2019 Air Results: Wide Receivers

Eric Hardter Posted On June 17, 2020

Back in 2014, as a wide-eyed dynasty optimist with time to kill, I descended into metrics madness trying to find a new way to better quantify receiving output. The result was the creation of the Adjusted Improvement Ratio, or AIR for short. This metric sought to contextualize inter-squad differences in order to place players onto a common pedestal, accounting for differences in efficiency between respective NFL offenses. By doing so, apples could be compared to apples, as opposed to attempting to differentiate between receiving results stemming from Patrick Mahomes dimes relative to Mitchell Trubisky YOLO bombs.

But instead of explaining the concept anew, I’ll defer to my past self to explain AIR, its derivation, and its purpose:

“AIR masks individual deficiencies by taking nearly every pertinent factor into account, including sample size, efficiency and the scope of a team’s offense.

To tabulate AIR, I created and followed the following formula:

AIR = % of Team’s PPR Receiving Points / % of Team’s Targets

The calculated ratio can then determine if a player was able to operate above the mean level of production for his offense. It also provided an answer as to whether a player’s fantasy points correlated to the volume of targets he received. Put another way, AIR describes a pass catcher’s impact relative to his team, as well as the efficiency at which he operated.

I know I’ve said a mouthful, and there’s only so much that can be described about metrics before the actual numbers are used. So in order to provide a sample of AIR in action, let’s consider the Browns’ Josh Gordon and his sterling 2013 campaign. In order to enact AIR, we first need to know how many PPR points were available to the Cleveland pass catchers:

*Team Receptions*	*Team Receiving Yards*	*Points from Yards*	*Team TDs*	*Points from TDs*	*Team PPR Points*
379	4372	437.2	26	156	972.2

As the table above shows, this number can be generated by taking the quarterback data and converting it to how the points would be scored relative to pass catchers. Each reception is worth one point, each yard is worth 0.1 points, and of course every touchdown is worth six points. For the Browns, this amounted to 972.2 possible points. As Gordon was responsible for 314.4 of these points, his numerator for the AIR equation stands at 32.3%.

The denominator is more straightforward. Browns’ quarterbacks attempted 681 (!) total passes, 159 of which were directed at Gordon. Therefore his AIR denominator (% of team targets) stands at 23.3%. Dividing the two numbers results in an AIR of 1.39 for the budding superstar, and also helps qualify the metric as a whole – the bigger the AIR, the better. AIR ratings approaching 1.0 (or below) represent expected, or subpar production.”

The original work can be found here, when I rolled through the results of the 2013 NFL season. Additional work was performed in subsequent iterations but has lamentably taken a backseat to real-world endeavors over the past few years. However, no longer is that the case!

This coming miniseries will include AIR results for both the top 50 wide receivers and the top 35 tight ends, along with breakdowns of results on a per-player basis, with specific focuses on veterans and then first- and second-year players. It will begin with the results for the receivers, ranked from highest AIR score to lowest.

However, prior to that, I’ll offer the following disclaimer: I’m not claiming AIR to be a predictive metric, as some preliminary (unpublished) work has shown it’s not overly sticky from year to year for determining top fantasy finishers. To that end, enough work has been performed and provided across varying fantasy websites to assert that volume is king, and stands as the best differentiator between higher and lower scoring fantasy assets. In other words, 160 relatively “poor” targets will still trump 80 “good” targets every time.

Where I believe AIR can be implemented is to potentially answer the question of “what if?” What if a supremely efficient receiver accrues more targets in subsequent years? Conversely, what if an inefficient receiver fails to sequester as many looks as in years past? This, in my estimation, is where AIR can be best utilized to afford an edge on helping to decide when to trade, or trade for, certain pass catchers.

With that preamble in hand, let’s get to the data! Below are summarized the AIR results for the 2019 top 50 wide receivers. Please note that any points from rushing have been subtracted and are not included – this is a receiving-only analysis. Subsequent articles will provide individual player breakdowns.

