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Scouting The Draft: A Different Perspective On The Tight Ends

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Brian Spurlock-USA TODAY Sports

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.

Player

Receptions

Yardage

Touchdowns

.

Evan Engram

65

926

8

David Njoku

43

698

8

Bucky Hodges

48

691

7

Jordan Leggett

46

736

7

Jacob Hollister

32

515

7

Gerald Everett

49

717

4

Jake Butt

46

546

4

Jonnu Smith

42

506

4

Jeremy Sprinkle

33

380

4

George Kittle

22

314

4

O.J. Howard

45

595

3

Cethan Carter

19

190

1

Now let's look at touchdown share.  The chart looks very different, with George Kittle leading the way with a 33% share of his team's passing touchdowns.  Engram and Njoku are right behind Kittle with about 32% each.  Jordan Leggett and O.J. Howard sink to the bottom of the list, beating out only our constant cellar dweller Cethan Carter in touchdown share.

Player

Catch Share

Yardage Share

Touchdown Share

.

George Kittle

18.2%

22.8%

33.0%

Evan Engram

24.5%

26.7%

32.3%

David Njoku

17.7%

21.2%

32.0%

Gerald Everett

22.8%

24.5%

29.0%

Jacob Hollister

17.7%

18.7%

28.1%

Jonnu Smith

19.0%

19.9%

23.0%

Bucky Hodges

17.2%

18.8%

22.6%

Jake Butt

21.9%

21.5%

21.6%

Jeremy Sprinkle

14.5%

12.0%

16.7%

Jordan Leggett

12.5%

17.0%

15.6%

O.J. Howard

17.0%

18.9%

11.5%

Cethan Carter

13.6%

10.0%

8.0%

The above charts adjusting the numbers to reflect share of the offense should not be taken as definitive rankings of the receiving abilities of the tight end prospects in the 2017 draft.  That would be investing far more weight in a single statistical adjustment than is warranted. However, the offense share numbers may give you some food for thought, and may provide a new perspective on the receiving abilities of some players whose raw numbers may not tell the whole story. In particular, the big loser here is Jordan Leggett, whose raw numbers look very good, but whose share of the offense was pretty pedestrian.  Did Leggett benefit inordinately from one of the most pass happy offenses in college football? Should we view his pass catching abilities with a bit more skepticism in light of the relatively minor role he played in Clemson's passing offense?

Likewise, George Kittle looks like perhaps the biggest winner here. Kittle's raw counting statistics are unimpressive. However, after adjusting for the four games Kittle missed and the extremely run heavy offense Kittle played in, George Kittle's offense share numbers rise to at or near the top of the pack in all three categories.  Should we view Kittle's receiving prowess in a more favorable light?  Is he unfairly dismissed as a threat in the passing game due to pedestrian numbers that were in large part beyond his control?

These are the kinds of questions each of you can decide for yourself.  Hopefully the charts presented here at least give you some food for thought and a bit of a new perspective on the 2017 tight end prospects.