We’re into the summer doldrums of the NFL calendar, but got a small dose of excitement over the last week. Two top wide receivers are in limbo: Jeremy Maclin officially hit the free agent market last week, and has been making the rounds to potential suitors; and Eric Decker – initially expected to be released by the Jets – is now reportedly the subject of trade talks.
It’s fair to assume that the two receivers will cost roughly the same in terms of a new contract, as both are at, or approaching, 30 years old and have been productive – when healthy – throughout their careers. Decker isn’t a free agent, so his cost is currently higher in draft capital, although I’d put a low likelihood on the Jets gaining more than a late-round pick for his services.
The receivers share fairly similar box-score stats, both averaging around 70 yards per game over the last four years. But Decker has been a more dominate touchdown scorer. While box score stats are good at measuring the effect a receiver has when he is targeted and catches the ball, it doesn’t fully capture his influence on the entire offense. (more…)
We have roughly a month until the 2017 NFL draft, when we will learn where our favorite (or not so favorite) prospects will land this coming season. While draft position and landing spot are huge factors for forecasting the success of any running back prospect, I’ve found that we can accurately predict whether a running back will be successful largely based on his production profile and athletic measurables.
We know that collegiate production isn’t everything for wide receivers, it’s the only thing. For running backs, the situation is wholly different. Production matters, but size-adjusted speed is king for determining which running backs will be successful in the NFL.
You can define success many ways, but I’m choosing to use a top-12 fantasy point season (PPR) for running backs. The model’s dependent variable for early NFL success is whether or not a player had such a season within his first three years in the NFL.
We used age, production, and combine measurables to train and test the updated 2017 running back model. The model used 350 running back prospects that entered in the NFL from 2000-2014, splitting the data roughly 2-to-1 into training and testing sets.
After plugging dozens of different production and combine statistics into the model and slowly taking away, one-by-one the least statistically significant, we were left with four (two combine, two production) that provide the most explanatory and predictive power (listed in order of statistical significance): (more…)