Absent names, which player do you want?
Both player A and player B scored a similar number of points, played the same number of games, and had similar point per game metrics (the X). Higher weekly scores are displayed higher on each player’s chart. The entire height of ‘whiskers’ is the total range of outcomes for that player.
There’s a lot of statistics that are all “baked into one” with the first down receptions stat. There’s a target, which is correlated to fantasy scoring. There’s a reception, also correlated to fantasy scoring. There’s the implied yards produced because presumably that first down reception will be a bit deeper on average than your typical target.
The utility of this particular model isn’t to predict fantasy scoring of course, but to predict first down receptions per game year-over-year on the basis of merely a player’s age. The predictive strength of which we can test in the same manner as we did with our points projection model.
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.
I’m not trying to build a complex model that takes into account barometric pressure or the last time a receiver got lucky, I’m simply trying to demonstrate the predictive power of specific statistics in fantasy football. In other words, I’m gonna go ahead and keep it simple.
Correlation to fantasy scoring isn’t all that is necessary to establish which metrics are most predictive. Critically you need to know how likely a player is to recreate a given performance for a specific metric, or how “sticky” that stat is year-to-year.
I was just trying to put together a point-projection model that might assist me in creating some projections I could contribute to my dudes over at @thecuttffb Instead, in true geek fashion, I found myself lost in a tangent of mathematical correlation. Exciting, I know!
