I’ve spend a lot of time recently messing around with the excellent air yards data you can pull from the NFL.com API using the nflscrapR package in R. After the kickoff game last week, I used air yards to show that Alex Smith has, in fact, not morphed into a bomb throwing passer. But there are many more applications for air yards data. The most interesting being for receivers.
If we know where a player was targeted on the field, we build league-wide historical averages for anything from expected yards, touchdowns, and receptions. Then, we can compare those expected number to what was actually posted in the box score. The differences between expected and actual stats can help us identify receivers who may be under- or overvalued based on unsustainable levels of efficiency.
I’ll say this in bold letter: Receivers will not all regress to league-average numbers! I’m not foolish enough to think that equal opportunity will eventually lead to equal stats for Antonio Brown receiving passes from Ben Roethlisberger and an unheralded wide receiver playing with one of the most inefficient quarterbacks in the league.
That said, these numbers can give up an idea of receivers’ potential upside, or downside, if notoriously volatile efficiency moves in the other direction. Continue reading “Using Air Yards to Enhance Expected Fantasy Points”