Scouting The Draft: A Different Perspective On The Tight Ends

Lies, damn lies and statistics. That is an expression most are familiar with. It is often attributed to Mark Twain, who attributed it to Benjamin Disraeli. It is meant to convey how statistics can be selectively taken out of context to bolster an otherwise weak argument. The phrase is often used to convey an outright contempt for statistics in sports discussions. As is sometimes said, all you need to know is in the tape, or "the tape don't lie."

This, I think, is going a bit overboard in dismissing the possibility that statistics, when properly used, have any value whatsoever in analyzing athletic performance. Certainly statistics by themselves cannot convey the wealth of information and judgments which can be obtained by a seasoned eye in tape review. But there is no need for a one or the other false dichotomy. Statistics can provide some level of objectivity and insight into areas we might otherwise miss. They can raise new lines of inquiry and cast doubt on certain "everybody knows this" kind of false certitudes. They can make you question whether you are truly capturing everything that is important about a player in film study alone. In short, they are just another piece of the puzzle. Properly utilized they can provide valuable information that might otherwise get overlooked or be misunderstood.

In any event, if you are somebody who has no use for statistics then this article may not be for you. For the rest of you, those who believe statistics have some place in analyzing players, let's take a look at some of the top rated tight ends in the 2017 NFL draft class. In particular I want to try to remove some of the distortions that comparing counting stats can cause in evaluating these tight ends.

The only thing we will concern ourselves with here is the receiving ability of the 2017 class of tight ends. Blocking abilities are obviously a big piece of the puzzle for tight ends, but they don't lend themselves as readily to statistical analysis.

The goal here is to make the counting stats more of an apples to apples comparison.  Some of the the problems that arise are as follows:

PLAYERS HAVE NOT PLAYED IN THE SAME NUMBER OF GAMES

If one player plays in nine of his team's 13 games due to injury or other circumstances and another player plays in all 13 of his 13 games we would expect the player playing every game, all other things being equal, would have approximately 40% more production. When we just use counting stats like receptions, yards and touchdowns, we lose the nuance of some players having a lot more opportunity than other players due to playing a higher percentage of their teams' games. We will attempt to adjust for this distortion by looking at production as a percentage of the team's production, with the team's production prorated for the percentage of games played by the player in question.   For example, suppose Player A played in seven out of 14 games his team played, missing seven due to injury.  Further, suppose Player A's team passed for 4000 yards on the season.  If we're only looking at counting stats, a production level of, say, 500 receiving yards for Player A would look fairly pedestrian. However, if we prorate the receiving yards to account for Player A only playing in half the games, those same 500 yards are now achieved in a 2000 yard passing offense.  It doesn't take a genius to realize that achieving a 500 yard season in a 2000 yard passing offense is a very different animal from achieving the same 500 yard season in a 4000 yard passing offense.  So we are going to try to level the playing field and compare apples to apples by ranking players based on percentage of a team's prorated passing attack rather than raw counting stats.

There are of course always limitations to any methodology.  As some say, the most importatnt ability for any football player is availability.   Player A misses half his games due to injury and Player B never misses a snap. That may well be telling us important information regarding the two players and their projected availability going forward. This is something difficult to get a handle on statistically based on one year of statistics due to small sample problems.  A player may miss games because of a freak injury unlikely to recur, or he may miss games because he is injury prone. Statistics won't solve that puzzle for us, so I'll leave it to you to determine what, if any, credit should go to the players who managed to stay on the field every game.

PLAYERS  PERFORM IN VERY DIFFERENT SYSTEMS

Some teams pass the ball a lot. Some don't. Clemson threw for more than 5000 yards. Iowa threw for less than 2000 yards. There is no realistic scenario in which an Iowa tight end could put up, say, 800 yards receiving. There just aren't enough receiving opportunities to go around. Likewise, if a Clemson tight end puts up 800 yards receiving, it's a nice year, but hardly the kind of eye-popping, this isn't humanly possible type season it would be in Iowa.

TIGHT ENDS HAVE MAJOR BLOCKING RESPONSIBILITIES

Unlike wide receivers, tight ends are often asked to block on passing plays.  This affects receiving numbers as the tight ends generally do not have as many opportunities as other receivers to make plays in the passing game.  A player like Evan Engram is rarely asked to block on a passing play, while more traditional in-line tight ends may spend a large portion of passing plays protecting the quarterback. Adjusting for differences in blocking responsibilities is beyond the scope of this article, but it is something to keep in mind when trying to determine the relative merits of receiving numbers among the tight ends.

Now let's get to the numbers.  Below you will find charts of the top tight end prospects in the 2017 NFL draft sorted by traditional counting statistics: receptions, yards, touchdowns.  Following the counting statistics charts are charts showing adjusted numbers, accounting for differences in games played and in opportunities based on how pass heavy the offense each tight end played in was.  As an example, if Player X played in only 7 of his team's 14 games, and his team passed for 150 receptions, 2000 yards and 10 touchdowns, then Player X's share of the offense would be based on 50% of his team's production, since Player X played in only 50% of his team's games.  So if Player X had 20 receptions for 250 yards and three touchdowns, that looks pretty poor in traditional counting statistics.  However, when adjusted for his lack of opportunities, Player X's production takes on a different complexion.  Player X's adjusted numbers, prorated as a percentage of the team's production for the percentage of games Player X actually played, accounted for more than 26% of his team's receptions, 25% of his team's yards, and 60% of his team's touchdowns, which would be terrific numbers if Player X had played all the team's games in a more pass happy system.   Had Player X been healthy all year and played in a more prolific passing offense, and his share of the offense remained constant, Player X looks a lot more like a major threat as a receiver.  For example, put Player X in a passing offense that throws for 340 receptions,3700 yards and 25 touchdowns and, assuming his share of the offense remains constant and he stays healthy all year, Player X's counting numbers would be 90 receptions, 925 yards and 15 touchdowns.

