Challenge Henley 2012 Analysis: The Impact of Inconsistency

What happens when the consistency goes? For one of my athletes it’s a tough day of racing and a slightly unsatisfactory end to an otherwise good season. In this case Challenge Henley was a race too far; after a successful performance in Roth, Kevin was unable to sustain the training needed to repeat in Henley. The race analysis itself is simple – the graphs show an athlete off his game. In one case overall fitness helps compensate for pacing errors, in the other he is on his own. More interesting then this analysis is how we went from an Ironman personal best to what Kevin labelled a disaster.

Let’s start in Roth. Despite a 90 minute improvement on Kevin’s previous best time, this was not a perfect race. A swim of 1:14 was within expectations, the bike performance was more conservative than his FTP would suggest and the run that followed was slower than planned, the second half dominated by walk breaks. Based on an estimated 260W FTP the graph below shows Kevin never gets into his stride, showing a highly variable power output (VI often above 1.10) with an overall declining profile. Given the apparently moderate pacing of his race this fluctuation in power may help to explain the decline on both bike and run. Although for the latter it a running schedule hampered by an existing achilles issue could equally be to blame.

Challenge Roth 2012: Kevin's Power Output Compared to Pacing Goals

The overall result was positive – a personal best. However, the execution clearly allowed room for improvement. Nutritional strategy could be refined, consistency of pacing could be addressed; in theory these two factors could help deliver a better bike and run performance. We thoroughly debriefed in an attempt to identify all potential contributors to the decline in performance. I can’t rule out an incorrectly set FTP, when an athlete fails to reach targets this has to be a consideration, on my side I clearly needed to prescribe more consistent checks. we had 2 months to prepare for Challenge Henley and every confidence we could iron out a better race strategy.

Challenge Henley 2012: Kevin's Power Output Compared to Pacing Goals

But what happened in September was more of the same. Worse. The graph above shows that although Kevin started on target, he experienced a more severe decline in power. His report matched the power profile: feeling uncomfortable from the start, unable to stay aero and then really suffering from about halfway. The data offered no specific reason for the failure to race, the response had been so immediate. If anything the riding was more controlled, less variable than before. Had something happened in the swim? It had been harder than in Roth, but sufficient to explain the rapid decline? I didn’t need to examine his run data in detail to understand why that had also failed to follow the plan.

Drawing a blank from the race data I looked back and reexamined the two months preceding race day. Following the debrief plans had been put in place to transition from recovery back to a short block of training designed to restore fitness to Roth levels and to practice race execution, but Henley proved to be a race too far. Kevin’s training was inconsistent, some initial success quickly fell apart as his motivation dropped. First the Olympics distracted him, then a period of illness derailed him and a break consolidated this. By the time we came to taper, there was little to taper from – a smattering of successfully executed rides and runs – very little swimming – over a two month period. Kevin was not race fit. At best he had maintained some form from his post-Roth recovery.

Roth had been the product of weeks of consistent training. While it hadn’t been perfect a period of 6 months preparation had done a enough to get him to the finish line in a new personal best. On reflection the decision to race again had been a mistake as the motivation wasn’t there to repeat the training cycle. Rather than building on success we stretched goals out without fully committing to the process. The signs were present in Kevin’s feedback and I tried to shift momentum back towards the race, but ultimately training has to be done for it to work. I’d already experienced this when I raced in Roth after weeks – months – of inconsistency; it doesn’t work.

There are lessons to be learnt on both sides. Kevin is taking a break, but will be revisiting Roth next season with fresh goals and a better appreciation of the importance of preparation, especially if he wishes to approach or even break 10 hours. And I need to be more emphatic in ensuring we follow the plan with clearer implications when we don’t. We cannot afford doubts around FTP or targets in training and racing; we need to ensure that run volume is maintained throughout the season, although achilles tendonitis makes this a challenge; we need to practice race strategy throughly. These are not new ideas, they have a place in a plan for a reason; it’s surprising how easily that can be forgot. Reviewing this season has been a reminder how important they are for both of us.

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  • Russ Brandt

    Hi Russ,
    I love your analysis of the race file. A question I have, when you are reconstructing a race, how much of it is from the files vs what the athlete tells you? Would you draw many of the same conclusions just looking at the race file alone or must it be done in combination with the athlete’s subjective report? Obviously things like nutrition must be reported, but I wonder if you can see a race file and “predict” how the athlete will report the race went subjectively?

  • Russ,

    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.




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