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Bears at the Mini-Bye Volume III: Defense & Playoff Odds

| October 15th, 2020

I already looked at a variety of statistics for the offense, including QB performance, run game woes, and explosive plays, and explored how Chicago has deployed their skill position players. Today I want to look at advanced defensive statistics from Pro Football Reference and think about Chicago’s playoff odds.


Missed Tackles

I highlighted missed tackles as a concern in the secondary heading into the season. As a team, the Bears are actually doing quite well with missed tackles right now; they rank 7th in the NFL with 22 through 5 weeks. The table below shows missed tackle stats (from Pro Football Reference) for all players with at least 10 tackle attempts, as well as cumulative totals for each position group.

For context, here’s how the positional averages compare to NFL peers over the last 2 years:

  • The median starting NFL DB misses right around 11% of their tackles, so Chicago’s secondary is about average here so far. That’s actually pretty good for them given the tackling concerns heading into the season with Kyle Fuller, Buster Skrine, and Eddie Jackson. Fuller in particular has struggled so far this year, but everybody else has been ok.

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Bears at the Mini-Bye Volume II: Offensive Personnel Usage

| October 14th, 2020


I already looked at a variety of statistics for the offense, including QB performance, run game woes, and explosive plays. Today I want to explore how the Bears are deploying their skill position players, using lineup data from the NFL Game Statistics Information System. This tracks how many plays the Bears have played with different combination of 11 offensive players, and splits the data into runs and passes, with yards gained for each. Combing through this data can provide valuable insights into how the Bears are deploying their personnel, and what packages have been most and least effective.


Tight Ends

The Bears completely overhauled this position in the offseason, following a disastrous 2019 campaign in which no player even hit 100 receiving yards. They gave Jimmy Graham a big contract, spent their 1st pick (43rd overall) on Cole Kmet, and brought in veteran journeyman Demetrius Harris.

I want to start by looking at Cole Kmet, who has been very quiet so far as a rookie despite receiving a good bit of training camp hype. Through five games, Kmet has played 102 snaps, seen 3 pass targets, and caught 1 ball for 12 yards. This is hugely disappointing, and worrisome for his future; when I looked at rookie seasons for TEs drafted in the 2nd round this offseason, I found that tight ends who are going to be good are typically involved in the offense right away. The only tight ends drafted in the 2nd round over the last 10 years to receive fewer than 30 targets in their rookie seasons are Vance McDonald, Adam Shaheen, Gavin Escobar, Drew Sample, and Troy Niklas. Of those, only Vance McDonald has done anything in the NFL. Kmet is currently on pace for 10 targets.

It’s fair to argue a rookie should see their production increase as the season wears on, so I looked at all 19 players in that study through the first five games of their rookie season. You can see the full list here, but Kmet has the 3rd fewest targets, least amount of catches, and the least number of yards through that time period. And for all of those categories, the bottom four (not including Kmet) are from the list of five names above. It’s early, but right now Kmet most closely resembles Troy Niklas and Adam Shaheen, which is very not good.

Because I was curious about Kmet, I split out lineups involving him vs. those who don’t, and also sorted by the number of tight ends on the field. The results, as you can see below, are certainly illuminating.

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Bears at the Mini-Bye Volume I: Offense

| October 13th, 2020

We’re five weeks in to a wild season in which we’ve already seen the Bears make a quarterback change and post three comeback wins from 13 or more points down. Since they’re on a mini-bye following their Thursday night victory over Tampa Bay, now is a good time to take a step back and see what we’ve learned so far.

Obligatory warnings:

  • These are still small sample sizes, especially given that each QB basically played 2.5 games. So think of any lessons learned here more as observations that are worth monitoring going forward than hard and fast conclusions.
  • Statistics for Bears are updated through 5 games, but all other teams only have 4 at the time of this writing, so NFL ranks may have changed a bit by the time this is published.

I have a lot I want to get to, so let’s dive right in.


Better Lucky Than Good

The Bears may be 4-1, but I don’t think anybody would argue they have played well so far this year (including Matt Nagy). As you can see from the pie chart below, which shows the % of offensive snaps the Bears have taken in a variety of score situations, they have actually spent the majority of the season trailing.

