The Analytics of Running

One definition of analytics is:

The systematic computational analysis of data or statistics.

The analytics of running  can be as simple as observing that I ran faster today than I did yesterday, or it can be as complex as plotting a graph of daily run times and looking for verifiable trends brought about due to the impact of any number of environmental variables. In general, I opt for the former.

Well, that’s not completely true. I opt for keeping it simple but I do like to know what I’m doing right and what I’m doing wrong. Suggesting that I could be doing some things right and some things wrong also suggests that there are variables over which I have control and, in fact, there are. How often do I run? How far do I run? And, how fast do I run?  These are simple questions, but they represent variables over which we potentially have a great deal of control.

Variables over which we have less control might include the time of day that we run, the food that we take in preparing for a run, and the temperature and other weather related conditions. Even though we might have less control over some or all of these variables, they can still have just as great an impact on our running success as those over which we have more control. In fact, as I’ve gotten older a new and very important variable has introduced itself to the mix. How quickly do I recover from a training run?

Oh, and here’s another interesting variable for you! As a musician, I am completely unable to focus on anything if music (that I enjoy) is playing. That includes reading, studying, pondering something important, and even sleeping. But, interestingly enough, listening to music on a run (I’m talking about a playlist of favorite songs that really pull me in) does a great job of distracting me from the discomfort associated with faster speeds and also tends to drown out the heavy breathing that is constantly trying to remind me how hard I’m working and how tired I’m getting. I don’t know if that makes sense to some of you, but it sure works for me.

Earlier I mentioned the time of day that we run. Unfortunately, I’m an evening runner. I’m at my best in the later afternoon and running has never come easy for me in the morning – especially early mornings when most races tend to start. Fortunately, I’ve discovered that if you keep everything else the same between training runs and races, the time of day factor can be psychologically pushed aside pretty easily. Perhaps that’s because over the years I’ve become more of a morning person – even though that has yet to spill over to becoming a morning runner.

Oh, speaking of variables, I have a great love for outdoor running and a massive hatred for that hideous and evil dreadmill. I’d just about rather take my chances in a lightning storm than run on one of those nasty devices!

For me, the analytics of running is a three step process:

  1. Determine the variables that contribute to the success of your training runs and races.
  2. Determine the order of importance of those variables upon your results.
  3. Prioritize your focus toward the variables on the top of the list.



2 thoughts on “The Analytics of Running

  1. This post really speaks to me. As someone who loves data, I definitely like to see the bigger picture in all of the details that I input from day to day — and figure out which things are affecting my training the right way (and vice versa).

  2. Hello utalap! We must be cut from the same fabric because I also enjoy collecting data and particularly enjoy analyzing it. Confirming and invalidating my assumptions is interesting, but even more interesting is discovering a data relationship where I had not even assumed one to be present. Data Science meets running? 🙂

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