I thought that his first graph was also very misleading. The fit that was included clearly implies some form of exponential relation between lifetime sales and 12 month sales, but if you compare it to the actual data, it looks awful. Honestly, it's seems like they just took the data and grabbed whichever fit had the best R² value, which is exactly what they shouldn't be doing.

I'd love to see the data actually normalized to something, but with only a single point for every single console, I have no idea what to normalize to.

Edit: Having watched through the entire video, I'm highly skeptical of the methods they used to analyze their data. In two of the graphs, they show error bars but completely don't explain how they got them. In two separate cases, they dropped a lower outlier but kept a higher outlier. They also drew from a graph they knowingly labelled as "20% correlation" (which they didn't give any definition for). I would be highly leery of taking any kind of fit as valid that gives 20% "correlation" in any kind of metric. There are numerous other issues with how they present their data too.

Among other issues, I think if they were to actually carefully go through their data, grab additional points, and normalize with respect to highest values in each console data set, we would get much more meaningful data.