Four New York Jets Linebackers: Context Stats Breakdown

The Story of Four Linebackers Told Through Analytics

A fact is the confirmation or validation of an event or object. Data is raw facts that describe the characteristics of an event or object. Information is data converted into a meaningful and useful context.

Not a lot of people know that there’s a significant difference between the three. It’s why numbers (whether football or otherwise) can lie. But eyes lie too.

As this writer delved into football analysis, my first inclination was to go the tape. After two years of “tape grinding” I noticed the massive flaws from a factual and argumentative standpoint. Anyone can find film of a player doing the same thing well and poorly. So I began drifting into stats, starting with the context stats project that’s been posted throughout the season on this website and now am moving onto a new personal project: analytics for linebackers.

Tape scouting without logging data has two big flaws. It can’t see the macro-view patterns and it is prone to personal biases. Analytics don’t have those issues but lack nuance and some domain knowledge that tape scouting provides.

Regardless, this is going to be a story of four recent Jets linebackers told through numbers and context..

The Stats

I wanted to see what it takes to play a non-pass rushing linebacker position via only analytics. So i got all the data from 2008-2016 and began working on building models and graphs that show what is and isn’t important about becoming a great linebacker. We subconsciously value many of these same traits when evaluating tape, but due to our inability to see the macro-view we miss out on some of the information. I know for certain NFL scouts value these traits, because the players who lead in these are usually higher draft picks.

The stats hierarchy goes like this:

  1. Solo Tackle % of team
  2. Total Tackle % of team
  3. Speed Weighted (Using the Speed Score formula)
  4. Age Breakout (what age the player hit 8% of his teams total tackles)

The remaining stats you’ll see just paint a full picture of the player. So far those don’t mean much.

It all makes sense though right? A player who enters the NFL and wants to play LB should have been good at producing against weaker CFB competition, so production translates the most. Speed is important because athleticism plays a huge part in running alongside routes and catching outside runs, along with bursting into the backfield. Age explains how long it took for you to figure it all out. If it took too long, then you were just a veteran beating kids.

All graphs you’ll see below show percentile ranking of each player. That means a player who is 91st percentile in Solo Tackle % is amongst the top 9% of all LBs in the system.

David Harris: 74 inches, 243 lbs

David Harris Analytics

David Harris is the first player in this story. Harris came onto the Jets via a second round pick in 2007. Jets fans have been regularly reminded that Harris is their only productive second round pick in nearly a decade.

Harris dominates the board in the most important categories. A dominant producer at Michigan, capable of taking on RBs one on one as shown by his 88th percentile Tackle Solo %. He was also dominant all-around via his 83rd percentile Tackle %. And he’s extremely fast in raw speed and weighted speed. The only mark on his resume was an extremely late breakout. But this is where data and information have their differences.

Harris suffered an injury in his freshman year that required 3 full years to heal. Seriously. Harris came back from that horrific injury to have one of the most dominant college seasons in my records, at least contextually. Harris wasn’t able to put up the type of tackle volume others put together which may have been a sign of his ultimate ceiling as he only had 96 total tackles despite owning 14% of his teams production. Regardless, Harris passed the three most important tests and became one of the Jets cornerstones on defense for years.

Demario Davis: 74 inches, 235 lbs

Demario Davis Analytics Profile

Jets drafted Demario Davis in the third round of 2012. He was drafted to play the weakside inside linebacker along Harris who took the strong side. He was supposed to bring speed back to the defense. He sorta brought nothing else.

Demario’s Jets career was two “development” years before seeming like he was turning the corner and then immediately falling apart.

We could’ve seen this coming from his college analytics. Demario was unable to produce in college. His production was anemic. Below 27th percentile in percentage of teams solo tackles, meaning he struggled in space; and 20th percentile in tackles, so he wasn’t even near the ball most of the time. He was an average age breakout so he wasn’t a prodigy either. All he had going for him was speed.

Here’s where the difference between data and information kicks in again though. If he was such an elite athlete, why was he struggling to produce against inferior competition? He played in the SUN BELT conference. The answer to that is the reason why his Jets career was what it was. He just wasn’t good. In the NFL, playing linebacker is a bit like playing running back. Simply getting the opportunity leads to production, but only the good ones keep their jobs in the face of competition.

Nick Bellore: 73 inches, 245 lbs

Nick Bellore Analytics Profile

Weird pick right? Follow me though. Nick Bellore was a UDFA the Jets picked up in 2011. He’s built to play strong side linebacker, but he lacks straight line speed so much that in order to be good he has to play with a significantly high set of instincts, preparation, and timing. And playing in the MAC, he was able to. He understood the game immediately as a freshman and had a dominant season. That dominance continued every year of his college career.

The Jets never gave Bellore a chance to start, but should it be surprising that a player who understood the game so cerebrally was one of the best special teams players during his time here? John Idzik re-signed him at the end of his initial contract because of his special teams value. But he couldn’t overtake Demario Davis’ position because he was too slow, and Harris was an iron man at his natural position; so he never saw actual defensive snaps. In 2016 with the 49ers, he got on the field after Navarro Bowman went down.

Bellore was a young breakout and a dominant producer despite being a sub-par athlete. He’s the anti-Demario Davis. In 10 starts, he produced as well as Demario Davis did in any year of his career with 7.5 tackles per game, and it was Bellore’s first time ever starting in the NFL. Demario’s best season only had 7.25 per game.

Darron Lee: 72 inches, 232 lbs

Nick Bellore Analytics Profile

We have no idea what to expect for Darron Lee so far. We picked him in the first round of 2016 to put at the Demario Davis position of WILB.

In college however, Lee was a completely unproductive linebacker. Despite being an elite athlete, like Demario, he never put up a volume of statistics for his team. In fact, he was close to never breaking out at all. Had someone else taken just one of his tackles in his 2015 season, neither of Darron Lee’s seasons would’ve had 8% of his teams tackles, meaning no breakout at all.

It’s crazy to think that the Jets drafted someone who never produced in college in the first round. But again, this is where information and data separates. Ohio State was one of the most stacked teams in college. Darron Lee was moved around the defense and wasn’t even on the field all the time. Does that affect his projection? or is it signs of him not being NFL starter caliber? Do you believe the coaches at Ohio State didn’t believe Lee was good enough to have a solid role? or was he so good that he could do anything?

I like to project young players by finding their closest analytics comparison. Darron Lee doesn’t have a clean comparison, there hasn’t been a LB at his size/weight that has his speed. However, there is one that’s close; and he may be a glimpse into Lee’s future.

That’s 72 inches, 226 pounder, former Seahawk and current Raider: Malcolm Smith.

Malcolm Smith Analytics Profile

The Point Is

All i’ve given you is some data. What information there is to get out of this is up for debate. You could’ve predicted what these players may have done based only off these numbers, a few minutes of watching their games, and looking up their history. Three of these linebackers had plenty of clues to their story entirely found through an intelligently contextualized look at their collegiate careers and athleticism.

But the fourth has a lot of variables.

Author: Edward Gorelik

My cat is a better analyst than me, that's why he ghostwrites my posts.