Using Air Yards to Identify Buy Low Candidates

Each Wednesday I’ll be breaking out wide receiver air yards and using the data to identify undervalued WRs and breakout candidates.


Air yards give us a complete view of a WR’s game. They show us what volume of yards are thrown at a guy, what amount he caught, and how many yards after the catch he created on passes he did catch. The NFL produces some very cool charts on select reciever performances using their NextGen Stats. Below is one for Kelvin Benjamin from Week 1 last season.


What I love about these visualizations is that they graphically depict the information captured in air yards.

From the above photo, here are the corresponding air yards data:

Air yards (white and gray lines combined): 107
Completed Air Yards (white): 65
Incomplete Air Yards (gray): 42
YAC (green): 26

From these raw counting stats, we can create new metrics.

aDOT1: 8.9
RACR2: 0.85
Market Share Air Yards3: 0.46


The basic premise of this series of weekly articles is to use air yards data to identify players that are getting lots of opportunity in an offense but have failed to produce. Early in the season, due to the variance inherent in NFL play, good receivers can have bad games. Since volume is a more reliable indicator of quality than efficiency in small samples, we will use it to help us sift through the noise and find undervalued breakout candidates.

NFL coaches deploy their WRs in very consistent ways from week to week. Because of this air yards stabilize quickly in-season. One way to measure that stability is to look at how well Week 1 air yards predict the remainder-of-season air yards. We can also compute market share air yards and compare both to how well standard receiving yards in Week 1 predict remainder-of-season receiving yards.1screen-shot-2016-09-13-at-9-52-33-amWhat this table tells us is that given just one week of data to work with, MS air yards are 38 percent more predictable compared to receiving yards.2 In fact, if we see how well just MS air yards predict receiving yards, we find that the r-squared is 0.266. In other words, after Week 1 the best predictor of future receiving yards is not actually receiving yards, it’s MS air yards. This is an important finding, and should give us an edge predicting future WR production compared to using just standard box score stats.


In a change from last year, players will also be sorted by WOPR, or Weighted Opportunity Rating. WOPR allows us to compare slot receivers who get lots of targets but not a lot of air yards with players who receive fewer targets but a greater share of the team’s air yards. WOPR takes share of team air yards and share of team targets and weights them based on how well they predict both PPR and standard fantasy points. Targets are given roughly twice as much weight as air yards, and when you combine the two you are able to accurately assess the opportunity each receiver is commanding. One good example of this is Marvin Jones and Golden Tate in 2016. Last year Jones and Tate saw completely different types of opportunity at different times of the season. Yet at the end of the year their season-long WOPRs were exactly equal at 0.50.



Also new this season will be an increased focus on DFS. Defensive matchups, Vegas odds, QB play and more will be integrated into the weekly analysis. Many of the tools on will be leveraged, and if you are curious on how to use them in your own DFS planning, these articles should serve as a good primer.


  1. aDOT was created by ESPN’s Mike Clay and can also be thought of as air yards per target  (back)
  2. RACR stands for Receiver Air Conversion Rate. It measures how many receiving yards a player creates for every air yard thrown at him. It’s the most predictive efficiency metric there is for NFL receivers, better even than yards per route run  (back)
  3. Market share, or share of team air yards, adjusts for a team’s total passing output and gives us a better idea of a receiver’s importance to the passing game expressed as a percentage.  (back)

Josh Hermsmeyer

Air yards, Numbers Game, and predictive modeling.
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