A Simple Box Score Metric for Valuing Wide Receivers

One of the projects I’ve been working on is creating a model of player value by position based on converting box score based metrics to a single number of points added. This model is by no means the be all end all of player value, but it would provide an evidence-based starting point for estimating the value of players across positions. In a previous post, I explained why measures of a players contribution should always be in terms of Expected Points.

For now I’m only going to deal with the value a receiver adds by catching passes. It is undoubtedly true that an NFL wide receiver can contribute to his offense scoring points by drawing defensive attention away from other players or by blocking well in the running game, but these contribution are probably an order of magnitude less important to a receivers full contribution than his pass catching and they are much harder to measure, so I’m going to set these considerations aside for now.

Receivers with under 20 targets in a given season over 2011-2015 averaged about 0.5 NEP/Target, so this can be considered the per target value of a replacement level player. Adam Harstad has written about the fact that for receivers, simply being targeted is an efficient play for the receiver because receivers who can’t get open aren’t targeted at all. This implies that if we forced a replacement level player to see a high share of targets, their efficiency would suffer, so we would expect the NEP/Target for a replacement level player thrust into a starting role to be marginally lower than 0.5. The 25th percentile of receivers is about 0.45 NEP/Target over the past 5 years, which strikes me as being about right. To calculate the value a receiver added in a given season over a replacement player, simply take the players’ total Net Expected Points and then penalize the player 0.45 expected points for each target they receive.  This method leaves approximately 90-100 receivers a season more than 5 points above replacement level, which is probably about right as well, generally players don’t stick around the league too long if they aren’t one of their teams three best receivers.

A weakness of this method is it does not account for quarterback quality. I’ll get back to this in a later post though it is very difficult to separate out a quarterbacks’ influence on his receivers, so it is best to leave quarterback quality out of the initial model.The top 10 Receivers from the 2016 Season and their Value over Replacement are presented below:

Name Targets NEP/Target Expected Points Added
Mike Evans 171 0.84 67.27
T.Y. Hilton 155 0.84 60.09
Julio Jones 129 0.89 57.06
Jordy Nelson 152 0.81 54.75
Brandin Cooks 117 0.84 46.21
Adam Thielen 92 0.95 45.7
Pierre Garcon 114 0.84 43.93
Antonio Brown 154 0.71 40.4
Tyrell Williams 119 0.78 39.07
Doug Baldwin 125 0.75 37.78

For the most part, the players at the top of the list got their on high volume and efficiency and are all part of the NFL’s elite passing attacks. Adam Thielen and Pierre Garcon probably are not top 10 receivers at this point in their career, but we would expect there to be significant variation between the 10 most skilled receivers and the 10 receivers who add the most value in a given season just due to luck and random variation. In other words, the observation that Adam Thielen was extremely efficient when he was targeted last season is a fact, but it is not by itself an argument to extrapolate that he is one of the 10 best wide receivers in the NFL. As stated, this is a descriptive metric meant to summarize a receivers’ entire stat line into a single value, so it is by definition going to suffer the same problems of any analysis drawn from the box score. Nevertheless, it is a far more useful starting point for evaluating the value a receiver added when targeted than simply using Yards, Touchdowns, Yards/Target, raw Net Expected Points, or any other box score driven statistic.

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