/worse performance numbers, but for the sake of normative data, this is from our population and gives us a benchmark for comparison.
2. Benchmarks can be exceeded. These numbers show ‘typical’. If a 12 year old has a 12 inch vertical jump, and the data sheet says ‘good’ is 10 inches, that doesn’t mean the kid is going to the NBA. It just mean’s that we can make an assumption that this athlete’s vertical jump, and the qualities associated with that performance, are on par with his age and current development. Follow me? Understanding below and above average helps determine the first few trainable qualities.
3. Some athletes should never see these numbers. I say this as a guy who has done over a thousand athlete on-boards. You need to know your audience when it comes to making a comparison. There is an old cliche that says “Comparison is the thief of joy”. Some athletes do not have the mindset or capacity to handle being compared, even if they are above average in their performance. And that is perfectly okay. We, as Coaches, are very close to the data here. We test, we measure, we manage, we want to help our athlete’s achieve their goals. Some athletes, just want to train and see that they are better than day 1. Normative data does not need to be a part of that process. Know your audience.

Okay, enough reading. Below is a video of how I explain the Parisi evaluation- give it a watch and let me know what you think!