A disappointing 2019 season is over, and it’s time to start thinking about what the Bears can do over the next several months to set up a 2020 rebound. To start the offseason, let’s take a look at where things currently stand for the roster.

Salary Cap Situation

The 2020 salary cap has been projected between $196.8M and $201.2M. We’ll play it safe and use the low end of that estimate. As you can see in the table below, the Bears don’t currently have a lot of money to work with (bottom row). All cap information courtesy of Spotrac.

So the Bears currently have around $13.5M in cap room for 2020, though that could be around $18.5M if the cap hits more optimistic projections. Now let’s look at who they lose from 2019.

NOTE: these numbers are before the Eddie Jackson extension. I’ll update once the exact figures for that come out, and then remove this note. I’m guessing they’ll only drop the 2020 space by 1/5 of his signing bonus, which will probably come out to $2-3M.

Key Free Agents

The Bears actually don’t have a huge number of free agents this year, at least in terms of players who were significant contributors. I’ll briefly list and discuss the main ones here, sorted by position.

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The Bears didn’t wait long to start attacking the offseason following a disappointing 2019. Just three days after their last game and one day after firing offensive line coach Harry Hiestand, they hired his replacement in Juan Castillo. He brings a wealth of experience to the role, having filled the same position in Philadelphia under Andy Reid from 1998-2010, in Baltimore from 2013-16, and in Buffalo from 2017-18.

Castillo is expected to be heavily involved in designing and coordinating the run game in Chicago, which will be revamped this offseason after 2 unproductive years under Hiestand and former offensive coordinator Mark Helfrich. Accordingly, I dug into how well run games have fared under Castillo in the 19 years he’s served as an offensive line coach and/or run game coordinator. I did this using DVOA rankings, from Football Outsiders, which are a generally solid all-encompassing metric to evaluate both the rushing and passing production from an offense. The ranks for rushing and passing DVOA can be seen in the table below.

There’s a lot of data to parse through here, and I think you can look at it fairly from both an optimistic and pessimistic viewpoint. Let’s take a brief look at each perspective:

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Secondary

I’m not going to look at safeties much because there hasn’t really been any rotation there and there aren’t many stats to quantify safety play (though I will say I feel good about my preseason pick of Eddie Jackson as the defense’s breakout star). Let’s jump right to the CBs then and take a detailed look at their performance using stats from The Quant Edge.

The first thing I want to note is that Chicago’s corners don’t move. Kyle Fuller has played exclusively on the right side (from the offense’s perspective) and Prince Amukamara has been exclusively on the left. Kevin Toliver II basically took Prince’s spot on the left post-injury, though he did play a few snaps in the slot. Nickel CB Bryce Callahan, meanwhile, played a few snaps outside after Prince got hurt but otherwise has been exclusively in the slot.

Next I want to look at how effective each CB has been in coverage, as well as how much man, zone, and press coverage they’ve played.

A few thoughts:

• At first glance it might seem like Kyle Fuller has been the worst CB on the roster, but look beyond the passer rating. That takes a big hit because he’s given up 2 passing TDs and none of the throws targeted at him have resulted in interceptions yet. He’s been very good at keeping the completion percentage down, and the yards per target on throws aimed at him are much lower than anybody else. Make no mistake: he is the best CB on Chicago’s roster, and he is very good.
• Fuller is actually the only CB credited with giving up a TD so far this year. He gave up two, which means the other 5 passing TDs the Bears have allowed are blamed on non-CBs. Meanwhile, five passes targeted at CBs have ended up intercepted. That’s a great ratio.
• I find the press splits interesting. Toliver has never played press, and Fuller only does it about half as much as Prince and Callahan. That’s perfectly fine; each player has their own style that is obviously working well for them so far.

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Chicago’s defense has been awesome in the first month of the season. They’re among the best in the league in nearly every category that matters, and are ranked first overall in DVOA. Now I want to look a little more closely at how well they’re performing against both the run and pass in different areas of the field.

Defending The Run

Chicago’s run defense was solid in 2017, but it has been fantastic so far in 2018. They have shut opposing run games down, and they’ve done it pretty much across the board, as we can see below.

Here’s the data for Chicago’s rushing defense by zone, courtesy of the NFL Game Statistics and Information System. The line at the bottom is the line of scrimmage, runs are split into 7 zones, and attempts and yards per carry are listed for each zone, with ranks relative to the rest of the NFL in parentheses. The height of the bar is proportional to yards per carry, and bars are colored green for top 10, red for bottom 10, and yellow for middle 12. Note expected yards per carry varies by region, so the colors are relative to their peers in that region.

