Modeling NFL Production in Young WRs
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I first ran across RotoViz when I Googled “Principal Component Fantasy Football” — it returned the first Google hit1 — because I’m a huge nerd. I quickly became enthralled with a fantasy football site that was all about metrics and analytics. Lo and behold, today I’m lucky enough to write for RotoViz,2 which in my humble (and almost surely biased) opinion is the best fantasy football site there is. That’s because we have groundbreaking articles such as Shawn Siegele’s 2014 piece Breakout Age is the Skeleton Key in addition to industry leading metrics like Jon Moore’s Phenom Index. Add to it the best apps on the web such as the RotoViz Screener, and we get a combination that lays the foundation for this particular article. I’ve pulled together Moore’s Phenom Index numbers from 2004 to present, including the individual age and market share components from the linked article above, and meshed them with each receiver’s NFL statistics using the RotoViz Screener App to create a statistical model that predicts PPR fantasy points per game for receivers in their first four years in the NFL to a high degree of accuracy. I’ll show you which variables are significant in predicting success in each of the first four years of a receiver’s career, and how those variables change in significance throughout a WR’s early career. Then I’ll use these models to find the best candidates for success in 2016.
The Model FactorsI created a separate model for each of the first four seasons of an NFL receiver’s career. The factors I looked at included:
- Year (SEAS)3
- Logarithm of draft position (L.DPOS)
- Draft age (AGE.Z)4
- Final year collegiate market share (MS.Z)5
- PPR points per game the prior year (PPR.N)
- Games played the prior year (GMS.N)