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Finding The Superior Athlete: Impact Defensive Ends In The 2016 NFL Draft

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We look at the measurables and the production of this year's edge rushing class to try to figure out which player might have the biggest impact at the NFL level.

Ronald Martinez/Getty Images

Last year on these pages, we introduced Cowboys Nation to the concept of SPARQ, a concept that resonated powerfully with many fans. What was previously a metric known only to a few advanced-stat-aficionados now has Cowboys social media all a-buzz and a-twitter.

Over the next days and weeks, we'll look at many different position groups in this year's draft class via SPARQ, so today I'll provide an extended (re-)introduction to SPARQ before we move on to this year's defensive end class.

SPARQ

Many elite athletes in the various college programs find that once they enter the NFL, their previously elite skill set is - at best - par for the course on an NFL team. As a matter of principle, NFL players are bigger, faster, stronger, and more talented than college players.

Which is why NFL teams are obsessed with athleticism over almost anything else, and which is why we as fans pore of 40-yard dash times and short shuttle times so much. You can teach most players to recognize when a defense is in man or zone, but you cannot teach a player to outrun a faster defender.

A little over a decade ago, Nike developed a metric called SPARQ. The idea behind SPARQ was to have a single composite number that would allow you to quickly assess the athleticism of a player with a single number. The Seattle Seahawks are one of the NFL teams using a SPARQ-like metric in their player evaluations, and some of their staff was involved in the creation of SPARQ.

Unfortunately, Nike never published the exact formula for the SPARQ metric. But an enterprising blogger, Zach Whitman, reverse-engineered an approximation of the formula, and while he doesn't divulge the formula either, at least he publishes the results of his calculations at 3sigmaathlete.com. Whitman uses eight inputs for his metric, which he calls pSPARQ: player weight, bench press, broad jump, vertical jump, forty-yard dash, ten-yard split, short shuttle and 3-cone drill.

Here's Whitman explaining how SPARQ can be used.

What’s the use of SPARQ? What we see often in pre-draft analysis is an over-emphasis on the forty-yard dash, for which there are two main reasons: (1) speed is important, and (2) we’re familiar with the common forty benchmarks. A 4.4s 40 is fast and sounds good, and there’s an inherent understanding of what it means. The problem is that the forty-yard time isn’t fully indicative of a player’s overall athleticism. Most people don’t know off-hand what a good broad jump is for a wide receiver, and even fewer are aware of what they should expect from a defensive end. SPARQ is a way to standardize these different parameters and gain a more circumspect view of a player’s natural ability. [...]

SPARQ isn’t perfect. Player test results have error and, even if they were perfect, don’t fully represent the ability of an athlete. The goal here isn’t to build an airplane. SPARQ is just a method by which we can better understand players, and it’s important to not let perfect be the enemy of good.

So what, if any, correlation does SPARQ metric have with actual NFL production?. Here's a chart courtesy of Zach Whitman at 3sigmaathlete.com explaining that exact correlation.

SPARQ vs AV

The chart uses Approximate Value (read up on that metric here) as a measure of NFL production and the SPARQ score as a measure of athleticism. SPARQ here is expressed as a player’s ranking relative to his peers at his position (a 0 z-score is average, a 2.0 is two standard deviations above the peer average). Whitman explains the rest:

What we see is that there’s a clear trend toward more athletic players producing a higher AV3. If there was no relationship between athleticism and production, this line would be flat, parallel to the x-axis (i.e., zero slope). This relationship is statistically significant with a p-value of approximately zero.

2016 Defensive Ends Class

Whitman publishes all the pSPARQ numbers on his website, and the following table summarizes the SPARQ data and adds the Production Ratio for this year's defensive ends (click on the blue column headers to sort):

