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…]

Adam Steele is back with more analysis of traditional box score stats versus Pro Football Focus’s big time throw and turnover-worthy play metrics. And we thank him for it.

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A couple of weeks ago, I compared TD/INT and BTT/TWP numbers for the 2021 season. Today we’ll be looking at the entire Pro Football Focus era going back to 2006.

Before compiling the data, I hypothesized that TD/INT and BTT/TWP would track in relative lockstep, though perhaps the upward slope of the PFF metrics would be less severe. That turns out to be true for 2006-07 and 2014-21, but oh boy was there some wackiness taking place in between. In the graph below, you’ll see league TD-INT difference in blue and league BTT-TWP difference in red: [continue reading…]

The following article originally appeared on the now-defunct GridFe website but never found its way to Football Perspective after Adam Steele and I decided to shut things down in our little corner of the internet. For the sake of having a reference, I have decided to republish in Chase’s space. Below is the article as originally published following the 2018 season.

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Last year, I unveiled the GridFe Hall of Fame, a group effort of football diehards dissatisfied with (and unencumbered by the logistical limitations of) the Pro Football Hall of Fame. ^[1]The GrideFe Hall of Fame Committee comprises research guru Topher Doll, standard human Bryan Frye, actual genius Adam Harstad, enigmatic fount of knowledge Raider Joe, potentate of … Continue reading This Hall of Fame has very few rules outside of a minimum five “yea” votes out of a possible six for enshrinement. We have no waiting period for induction. If it’s obvious that Tom Brady belongs, he’s in; if we need to take some time to put Julio Jones‘s stats into perspective, we will. We don’t have contribution silos. I didn’t vote for John Madden solely as a coach but as a coach, influential broadcaster, and video game pioneer. [continue reading…]

The following article originally appeared on the now-defunct GridFe website but never found its way to Football Perspective after Adam Steele and I decided to shut things down in our little corner of the internet. For the sake of having a reference, I have decided to republish in Chase’s space. Below is the article as originally published following the 2018 season.

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The GridFe Hall of Fame 2019 class features no quarterbacks and is heavy on running backs, tight ends, and linemen. ^[1]The only quarterback who received votes got just two of them. Meanwhile, several linemen just missed the cut. Unlike the defensive hall of fame class, the offense features no active players. In fact, the most recent player last played in 1988. Perhaps that’s indicative of more clearly worthy defensive players in today’s league, or maybe it simply means more voters have taken a wait-and-see approach with regards to positions that have seen significant stat inflation in recent years. ^[2]An alternative theory is that we voted for all the worthy offensive players in the inaugural class. Tom Brady, Drew Brees, Aaron Rodgers, Adrian Peterson, Larry Fitzgerald, Jason Witten, and Antonio … Continue reading It’s outlandish to believe that with greater talent than ever before, only one hall of fame caliber wide receiver has entered the league in the last twenty years. Is it possible we have exercised too much caution with modern players? I don’t know, but it’s certainly possible. Below are eight inductees for this year’s class. Read and determine for yourself. ^[3]Others receiving votes: Len Dawson, Curtis Martin, Ollie Matson*, Bobby Mitchell*, Elroy Hirsch, Pete Pihos*, Rayfield Wright*, Jim Tyrer*, Gary Zimmerman*, Joe DeLamielleure*

GridFe Hall of Fame Offense

Marion Motley (1946-1955)
Cleveland Browns, Pittsburgh Steelers
5 First Team All Pros (4 AAFC/1 NFL), 1 Pro Bowl, 6 Title Wins, 3 Title Losses, 1 GridFe World Award (AAFC), 1 GridFe Sweetness Award, 1 GridFe Supersonic Award, 7 GridFe Motley Awards (4 AAFC/3 NFL) ^[4]The Pro Bowl didn’t exist when Motley played in the AAFC, but he was worthy of the honor all four years. [continue reading…]

Adam Steele is back with the crazy notion that we should stop using stochastic, binary events in ratio form as the basis for judging quarterbacks. Fancy that. We thank Adam for his ideas and analysis.

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The most commonly cited quarterback stats in mainstream analysis are touchdown passes and interceptions, usually presented as TD/INT ratio. This essentially functions as shorthand to compare the quantity of a player’s great plays against his terrible plays. But this is quite unfortunate since both stats are very noisy and situation dependent. TD/INT ratio not only lacks important information but it can be downright misleading at times.

