It was analytics-in-the-NFL week last week at the MMQB, with Albert Breer first looking at what analytics even are in the context of professional football, how these metrics are developed and how they are being used and by whom. He broke this down the next day to explore further just how all 32 teams use the ever-increasing amount of specific team and player data available and how much the data informs their decision-making. Unsurprisingly, a good deal of the first piece centers around the Cleveland Browns.
Indeed, the Browns have become synonymous with the word “analytics” in the NFL. That’s even despite Breer’s noting that “Most teams don’t shy away from analytics,” incorporating the data from both a “player acquisition” standpoint as well as, “On the coaching side... to make staffs more efficient.” Though some teams incorporate the use of analytical data more than others—”More than three-quarters of NFL teams employ either a director of analytics or have a full-blown analytics department,” writes Breer—and data-providing services like Pro Football Focus and Stats LLC work with 27 and 26 of the league’s teams, respectively, the Browns have quickly become the poster children for such an approach.
The reason is simple: The Browns made many, public moves to take a more analytical approach to their front-office happenings, particularly when it comes to adding and subtracting players. Executive Vice President of Football Operations Sashi Brown brought aboard former Oakland Athletics executive Paul DePodesta to serve as Chief Strategy Officer. Former Dallas Cowboys analyst Ken Kovash has been in Cleveland since 2013 (predating Brown and the Hue Jackson-led coaching staff), but last year was promoted to Vice President of Player Personnel alongside Andrew Berry, who holds the same title. Breer also notes that “[t]hree others in player personnel have ‘research,’ ‘strategy’ or both in their titles”—those would be Kevin Meers (Director of Research and Strategy), Dave Giuliani (Football Research Analyst) and Andrew Healey (Senior Strategist for Player Personnel).
Brown has tried to downplay just how much influence this group has on the Browns’ overall strategy, saying:
“Our structure isn’t designed to give rise to analytics, but to the end we want to incorporate it, I think it’s a desire to win. If you tell me there are some answers out there or some information out there that can improve our team, stuff that we don’t have, I don’t care what you call it, I’m interested. Now, it’s gonna have to prove its mettle. But if you are honest and open about your objectives, humble enough and non-territorial enough, then I think it’s a natural thing that most people would want... It’s a new century.”
But it’s hard not to see how this approach has already managed to reshape the Browns. Though the tenure of the current administration has been short, the fingerprint of the analytics department has been felt primarily through the team’s actions regarding trading players and amassing draft picks. It was not a coincidence that Cleveland came away with 14 drafted players a year ago, 10 this year and have 12 picks already in 2018 (five of which are in the first two rounds). And the trade with the Houston Texans for quarterback Brock Osweiler, in which the Browns brought him aboard not for his talents under center but the 2018 second-round draft pick that came along with his acquisition, also fits the mold.
Breer has noted, though, that the Browns are “trying to figure out where analytics lines up with scouting and coaching.” The front office’s desires aren’t absolute; for example, Breer reported that the data would have led to the Browns drafting quarterback Mitchell Trubisky with the first-overall pick in the 2017 draft but that the coaching staff preferred—and got—linebacker Myles Garrett.
These desires should not be absolute, as player attributes, both tangible and less-so, must also be taken into account when bringing a player on to (or off of) the roster. As an AFC personnel executive said to Breer, “Maybe your model doesn’t like a guy that your scouts like, so let’s find out why the model doesn’t like the player... It could be position specific. It could be a stat. There’s lots of reasons why a scout may like a player and your model wouldn’t.”
This is the balance the Browns, and all teams in the league, need to strike. As the AFC executive said, “In a perfect world, the scout and model both like the player.” There must be a way to evaluate a football player as a person and athlete as well as a collection of numbers and data points, to incorporate more advanced methods of evaluation with the traditional methods of a scouting department traveling the country and breaking down game tape.
This appears to be the Browns’ goal, though of course determining whether this process proves successful ultimately hinges on the numbers of wins it produces. But it should be noted that the Browns’ dependence on analytics is not an outlier in the NFL as a whole; it was just a more public, auspicious version of a transition that nearly every club in the league has been making to some degree or another for years.