I recently reopened a discussion about sacks Chase started years ago. Today, I’m going to rehash another topic our prolific host has covered time and again (and again): adjusting interceptions for era. Unlike sacks, which have official number dating back to 1982 and unofficial ones published as far back as 1960, interceptions have official records as far back as 1940. This gives us much more data to work with, but it also provides similar challenges that Sid Luckman presents when adjusting passing stats: the game is so different now from what it was in the 1940s that trying to compare the numbers side by side ends up killing newer players when adjusting for dropbacks or hurting older players when adjusting for passing environment. But getting it 85% right is better than not doing anything at all, so I’m going to do it anyway. As I did with the sack posts, I will go through my progressions of adjustments one step at a time, so that you can see how we arrived at the final numbers.

Normalizing for Volume

It stands to reason that intercepting ten passes against 300 attempts is more impressive than intercepting the same number of passes against 600 attempts—at least, as far as getting interceptions is at all impressive when divorced from other aspects of play. Because of this, it is necessary to put players on a more even playing field. Using Bill Dudley‘s frankly ridiculous 1946 season as an example, follow the table like so: Dudley, playing in 1946 for the Steelers, appeared in 11 games and snagged 10 interceptions against 162 opponent pass attempts. That comes to an outlandish 6.17% interception rate. If we adjust for volume by giving all players credit for their interception rate multiplied by 500 attempts, Dudley’s 1946 comes to 30.9 picks per 500 passes. If you look at that number and mutter “well, that’s just too high,” then we are in agreement. Thus, I took the average of their actual picks and their attempts per 500 passes to find the number in the last column, which I have labeled Mid. Using that adjustment instead, Dudley’s season was only good for 20.4 volume-adjusted interceptions.

If we stop here, it’s easy to see the glaring issue: the much higher interception rate in the days of yore leaves us with a list that doesn’t feature it’s first player after the year 2000 until the 210th spot. Even using the mid number, Ty Law‘s 2005 doesn’t show up until 115. Clearly, we need to keep going.

Incorporating League Environment

The next step is to incorporate the league average interception rate for each season. To do this, I used all seasons from 1940-2021 and found the three-year rolling average, with each given year in the middle (So 2017 would include the average of seasons 2016-2018). Then, I found three numbers: the cumulative interception rate from 1940-2021 (4.02%), the average of averages for each year in the sample (5.18%), and the median rate from the sample (5.21%). Then I took the average of those three numbers (4.80%) and used it as the historical baseline.

The next two tables use this step. The first of the two displays adjusted interception rates, while the latter of the two displays adjusted totals. Using Xavien Howard‘s 2020 as an example, read the table thus: Howard played 16 games and had 10 interceptions against 545 attempts, good for a 1.83% pick rate. The rolling average for 2020 is 2.28%, so Howard gets a boost of 210.5% (4.80/2.28) in the column labeled Mod. If you think that’s too higher, I included a softened version, which is the average of Mod and 100% (in this case, the Soft number is 155.2%). When using the Mod figure to adjust his interception rate, Howard gets credit for a rate of 3.86% (that’s 1.83% * 210.5%), the highest number on record. Using the softened version gives him 2.85% (1.83% * 155.2%), which ranks 18th.

This one is interesting to me, because the modified version seems too skewed in favor of modern players, while the softened version doesn’t feel harsh enough toward the old guys. We’ll go to the table below to see what that looks like in terms of interceptions rather just the more abstract percentages.

Incorporating League Environment (Again)

Let’s use J.C. Jackson as our example this time. In 2020, he played 16 games and hauled in 9 interceptions. We know his adjusted rates from the table above. Using the full modifier on his actual interceptions gives him 18.9 adjusted interceptions, while using the soft modifier gives him 14.0. Jackson is the rare current player who actually gets a boost from using per 500 attempt numbers, albeit a small one. Using the full modifier multiplied by his interceptions per 500 attempts (9.1 from the first table) leaves him with 19.2, while using the softened version gives him credit for 14.1. Note, I did not use the Mid figure from the first table, because too many columns makes these things unwieldy, in my opinion. Instead, I saved that for the last table.