SPARQ & Production Ratio, 2016
POS CBS
Rank
Player School Proj.
Round
Ht Wt SPARQ z-score NFL
Perc
Prod.
Ratio
DE 3 Joey Bosa Ohio State 1 6-5 269 125.6 0.4 67.2 2.06
DE 13 Shaq Lawson Clemson 1 6-3 269 130.5 0.8 79.3 1.89
DE 29 Noah Spence Eastern Kentucky 1-2 6-2 251 122.4 0.2 58.3 3.09
DE 30 Emmanuel Ogbah Oklahoma State 1-2 6-4 273 131.8 0.9 82.0 2.25
DE 50 Shilique Calhoun Michigan State 2 6-4 251 119 0.0 48.2 1.70
DE 68 Carl Nassib Penn State 2-3 6-7 277 113.2 -0.5 31.4 1.58
DE 69 Kamalei Correa Boise State 2-3 6-3 243 108.5 -0.8 20.3 1.81
DE 92 Shawn Oakman Baylor 3 6-7 287 112.6 -0.5 30.0 1.98
DE 112 Bronson Kaufusi BYU 3-4 6-6 285 124.3 - - - - 2.02
DE 114 Jason Fanaika Utah 3-4 6-2 271 119.2 0.0 48.9 1.12
DE 120 Charles Tapper Oklahoma 3-4 6-2 282 133.7 1.1 85.4 1.06
DE 133 Matt Judon Grand Valley State 4 6-3 275 114.4 -0.4 34.7 2.73
DE 173 Romeo Okwara Notre Dame 5 6-5 265 109.6 -0.8 22.6 1.13
DE 183 James Cowser Southern Utah 5-6 6-3 248 110.4 -0.7 24.4 3.02
LEO 190 Dadi Nicolas Virginia Tech 5-6 6-3 235 120.7 0.1 53.1 1.38
LEO 199 Victor Ochi Stony Brook 6 6-1 246 109.8 -0.7 23.0 2.71
DE 205 D.J. Pettway Alabama 6 6-2 265 85.4 -2.6 0.5 0.45
DE 207 Yannik Ngakoue Maryland 6 6-2 252 115.5 -0.3 37.8 1.88
LEO 231 Stephen Weatherly Vanderbilt 6-7 6-4 267 125.4 0.4 66.9 1.25
DE 326 Alex McCalister Florida 7-FA 6-6 239 129.4 0.7 76.8 1.43

A few notes on the data:

  • pSPARQ is the single metric designed to summarize a player's athleticism.
  • z-score calculates a player’s ranking relative to his peers at his position. A z-score of 0 means a player is average, while a 2.0 means he’s two standard deviations above the peer average.
  • NFL perc. is the z-score translated into percentiles. A 50.0 percentile would represent a player who rates as a league-average NFL athlete at the position.
  • Production Ratio shows the number of sacks and tackles for loss per game over a player's last two college seasons. A number above 1.5 is often indicative of premier production for a pass rusher. Production ratios marked in yellow indicate a player is from a small school, and that his high production ratio is at least in part the result of playing against inferior competition.

Going by the pSPARQ score, the top defensive ends in this year's draft class are Emmanuel Ogbah, Shaq Lawson, and Alex McCalister, all of whom score in the top 70 percentile relative to their NFL peers. Add Joey Bosa and Noah Spence, both of whom also show above average athleticism, and that's already it for this year's DE class.

For comparison, last year's Cowboys draft picks Randy Gregory and Ryan Russell had pSPARQ scores of 132.8 and 122.3 respectively. Some of the better pass rushers to enter the league in recent years like J.J. Watt, DeMarcus Ware, Jadeveon Clowney, Justin Houston, or Cameron Jordan all scored above 140.

So now we know who the superior athletes in this defensive ends class are. But by itself, that won't help us all that much. After all, the history of the NFL draft is littered with superior athletes who never made it in the NFL.

Back in January this year, we looked at the college production of the defensive ends in the 2016 draft class. To do that, we used a metric called the 'Production Ratio' that adds up sacks and tackles-for-loss and divides the sum by the number of college games played. The resulting ratio is one tool among many - albeit a pretty good one - that measures the playmaking potential of front five players coming out of college.

If we combine the two metrics, SPARQ and the Production Ratio, we should be able to find the most productive AND the most athletic DEs in this draft. The graph below plots the Production Ratio against the SPARQ score for the 19 DEs from the table above.

Tapper

The two red lines divide the graph into above average and below average performers. Players with a Production Ratio of 1.5 or more (the top two quadrants, "A" and "C") delivered an above average production in their last two college seasons. Players with a SPARQ score of more than 120 (the two quadrants on the right, "A" and "B") are above average athletes relative to their NFL peers.

The A quadrant (top right) shows the players most likely to succeed at the NFL level. They have a strong track record of production and have the pre-requisite athleticism that should allow them to compete at the NFL level. And the four players we see in this quadrant, Joey Bosa, Shaq Lawson, Emmanuel Ogbah, and Noah Spence shouldn't really surprise anybody, as they are the consensus top four defensive ends prospects.

