Scott Long
12/18/20
After Moneyball took baseball took baseball by storm, analytics proved they have a place in baseball and can help teams win games in different ways. Shortly thereafter, other sports began to join the movement towards analytics becoming an integral part of the way that teams are built, and players are evaluated, with football, basketball and hockey among others joining the fold. Football has joined the analytics movement, however, has not gotten nearly as far as baseball has yet when it comes to analytics, and there are plenty of new things to be evaluated in the sport. One of the ways that the sport can be improved is by incorporating a new stat based off of separation between the offensive and defensive players in the passing game.
One of the things that has taken hold of the NFL when it comes to analytics is the expansion of the passing game. Teams have begun to see that it is much more efficient to move the ball through passing plays rather than rushing plays, and as a result of that, passing yards per game have gone way up in the last 10 years from where they had been for the rest of the NFL’s history. This has been fairly successful for teams in the NFL, however there are always ways to refine and improve the way the game is being played. This is where my idea for a stat based off of separation between the receiver and the defender comes in. Passing the ball is efficient but the best way to make it even more efficient is by getting players who are open, and the way to judge this would be to create a stat based off of this. The idea here would be that however many yards of separation a player generates on every play he runs a route on in the passing game would be averaged together to get a certain number for what their average separation per play would be. Similarly, the same would be done for any defensive player for any passing play that they drop into coverage on. Obviously, for any offensive player the goal would be to find players who create more separation, while for defensive players the less separation they allow the better. If a team begins to evaluate based off of a stat like this it will greatly benefit their team by not only making their offense more efficient by having more open receivers on each play, but also it will help them defensively, as they will have tighter coverage on each play. However, like all stats this stat would have its limitations, as it would only work for evaluating a player in man coverage or press coverage. Zone coverage wouldn’t really apply to this stat because there are openings that aren’t covered in zone, so naturally a player will create separation there, and naturally defenders will allow separation in zone. While it is a skill for a player to be able to find the soft spot in the defense’s zone, that is not applicable to this stat, and that’s a stat for another conversation. These limitations do exist, but this doesn’t change the fact that there are plenty of advantages like I have already mentioned.
Typically, when drafting players teams look for players who are really big, strong or fast, or who have put up big numbers. However, through this stat it will help find diamonds in the rough, like Billy Beane was able to do by looking for players who walked a lot. Like he proved with baseball, just because a guy doesn’t look like a perfect athlete doesn’t mean they can’t draw walks, and the same applies to getting open. Obviously, physical tools help as well, but when evaluating college players, sometimes certain players don’t put up eye-popping numbers at wide receiver, and thus get drafted much later or don’t get drafted at all. Part of the reason for this is because of how run-centric college football still seems to be, since the NFL is passing oriented, but college football still leans on the running game. This stat will help remove the quarterback from the equation of evaluating a receiver as it will allow teams to look solely at the fact that they were able to get open by “X” amount. Focusing solely on the receivers skill will remove their quarterback from the equation, and take out the fact that they may not have had great stats because of bad yardage and reception totals because they have a bad quarterback, or the fact that maybe they only had great stats because their quarterback put the ball in the perfect spot every time. One player who would greatly benefit from this stat is Diontae Johnson, a wide receiver for the Pittsburgh Steelers who played college football at Toledo. While Johnson was effective in college, he didn’t put up eye-popping numbers, partially because they were in a run heavy offense and didn’t play at a powerhouse football school, which is a large reason why he fell to the third round in the 2019 NFL Draft where the Steelers selected him 66th overall. Meanwhile, now you look at his tape in college and in the NFL, he is a pristine route runner, and as a result of being able to make quick cuts he creates a lot of separation from defenders leaving him as an easy open target for his quarterback very often. As a smaller receiver who stands at only 5’10” and weighs only 183 lbs., Johnson needs to be able to run effective routes to get very open because he’s not a big target who can outmuscle most defenders, and he has seemed to master getting open. Furthermore, him creating separation and being wide open allows him to run with the ball after the catch, as many teams look at him as a yards after catch (YAC) receiver who is able to make people miss and go the distance any time he gets the ball in the open field. Had Johnson played at a bigger school, or had teams had access to a statistic that showed how much separation he generates, he likely would’ve been selected a lot higher in the draft. Johnson is one of many receivers who went after the first round but have proved that due to precise route running and creation of separation they can do a lot of damage with the ball in their hands and provide their quarterback with an easy target. Antonio Brown, Cooper Kupp, Wes Welker, Steve Smith and Adam Thielen are just a few more of the many receivers, who despite the lack of size and blazing speed have made successful careers out of gaining separation with precise route running and providing their quarterbacks with an easy target.
Similarly, on the defensive side, there are quite a few cornerbacks who have developed a reputation as feisty and effective cornerbacks who have been able to stay with some top tier wide receivers despite lacking elite size or speed. The two players that best fit this bill are Cortland Finnegan and Brent Grimes. Both are 5’10”, and don’t have elite speed, but have made up for that by having elite technique and the toughness to stay up close on some of the league’s biggest and most dominant wide receivers when they played like Calvin Johnson and Andre Johnson. Grimes was an undrafted free agent and Finnegan was a seventh round pick, but if any team had access to a statistic that showed just how effective they were at preventing separation, not only would both be drafted higher, but they would likely both be first round picks. Grimes and Finnegan both possessed rare toughness for cornerbacks their size and that allowed them to go up toe to toe with some of those big receivers like Andre Johnson and play tight coverage, and make it hard for quarterbacks to reach their top targets.
Clearly, while football has already broken into the game of analytics, there is still much more work that can be done for teams to find players that are undervalued. Separation is a thing that many announcers on broadcasts like to discuss but there is seemingly no way that it has been measured. The creation of a metric that shows how much creation receivers create, and how little separation cornerbacks allow could be a great way for teams to find players who are undervalued, and not only allow them to more easily move the ball on offense, but make it much harder for opposing teams to move the ball by getting much tighter coverage on the defensive side of the ball from their cornerbacks.