With an idea of how work output is calculated and how we estimate a model for looking at average work output over time, we are now ready to talk about fitness scores. At a high level fitness scores provide a snapshot of your general level of metabolic conditioning. Let’s imagine that you’ve just completed entering in a months worth of workouts, assuming you’re on a 3 days on, 1 day off schedule for your training that adds up to about 22 workouts (we like round numbers so call it 20).

Next, let’s suppose you were to add up all the work done (in terms of ft-lbs) across those 20 workouts. If the time domains of those workouts were pretty evenly spread out over 0 to 20ish minutes (e.g. you did a workout that was around 1 minute, 2 minutes, etc.), that total work value would give you an idea of your “total work capacity” in the 1 to 20 minute time domain. This is essentially what we call your fitness score.

However, a couple of issues come up with estimating fitness score in the way just described. First, while the distribution of times we hit over the course of 20 workouts will generally tend to cover the 0 to 20 minute time domain, rarely will they be as evenly distributed as described above. Thus if we happened to be even moderately biased in short, medium or long time domains (as shown in the figure below), that would throw off the fitness score calculation. Second, as we saw from before, there can be a TON of variation in work output between workouts, even those that take about the same amount of time to complete (recall our example of Cindy vs Mary). This makes it difficult to meaningfully compare fitness scores over time. In order to alleviate this issue we use the estimated work output values from the work capacity curve over the 0 to 20 minute time domain to get a more reliable estimate of fitness score.

Returning to the example shown in the figure above, each of these is actually generated from the same underlying work capacity curve, and should produce the same (or similar since we added some noise) fitness scores. If we simply add up the work output values we see that there is considerable variation in the fitness score estimates (see table below), however using the area beneath the work capacity curve (basically summing up the work estimates at each point along the curve, corresponding to the shaded region in the plot below) the estimates are very close. The reason we see a discrepancy in the values is that the number of points being summed over is different using the work capacity curve as compared to the raw data.

Sum Raw Work Output Sum Based on Work Capacity Curve

592,874 ft-lbs 46,442,732 ft-lbs

791,343 47,356,813

1,032,530 46,735,558

830,705 48,777,134

It’s important to note that we’re not saying that looking at the sum of work output values over a week or month is not a useful metric, but it doesn’t lend itself as nicely to tracking progress over time. In our next post we’ll look at tracking fitness score over time and what the trends can tell us about our training.