Monday, March 26, 2012
Training isn't perfect, but we can learn to do it better
I came across this PMC chart for an athlete who got hurt a few years ago, and unfortunately was not able to start their Ironman event. Anytime you work with an athlete, you're dealing with a unique experiment of one. You can bring experience from other athletes and training philosophies, but in the end, no two athletes are the same.
From reading this blog, you know I am big on data, and this photo helps illustrate why. This athlete got injured, and I was wondering what might have been the trigger to getting injured. Was there a mistake I made in planning, or they made in possibly not following the plan, that we can learn from for doing better training next time?
In the above photo, you can see where I identified 2 instances where Acute Training Load, (ATL), shows a very large jump, followed not much later by an injury. There's even another moment where the athlete is sick, and identified in the graph.
This information showed me what some key metrics and numbers to avoid with this athlete, and how much risk is reasonable in training, versus just likely to injure them again.
If you're using technology on a consistent and committed basis, you can begin to learn these things about your own "experiment of one". Training is never perfect, but with the power of data and retrospect, we can certainly learn to do it better, making less errors, or at least making training judgments with the odds in our favor.
If you're looking for some help with this, I am happy to review your files and tell you what I see, and what I think. But look for trends yourself, and see if you can learn anything from them as well.