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Advanced Defensive Stats: Pass Rush

| July 14th, 2020

I’m continuing to look at Chicago’s defense using advanced defensive statistics from Pro Football Reference (PFR). I already looked at missed tackles and coverage, and today I want to look at pass rush.


Expected Sacks

In general, sacks are fairly variable from year-to-year due to their small sample size. Accordingly, they are not a very good way to evaluate a pass rusher, just like rushing or receiving touchdowns (which also have a small sample size) are not the main way we evaluate skill position players.

This is where advanced statistics can give us a more helpful overall picture of a pass rusher’s performance. The PFR database tracks total QB pressures, which gives you a larger sample size and thus should be more reflective of the player’s performance.

I was curious about the relationship between total pressures and sacks, so I took the following steps to investigate:

  • I examined all rushers between 2018-19 (the only 2 years this database has) who had at least 15 pressures in a year; I chose this threshold to look only at full-time pass rushers. This gave me a data set of 215 seasons, or roughly 3.4 rushers per team per year.
  • I found that the typical ratio was 3.8 pressures per sack, though this had a very high standard deviation (4.0), highlighting how much it varies from person to person.
  • When I looked only at 30+ pressures in a season (63 samples, so roughly 1 player per team per season), the average stayed virtually identical at 3.7 pressures per sack, but the standard deviation dropped to 1.2. This suggested to me that the typical number of around 3.8 pressures/sack is legitimate, and the high standard deviation with the 15 pressure cutoff was largely due to small sample sizes; you get lots of fluctuation in pressure/sack ratio when the pressure number is small.

Using that 3.8 pressures/sack as the norm, then, you can come up with how many expected sacks a player has for a season. If a player has 38 pressures, they are expected to have 10 sacks (38/3.8). You can then easily get a sack differential; a player with 10 expected sacks who actually posted 7 would have a differential of -3, indicating they were 3 sacks below what they should have normally had.


2019 Bears

I included all DL and OLB who registered pressures in 2019, as well as Robert Quinn and Barkevious Mingo. Players with a sack differential of +1 or better are highlighted in green, while those with a sack differential of -1 or worse are highlighted in red. I also included 2018 data to give you an idea of whether 2019 results were consistent with the year before.

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Advanced Defensive Stats: Coverage

| July 9th, 2020

I’m continuing to look at Chicago’s defense using advanced defensive statistics from Pro Football Reference (PFR). I already looked at missed tackles, and today I want to look at coverage.


Baseline Rates

There are a whole host of advanced coverage stats available, including completion percentage, yards/target, target depth, yards after catch allowed, TDs, INTs, and passer rating. In order to keep it simple, I’m going to look only at yards/target, as that is a good baseline metric for how effective teams are when targeting a player. I’m intentionally not looking at passer rating because that gets skewed by touchdowns and interceptions, which are notoriously random statistics within a small sample size like this.

I compiled all yards/target stats from the PFR database for 2018 and 2019, the only 2 years it has, and sorted them by position. In order to compare starters to starters and avoid rates skewed by backups, I assumed a base nickel package of 4 defensive linemen (DL), 2 linebackers (LB), 3 cornerbacks (CB), and 2 safeties (S). For all 32 teams over a 2 year span, this would mean 128 LB, 192 CB, and 128 S. This gave thresholds of 30 targets for LB, 40 for CB, and 20 for S.

Looking at those sample sizes, you can see the spread of missed tackle rates in the table below for each position group.

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Advanced Defensive Stats: Missed Tackles

| July 1st, 2020

I’ve written quite a bit about Chicago’s offense so far this offseason, but not as much about the other side of the ball. I want to change that in the next series of articles, using advanced defensive statistics from Pro Football Reference (PFR). We’ll start today by looking at missed tackles.


Baseline Rates

Let’s start by establishing a baseline for what is a normal rate of missed tackles.

I compiled all missed tackle stats from the PFR database for 2018 and 2019 (the only 2 years it has) and sorted them by position. In order to compare starters to starters and avoid rates skewed by backups, I assumed a base nickel package of 4 defensive linemen (DL), 2 linebackers (LB), and 5 defensive backs (DB). For all 32 teams over a 2 year span, this would mean roughly 256 DL, 128 LB, and 320 DB. This gave thresholds of 20 tackles for DL, 60 for LB, and 40 for DB.

Looking at those sample sizes, you can see the spread of missed tackle rates in the table below for each position group.

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