Racetrack charts refuse to die. For old time's sake, here is a blog post from 2005 in which I explain why they don't make good dataviz.
Our latest example comes from Visual Capitalist (link), which publishes a fair share of nice dataviz. In this infographics, they feature a racetrack chart, just because the topic is the lifespan of cars.
The whole infographic has four parts, each a racetrack chart. I'll focus on the first racetrack chart (shown above), which deals with the product category of sedans and hatchbacks.
The first thing I noticed is the reference value of 100,000 miles, which is described as the expected lifespan of a typical car made in the 1970s. This is of dubious value since the top of the page informs us the current relevant reference value is 200,000 miles, which is unlabeled. We surmise that 200,000 miles is indicated by the end of the grey sections of the racetrack. (This is eventually confirmed in the next racettrack chart for SUVs in the second sectiotn of the infographic.)
Now let's zoom in on the brown section of the track. Each of the four sections illustrates the same datum = 100,000 miles and yet they exhibit different lengths. From this, we learn that the data are not encoded in the lengths of these tracks -- but rather the data are to be found in the angle sustained at the centre of the concentric circles. The problem with racetrack charts is that readers are drawn to the lengths of the tracks rather than the angles at the center, which are not explicitly represented.
The Avalon model has the longest life span on this chart, and yet it is shown as the shortest curve.
The most baffling part of this chart is not the visual but the analysis methodology.
iSeeCars analyzed over 2M used cars on the road between Jan. and Oct. 2022. Rankings are based on the mileage that the top 1% of cars within each model obtained.
According to this blurb, the 245,710 miles number for Avalon is the average mileage found in the top 1% of Avalons within the iSeeCars sample of 2M used cars.
The word "lifespan" strikes me as incorporating a date of death, and yet nothing in the above text indicates that any of the sampled cars are at end of life. The cars they really need are not found in their sample at all.
I suppose taking the top 1% is meant to exclude younger cars but why 1%? Also, this sample completely misses the cars that prematurely died, e.g. the cars that failed after 100,000 miles but before 200,000 miles. This filtering also ensures that newer models are excluded from the sample.
In the Trifecta Checkup, this qualifies as Type DV. The dataset does not answer the question of concern while the visual form distorts the data.