You can see from the chart that the distribution of my model is not dissimilar from the distribution of the Fantasy Pro model, with R² values that aren’t all that different either. This indicates that the two models have a similar level of accuracy, despite the dramatically dissimilar degree of effort invested.

While this does demonstrates that the Fantasy Pro model is a better "fit", and you can see the greater degree of distribution or range of the simple algorithm's projection, the simple algorithm we derived using this method still get’s within a stone’s throw of the same degree of accuracy, with a tiny fraction of the time invested.

Additionally, I spent literally zero time looking for contextual reasons to inflate or fade a player’s potential first down reception per game production. Numerous contextual factors could potentially influence that year-over-year change, such as a new quarterback, an injury to a key player(s) on their team (offensive or defensive, or, quite frankly), a player changing teams. Perhaps the most obvious contextual change year-to-year: age.

One can have a reasonable expectation that players will exceed production from their rookie season in their second seasons in the league. One might also presume that a player at the zenith of his career may be due for negative regression based simply upon age. But what happens in between?

Perhaps year over year changes as it relates to age can improve our projection?