A few thoughts:

• That’s a whole lot of green. Awesome. Last year’s version featured a lot more yellow and red. This is a good time to remind you that Khalil Mack is an elite run defender in addition to being one of the best pass rushers in the league.
• Speaking of Khalil Mack, he and Akiem Hicks usually line up opposite the RG and RT. Notice where defenses are running towards? There are 38 runs to the left side – away from Mack and Hicks -compared to 21 towards them on the right.

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Now that we’ve seen Chicago’s new offense play four games, it’s time to examine what exactly it looks like. We’ve seen them run 271 plays, and while that’s still a fairly small sample size, it’s big enough that we can begin to pick up trends, search for predictable patterns that opposing defenses might begin to pick up on, and see if there are any situations their current approach could be improved.

Now we focus on the wide receivers and tight ends, examining how much they’re playing, how effective they’ve been, and how they’re being utilized.

Snap Counts and Predictabilities

First I want to look at how frequently each target is playing, and how their presence on the field impacts the offense’s performance. Data is from The Quant Edge.

A couple things to note about the table below:

• I’m using success rate here instead of yards per play. That is to account for down and distance context. A two-yard play on 1st and 10 is bad, while a two-yard play on 3rd and 1 is good. The general idea is that a successful play keeps you ahead of the chains, but an exact definition is available here if you’re curious.
• I didn’t include Allen Robinson, Taylor Gabriel, or Trey Burton here because they’re almost always on the field; they’re all playing least 83% of the offensive snaps so far. This is more to look at the players who are situational and how they’re impacting the offense.
• Anthony Miller’s data only includes the 3 games for which he was active.

A few thoughts:

• On the surface, it looks like Anthony Miller has hurt the offense. Maybe he has. But he basically only plays in 11 personnel groupings, where there are 1 RB, 1 TE, and 3 WRs on the field, and in general that grouping has been the least efficient passing formation in the NFL. In terms of the run game, I don’t actually know much about Miller as a blocker. It’s possible that he’s not blocking well and that’s hurting the run game, but it’s also possible something else is causing the difference.

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Now that we’ve seen Chicago’s new offense play four games, it’s time to examine what exactly it looks like. We’ve seen them run 271 plays, and while that’s still a fairly small sample size, it’s big enough that we can begin to pick up trends, search for predictable patterns that opposing defenses might begin to pick up on, and see if there are any situations their current approach could be improved.

Today we’re going to focus on running backs Jordan Howard and Tarik Cohen, examining how much they’re playing, how effective they’ve been, and how they’re being utilized.

Snap Counts and Efficiency

First I want to look at how frequently each running back is playing, and how their presence on the field impacts the offense’s performance. Data is from The Quant Edge.

(Note: I’m using success rate here instead of yards per play. That is to account for down and distance context. A two-yard play on 1st and 10 is bad, while a two-yard play on 3rd and 1 is good. The general idea is that a successful play keeps you ahead of the chains, but an exact definition is available here if you’re curious.)

A few thoughts:

• Howard is actually playing typical lead RB snaps for an Andy Reid offense. As I noted this offseason, Kareem Hunt played 65% of the snaps in Kansas City last year. This is in stark contrast to the Philadelphia Eagles style-rotation I thought would make more sense. It’s worth noting the 2 split snaps almost exactly 50/50 in the Tampa Bay game. I wonder if that’s more what we’ll see going forward.
• The run/pass splits for when both of these players are in vs. out of the game are too lopsided. A 30% swing when Howard exits the game and 20% swing when Cohen enters the game should not be the case. This is too predictable and makes it too easy on the defense.
• Some of these numbers are related, since Howard and Cohen basically swap being on the field. They’ve only shared the field on 23 snaps so far this year, so the run game being more effective with Howard off the field is the same as saying the run game gets more effective when Cohen is on the field. Again, I think this might have more to do with defenses gearing up to stop the run when Howard is in the game and not expecting it when Cohen is in.

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Now that we’ve seen Mitchell Trubisky play four games under Matt Nagy’s tutelage, it’s time to examine how he’s doing. We’ve seen him play 269 snaps and throw 130 passes, and while that’s still a fairly small sample size, it’s big enough that we can begin to analyze how he’s performing in a variety of situations.

Growth Through Each “Quarter”

Last offseason I looked at Trubisky’s performance in 4-game snapshots, borrowing the idea of breaking an NFL season down into quarters from Lovie Smith. There I found that Trubisky got progressively better in every “quarter.” Since Trubisky has played 4 games this year, he now has 16 in his career, giving him a full 4 “quarters” that we can track. Let’s take a look.

Well that looks pretty good. I said last offseason that, statistically speaking, Trubisky needed to throw more TDs while keeping everything else the same. Here we see that he has managed to throw more TDs, and everything else has stayed the same or improved. That’s good growth to see from a 2nd year QB.

