Tuesday, October 23, 2012

Visual Analytics and the Athlete: Part 3

"Sometimes you learn more from what you don't know than what you do." - Another anonymous comment.

So now you've got the data. You're doing all your weekly workouts. Weight is good. Eating is good. The "A" race comes up and boom...you come up short. What happened? Let's go back to the very first charts from the very first post.
What do we know? We know that in the past there were two defined peaks, both of which correlate to a race. We know that from the first to the second there is a rapid drop and climb, some somewhat jagged times, followed by a leveling and a climb to another peak. i.e. Following Lake Placid in 2009 I shut it all down for a time to give some attention to my life. 2010 was defined as a "bridge" year to get to 2011 without a significant degradation in ability. And 2011 was the build up to Coeur D'Alene. And if that's all you got, that is actually a pretty fair interpretation. But let's dive a little more into the weeds which means discussing the negative space, i.e. the stuff we don't see and how it influences the chart as much as the training which we've quantified and recorded.

See that climb and baby peak just before Lake Placid followed by the immediate retreat? That's Mooseman 2009, arguably one of my best races ever. The lead up to that race was very trying emotionally and again following it my family life came under such duress and the very thought of competing in Lake Placid seemed unrealistic. I won't rehash it here, but it is documented elsewhere in this blog. Anyway once it was determined LP was a go there's the final push to the race and race day itself. So the chart has the steep slope not because the process of peaking for the race brought about some great physiological improvement, but because the data leading up to the race was fairly inconsistent and spotty. Life didn't normalize much until the middle of 2010. With the exception of one dip which I believe was related to late summer, kids, family, time off, etc... The lines actually start to track on a fairly consistent pattern to Coeur D'Alene with a climb into race day which is supported more by peaking than anything else.

The point of all this is that forces outside of the data and outside of the graph are influencing what is in the graph in the first place. It's based on the premise that a type-A athlete, and if you're going through this stuff you're type-A, will perform precisely on plan if there are no other "interruptions" from life. So when we see that they are not tracking to plan, we can assume something is going on. The components of the negative space, the items that are not cast on paper or on a computer screen, actually contribute to the uniqueness of each athlete's graph as much as does their training plan. As a coach or someone evaluating the visual, the mere existence of randomness in the patterns is an indication that something might be going on with the athlete, so maybe it's worth a follow-up.

"But if this other data is so important, does that mean we should stop recording our current data?" No. But if feasible you should find a way to record these other events. This may come in the form of a training log, comments, a blog, etc... And yes to build a predictive model we will need to somehow quantify these outside influences, but that is a topic for the next post.