Looking at the Mod and Soft multipliers applied to interceptions, without accounting for volume, just leaves us with a huge list of recent players. While I believe modern defenders to be both superior and in a more difficult position because of rules and schemes, I don’t think it makes sense to give them this much of a boost. Especially when the point of this whole exercise is not to measure the quality of a player, but rather use a variety of factors to more appropriately compare his interception totals to those of other defenders. One need only look at the career of Darrelle Revis to know that having a relatively low turnover total doesn’t preclude a player from greatness. And Ken Riley‘s career makes it evident that a player can find himself quite high on the career pick list without having been the best cornerback on his own team during his prime.

Putting it All Together

Below is the final table for today. Here, I have tried to strike a balance between adjusting for volume and adjusting for environment, but I kept battling with myself over whether I preferred full rate modifiers or soft ones. So I decided to just present both and let the reader decide. Using the controversial 2021 Trevon Diggs season, read the table thus: in 16 games, Diggs had 11 interceptions against 612 passes, good for a 1.80% rate. His Mid volume adjustment (from the first table) is worth 91%. That, combined with his 211.2%environmental modifier (Mod from the second table) gives him 21.1 adjusted interceptions in the Mod-Mid column. Using the Soft modifier instead gives him 15.5.

Instead of using the 4.80% historical baseline that I found, Chase most recently used 3.5%. Doing so doesn’t do much to the orders of the lists any, but it does have a significant impact on the totals by degree. So Diggs would still rank first in the Mod-Mid column and second in the Soft-Mid column, but he would have something closer to 15.4 and 12.7 as his adjusted interception total. While these numbers are more or less abstract and don’t really matter, I do think having the lower baseline Chase used produces results that look more realistic, even if the 3.5% figure was chosen at random (and I don’t know if it was or was not chosen at random). In fact, when I looked at career totals, I actually preferred to use an even lower baseline of 3.16%, which represents the last 40 years of football and covers basically the entire period of post-1978 rules changes that help permanently drop leaguewide interception rates below five percent.

When looking at the results above, the last column seems to produce the most even mix of old and new players. Oddly, however, I may prefer the Mod-Mid column when looking at career totals, which we will see later.  ^[1]How much later, I simply cannot say. Regardless, I think accounting for both volume and passing environment, in some form or fashion, helps put the numbers into more proper context. Even if it does take a little shine off my man Dick Lane.

 

Recently, I reintroduced the concept of adjusted sack numbers for individual player seasons. ^[1]I say recently because Chase published an article about the idea as early as 2015, and I recall reading articles that touched on the issue at the old PFR blog. The logical next step, to me, is to take a look at those stats in the context of full careers. I liked the idea of presenting career data in terms of per-500 dropback metrics and cumulative totals. On top of the methodologies we discussed in the last post, I also wanted to introduce two new ways to look at the information. I hope it proves interesting, and I apologize in advance for my spectacular inability to come up with better acronyms, initialisms, and abbreviations.

Normalized for Volume

The table below contains every player in history with at least 30 sacks (or 30 in the last column) since 1960. Read it thus: Deacon Jones played in 191 games and recorded 173.5 sacks against 5891 dropbacks. His 2.95% career sack rate means he was good for 14.7 sacks per 500 dropbacks over the course of his entire career. If we take his sacks per 500 dropbacks totals from each season and add them together, we get 203.3, which is easily the highest mark ever.

As you probably expected, older players and T.J. Watt dominate the S/500 column. Guys like Robustelli and Marchetti played against offensive linemen hamstrung by rules, while Watt is a quarterback killer still in his prime. When we look in the last column, we can see how much the small differences in each season add up to a big gap between Bruce Smith and Reggie White. Three members of the Purple People Eaters appear in the top eight, though one guy took an additional sixty-ish games to get there.

