The offseason is winding down as we are now firmly in training camp and shifting into preseason mode, but before the real games start I want to sneak one last series examining part of the roster to give us a better sense of what to expect in the 2024 season.

I’ve spent quite a bit of time looking at the offense in the last few months, as we’ve learned together about new OC Shane Waldron, the WRs, the TEs, and expectations for rookies Caleb Williams and Rome Odunze. However, I’ve completely ignored the offensive line, and want to spend a little time this week focusing on the big boys up front who will be tasked with protecting the quarterback and opening lanes in the run game.

We’ll start today with the interior, and look at tackles tomorrow.

Guards

Pass Protection

Let’s start with guards, and then begin by examining pass protection, which is the most important part of an offensive lineman’s job. There were 62 guards (1.9/team) who had at least 300 pass blocking snaps in 2023, which is roughly starters. Chicago had three players in this sample: Teven Jenkins, Nate Davis, and Cody Whitehair. The table below shows how they help up protecting the passer compared their peers around the NFL. A few quick notes:

• All data is from Pro Football Focus (PFF).
• True pass sets are intended to remove plays designed to minimize the pass rush and make the OL’s job easier, such as screens, play action, and rollout.
• Pass blocking efficiency measures overall pressure allowed, but weights sacks more heavily than other pressures. A higher number is better.
• In a 62 player sample, 31st would be the middle rank, while cells highlighted in green indicate they ranked in the top 25% (top 16) and red indicates the bottom 25% (47th or worse).
• Ryan Bates is also included, since he will be competing for a starting spot this year.
  • He didn’t play much in 2023, so his data is from 2022, and thus doesn’t have any ranks indicated.
  • The Bears plan to have him compete at center, but he’s only played 203 career snaps at C spread across 4 seasons, so that’s not much to go on. He played 821 snaps at guard in 2022, the only season where he’s had starting snaps, so that’s what we’ll evaluate.

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The Bears have three rookies with a chance to play meaningful roles on offense or defense this year, so I want to take some time this week to look at what history can tell us about what to expect for those players, both in their rookie seasons and in their careers. We started with QB Caleb Williams, looked yesterday at WR Rome Odunze, and today we’ll end the series with a look at DE Austin Booker.

The Setup

Booker was drafted in round 5 this year, so to look at historical comparisons I examined all 29 defensive ends selected in round 5 over the last 10 drafts. Full data set can be seen here.

Some might argue that Booker was projected as a 3rd round pick before the draft (he was 85th on the Consensus Big Board), so he should not be compared to typical 5th rounders, but Arif Hasan, who runs the Consensus Big Board, has found that “steals” – players who are drafted appreciably later than their pre-draft media projection – end up performing more in line with their draft slot than their media projected slot, so comparing him to other 5th round picks is still a valid approach.

Rookie Performance

With that in mind, let’s examine how these 29 defensive ends performed as rookies.

Overall, defensive ends drafted in the 5th round didn’t do much; their average stat line was 202 snaps, 12.5 tackles, and 1.5 sacks. Of course, there was a wide fluctuation of outcomes among 29 players, so the table below breaks them up into groups based on how many snaps they played.

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The Bears have three rookies with a chance to play meaningful roles on offense or defense this year, so I want to take some time this week to look at what history can tell us about what to expect for those players, both in their rookie seasons and in their careers. We looked yesterday at QB Caleb Williams, and today will focus on WR Rome Odunze, before ending tomorrow with a look at DE Austin Booker.

The Setup

To get a baseline for Odunze, I looked at all WRs drafted near him in the last 10 years. Odunze was drafted 9th overall, so I went +/- 5 picks and looked at WRs drafted between 4th and 14th. This gave a list of 17 players who were viewed fairly similarly to Odunze coming out of college, and enables us to see what history has to say about reasonable expectations for Odunze, both as a rookie and in his career.

Rookie Performance

With that setup in mind, let’s take a look at how those 17 WRs fared as rookies. Full data can be seen here, but the average stat line for these players was 13 games and 680 snaps played, with 91 targets leading to 54 catches for 766 yards and 5 TD.

That’s not bad – those values would have ranked 41st among WR in targets, 46th in receptions, 40th in yards, and 28th in TD. Basically, the average player in this sample has performed like a mid-tier WR2.