Luckily for us the good folks at Pro Football Focus have come up with a much better alternative: Big Time Throws (BTT) and Turnover Worthy Plays (TWP). These stats are tabulated by watching film so they capture far more signal than the process-blind box score numbers. Passers get credited with a BTT when they make a throw that goes well beyond what’s expected on a given play, and this includes passes which are dropped or wiped out by penalty. Meanwhile a TWP is charged when a throw is made that has a good chance of being intercepted (whether it’s actually picked or not), or when the QB gets careless with the ball during his dropback and fumbles when such an error could’ve been avoided. [continue reading…]

Adam Steele comes to us today with a philosophical question regarding the nature of fandom. We thank him for it.

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After a weekend in which all four games were coin flips decided on the final play, I started thinking about how much the results actually matter to fans in the long run. We’ve had the primacy of titles drilled into our heads throughout our lives – hoisting the Lombardi trophy is why you play the game, there’s only one winner and 31 losers, etc. And sure, in the most literal sense, attempting to win a championship is the reason we hold a season every year.

But how much do championships really matter to fans? My sense is that they matter far less than you’d initially think. If you ask a group of fans to name their most cherished football memories, a majority of their answers will probably not be related to winning it all.

Think about all the amazing player seasons throughout football history. The vast majority of them did not result in a ring. Did fans of Randy Moss, Barry Sanders, Dan Marino, or J.J. Watt enjoy their heroes less because they don’t have the jewelry? Doubtful. It’s more likely that fans will wax poetic about how they got to watch these legends play.

In many cases, even average players and coaches on perennially losing franchises become local heroes in their communities. Fans fall in love with players they feel a connection with irrespective of the number of titles those athletes bring home. This is why long suffering fanbases of ringless teams often have the most loyal and devoted followers; it’s more about the journey than the destination.

Quantifying Fan Priorities

There’s actually pretty strong empirical evidence that championships are not the most important thing to fans. From 2003-2016, ESPN ran a series called Ultimate Standings (insert hyperlink ESPN The Magazine’s 2016 Ultimate Standings). They surveyed fans across all four major North American sports to come up with a formula for determining which teams reward their fans the most. The responses were whittled down into seven broad categories, weighted by importance:

Fan relations – 27%
Money spent per win – 27%
Players – 15%
Ownership – 13%
Stadium experience – 12%
Championships – 4%
Coaching – 3%

Well look at that! Championships are way down the list of things that fans consider important. Teams that make a genuine effort to connect to their communities engender loyal fans regardless of on-field results. Regular season wins matter but only if fans aren’t being gouged in the process; less frequent winning is acceptable if being a diehard fan is affordable for the average Joe. Players are judged by their effort and likability more than their performance. Having a solid ownership situation and a fun stadium to attend are also several times more important than past or potential championships.

Does this post resonate with you? What are your favorite memories as a sports fan? Would you trade those memories for a championship? Let me know in the comments.

Adam Steele is back again, this time with a look at teams resting their starters over the years. Bless him.

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Over the past few years I’ve been documenting the historical instances of teams resting their starters in late season games. I like to remove such games when comparing teams since even a single upside down result can warp a club’s statistical profile (especially since these meaningless games disproportionately affect the best teams in a given season). Now that the 2021 regular season is complete, I figured I might as well share this database with FP readers in hopes that some of you might find it useful or interesting. [continue reading…]

The penultimate week of the season was an odd one from a statistical standpoint. QBR and PFF mostly agreed (for once), but some of the boxscores straight up lied to us about how well quarterbacks played. We saw 45 touchdown passes and 32 QB turnovers, and that’s standard fare for a late season week in today’s NFL. However, according to PFF graders, quarterbacks registered 36 big time throws and a whopping 55 turnover worthy plays!

Let’s look at the week 17 rankings then take a closer look at some of these misleading statlines: [continue reading…]

I’m short on time right now so this week’s QB rankings will be presented without commentary. [continue reading…]

Adam Steele is back to give us his thoughts on this week’s quarterbacks. It’s been fun to follow his progression from hopeful fan to ranting madman over the course of just fifteen weeks.

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This is starting to sound like a broken record but we just witnessed yet another week of terrible quarterbacking. Only 9 of 32 qualifiers even cracked a QBR of 50! We can’t even blame this on backup QB’s dragging down the average as the bottom 10 were all regular starters aside from Mike Glennon. If anything, the backups outperformed the starters with Tyler Huntley taking the week 15 crown and Nick Mullens placing eighth. [continue reading…]

Adam Steele is back with another quarterback recap. He has a broken arm and a detached retina, and he’s ready to win it.