The B quadrant (bottom right) shows superior athletes whose college production has been below average. And while this doesn't automatically invalidate them as potential prospects, it does raise questions. Teams need to understand why these guys didn't have the kind of production other players, often with inferior athleticism, did. Was it the scheme they played in, the players they played next to, the opponents they played against, the role they were asked to play, or are they simply not very good football players?

The numbers here won't answer those questions, but those are questions teams will have to answer satisfactorily via film study, player interviews, coaching interviews or other means.

In this quadrant, Dadi Nicolas and Stephen Weatherly both project more as outside linebackers; Alex McCalister has intriguing athleticism but a below average track record of production, which may be why he's projected as a late-round prospect.

The C quadrant (top left) features players with a strong record of production at the college level, but who have questions regarding their athletic ability. Three players here exemplify the challenges of this group: James Cowser (Southern Utah), Matt Judon (Grand Valley State), and Victor Ochi (Stony Brook) hail from small schools and put together excellent numbers at those schools - but with average or below average athleticism.

Again, being in this quadrant is not necessarily a bad thing - Demarcus Lawrence for example was a C Quadrant player (113.8 SPARQ, 2.28 Production ratio). However, if you don't have the athleticism to compete at the next level, you're going to struggle mightily - regardless of your college production. It's just an extra question teams will have to answer.

If the Cowboys miss out on the A-quadrant players and still want to add a pass rusher to their D-line in the draft, Shilique Caloun and Yannick Ngakoue, and perhaps Carl Nassib, could be good options. After that, they'd probably be best advised to take a late-round flyer on one of the small-school guys.

The D quadrant (bottom left) is a tough one to be in. Below average production and below average athleticism don't promise a great future in the NFL, but once more, you need to understand each individual case before closing the book on a prospect.

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The defensive matrix above suggests a rigid, definitive picture of the 2016 defensive ends, but that's not necessarily correct.

Fluctuating production

For the calculation of the Production Ratio, I generally use the last two years of a prospect's college production, but not every prospect is best served by that approach.

  • Penn State's Carl Nassib had a 2.69 ratio in 2015, but just 0.34 in his previous two seasons. Which version of the player will show up in the NFL? That's a question NFL teams will have to answer and figure out what they have to put in place to ensure they get the 2015 Nassib and not the 2014 Nassib.
  • Clemson's Shaq Lawson faces similar questions. 12.5 sacks and 25.5 TFLs give him a ratio of 2.53 in 2015, but his 1.15 ratio in the previous two years is far more pedestrian.
  • Yannick Ngakoue, who the Cowboys had a private workout with, had a ratio of 2.29 last season, 1.50 in 2014, and 0.50 in 2013. We'll see whether the Cowboys are just doing their due diligence or whether there is real interest there.
  • Two of the better-known names in the draft saw the reverse effect, with their 2015 performance dropping off versus previous years. Joey Bosa had a 2.30 ratio from 2013-14, but dropped to 1.75 in 2015. That's still a pretty strong number, but is something teams need to understand. Baylor's Shawn Oakman has a very similar trajectory, going from 2.35 from 2013-14 to 1.58 in 2015.

Production ratios are going to fluctuate from year to year, that's to be expected. But when they fluctuate too strongly, you need to understand whether there's an issue with consistency, whether there are other factors in play, or whether you're simply seeing ghosts in the numbers.

Combine versus Pro Days versus any other time of the year

The benefit of the Combine is that it allows teams to collect data from prospects performing in the same environment and under the same circumstances. But players can have an off day, some players perpare better for the event, some players are still recovering from injuries; in short, the athletic markers that go into the SPARQ calculation can differ depending on where and when they are measured.

Joey Bosa for example reportedly improved his 40 time from 4.86 at Combine to 4.78 at the OSU pro day, improved from 24 to 28 reps in the bench press, and added an inch to his 10-foot Combine broad jump. That performance would easily give him a higher SPARQ score; other prospects may have peaked at the Combine and won't be able to repeat that performance.

Star_medium

With the limited data we have at our disposal, we can only provide the snapshot we see in the chart above. But with the understanding that the datapoints for each player may not be quite as rigid as the chart suggests, our snapshot nevertheless provides a good starting point from which to discuss these players.

The mandatory caveat: There are a multitude of factors that determine how well a prospect will do in the NFL. College production and athletic markers are just some of them, but at the very least, they provide some interesting input into the evaluation process.

Given these numbers, and given what you know about these prospects, in which rounds would you be looking for a defensive end, and which one would it be?

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As an addendum, here are some historical numbers for defensive ends as a comparison.

SPARQ DE Historic