Friday, October 05, 2012

Visual Analytics and the Athlete: Part 1

"There is no failure. Only feedback. "
~ Robert Allen

Athletes are human beings. Human beings are physiologically, mentally, and emotionally creatures of habit. We typically gravitate towards those things we do well, especially during times of stress or failure in other areas of our lives. So what if you could somehow capture these retreats to familiar ground over time in a picture? Maybe the picture would be an affirmation. Or maybe it would indicate a destructive tendency.

People think of analytics simply as a way of validating, or “scoring”, a current course of action, i.e. what went right or wrong and am I doing better or worse. And while they are very capable, you quickly discover the usefulness of this type of scoring is limited, because more often than not you create a picture that you already know, especially if it is simply a picture of what you are doing right now. Visualizations, especially those that span time, extend analytics allowing you to see your habits, both known and more importantly unknown. And these habits are frequently the things that prevent us from reaching a new level of performance. Let’s face it if what we did over and over and over were really working, we wouldn’t still be searching for ways to improve! “But these things worked for me before?” Yes, they did. And when they worked they were reasonably if not entirely new to you. At that point in time they were an unfamiliar stressor on your mind, body, or soul. And you adapted to handle them. And those adaptions led you to some success. And you did it again. And maybe had a bit more success, though not quite as striking a change as before. And again, and less striking success. You see as you adapt those stressors become the norm. They become “comfortable,” a refuge.

Visual Analytics Basics: I know how you THINK you did, but what do your EYES tell you?

So as an analyst what am I looking for? Well I’ll start by telling you what I’m not looking for…counts, absolute numbers of any kind (total miles, total hours, etc…). While they are usually the easiest measures to capture, they tell you so little really, especially if you are trying to normalize your findings across multiple datasets (e.g. compare two or more athletes). Sure they might relevant to each individual, but they don’t explain much. Changes, slopes, ratios, area under the curve or between two curves…those tell you more. Wow, this sounds a whole lot like geometry and calculus! Yes…yes it does. And that is why we use visuals, because many times our eyes can tell us the story without having to write the equation. And for the mathematically inclined, the visuals show you which equations to write!

Because we humans are creatures of habits, I look at these visuals for patterns, both repeating and original; consistency and breaks, expected and especially unexpected. At the bottom you’ll see 4 line charts. These basic graphs are all the same data set. I’m using a technique I’ve really come to appreciate, masking all series, labels, and legends. Sure I know which measures were put on the chart, but I don’t know which line is which. I also know the full date range used, but cannot see which day is which. Finally the charts follow a traverse down my time “dimension”, i.e. to finer and finer slices of time. The first is a yearly view (3 yrs). Next you see the quarterly view of the same data. We move then to monthly, and then finally daily (which is only a partial chart). Traversing the time state is useful in finding the right amount of detail to tell you the story but not confuse things with outliers.

Okay, anyone still awake? No? Well, if you care to learn more, stay tuned for Part 2 where I get crazy with more detail about creating relevant measures, and perhaps delve into “Negative Space” analytics (my term so don’t bother looking it up!).