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This may have been the least interesting week of the 2021 season for overall game quality (favorites were 12-2 with some totally noncompetitive matchups), but it was still a fascinating slate for analyzing quarterback performance. [continue reading…]

As has been the case for nearly two months now, the steady decline of NFL offenses continued in week 13. Scoring has dropped from 24.8 points per game last year to 22.9 this year. The league is currently averaging 11.0 yards per completion; if this holds it will be the lowest in NFL history. There’s also been a marked shift in touchdown passes vs. interceptions. Early in the season there were 3.5 TD passes for every INT; that ratio is now below 2 to 1. For the first time in several years the NFL has found a nice equilibrium between offense and defense. It’ll be interesting to see if the competition committee devises rule changes to boost offense again in 2022.

Here are the week 13 rankings: [continue reading…]

This week I’m excited to introduce a new data set into my quarterback rankings, courtesy of Pro Football Focus. I’d like to give a helmet knock to our good buddy Bryan Frye for working out some details behind the scenes to make this possible. ^[1]Note from Bryan: all I did was ask.

From this point forward, I will be using two metrics to rate quarterbacks: QBR and PFF offensive grades. This makes me giddy because both systems attempt to isolate the QB’s contribution from that of his teammates. That’s a significant step up from ANY/A, DYAR, and EPA which simply assign team offensive statistics to the QB taking the snaps. I can live with that for historical comparisons where we don’t have anything better, but in today’s world of robust data there’s no reason to settle for such a high degree of entanglement.

As neither QBR nor PFF grades account for workload, I needed to make an adjustment to prevent low usage QB’s from hogging the top of the rankings. After experimenting with a few ideas I settled on adding a z-score for play count (based on qualifiers only) and giving it half weight compared to the z-scores for the two metrics. It’s not perfect but it gets the job done without too many arbitrary decisions.

Let’s see how the new system looks for week 12: [continue reading…]

Adam Steele is back, and he’s eschewing the expository fluff (which I am re-adding, right here). Enjoy, friends.

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Here are this week’s quarterback rankings: [continue reading…]

As with the rest of these recaps, the ideas and analysis presented here are from Adam Steele. I’m just a dunce with admin rights.

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We’ve seen a steady decline in quarterback play across the last month, and this week hit a new low. The unweighted average QBR for week ten qualifiers was a dismal 44.7. That would have ranked 27th in the league last year! This isn’t a surprise as offense tends to decline in the second half of every season as defenses jell and the weather starts to make an impact.

Here are this week’s numbers: [continue reading…]

Adam Steele is back, continuing to refine his methods in real time, and we get to benefit from it. Thank you Adam, for working it out publicly.

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After tabulating the numbers for week nine I realized I needed to make one more tweak to the formula. Since EPA per play and QBR are both agnostic to volume, QB games with a low number of plays were disproportionately clustered at the top and bottom of the rankings. Obviously it’s harder to maintain an extreme performance over a larger sample than a smaller one. My solution was to regress EPA/P by adding 20 plays of 0.1 EPA (roughly league average) to everyone’s stat line before calculating their z-scores. This fix strikes a nice balance between efficiency and volume.

Onto this week results: [continue reading…]

Adam Steele is back with a brand new convention, and we thank him for keeping the site humming.

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With the NFL trade deadline taking place this week, I decided to make a trade of my own. I’m ditching DYAR in favor of EPA per play. After Davis Mills placed seventh in DYAR by turning into Dan Marino down 38-0, I knew I had to switch to a metric that filters out garbage time.

Thanks to Ben Baldwin and his nifty site rbsdm.com, it’s easy to query EPA/P with various amounts of garbage time removed. After some experimentation I settled on a 4% filter; plays which occur when win probability is below 4% or above 96% are thrown out. The vast majority of plays are still counted but nonsense like the Davis Mills experience is rightfully ignored. To wit, Mills drops from -.149 to -.474 EPA/P with this filter applied.

Here are the week eight numbers: [continue reading…]

Guys, gals, and nonbinary pals, Adam Steele is back with his quarterback recap. And we thank him for it.

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I’m going to keep the commentary short and sweet today, so here are the week seven rankings: [continue reading…]

After offenses lit up the scoreboard in week five, we were treated to the poorest quarterbacking of the year in week six. Both DYAR and QBR hit their low points in the 2021 season. What’s shocking is that this happened with the Jets on their bye week!
[continue reading…]

Finally, a week in which the best QB games were more extreme than the worst. The league saw DYAR and ANY/A reach their high points for any week in 2021. Oddly, this was the second worst week for QBR despite the meteoric conventional stats. There seemed to be an unusual number of highlight reel catches and long completions on busted coverages, both of which are likely discounted in QBR.

Fittingly, old man Brady tops the chart in the same week he became the all time leader in total DYAR. Brady and Lamar Jackson had the two best games of the year according to DYAR but fared much worse in QBR. In Brady’s case it’s likely because he faced almost zero pressure vs Miami, while Jackson gets taken to the woodshed for his goal line fumble (another example of QBR overweighting running plays).

