Helping athletes see a bigger picture

And shift focus from short-term to long-term

Around New Year 2017 we created an online survey to get a better understanding of how users incorporate software into their everyday training. Specifically, which software (or combination of software tools) they use, and why. The ultimate goal is to help the development of a new user experience for the Open Source training software GoldenCheetah, but we hope that the results are found to be helpful beyond this scope. You can download the report here for results and a bit more context. In a series of articles, I want to reflect on the findings, and explore new insights. You can read the first article here.

This is going to be a really short piece in which I want to highlight a gap between athletes goals and their software usage, and how software might both to blame and be the solution.

Most athletes look very focused on their single ride data. It is the single most important software feature both for recreational and coached or self-coached athletes. I can think of two reasons for this: First, the single ride (or single workout for that matter) is the atom of your training. The single workout, repeated again and again, defines if you will reach your athletic goals. It also reflects „what you have done“ in the most direct way imaginable. Not analyzing it would mean never looking at what you actually do.

The second reason is that at the workout level data is richest, and most information-dense. While a stress/fatigue/fitness chart contains a host of valuable information, it has a lot less data points than a long, hilly ride.

Additionally, the further we remove the analysis from the single workout, the more abstract it gets, and its meaning is less intuitive.

Interestingly, this is quite diametral to most endurance athletes main goal, which usually is long term improvement. Unless you are studying the data of a race or a race-simulation, your focus should be on long term performance tracking, not on analyzing a single ride (beyond the point of checking if you followed your training plan).

The question is: can we nudge training software users into a direction that is beneficial for them? Can we help them taking a step back and looking at their data from a different perspective? Looking at the bigger picture, instead of a single fragment of performance?

I think we can. One way would be to give context to the data provided. Most workout data is likely to quite repetitive; Most people tend to do a limited set of workouts, or ride a limited set of roads (and most likely workout types are coupled to roads as well).

This allows to automatically compare performances over time and helps establish patterns. Those can be stress/fitness/fatigue metrics as well as terrain comparisons or other similarities or differences between workouts.

Wouldn’t it be nice if a software would be able to tell you that you perform better on your 5 minute VO2 intervals when you’re doing them on a 4% climb, instead of a 7% climb? Maybe you already know that already, but for a lot of cyclists this sort of information would open up a new perspective on their training and the sport in general.

Or when the software helps you determine training load levels under which you perform well? Of course, this information is already in the data, but extracting it is beyond most peoples skills.

Turning data into insights is one big step for moving training software from really nice spreadsheets to powerful analytical tools for everyone.