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The Bears have three rookies with a chance to play meaningful roles on offense or defense this year, so I want to take some time this week to look at what history can tell us about what to expect for those players, both in their rookie seasons and in their careers. We’ll start today with QB Caleb Williams, shift tomorrow to WR Rome Odunze, and end with a look at DE Austin Booker.

The Setup

To get a baseline for Caleb Williams, I looked at the last 10 QBs drafted 1st overall, going back to Sam Bradford in 2010. I only wanted to look at QBs drafted 1st overall because they are significantly different than other highly drafted QBs in a few notable ways:

• They start immediately. 9 of the 10 #1 picks in this sample started at least 10 games as rookie, with an average of 14.3 starts. 5 of the 19 QBs drafted 2-10 in the same years started fewer than 10 games, with an average of only 10.8 starts for the sample.
• They have better career outcomes. 7 of the 10 #1 picks in this sample earned significant 2nd contracts to be starting QBs, with the verdict still out on 2023 pick Bryce Young. Only 8 of 19 drafted 2-10 hit the same threshold (I am assuming CJ Stroud will as one of those 8), with the verdict still out on Anthony Richardson.

QBs are by far the most valuable players in the NFL, and so any QB who is widely regarded as a top-level prospect is going to get drafted #1 overall. Accordingly, I want to compare Williams directly to his peers, not to others who might get drafted highly out of desperation.

Rookie performance

With that setup in mind, let’s take a look at how these 10 QBs fared as rookies. Full data can be seen here, but the average stat line for these players was 504 pass attempts, 60% completion, 6.7 yards/attempt, 17 TD, 13 INT, and an 80.2 passer rating.

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This is the final installment of a 3-part series looking at Chicago’s 2023 pass rush.

• In part one, we learned that the Bears had one of the worst pass rushes in the NFL, though it improved a bit after trading for Montez Sweat, especially when they were willing to blitz.
• In part two, we saw that Montez Sweat is a good but not great pass rusher, while all of the other defensive ends on the roster are bad at rushing the passer.

Today, we’re going to end the series by exploring Chicago’s defensive tackles.

Overall Efficiency

We’ll start with a season-long look at how Chicago’s main defensive tackles performed when rushing the passer. The table below shows a variety of per-snap metrics, including how they ranked compared to the 98 DTs league-wide who had at least 200 pass rush snaps. A few quick notes:

• All data comes from Pro Football Focus (PFF).
• Win rate is the percentage of snaps where PFF determines that the rusher has beaten the blocker at any point in the snap. This is admittedly subjective, and thus should not be used on its own, but can be a helpful part of a larger picture.
• Pass Rush Productivity is a unique PFF stat that accounts for all sacks, QB hits, and pressures on a per-snap basis, with an added weight given to sacks; a higher value is better.
• True pass sets look only at plays that do not give offensive linemen a built in advantage: no play action, no screens, and the throw time has to be at least 2 seconds. This lets us see how effectively a player rushes the passer when they most likely know they are getting after the QB, and the offense knows they have to block for a while.
• Values in the top 25% are highlighted in green, while those in the bottom 25% are highlighted in red.

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This is the 2nd installment of a 3-part series looking at Chicago’s pass rush from 2023. In part one, we learned that the overall team pass rush was among the worst in the NFL.

Today, we’ll be examining how effectively Chicago’s defensive ends rushed the passer last year.

Overall Efficiency

We’ll start with a season-long look at how Chicago’s main defensive ends performed when rushing the passer. The table below shows a variety of per-snap metrics, including how they ranked compared to the 95 edge rushers league-wide who had at least 200 pass rush snaps. A few quick notes:

• All data comes from Pro Football Focus (PFF).
• Data for Montez Sweat is only for the 9 games he played in Chicago.
• Win rate is the percentage of snaps where PFF determines that the rusher has beaten the blocker at any point in the snap. This is admittedly subjective, and thus should not be used on its own, but can be a helpful part of a larger picture.
• Pass Rush Productivity is a unique PFF stat that accounts for all sacks, QB hits, and pressures on a per-snap basis, with an added weight given to sacks; a higher value is better.
• True pass sets look only at plays that do not give offensive linemen a built in advantage: no play action, no screens, and the throw time has to be at least 2 seconds. This lets us see how effectively a player rushes the passer when they most likely know they are getting after the QB, and the offense knows they have to block for a while.
• Values in the top 25% are highlighted in green, while those in the bottom 25% are highlighted in red.