Josh Allen had the opposite result – dominant in QBR but merely good in DYAR. He was very successful on his runs, completed his average pass a whopping 13 yards downfield, and didn’t have enough plays to pump up his counting stats. [continue reading…]

With it being the (almost) quarter point of the 2021 season, I’m going to skip commentary on the week four games and focus on quarterback performance across the first month of the season. [continue reading…]

Adam Steele is back with his quarterback recap for week 3, 2021. Thank you, Adam, you beautiful man.

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The theme of week three is the same as week two: terrible rookies. Remarkably, the eleven worst QB games this season have all been more extreme than the single best game. Anyone reading this is well aware of Justin Fields‘ spectacularly awful sack-fest performance against Cleveland. But according to DYAR, that wasn’t even the worst game this week!

Here are the week three numbers: [continue reading…]

The following post attributes authorship to Bryan Frye, but everything under the line comes from the mind of Adam Steele. We thank him for his contributions to the site and to football discussion.

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For the second week in a row, the worst quarterbacks had more extreme performances than did the best. By absolute value, the eight worst games this season have been more extreme than the single best game. I don’t have a good explanation for this other than sheer randomness. [continue reading…]

Today’s article comes from friend of the site Pierce Conboy, whom you can find in the comments as pgc or on Twitter as @pgconb. Below the line are his words. Enjoy.

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Pro Football Reference is undoubtedly one of my favorite websites, so initial apologies for what amounts to taking pot shots at Approximate Value (AV) in a way that they did not intend, though consistent with how it’s widely misused by football fans at large.

This project is fairly nebulous and has no actual rules aside from a general attempt to create an extremely high AV team that would get crushed by a low AV team. I held my offense and defense below 100 AV apiece which was my arbitrary self-imposed ceiling along with keeping the average era from each of the two teams relatively close.

Here are the squads I came up with, having also consulted with Bryan, Bipedal-Moose from Reddit and cribbing from Turney and Troup articles at Pro Football Journal. [continue reading…]

The following post attributes authorship to Bryan Frye, but everything under the line comes from the mind of Adam Steele. We thank him for his contributions to the site and to football discussion.

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With Chase being such a busy man, I have taken over posting weekly passing reviews during the 2021 season. While the classic ANY/A formula has served its purpose over the years, I’m going to tap into a couple of more advanced metrics to rank quarterbacks on a weekly basis.

Each week, qualifying QB’s will be scored using Football Outsiders’ DYAR and ESPN’s QBR metrics. I think this will give us a nice balance between play-by-play and charting stats, as well as a balance between counting stats and pure efficiency. The qualifying players will have their z-score calculated for DYAR and QBR then averaged to create their overall score.

Here are the week 1 results: [continue reading…]

We’ve come to the end of the line. After several posts ranking and reranking, thinking and rethinking, quarterbacks with Total Adjusted Yards per Play and its descendants, this is the one I imagine most readers really want to see. Today, we are looking at measured performance in the regular season and playoffs combined. This is where guys like Y.A. Tittle, who feasted in the regular season but nearly always faltered in the postseason, see their positions fall down the list. Where passers like Jim Plunkett, whose regular season performances left much to be desired but went full tilt bozo in the playoffs, rise up the ranks. As far as the NFL record book is concerned, the playoffs don’t count toward career stats or win-loss totals. While I understand not rewarding players for getting to participate in more games, I can see the argument that it is equally unfair not to reward them for playing well enough to continue the march toward a championship. In order to balance those ideas, I have only counted playoff performances that measured above average by TAY/P.

A quick word on the numbers I’m using. You can find more detail in previous articles in the series, but this should be sufficient to introduce the rookies and refresh the veterans. [continue reading…]

We’re back at it with quarterback rankings based on Total Adjusted Yards per Play and its abundant offspring. This time, we’re getting into combined regular and postseason stats for single seasons. For the purposes of this article, I will refer to this as a full season. None of the stats are new and have been explained in what I hope is sufficient detail in previous posts. ^[1]Here are links for the base methodology, the introduction of Z Value and positive value, the methodology and refinement of championship leverage, and a brief explanation of retroactive leverage. Fun … Continue reading As fun as it would be to call this “the greatest quarterback seasons in history!” or something like that, I seem to have a deeply held grudge against page views and web traffic, because I can’t get behind calling it anything of the sort. This is one measure of how much quarterbacks produced in a given full season. I believe it is the best measure when trying to compare across eras in which superior metrics don’t exist, but that’s about as far as I can go on the hubris tip. Anyway, these are my numbers. I hope you like them. [continue reading…]