It is, of course, not as simple in real life as magically transporting a player from one offense to another and watching his numbers shrink or grow perfectly in synch with his share of his original team's offense.  Levels of competition widely vary, there are sample size and random fluctuation issues, there are issues with reduced production when and if defenses start keying on a tight end as a major receiving threat rather than mostly a pass blocker, etc.  So you should take the adjusted numbers presented here with a grain of salt.  They should, however, help to present context to raw counting numbers and perhaps change your perspective with respect to certain players who  may have put up big numbers in pass happy systems when comparing their receiving prowess to players who simply lacked opportunities, whether due to injuries or run heavy systems.

We'll start by sorting the counting statistics by descending order of number of receptions, which produces this chart:

Player	Receptions	Yardage	Touchdowns
			.
Evan Engram	65	926	8
Gerald Everett	49	717	4
Bucky Hodges	48	691	7
Jake Butt	46	546	4
Jordan Leggett	46	736	7
O.J. Howard	45	595	3
David Njoku	43	698	8
Jonnu Smith	42	506	4
Jeremy Sprinkle	33	380	4
Jacob Hollister	32	515	7
George Kittle	22	314	4
Cethan Carter	19	190	1

Here Evan Engram looks like the clear leader, as he will in almost any analysis of receiving numbers, because Engram was mostly used as a big wide receiver, rather than in a traditional tight end role with major blocking responsibilities.  Gerald Everett, Bucky Hodges, Jake Butt and Jordan Leggett are tightly bunched behind the clear leader Engram. Cethan Carter and George Kittle are buried at the bottom of the chart.

Now let's see what happens when we adjust for opportunities, taking into account percentage of games played and the varying levels of passing offenses each player performed in.  The numbers are converted into a percentage share of the passing offense for each player, and the chart now looks like this.

Player	Catch Share	Yardage Share	Touchdown Share
			.
Evan Engram	24.5%	26.7%	32.3%
Gerald Everett	22.8%	24.5%	29.0%
Jake Butt	21.9%	21.5%	21.6%
Jonnu Smith	19.0%	19.9%	23.0%
George Kittle	18.2%	22.8%	33.0%
David Njoku	17.7%	21.2%	32.0%
Jacob Hollister	17.7%	18.7%	28.1%
Bucky Hodges	17.2%	18.8%	22.6%
O.J. Howard	17.0%	18.9%	11.5%
Jeremy Sprinkle	14.5%	12.0%	16.7%
Cethan Carter	13.6%	10.0%	8.0%
Jordan Leggett	12.5%	17.0%	15.6%

Evan Engram and Gerald Everett remain at the top of the chart in terms of their share of their respective teams' receptions (catch share).  Cethan Carter remains near the bottom.  But Jordan Leggett falls all the way from near the top to dead last when adjusted for opportunities in Clemson's pass happy offense, and George Kittle rises from the floor to middle of the pack, ahead of such luminaries as David Njoku, O.J. Howard and Bucky Hodges.

Now let's sort the chart in descending order of receiving yardage.  Again Engram leads the way, with Leggett, Everett and Njoku following.  Again Kittle and Carter bring up the rear.

Player	Receptions	Yardage	Touchdowns
			.
Evan Engram	65	926	8
Jordan Leggett	46	736	7
Gerald Everett	49	717	4
David Njoku	43	698	8
Bucky Hodges	48	691	7
O.J. Howard	45	595	3
Jake Butt	46	546	4
Jacob Hollister	32	515	7
Jonnu Smith	42	506	4
Jeremy Sprinkle	33	380	4
George Kittle	22	314	4
Cethan Carter	19	190	1

Now let's make our adjustments to reflect share of the offense. Here is the adjusted chart.

Player	Catch Share	Yardage Share	Touchdown Share
			.
Evan Engram	24.5%	26.7%	32.3%
Gerald Everett	22.8%	24.5%	29.0%
George Kittle	18.2%	22.8%	33.0%
Jake Butt	21.9%	21.5%	21.6%
David Njoku	17.7%	21.2%	32.0%
Jonnu Smith	19.0%	19.9%	23.0%
O.J. Howard	17.0%	18.9%	11.5%
Bucky Hodges	17.2%	18.8%	22.6%
Jacob Hollister	17.7%	18.7%	28.1%
Jordan Leggett	12.5%	17.0%	15.6%
Jeremy Sprinkle	14.5%	12.0%	16.7%
Cethan Carter	13.6%	10.0%	8.0%

Again Engram leads the way, but Kittle now rises all the way to third in terms of yardage share of the offense, and Leggett falls all the way to third from last on the chart.

Finally, let's take a look at touchdowns.  This final statistic is the shakiest due to tiny sample size.  In the real world the difference between say, six touchdown catches and four touchdown catches may simply be random fluctuations in the accuracy of passes thrown, the kind of red zone plays called, favorable or unfavorable matchups, or any of a myriad of other factors which the player has no control over.  As a result, what looks in the numbers like a huge (say, 50%) difference, may in reality have little or no meaning. This small sample problem runs throughout these statistics, but is most prominent in the touchdown numbers, so the touchdown numbers should be viewed with an extra helping of skepticism. Nonetheless, for what they are worth, here is the chart sorted by number of touchdowns.

Engram leads the way, as usual, but he is tied for the lead with David Njoku, with Hodges, Leggett and Jacob Hollister all only one touchdown behind. Once again Cethan Carter brings up the rear.