They’ve taken 2/3 of their offensive snaps while trailing (33% by 2 or more scores) and only 19% with a lead. To somehow go from that to 4 wins in 5 games is remarkable, but it should not be expected to continue going forward. The Bears need to play better if they want to keep winning games. The good news is that they started to look better in week 5; the defense in the 2nd half looked the best it had since week 4 of the 2019 season, and the offense was something approaching competent for the last 40 or so minutes of the game.


QB Comparison

The Bears switched from Mitchell Trubisky to Nick Foles in the 2nd half of week 3, which means both QBs have actually played a similar amount of snaps so far this year (Foles is at 168, Trubisky 169). Let’s see how each performed. The table below shows stats for each passer, as well as the average for the entire NFL this year, broken up into deep and short throws (anything that travels 15+ yards in the air past the line of scrimmage is considered deep). YPA = yards per attempt.

A few thoughts:

  • Keep in mind that Nick Foles has played 2 of the best defenses in the NFL the last 2 weeks, while Trubisky played all of his snaps against 3 of the worst defenses in the league. Still, it’s hard to argue Foles has been better so far, at least on a statistical basis. He needs to play better going forward.

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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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Ryan Pace Has Gone All-In on the 2020 Season

| June 24th, 2020

After a disappointing 8-8 season, Ryan Pace moved aggressively this off-season to revamp the Bears for 2020.

On defense, he re-signed Danny Trevathan, upgraded Leonard Floyd with Robert Quinn, signed Tashaun Gipson as a cheap replacement for HaHa Clinton-Dix, and drafted Jaylon Johnson to replace the aging Prince Amukamara.

On offense, he traded for Nick Foles to compete with upgrade Mitchell Trubisky, replaced oft-injured veterans Taylor Gabriel, Kyle Long, and Trey Burton with Ted Ginn, Germain Ifedi, and Jimmy Graham, and drafted Cole Kmet to hopefully give Chicago their first long-term solution at tight end since Greg Olsen was shipped out of town a decade ago.

That’s an impressively long list of moves for a team that entered the off-season with surprisingly low amounts of cap space and draft capital. And it has left the Bears with what appears to be a pretty solid roster, at least on paper, though it’s fair to say that questions at quarterback certainly limit the optimism.

But things start to look much more questionable when you gaze beyond 2020. You see, the only way Pace could spend money this off-season was by borrowing from the future salary cap, and he did that quite heavily. Several players have had their contracts restructured within the last year+ to clear up immediate cap space by moving money to 2021 and beyond. This totaled around $20M from a combination of Khalil Mack ($7.8M), Kyle Fuller ($4.5M), Charles Leno ($4.2M), and Cody Whitehair ($3.2M).

On top of that, most contracts Pace handed out this off-season were absurdly back loaded.

  • Robert Quinn has a $6M 2020 cap hit on what is essentially a 3 year, $43M deal (a 2020 savings of over $8M from the average cap hit for the deal). The downside is he will still have total cap charges of $37M remaining in 2021 and beyond, and will likely only play in Chicago for 2021-2022. To make matters worse, those will be his age 31 and 32 seasons, when his play will likely start to slip. He’s a speed rusher that relies heavily on that one skill, so it’s possible that decline will be very pronounced.
  • Danny Trevathan has a $4.2M 2020 cap hit on what is essentially a three-year, $21.7M deal. That saves about $3M in 2020 cap, but means the Bears will still have $17.5M on cap charges for his remaining 2 seasons, in which he will be 31 and 32 and likely start to see his play decline.
  • Jimmy Graham has a $6M 2020 cap hit on what is essentially a one-year, $9M deal. That saves $3M in 2020, but means the Bears will have that cap hit in 2021 when he is likely not on the team (if he is on the team, he’ll have a $10M cap hit, which is not ideal for a player who will turn 35 during that season and has already started showing signs of decline).

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What Should Teams Do at the Goal Line?

| June 9th, 2020


It has become common knowledge that passing is far more valuable than running in the NFL. But I have surprisingly seen very little data about how that changes as teams approach the end zone and the real estate tightens.