Also, is it possible that the Hall of Fame actually doesn’t like pass rushers as much as people think?

League Environment Incorporated

In order to account for the easier environment for getting into the backfield long ago, we will use the modified and soft-modified conversions we used in the last article. The table contains any pass rusher who recorded at least 30 actual sacks or reached 30 in either of the last two columns. Read it thus: Alan Page played 218 games and recorded 148.5 sacks against 6348 dropbacks, which is good for a rate of 2.34%. His modifier is worth 91.5%, which becomes 95.7% when softened. For his career, he had 10.7 modified sacks per 500 dropbacks and 11.2 soft-modified sacks per 500 dropbacks. If we take the cumulative totals of those two stats, Page had 160.7 and 169.9, respectively.

When we look at the Soft500 column, we get a fun mix of characters. The Deacon is on top, followed by the younger Watt. Then we get another legend followed by another guy with scant games under his belt. Marchetti and Robustelli have a significant chunk of their careers omitted from this study because Turney and Webster haven’t finished their work on pre-1960 sacks, but the fact that they rank so highly on a per season basis despite not having years prior to their mid-thirties demonstrates how apt they were at pass rushing.

Alex Karras probably would have been in the Hall of Fame earlier were it not for his gambling controversy, while Claude Humphrey likely belongs on more lists of greatest pass rushers. Watch his tape, and you’ll see a guy whose athleticism stands out in the same way that Len Ford‘s did earlier or Julius Peppers‘s did later. Coy Bacon ranks tenth in the SoftTot column. He is a mere 4.3 below Jim Marshall, despite appearing in over one hundred fewer games.

Concentration Accounted For

Now it’s time to take pass rushing depth into consideration by applying a league concentration adjustment to each player. Here’s how to read the table, using Reggie White as an example: White’s career concentration adjustment is worth 1.036, meaning he gets a 3.6% boost to his stats from previous steps. For comparison’s sake, he had 198 actual sacks. When applying the concentration adjustment to his sacks per 500 dropbacks, his number comes to 11.7. If we include the modifier for league sack environment, that number jumps to 12.1. Softening that modifier brings his number down a bit, this time to 11.9. When we add all of White’s single-season figures in concentration-adjusted sacks per 500 dropbacks, his career total is 177.7. The cumulative number for the modified version of that comes to 181.1, and the softened iteration totals 179.4.

The thing that stands out to me is the placement of John Abraham. He is tied for eighteenth on the official sack list but jumps to thirteenth when sorting by the penultimate column. Abraham made five Pro Bowls and three all pro first teams, which doesn’t scream “Hall of Fame,” but he had eight seasons with double digit sacks and two more seasons in which he missed games but still notched 9.5 sacks. In 2003, he played in just seven games but managed 6 sacks. Had he stayed healthy in 2003-04, he likely would have had five consecutive seasons with 10+ sacks after becoming a starter. Abraham was a few injuries away from retiring with eleven seasons in the double digits. I remember watching footage of the highly celebrated Robustelli and thinking his postseasons honors indicate a Reggie White level of play but the tape suggested he was more akin to John Abraham. The per-season numbers in this table support that notion. If the second best Bengals cornerback of the 1970s can make it to Canton, maybe Abraham has a chance at a senior nod one day. ^[2]Note, I wouldn’t put him in, but with the bar being set at the Sprinkle and Riley level, I don’t think I know what a HOFer is anymore.

Dominance Exalted

The table below displays what I think is a more accurate representation of what we think about when we think about great pass rushers. Instead of career compilation, we’re looking at career value over a given baseline. ^[3]Refer to the previous article for the methodology. Read the table thus: Jack Youngblood played in 202 games and recorded 151.5 sacks. For his career, his sack rate was 1.04% better than the league baseline, giving him 5.2 extra sacks per 500 dropbacks. When summing his individual seasons in that metric, he was worth 74.3 sacks above baseline. If we apply the concentration adjustment to his career numbers, he was worth 69.8 added sacks. When we get rid of all seasons that are below average and look only at what might be considered peak production, Youngblood’s value jumps to 72.4.

