Friday, January 6, 2012

Making sense of the data

As you probably know from reading this blog, I am big "data-guy" when it comes to coaching athletes. Yes, I believe in coaching technique and especially the mental side when athletes have high goals, but in the day-to-day training aspects, data tells us how the athlete is progressing.

Much of the posts I've shared here have been where the data is clearly showing improvement for an athlete, but it isn't always easy to see improvement, and sometimes different data collections may tell a different story. So, making sense of the data collected is the biggest challenge, especially when you can get conflicting stories.

Here's an example of a PMC chart for running files from an athlete: (click on images to enlarge)

You can see I've highlighted an area where it appears we have some plateauing, or even some possible regression. The dots with lines represent the 10 best performances in the time period, over certain time intervals, such as 30 seconds, 1 min, 6 min, etc. Ideally, we want to see these lines on an upward slope, representing faster speeds produced.

However, we still see some of the 10 best performances of the time period so far. Not only do we see these happening, the 60 min samples are the best so far, and the ATL, (pink line, Actue Training Load representing load or fatigue of the athlete), is far and away the largest the athlete has experienced so far. The athlete went on a holiday vacation and was able to run a lot more. The fact he was still able to show some of his best performances in such a fatigued state, is encouraging. But again, this is only one data collection, so let's look at some other data and try to see a more complete picture.

Here is a chart from the same athlete, which tracks their average run pace per week, for ALL runs. This shows his average pace is at its highest. This data could be skewed if a certain workout was designed to be faster, but that hasn't been the case. This athlete has focused on Zone 1 run economy, and one run per week with 15 second surges, so the training has been consistent and inferences of improvement can be drawn.

In fact, this athlete lives in a very flat place in the US, and for this vacation went to a very hilly part of the US. The fact these paces were achieved in a more challenging terrain, under higher fatigue levels is encouraging. But let's continue to look at the data in a different perspective...

These charts take the best 30 min sample in a single week of run paces on the left, watts on the right, and plots them on the graph. You can see the trends are continuing up. The outlier on the left chart is a test workout. When we look at this, considering the whole picture of the data, and then look at the summary chart of the training so far, we see improvement across the board. The only value not showing improvement so far is 20 minute pace, because of a test workout to determine run FTP.

So even though the run PMC chart at the top showed a plateau of performance, when we considered all the data points, and even the subjectivity of where the athlete was training over the holidays, we can be fairly certain and excited that the athlete is progressing quite well with the current training focus, and there is no need to change the current course, yet.

If you have data collected, and are interested in having me view it and share what I see, contact me at coachjimvance at g mail dot com.

Coach Vance

1 comment:

Jim said...

where do you get the "average pace" chart? I can't seem to find it in Training Peaks. thanks!