Good question.

The point of an analysis is to explain the outcome of the race – good or bad. We’re looking for what happened so that we can either replicate it again, train for it or better plan for it in the future. A successful analysis should leave the athlete feeling they have a good understanding of how the race unfolded and how that might relate to their preparations. From my perspective as a coach I start by looking at their result and considering how that stands against my expectations and their performance during training. Is it reflective of how they’ve prepared?

The data analysis side is pretty automated, my spreadsheets produce the graphs from the data and unless I come up with a new way to examine it that side is pretty much done. Sometimes a graph says it all, the second example above from Challenge Henley – I would be pretty confident in saying this was not a good race from seeing that. You can race well and see a drop off in power, a recent example was my athlete in Kona, it is likely to be a relatively small drop off in power though if the race is going to continue strongly. Had I been given that second graph above blind I would have guessed the race did not go well – struggled through the bike, particularly later when average power is so much lower than FTP and if that was happening it’s hard to imagine a positive run.

There are limits though and many more subtle patterns than the one above. In the first graph my sense is we hadn’t dialled in FTP correctly and I’m still not convinced, we had done tests, but results were unsatisfactory and I allowed that to go a little rather than nailing a figure down. Coach can’t excuse his blame there. However during the race Kevin is far too variable in power output and the average power output is far lower than desired. Kevin is capable of more than he did there, but it seems his approach to riding at that point in time leaves him a little exposed. The drop off that occurs in Roth is undesirable, but manageable. It was hard to make firm conclusions, but discussing nutrition afterwards it was also clear he was under eating during the race – this was something we attempted to fix for Henley. It would have been impossible to reach a good conclusion without Kevin’s feedback and discussion and generally that’s the case.

That said athletes do forget and it’s interesting looking through our review of the year how perception of what has been done can differ from the reality. So while I read into their subjective view of the race I’m also trying to tally that with the way they’ve performed in the data if that makes sense. It doesn’t always match. The classic example is the athlete who had the great bike and then died on the run – it wasn’t a great bike, you biked too hard and didn’t run well. Perception was they rode well, and in a cycling-only sense they did, but in an Ironman sense they did too much.

In the case of Henley, with such a huge drop in power output and knowing how difficult it had been for Kevin to stick to plans the past month or two I ultimately felt that the issue could simply be focussed back on this. We obviously planned to be better prepared, but it just didn’t happen. Te thing is do you pull out of a race like that because you’re underprepared or do you do it because it costs a lot. We were probably both a little surprised by how tough his day was, but even so he still went faster than his pre-Roth personal best.

So, before I go on for too long, you can pick up a lot from the file, it helps to have things like a course profile and conditions too to help understand performance. But you do need athlete input to build a full picture and understand how they experienced the race and what they thought was going on.

Russ