The NFL draft, which begins on April 29, is just a few weeks away, and free agency has quieted down significantly. That means we know roughly what the Bears’ roster will look like heading into the draft, which can be seen in their current presumed depth chart below.

With that depth chart in mind, let’s look at Chicago’s biggest needs as they prepare for the draft. I’m going to start with immediate needs, spots where the Bears need to find somebody who can step in and start on day 1.

• Cornerback. Teams need 3-4 good CBs, and right now the Bears might have 0. Sure, Desmond Trufant was good in 2018, and Jaylon Johnson played well for a few games in his rookie year before falling off hard down the stretch, but there’s not a single CB on the roster you can confidently rely on. This is easily the biggest immediate hole on the team. The bad news is that a rookie is unlikely to help much in the here and now, as the adjustment to the NFL is a steep one. Still, Chicago should be looking to invest a premium pick in this premium position to make up for the loss of Kyle Fuller.
• Offensive Tackle. Charles Leno is nothing special, but he’s an adequate left tackle, especially when the guard playing next to him is good (his play noticeably improved in 2020 after Cody Whitehair moved back to left guard). Germain Ifedi is ideally suited to be a swing tackle, just like current swing tackle Elijah Wilkinson. This is a group that looks like a weakness right now, but could easily be a strength if the Bears draft a tackle somewhere in the early rounds in what is supposed to be one of the best OT draft classes in years. Given that Leno, Ifedi, and Wilkinson are all free agents after 2021, double-dipping with a developmental prospect on day 3 wouldn’t be a bad idea either.

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There seems to be a prevailing consensus among Chicago fans that the Bears still have one of the best defenses in the NFL. Unfortunately, there is no reason to believe that is true.

The Bears had a great defense in 2018, but that was 3 years ago, an eternity in the NFL. They led the NFL in pretty much every defensive stat imaginable that season, but we’ll track their decline since then with a simple one: points allowed.

• 2018: 17.7 points/game, 1st in NFL
• 2019: 18.6 points/game, 4th in NFL
• 2020: 23.1 points/game, 14th in the NFL

That’s right, Chicago’s defense was more average than good in 2020 (with 32 NFL teams, 16th is exactly average). That feels weird to say, right? I know I certainly didn’t think of them that way last year. But a broader look at the statistics paints exactly that picture, as you can see in the table below. DVOA is a measure of total defensive performance from Football Outsiders, and net yards/attempt (which factors in sacks and pass attempts) is from Pro Football Reference.

The only major stat where the defense ranked in the top ten was yards/run allowed. Everywhere else was mostly middle of the pack besides forcing turnovers, which they were bad at.

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I’ve been tracking explosive plays for a few years now because they have a strong relationship to points scored. To put it simply, good offenses produce plenty of big plays. Let’s look at how Chicago did in this department in last season.

Basic Overview

The table below shows how many explosive passes, runs, and total plays the Bears produced in 2020, as well as how those results ranked compared to the rest of the NFL. Explosive passes are those that gain 20+ yards, while explosive runs gained 15+ yards. All data is from Pro Football Reference, and explosive play data is from the Game Play Finder.

A few thoughts:

• If these results look awfully familiar, it’s because they are. The Bears were the least explosive team in the NFL in 2019 with 49 explosive plays. If you want to look on the bright side, this year actually showed a slight improvement, though they were still one of the least explosive offenses in the league.
• You can view the full results here, but Chicago’s totals put them right in line with teams like the Washington Football Team, Giants, Jets, and Bengals. Yuck.
• I found last offseason that there is very little year-to-year correlation for explosive plays. Based on this, you could argue that finishing horribly in this category 2 years in a row is random bad luck, but I’m more inclined to think it’s an indictment on the personnel and/or scheme.

Explosive Players

I also want to look briefly at who produced the explosive plays. I want to caution that I’ve found there is very little year-over-year consistency in these results, so a player having an explosive or non-explosive 2020 doesn’t necessarily guarantee it will repeat in 2021.

Let’s start with a look at the quarterbacks, and I want to note that passes here include sacks for a more accurate reflection of total pass plays.

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After yesterday’s piece highlighting the Bears’ need to prioritize keeping Allen Robinson around this offseason, today will build on that with a closer look at Robinson’s value to the Bears. I’ll start with examining his individual performance, and then look to the importance of that performance in context to building a roster.

High Volume

To start with, Robinson is the team’s highest volume pass weapon by a wide margin. More than 1 in 4 passes the Bears threw last year went Robinson’s way, and he finished 3rd in the NFL in targets with 151 (9.4/game). Nobody else had more than Darnell Mooney’s 98 (6.1/game). Replacing that kind of volume would be difficult.

However, you could reasonably argue that high volume is not indicative of quality. In fact, if Robinson drew a lot of targets but had limited production with them, it could be argued that distributing those targets elsewhere is a good idea. And at first glance, Robinson was not a terribly efficient target.

• Although Robinson was 3rd in the NFL in targets, he was 6th in receptions and 9th in yards, which means other players around the league out-produced him while needing less volume to do so.
• Of the 42 players who saw 100 targets in 2020, Robinson ranked 21st in both catch % and yards/target, meaning he was middle of the pack in efficiency.

It is important to remember, however, that a pass catcher is dependent on their quarterback, and Robinson was working with bad quarterbacks last year. The players who caught more passes than him were catching balls from Josh Allen, Kyler Murray, Aaron Rodgers, Derek Carr, and Patrick Mahomes. Those who finished with more yards caught passes from those QBs plus Russell Wilson, Kirk Cousins, and Matt Ryan.

High Efficiency

With that in mind, let’s compare Robinson’s efficiency to the rest of the team’s pass catchers. The table below shows the basic statistics for every player with at least 10 targets in 2020.

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Yet again in 2020, we see that the Bears have one of the best defenses in the NF,L coupled with one of the worst offenses. This combines to give them a team that is not good enough. It’s Groundhog Day all over again, a continuation of 2018-19, all of the Lovie years, and the 1980s after Jim McMahon got hurt.

Normally I’d use the bye week to do an in-depth look at the numbers for Chicago’s offense and defense, but honestly I don’t see the point. Their defense is really good, their offense is really bad, and you don’t need advanced stats to tell you more than that. I’m sure I’ll still do some of that analysis in the offseason but for right now I want to focus on a bigger question: WHY is the defense so much better than their offense?

The answer here is really not that surprising: the Bears are investing more in the defense. The table below shows how much money they have invested in the defense compared to the offense, as measured in 3 ways:

• 2020 cap dollars. How much current money is being spent.
• Average yearly salary. This accounts for the fact that contracts don’t have even distribution of cap hits every year. For instance, Robert Quinn has an average salary of $14M per year in his contract, but only has a 2020 cap hit of $6M. This will give a better picture of true spending.
• % of salary. This looks at how much of your total spending is focused on one side of the ball, based on the average annual salary of players. It’s a good measure of how lopsided your investment is on offense vs. defense.

The table below shows the Bears’ values for offense and defense in each category, as well as the NFL average and where the Bears rank. All data is from Spotrac.

A few thoughts:

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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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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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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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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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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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