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This is the start of a 3-part series examining Chicago’s pass rush in 2023 and what expectations should be for 2024. The content covered in each part roughly breaks down as follows:

Part 1: Overall team pass rush, and the impact of trading for Montez Sweat.
Part 2: Defensive end individual pass rushing efficiency.
Part 3: Defensive tackle individual pass rushing efficiency, including rookie progression for Gervon Dexter Sr. and Zacch Pickens.

Let’s dive right in!

Overall results

We’ll start by examining Chicago’s overall team performance rushing the passer last year. The table below shows how the Bears ranked out of 32 NFL teams in generating sacks and pressures, as well as how often they blitzed. All values in the top 25% are highlighted in green, while those in the bottom 25% are in red. All data for this article, unless otherwise noted, is from Pro Football Reference.

Side note: sorry if the tables don’t show up well in the article. You can click on them to view in full on a separate page. 

A few thoughts:

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This is the final installment of a 4-part series looking at Chicago TEs Cole Kmet and Gerald Everett.

• In part one, we explored how each player has been used in the past, and how this might match up with how offensive coordinator Shane Waldron has deployed his tight ends.
• In part two, we examined how frequently and effectively Kmet and Everett were asked to block in both the run and pass games.
• In part three, we saw how they fared as pass catchers in terms of overall efficiency, against man and zone coverage, and in different depths of the field.

Today we’ll wrap up the series by continuing to focus on their impact catching the football, especially on 3rd/4th down, in the red zone, and out of the slot.

3rd + 4th Down

Let’s start by looking at 3rd and 4th down, when stakes are high and players need to produce a first down to move the chains and avoid a punt. The table below shows how frequently and effectively Kmet and Everett were targeted in these high-leverage situations in 2023. Data is from Pro Football Reference’s Game Play Finder, ranks in parentheses are compared to the 26 NFL TEs with 50 or more targets, and ranks in the top 25% are highlighted in green, while those in the bottom 25% are highlighted in red.

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This is part 3 of a 4-part series looking at Chicago TEs Cole Kmet and Gerald Everett.

• In part one, we explored how each player has been used in the past, and how this might match up with how offensive coordinator Shane Waldron has deployed his tight ends.
• In part two, we examined how frequently and effectively Kmet and Everett were asked to block in both the run and pass games.

Today, we’re going to switch gears to focus on the tight ends as pass catchers. We’ll look at overall efficiency, how they performed against zone and man coverage, and how frequently and effectively they were targeted at different depths of the field.

Basic Efficiency Stats

We saw in part 1 that Kmet and Everett both have starting TE volume in the passing game, but volume stats don’t tell you the whole story; we also need to look at how efficient players are with their targets. The table below shows that data for Chicago’s TEs, as well as their ranks compared to 26 TEs with at least 50 targets. Values in the top 25% are highlighted in green, while those in the bottom 25% are in red. NFL best, average, and worst values among those 26 players are also included to help contextualize the data. All data is pulled from Pro Football Reference’s Game Play Finder.

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This is the 2nd installment of a 4-part series looking at Chicago TEs Cole Kmet and Gerald Everett. In part one, we explored how each player has been used in the past, and how this might match up with the way offensive coordinator Shane Waldron has deployed his tight ends. Today, we’re going to focus on blocking, which is an often overlooked part of a tight ends’ role.

Pass Blocking

Let’s start by looking at how frequently and effectively Chicago’s TEs pass blocked. The table below shows some basic pass blocking stats from Pro Football Focus (PFF) for Kmet and Everett in 2023, and their rank compared to 45 NFL TEs with at least 25 pass blocking snaps. Ranks in the top 25% are highlighted in green, while those in the bottom 25% are highlighted in red.

A brief explanation of some of the stats:

• Pass Block % is the % of pass plays where a player is on the field and is asked to block instead of running a route.
• Pass Block Efficiency is a PFF metric that “measures pressure allowed on a per snap basis with a weighting towards sacks.” A higher number is better.

Side note: sorry if there are formatting issues for the tables. You can view them in full by clicking on them. 

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