It's been an eventful couple of weeks for the NFL's new guard.

Chip Kelly was never the "Moneyball" coach some mistook him to be, but his firing from the Philadelphia Eagles last week sacked the sports science movement's highest-profile practitioner. This week, the Cleveland Browns' hiring of Moneyball pioneer Paul DePodesta turned one of the NFL's worst-run organizations into a public laboratory for testing whether an analytics-driven approach can succeed.

The Browns will ultimately succeed or fail based mostly on their quarterback, not their new chief strategy officer (DePodesta's newly created title). The same was true for Kelly, who once pointed out that the revolutionary Moneyball approach worked for the Oakland A's to the extent that three little-mentioned pitchers allowed it to work.

While Browns ownership deserves every bit of skepticism the football world can muster, the day is coming when hiring leaders with backgrounds in analytics will open eyes to the possibilities instead of making those same eyes roll. Thing is, that day arrived some time ago for those familiar with a few of the ways analytics are helping teams already:

Injury prevention: The Browns were already among a growing number of teams using tracking technology to monitor players' workloads during practice. One of their receivers, Andrew Hawkins, discussed the technology while appearing as a panelist at the MIT Sloan Sports Analytics Conference last year. He said the Browns successfully used the technology to help Miles Austin manage chronic hamstring injuries, regulating his practice reps with far greater specificity than was previously possible.

Ron Rivera made a shift in philosophy early in his coaching career based around what the numbers were telling him. Hannah Foslien/Getty Images

Game management: Try a field goal on fourth down or go for it? Accept that penalty or replay the down? Call timeout or let the clock run? Head coaches sometimes say they prefer to go with gut feelings when making in-game decisions, but the people they consult over their headsets during games increasingly have crunched the numbers across hundreds or even thousands of plays, and coaches increasingly have foresight on what the numbers say in various situations. Ask Riverboat Ron Rivera.

Game preparation: Even the oldest-school coaches have focused hard on tendencies, both for themselves and for their opponents. Does the quarterback always look the same way first against a specific coverage? Does a certain running back always free release when he's in the game? Does the free safety always blitz from a certain alignment? Some teams have had that information logged into their video systems for years, allowing them to pass along coaching points to players and/or devise specific play calls as countermeasures.

Performance analysis: It's not enough to know Adrian Peterson leads the league in rushing yards. Teams know how many yards he averages before (2.6) and after (1.9) contact, and how that compares to other runners, and in what situations. They can know which routes receivers are running against which coverages, or whatever else they want to track or are willing to purchase from companies such as Pro Football Focus. The larger the analytics staff, the greater the granularity of the data becomes. And that goes back into game prep.

Roster construction: How many high-priced players can teams typically afford? What are the best ways to allocate salary-cap resources? Analyzing years of salary data can provide answers for teams looking for smart strategies.

Scouting: Analyzing 10 or 20 years of data from the scouting combine can devolve into a mind-numbing search for causation that might not exist, but teams have long wanted to know how traits correlate with performance and scheme fit. Teams also might be able to determine which traits could become more important as the NFL game models the college version featuring a proliferation of screen passes. Analytics helps streamline raw scouting data from things like the combine, in addition to all that is collected from the scouting staff.

The Browns' decision to promote their general counsel to a position with roster control and then hire a data-driven baseball executive over him was unprecedented in the NFL. It could backfire like just about everything else has backfired for Browns owner Jimmy Haslam. But if one of the NFL's model organizations were making similar hires, they might be seen as a forward-thinking team charging into the future.

The questions DePodesta's hiring raise for the Browns have more to do with organizational structure than the increasingly ubiquitous leveraging of data to better inform decisions. Analytics are not new to the Browns, and they are certainly not new to the NFL.