Branding For Growth
Many of us are gathering data and turning that data into information.
Sometimes, I am looking at data that is something like this:
1000, 1030, 1022, 988, 1030, 1029, 970, 1000, 1022, 1030, 988, 1029, 970, 1022, 1001, 1022, 988, 1030, 988, 1030, 988, 990
Of course, this data means something to me, because I designed the process of collecting this data and I know what it measures and what it means. But, for others to understand it, I needed to change that simple numerical form to be something like this:
This graphic gives you a way of understanding how I transform that data into information.
The graphic is “in” the “form” that is more easy for folks to understand. That’s another way of understanding what ‘information’ is. It is data that has been put into a form that is easier to understand.
Sometimes the form that is used for making data more understandable ends up conveying an illusion that gets attached to or extracted from the data. Sometimes, for instance, if I’m putting together a graphic representation of numbers in a bar graph, viewers might assume that tall bars or long bars are ‘good’ and short bars are ‘bad.’
Sometimes, the information representations we come up with for data about new concepts, end up encouraging assumptions about the data that are not warranted and these assumptions become part of the unwritten beliefs we have about the original data source.
An Example: Slow Twitch and Fast Twitch Muscle Fiber Recruitment
Muscle fiber recruitment (MFR) is a significant addition to the art and science of strength training and conditioning. Within that conceptual field of recruitment (activations) of muscle fibers, the information about these two different types of muscle fibers (fast-twitch and slow-twitch) has become an important basic concept with respect to which kinds of sports activities need which kinds of muscle fibers to be strengthened and conditioned. Some sports activities rely on one and other sports activities rely on the other, and many sports activities need both for differing phases of the specific skills of those sports.
When I did an online search for graphics to display fast-twitch and slow-twitch activation graphics, I found many online images. I chose these 3 to show a particular concept about muscle fiber activations. They all show the activation signal originating at essentially the same time, and they show the differing responses (on the timeline) of the different types of the muscle fibers.
Those specific points of information are very important for strength and conditioning. If the signals for the different types of muscle fibers were to receive their signals at the same moment, these graphs display the timing differences that are inherent for those types of fibers responses to those activation (recruitment) signals.
What Are The Consequences Of This Information?
Of course, this view of these fiber responses to activation signals are correct.
The problem is that muscle activation signals don’t occur at precisely the same time. Even MFRs for the same type (fast-twitch, for instance) don’t occur all at the same time.
Normally, muscle fiber activations occur in a symphonic cascade, which builds up the specific muscle fiber sequencing of contractions needed for the desired movement or activity. But, there is even more variation that this symphonic cascade, which is that appropriate timing of the MFR signals. There is a variation in when the MFRs occur, which introduces errors in performance.
MFRs Should Occur When They Should Occur
Muscle fiber activations activations don’t all occur at the precise moment they need to be activated.
What we are discovering is that the timing of muscle fiber activations, as defined in the athlete’s skills, are not what the brain delivers to the muscles. That first set of data points I showed and the graph I showed at the beginning of this article is a visual representation of how imprecisely timed muscle fiber activation signals are being sent from the brain to the muscle fibers. The test subjects for that data have a very precise rhythmic task, and the accuracy of the needed MFRs to accomplish that task are being measured.
The center horizontal line represents accurate performance of the task. The first horizontal lines above and below the center line, represent 50ms too early or too late in performing the task.
This example shows us, for this test subject, that the activation timing accuracy fluctuates within a range of plus or minus 30 milliseconds (or a 60ms range of timing imprecision).
To put this into perspective for athletic performance, consider that for a baseball batter to hit an incoming pitch, the margin of error for successfully hitting the ball in play (a good hit), is plus or minus 7ms. In this graphic, this test subject was within that timing range (of plus or minus 7ms) 3 times and was close to that timing range 8 more times (out of all 22 test points). So, this athlete could have a reasonable chance at hitting the ball about 50% of the time.
In watching NFL and NBA film, I often see passes that are not caught by the receiver, because their hands close to grasp the ball too early or too late. They are taking actions that are part of their sport-specific skill, but the timing of the executions of those skills are noticeably too early or too late to be successful with that task. So, I can test in an isolated situation and find athletes executing their skills too early or too late, and I can see on the field examples of professional athletes executing their skills too early or too late.
What can we learn about athletes from these graphs?
When we look at those three pictorial graphs showing the relative timing of slow and fast twitch fibers, we see those stereotypic activations all starting at the same time, but of course, the completion of the activations are different based on the types of fibers.
Yet, it is easy to see these graphics and assume that MFR signals from the brain occur at exactly the times they are needed. But, in real life, in vitro, these signals are not precisely timed.
The most precise athletes are the ones who operate more closely to the center horizontal line in my graphs.