I found this excellent article looking at all goal-to-go plays, which found that passing is still more valuable than running and highlighted specific types of runs and passes that work better than others. But that groups plays from the 8 or 9 yard line together with plays from the 1 or 2, and those are drastically different scenarios.

I spent about 15 minutes on Google trying to find something detailing what’s most effective for teams to score a TD from the 1 or 2 yard line, and couldn’t find anything, so I decided to do it myself. I started by using the Pro Football Reference game play finder to get a basic look at how often, and how successfully, teams run vs. pass from the 1 and 2 yard line. The table below shows that information for the years 2016-19. I chose that specific time range to be consistent with available information from later in the study.


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How Consistent are Explosive Players?

| June 2nd, 2020


Recently, I’ve found that explosive plays are really important to overall offensive production and explosive plays are extremely inconsistent from year to year on a team level. Today I want to look at explosive plays on an individual level to see if players can be fairly reliable counted on to be more or less explosive than expected.

The Set-Up

Like with the team-level data, I used performance from 2014-19 as my sample size. I used the Pro Football Reference Game Play Finder to identify all players who had at least 200 pass attempts, 50 pass targets, or 100 carries in each season. I chose these numbers as somewhat arbitrary thresholds to get a good mix of a sufficient data sample each year and a big enough sample size within each data point to make the data as reliable as possible.

I then looked up the explosive plays (runs of 15+ yards, passes of 20+ yards) each of those players achieved in those seasons. I used the data in aggregate to get average explosive play rates for each. Full data can be seen here.

  • Passing: on average, 8.7% of all passing plays (including sacks) resulted in explosive passes. This data did not seem to change much from 2014-19, with each year fluctuating between 8.3% and 9.1% and no clear year-to-year pattern. I also double checked that smaller sample sizes didn’t skew the data, but the rate stayed the same when I only looked at player seasons with 300+, 400+, or 500+ pass attempts.
  • Rushing: on average, 4.8% of all running back carries resulted in explosive runs. I’ll note I excluded QBs with 100+ carries in a season from this, because many of those are scrambles and thus have a much higher explosive rate, and the sample size of QBs with 100+ carries was too small to study independently. Again, this number didn’t change much year-to-year or if I had a larger carry threshold for inclusion (I checked 150+, 200+, and 250+ carries).
  • Receiving: I split this one up by position, since WRs, TEs, and RBs are used quite differently in the passing game. Overall, 5.5% of targets to running backs, 11.1% of targets to WRs, and 9.3% of targets to TEs resulted in explosive completions. Again, there was little variation year-to-year.

I then used those rates as a baseline for how many explosive plays an individual should be expected to produce based on their volume for the year. For example, a RB with 100 carries and 100 pass targets should be expected to have 4.8 explosive carries and 5.5 explosive receptions. If they actually produced 6 explosive carries and 4 explosive receptions, they had 1.2 more explosive runs and 1.5 fewer explosive catches than expected.

To save words, from here on out I’m going to refer to that as the explosive differential.

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Nick Foles Will Be the Starting Quarterback

| June 1st, 2020


For the Bears, there is no more important issue looming than which man will be under center receive the shotgun snap when the Bears take the field against Detroit in Week One. Today I want to dig into the stats to see what we can learn about Foles vs. Trubisky, as well as what to expect from whoever wins that derby compared to other QBs around the NFL.

The table below shows basic efficiency statistics for Trubisky and Foles in the Reid offense (so Trubisky in 2018-19 in Chicago and Foles in 2016 in KC and 17-18 in Philadelphia), plus the other three notable recent Reid QBs (Smith 13-17, Mahomes 18-19, Wentz 16-19). I’ll note I included playoff stats for everybody because otherwise Foles’ sample size is just so small (less than 350 with just regular season, just over 500 with playoffs included). I also included the NFL average for 2018-19 as a frame of reference for what’s roughly normal around the league. I split up the data into short and long passes (targeted more than 15 yards past the line of scrimmage) using Pro Football Reference’s game play finder.

That’s a lot of information to digest, so let’s look at short and deep passes separately.


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