This table is a numeric representation of why Jim Marshall can rank 23rd in career sacks and not make it to the Hall of Fame. For his career, he was barely above the baseline, meaning he was ultimately worth about 19 extra sacks. Compare that with Bacon, who shares a ranking on the unofficial career list. Because his sack performances were more dominant, his career sack value is 54.7, which puts him in elite company. Cedrick Hardman, Simeon Rice, Harvey Martin, and Jack Gregory are a few other players who stand out as dominant sack artists who may be underrated now.

Something New

I figured I would throw in a few new concepts just to round out the discussion. I have long been a fan of Pro Football Reference’s passing index scores, and I have created my own versions of them for several different stats. This time, I applied the methodology to defensive sack rates. Also, because the results of the single season and career numbers still seem to favor older players, despite the entire purpose of this exercise being to translate across eras, I wanted to incorporate the league strength modifiers I have been working on for the past several years. ^[4]These take into account things like integration vs segregation, positional specialization, league attractiveness vs other sports, pay, the existence of rival leagues, U.S. population of NFL-aged men, … Continue reading People who lament that football today isn’t like the football idealized by marvelous NFL Films creations may not like this.

The below chart shows every player with at least 3000 dropbacks faced. Using Jared Allen as an example, read the table thus: Allen had 134 sacks against 6426 dropbacks for a 2.09% sack rate. His sack rate was nearly a full standard deviation above the median, giving him a sack rate+ of 113.5. ^[5]Highest on record, min 3000 dropbacks faced. T.J. Watt will take over the top spot soon. He currently has a rate+ of 117.2. Nick Bosa and Micah Parsons are also higher than Allen, though they are … Continue reading His concentration-adjusted career sack value, after accounting for league strength, is 61.1. When we look at only his positive value seasons, it raises slightly to 61.7.

I believe the top ten, as ranked by the last column, is a great list of stellar sacksmiths. A decent era range shows up, and there doesn’t seem to be too much skew toward older or newer players. However, this may be because it more closely lines up with my subjective view of these players, and we love to have our priors confirmed.

The lowest ranked players on the list are linebackers who had a decent number of sacks but played in coverage too often to reasonably compete with edge rushers, as well as interior linemen who played a ton of snaps but weren’t primarily pass rushers.

What stands out to you?

 

In 2015 (and, again, in 2018), Chase published his methodology for comparing individual sack seasons across eras. At the time, we had only the official numbers available, so the comparisons didn’t capture any performances prior to 1982. Now, thanks to the work of dedicated researchers John Turney and Nick Webster, we have reliable sack data dating back to 1960 (with more likely to come in the future). ^[1]Thanks to Webster, specifically, we also have the numbers for Len Ford‘s outlandish 1951 campaign. Although I don’t have the same context for that season, I will be including it with … Continue reading With all the new information available, I was excited to pick up where Chase left off and include the additional 22 years of preceding data. Because of the new seasons included, the results of this post will differ from Chase’s, even among players included in the original article, so this should offer some new insight beyond adding names to the list.

Normalizing for Volume

The first step is to account for the fact that teams throw the ball more frequently today than they did in the sixties, eighties, or even the aughts. To do this, I am going to do what Chase did, because it seemed like a reasonable first step to me. That first step is to find the number of dropbacks a player’s team faced that season and calculate the percentage of those plays on which he sacked the quarterback. ^[2]There is a case to be made that one should only include dropbacks in games which players participated. So Jared Allen would only count as having played 14 games in 2007, rather than 16 games. … Continue reading Next, we multiply that number by 500 in order to put pass rushers on a more even playing field.

Take Cleveland Elam‘s 1977, for example. He dropped opposing quarterbacks 17.5 times while the 49ers faced just 312 dropbacks. That gives him an incredible 5.61% sack rate, which translates to 28.0 sacks against 500 dropbacks. [continue reading…]