PPR Rank	Player	Team	Target	Points	Team Targets	Team Points	AIR	AIR Rank
29	Terry McLaurin	WAS	93	191.9	479	687.2	1.438	1
9	Kenny Golladay	DET	116	250	571	902	1.364	2
28	Marvin Jones	DET	91	193.9	571	902	1.349	3
2	Chris Godwin	TB	120	273.3	630	1064.5	1.348	4
11	DeVante Parker	MIA	128	246.2	615	883.4	1.339	5
37	Darius Slayton	NYG	84	170	607	929.1	1.322	6
21	AJ Brown	TEN	84	205.1	448	829.2	1.319	7
47	Tyrell Williams	LV	64	143.1	523	891.6	1.312	8
4	Cooper Kupp	LAR	134	270.1	632	978.9	1.301	9
22	John Brown	BUF	115	214	513	747.9	1.276	10
27	Calvin Ridley	ATL	93	191.6	684	1104.4	1.276	11
20	Stefon Diggs	MIN	94	212	466	827.3	1.270	12
15	DJ Moore	CAR	135	228.5	633	849	1.262	13
18	DJ Chark	JAC	118	221.8	589	884	1.252	14
19	Courtland Sutton	DEN	125	219.2	504	719.5	1.228	15
13	Tyler Lockett	SEA	110	235.7	517	906.1	1.223	16
40	Diontae Johnson	PIT	92	157	510	721.1	1.207	17
32	Tyreek Hill	KC	89	186	576	1007.8	1.194	18
10	Amari Cooper	DAL	119	245.9	597	1043.1	1.183	19
44	Golden Tate	NYG	85	152.6	607	929.1	1.173	20
50	Breshad Perriman	TB	69	136.5	630	1064.5	1.171	21
34	Cole Beasley	BUF	106	180.8	513	747.9	1.170	22
7	Allen Robinson	CHI	154	254.7	580	820.1	1.170	23
41	Robby Anderson	NYJ	96	159.9	521	748.1	1.160	24
16	Mike Evans	TB	118	230.7	630	1064.5	1.157	25
33	Emmanuel Sanders*	DEN	97	182.9	504	719.5	1.153	26
12	Jarvis Landry	CLE	138	236.4	539	805.4	1.146	27
43	Chris Conley	JAC	90	154.5	589	884	1.144	28
17	Tyler Boyd	CIN	148	224.6	616	829.2	1.127	29
26	Jamison Crowder	NYJ	122	197.3	521	748.1	1.126	30
1	Michael Thomas	NO	185	375.5	581	1058.4	1.114	31
35	Larry Fitzgerald	ARI	109	179.4	554	822.7	1.108	32
39	Mike Williams	LAC	90	161.1	597	980.6	1.090	33
46	Marquise Brown	BAL	71	146.4	440	833.5	1.089	34
8	Julian Edelman	NE	153	247.7	620	924.1	1.086	35
36	Curtis Samuel	CAR	105	152.7	633	849	1.084	36
30	DK Metcalf	SEA	100	190	517	906.1	1.084	37
23	Davante Adams	GB	127	212.7	573	885.3	1.084	38
3	Julio Jones	ATL	157	274.4	684	1104.4	1.082	39
24	Michael Gallup	DAL	113	212.7	597	1043.1	1.077	40
42	Randall Cobb	DAL	83	155.8	597	1043.1	1.074	41
6	Keenan Allen	LAC	149	259.9	597	980.6	1.062	42
5	DeAndre Hopkins	HOU	150	262.5	534	895.3	1.044	43
48	Sterling Shepard	NYG	83	132.6	607	929.1	1.044	44
31	Deebo Samuel	SF	81	155.2	478	878.2	1.043	45
25	Odell Beckham Jr	CLE	133	201.5	539	805.4	1.014	46
14	Robert Woods	LAR	139	215.4	632	978.9	1.000	47
45	Dede Westbrook	JAC	101	150	589	884	0.990	48
38	Christian Kirk	ARI	108	156.9	554	822.7	0.978	49
49	Sammy Watkins	KC	90	137.3	576	1007.8	0.872	50

*AIR value is a weighted average of statistics accrued from stints in Denver and San Francisco

Find me on Twitter @EDH_27 (even though I rarely post anymore)

Author
Recent Posts

Eric Hardter

Contributor at Dynasty League Football

Eric is a Boston College chemistry grad school survivor with a minor in dynasty football. He prefers to utilize both statistics and sarcasm whenever possible, believes in process over results and thinks "Hot Takes" are the scourge of the fantasy landscape.