Meaning from Math: Snap Counts and what they say about scheme

A peek behind the math curtain

One of my favorite things about the sport of football is the sheer variety of ways in which the game can be understood. For me, a common path to illumination is the field of mathematics. Let’s rewind to my early days as a Buffalo Rumblings contributor. A young(er) straw lad looking to establish himself in the world of football writing.

The writer who had been in charge of the weekly snap-count feature was leaving, which seemed like an easy place to get situated. It wasn’t. Sure, it’s easy enough to discuss “This guy played more than this other guy.” But how could I offer more than mere regurgitated numbers to the readers? The answer was of course embedded in the math.

More than just a pile of numbers

If you’d like to follow along with the exercises below, click on this link, which should bring you to right to the snap count data for the Buffalo Bills at Kansas City game last season courtesy of Pro Football Reference. In my search for a way to use snap counts as a means for a deeper understanding of the game, it hit me. A simple spreadsheet and some rapid calculations can give an incredibly fast and accurate method of analyzing the strategy for a particular game.

I can’t tell you how many times I’ve seen someone ask in a snap-count piece “When you rewatched the game did you notice…” Fun fact I had to admit just as many times: I hadn’t rewatched the game yet. Sometimes the replay wasn’t even available yet. Everything was from the snap counts.

Basic Premise

If you took the time to check out the link above, you’ll see that snap-count data often comes in a raw number (x number of snaps) and percentages. I prefer to work with the percentages, although admittedly sometimes you may need to check out a rounding error using the raw numbers.

The idea is overall pretty simple. At any given time your team has 11 players on the field. Because this is true for every snap, adding up the percentages for every player should yield 1,100% on each phase of offense. Did you read that as “11 hundred?” You should. It’s really that easy. Each spot on the field accounts for 100%.

By counting how much each position type accounts for in that 1,100, you can quickly make excellent judgments on scheme tendencies. Defense can be easier to conceptualize as that phase is often labeled as something like a 4-3 base. In a “perfect” 4-3 you’d see 400% dedicated to the line (two tackles, two ends). Another 300% would be associated with linebackers, leaving 400% for defensive backs (two corners, two safeties).

Ready to try it out?

Defense

From the KC game I promised we’d look at, here is the raw data for the defensive snap counts.

So right away we have 19 players who participated in the game, making our 1,100% a bit complex right out of the gate. I don’t need to drag this out though, so take a look at this color-coded version with some quick formulas tossed in.

Right off the bat, the defensive backs jump off the screen. One disclaimer before we move on though. This gives an incredibly good rough look, but there’s always the chance a snap or two throws things off. That’s OK, perfect precision is usually a fool’s errand to begin with.

So those defensive backs. They top out at over 500%. Remember that 100% = one player/position. That means Buffalo played nickel the entire game (or close enough to call it that). In fact, that extra 18% means they played in dime defense for about 12 of the 67 snaps.

The rest of the information corroborates that. Our linebackers are under 200%, which is the usual shift from a 4-3 to the Buffalo nickel. And our defensive line is a bit shy under 400%. That supports a dime defense for sure, as you’re gunning for speed. Shifting a big guy to a little guy is exactly what you’d expect.

For this game, without looking at a single snap on film, we can safely say that Buffalo ran with a fast and light defense pretty much the entire time. Did it work? They kept KC to 20 points, forced two turnovers, and sacked Patrick Mahomes three times and registered nine QB hits.

Offense

There’s no need to put you through the raw chart, so here’s our color-coded one. Notice on both that using percentages does often lead to small rounding errors.

Offense tends to be a bit trickier, but there’s still a lot we can glean from this. A real quick observation is that on 3% of plays (two snaps, check out Bobby Hart), Buffalo used an extra lineman. That’s likely for a short-yardage play, but of course there are limitations to a by-the-numbers-only analysis.

Next up we have our fullback in for 29% of the time, which is highly unusual for Buffalo. Two of those 21 snaps also likely occurred with Bobby Hart on the field, but… see above. That still leaves a good chunk of time where Gilliam was on the field. Our running backs come up to exactly 100%, which means that the only two-back sets Buffalo used involved a FB/RB duo rather than two RBs.

I usually leave receivers last for a reason, which means we’re on to tight ends. You’ve likely heard the terms “11 personnel” or “12 personnel” and similar in the past. The first number is the number of running backs, the second is the tight ends. With the tight end group seeing 112%, that means Buffalo ran a two-tight-end set roughly 12% of the time (nine snaps). Now we can’t say for sure if that was a 12 personnel or 22 personnel play from the above, but we do know that second digit was a “2” for a handful of plays.

There are five “skill position” players allowed to be on the field at a time, which means that when you designate 11 or 12 or 22 or whatever for the play type, you add the two digits up, subtract from five and you end up with the number of wide receivers on the field. For this game, the wide receiver group was at 257%. That means most if not all plays featured two WRs, and about 57% featured three.

These are less likely to be exact, as a play featuring Bobby Hart as a sixth lineman for example would have him as closest to being a tight end. But as we all know, Hart isn’t really a tight end.

I still find the numbers on offense illuminating because many of the oddities have an obvious reason. For example, would any of us be shocked if one of the Bobby Hart plays was the QB sneak on 4th & Short late in the game? When we factor in some common sense rationale, the tight end and running back numbers offer a wealth of tendency information.

I’m closing with this because the intent of this article is to help a reader or two take some data and run with it as they enjoy this football season. Will the tight end numbers routinely be higher than 112% this season with Dalton Kincaid being a factor? Will the sixth lineman and RB numbers fluctuate greatly based on opponent? How much dime defense should we expect to see?

These are all questions I see being asked quite a bit already. A quick snap count look can be the fastest way to an answer.