Explaining SwingTracker’s Overall Score Calculations

NORTH SHORE, PGH – In terms of maximizing your swing output and swing potential, it is necessary to understand every piece of information that SwingTracker gives you. Within this framework, let’s take a look at the Diamond Kinetics’ Diamond Chart.

With this post, we will look at why the scores look the way they look in the Diamond Chart. We will also look at the science behind how we get to the numbers (and shapes) you see within the Diamond Chart.

Let’s get started…

As you can see in the Diamond Chart graphic below (Illustration 1.0), there are four components to SwingTracker:

1) Speed, 2) Power, 3) Quickness 4) Control.

Web App Screenshot

(Illustration 1.0)

Each score taken from each swing is based on a scale of 1-10. The Power score calculates three metrics, the Quickness score calculates one metric, the Control score calculates three metrics and the Speed score calculates three of the four Speed metrics.

After each swing, these 14 metrics and components are averaged together, then ranked against existing swings within your skill set (i.e. Youth, Junior Varsity, Varsity…etc.) in the SwingTracker database

From there, we arrive at the score you see on your tablet, smartphone or computer.

This score is used two places…

  • The raw numbers listed next to the four swing components (Speed, Power, Quickness, Control)
  • The white, diamond-shaped line which visualizes your scores within the Diamond Chart.

In Illustration 1.0 above, the white, diamond-shaped line within the Diamond Chart shows where that specific swing ranked relative to that user’s specific skill level. The gray area represents the database of swings for that particular skill level.

As we can see, most of the scores from the swing in Illustration 1.0 are either average, or slightly above average. This is why the white, diamond-shaped line is drawn throughout the ‘middle’ of the grey area.

Over time, as more swings are collected, the ‘shape’ of the gray area will change as the database of that particular skill level increases.

Look below at Illustration 2.0. This is another swing from the same database as Illustration 1.0, therefore the gray area is exactly the same ‘shape’.

But take a look at the Quickness score.

As we can see, the score is very high. So high, in fact, the white, diamond-shaped line is actually outside of the gray area at that specific point on the Diamond Chart.

DiamondCLUB Image Part Two

(Illustration 2.0)

This means that the player who took this swing had a Quickness score that was one standard deviation higher than the average Quickness score of swings in this particular database. Simply put, this player’s Quickness score was in the top one-third (1/3) of all Quickness scores in this database. More specifically, this player’s particular Quickness score was in the top one-fourth (1/4), relative to this database. That is why the white line dips out of the gray area, because it is one full standard deviation higher than the average quickness score, which places the swing in the upper echelon of swings.

While this is a great accomplishment, this player shouldn’t be sending in his MLB Draft application quite yet.

Because we are in the early stages of SwingTracker, the database for this particular skill level is currently less populated than it will be a month from now (much less six months from now), after thousands and thousands of swings have been recorded.

As more and more swings are taken within this skill level – and the database begins to grow and grow – the gray area will change relative to those swings.

And this same swing, while still an excellent, above-average score, may not be ranked as high a month from now, after more swings (and better swings) are collected.

Keeping that in mind, let’s take a look at an example from a different database.

See Illustration 3.0 below:

DiamondCLUB

(Illustration 3.0)

As we can see, the raw Quickness score is higher in Illustration 3.0 compared to Illustration 2.0 = (8.1 to 7.8)

However, the white, diamond-shaped line in Illustration 3.0 does not go outside the gray area as it does in Illustration 2.0, even though the raw score in Illustration 3.0 is higher.

Why is this? The answer is simple.

At this particular skill level, the Quickness scores in the database in Illustration 3.0 are higher than the database of Quickness scores in Illustration 2.0. Not the actual number of swings, but the raw data of swing scores is higher.

This is why a 7.8 raw data score is deemed ‘better’ than an 8.1 raw data score. It’s ‘better’ as it applies to the particular skill level shown in Illustration 3.0 as compared to Illustration 2.0.

Here’s a simple way to look at it:

Let’s assume both databases in Illustration 2.0 and Illustration 3.0 each have 100 swings. The swings in the Illustration 3.0 database have players who are really quick to the ball (high Quickness scores) but lack control (low Control scores). Because of this, the gray area (representing the database of swings for that skill level), skews more toward one particular component than another (in this case Quickness as compared to Control).

Over time, as more and more swings are taken by players who have higher Control scores, but lower Quickness scores, the ‘shape’ of the gray area in Illustration 3.0 will change and become more balanced.

Here’s another way to look at it.

Let’s assume a player takes the same exact swing on July 1st as they did on January 1st. They will see the same raw data numbers, but a completely different visualization on the Diamond ChartThe 7.8 Quickness score will still be a 7.8, but the white, diamond-shaped line will likely not be dipping below the gray area as it once did six months ago.

We hope this blogpost has made it easier to understand how the SwingTracker score is calculated.

The ultimate goal of SwingTracker is to help you improve your swing, but also see how you measure against your peers. We believe science, accuracy and customer trust are critical. We hope this has been beneficial in meeting the requirements of all three.

To dive even further into this topic, please check out this video on SwingTracker’s score calculations.

#DKBaseball

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