Of course, four games is a small sample size, and this doesn’t look quite as rosy if we remove the Tampa game from the equation. Then his yards per attempt drops to 5.7, TD percentage to 1.9%, and his INT % (2.9%) and sack % (8.0%) both rise a bit higher than they were late in his rookie year.

Through three weeks, the stats suggested Trubisky was actually playing worse than late in his rookie year. That’s not entirely surprising given that learning a new offense often results in a step back at first.

Adding the TB game in there makes this look good, but now the question is whether the TB game was an aberration or a sign of things to come.

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Now that we’ve seen Chicago’s new offense play four games, it’s time to examine what exactly it looks like. We’ve seen them run 271 plays, and while that’s still a fairly small sample size, it’s big enough that we can begin to pick up trends, search for predictable patterns that opposing defenses might begin to pick up on, and see if there are any situations their current approach could be improved.

Down & Distance

Let’s start by looking at what they’re doing in different down and distance situations. All statistics come from the NFL Game Statistics and Information System unless otherwise noted.

First Down

The offense has been extremely balanced on 1st down so far, with exactly 58 runs and 58 passing plays (passes, sacks, or scrambles).

The passing game has thrived with an average of 7.8 yards per play. According to Pro Football Reference’s Game Play Finder, Mitchell Trubisky is completing 69% of his passes on 1st down, with 6 TD, 2 INT, and a 115.9 passer rating.

The running game, on the other hand, has been extremely ineffective, averaging only 3.0 yards per carry. Most of the running attempts (34) have come from Jordan Howard, who is averaging 3.2 yards per carry, but Tarik Cohen also has 17 carries at only 2.9 yards per clip. It would appear the Bears are either making it obvious when they’re going to run or defenses are worrying about stopping the run first to make Trubisky beat them.

Second Down

When it comes to 2nd down, context is needed. A 3 yard gain is great on 2nd and 2, pretty good on 2nd and 5, and awful on 2nd and 10. With that in mind, I split the data into 4 groups based on the distance required to get a 1st down. The table below shows the results.

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Now that Ryan Pace has been here for a while, we can start to look at his past drafts to see what lessons we can learn from his approach. This can help us cautiously look ahead to the 2018 draft to see what he might be thinking.

With that goal in mind, I’m going to spend the next three weeks looking at how Pace has approached the three days of the draft, and then applying that approach to 2018 to see what players are likely being considered for the Bears this year. We’re starting today at the top of the draft. Let’s look first at the history, and then we’ll examine lessons learned.

Draft History

2015: Kevin White, WR, 7th overall

2016: Leonard Floyd, OLB, 9th overall (trade up from 11)

2017: Mitchell Trubisky, QB, 2nd overall (trade up from 3)

Trend 1: Go get your guy

The first thing we should observe is that Ryan Pace is not shy about trading up in round 1 to get the player he has identified as his main target. So keep that in mind as we look at mock drafts with players who might be good fits for the Bears but are projected to go higher than #8.

It’s worth noting that these have all been relatively minor trades just moving up a few spots, which keeps the cost down. Despite reportedly exploring moving up to the top of the draft for Marcus Mariota in 2015, Pace has not been willing to give up multiple high picks in these moves.

Trading up becomes a bit more difficult this year because the Bears are already without a third round pick due to trading up for Trubisky last year, but they do have an extra fourth round pick they could use.

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In the last two weeks, I’ve outlined both what the Bears need to add at WR this off-season and what players in free agency should fit that profile/the new offense. At the end of that work, I came up with the following two lists, suggesting that the Bears work to sign one player from each group.

Tier 1 (750+ yard receivers)

Marqise Lee, Jordan Matthews, Mike Wallace, Emmanuel Sanders (if cut)

Tier 2 (500+ yard receivers)

Albert Wilson, Kendall Wright, John Brown, Taylor Gabriel, Paul Richardson, Jaron Brown

Now I want to look at what types of contracts those players should expect in free agency to see how expensive these moves would likely be for the Bears. In order to do that, you need to compare the contracts signed by similar players (in both age and past production) who hit free agency in recent years. This gives you a general baseline for the ballpark a new contract should probably be in, though of course there are no guarantees this is exactly how it works out.

In an effort to be as accurate as possible, I also accounted for inflation, since the cap keeps going up every year. It’s jumped by about $10 million a year every year since 2015, and is expected to do the same again this year. Thus the comparable contracts were multiplied by the following scaling factors to get the predicted value, depending on when they were signed (some slight adjustments were made for greater/worse production):

• 2015: 1.24
• 2016: 1.15
• 2017: 1.07

Let’s look through each target 1 by 1, with a few brief comments. Full data for production of targets and free agent contracts can be seen here. All contract information is from Spotrac.

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