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Bill Cowher And Coaches Retiring Early

It’s been nearly a decade since Bill Cowher stopped coaching, but that hasn’t done much to keep his name out of the rumor mill every December and January. After all, Cowher was both very successful and very young when he retired, and NFL folks believe those dots can be connected to mean he won’t stay retired forever.

That made me wonder: how much of an outlier is Cowher with respect to his age and how successful he was? In particular, Cowher was successful at the end of his stint, which differentiates him from someone like Jon Gruden. Defining “success” is challenging when it comes to coaches, but I want to just generate a set of comparable modern coaches and see how they fared at the ends of their careers and when they retired. I don’t need a particularly precise coaching formula, just something that gets the job done.

As it turns out, six years ago, I created a rudimentary formula to rank head coaching records. Let’s use Cowher’s last three years as an example. This formula gives credit for wins above losses, so Cowher gets a 0 for his work in 2006, his final year, when Pittsburgh went 8-8. The prior year, the Steelers went 11-5, so that’s +6, but I also gave a 12-point bonus for winning the Super Bowl, so he gets a +18 for that season. And in ’04, Pittsburgh went 15-1, so that’s +14. Add it up, and Cowher has a +32 score over his last 3 years. And he was just 49 years old during his final season. [click to continue…]

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Correlating Wins in Year N and Year N+1

There are many advanced projection systems that do a great job of projecting teams wins. I’m not interested in recreating that or coming up with my own system, but rather setting a baseline for what a projection system should hope to accomplish. You’ll see what I mean in a few moments.

Test #1: Every Team Is The Same

This is the simplest baseline: let’s project each team to go 8-8. If you did that in every season from 1989 to 2014, your model would have been off by, on average, 2.48 wins per team. This is calculated by taking the absolute value of the difference between 0.500 and each team’s actual winning percentage, and multiplying that result by 16. So that should be the absolute floor for any projection model: you have to come closer than that.

Test #2: Every Team Does What They Did Last Year

Looking at all teams from 1990 to 2014, I calculated their winning percentages in that season (Year N) and in the prior season (Year N-1). If you used the previous year’s record to project this year’s record, you would have been off by, on average, 2.84 wins per team. That’s right: you are better off predicting every team to go 8-8 than to predict every team to repeat what they did last season. [click to continue…]

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2014 Defensive Pass Identity Data

Yesterday, we looked at offensive Pass Identity grades. Today, we are going to use the same process to analyze the data for defenses.  Yesterday’s post is required reading to understand how Pass Identity grades are calculated, but here’s one update.  While we can use the same numbers for Game Script (including the 3.27 number for standard deviation and 0 for average), that’s not the case for defensive Pass Ratio. There, while the average is roughly the same at 58.29%, the standard deviation is much smaller at 2.84% (it was 4.66% for the offenses).

Let’s use the Lions as an example.  Detroit had an average Game Script of +0.4 last year, meaning the Lions were leading by, on average, 0.4 points during every second of every game.  That was 0.11 standard deviations above average. [click to continue…]

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Final 2014 Game Scripts and Pass Identity Data

As we did last year, today I’m going to calculate the final 2014 Game Scripts and Pass Identity data.  Every week during the season, I write about the Game Scripts from the previous weekend. For new readers, the term Game Script is just shorthand for the average points differential for a team over every second of each game. You can check out the updated Game Scripts page, which shows the results of all 256 games from 2014, and you can read the history behind the metric here.

Let’s begin by looking at the 2014 Game Scripts numbers. The Packers held an average lead of 6.9 points during their regular season games, the highest average in all of football. Because Green Bay was so good, Aaron Rodgers and the Packers weren’t very pass-happy; in fact, the Packers ranked just 21st in pass attempts. That’s why Jordy Nelson and Randall Cobb, as good as their raw numbers were, look even better in some advanced metrics. In some ways, the Packers were the victims of their own success last year, as Green Bay was — by far — the best first half team in the NFL in 2014. That led to the high Game Script number, and a lot of casual dress second halves. [click to continue…]

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Who Were The Best Punters In 2014?

Yesterday, I looked at a new way to measure punting statistics. Let’s review by using the top single performance from 2014, which surprisingly came from Jets second-year punter Ryan Quigley in a 31-0 loss to San Diego. Yes, the Jets were terrible, but that doesn’t mean it was Quigley’s fault! He had 8 punts, and all but 1 was an above-average punt. Let’s review:

  • Punt 1: Quigley punts from the Jets 39-yard line. On average, when a team punts at the 39, the opposing team takes over at the “78.9” yard line, which means just a hair in front of that team’s 21-yard line. Instead, Quigley pinned San Diego back to their 11; that 51-yard punt therefore provided 11.1 more yards of field position than we would expect.
  • Punt 2 was a 44-yard punt from the Jets 29. On average, punts from the 29 pin the other team back at their 29.7 yard line. The 44-yarder had no return, giving San Diego the ball at their 27, so Quigley added 2.3 yards of field position over average.
  • Punt 3 was from the Jets 20, so San Diego would have been expected to take over at their 38.4-yard line. Instead, following a whopping 64 yard punt, a 2-yard return, and 9-yard lost by San Diego due to an illegal block, and the Chargers were back at their own 9-yard line. That goes down as +20.4 for Quigley. Is it fair to give the punter credit when the return team loses yards on a penalty? I don’t know, although I’m not sure if that’s more or less fair than return yards that team gains (or yards the punting team loses due to a penalty). Think of these more as punt unit ratings than punter ratings, I guess.
  • Okay, even I don’t have the energy to go through all 8 punts.  But on the other 5, Quigley gained 16.8 yards over expectation, 11.9, 10.4, 10.2, and on one bad punt, -6.0.

[click to continue…]

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Yes, you read that title right. Not only is today about punters, guess what? Tomorrow will be, too. Today, I want to dive into punting statistics. The two key numbers the media focuses on with punters are usually net punting average and gross punting average. But both numbers are pretty heavily influenced by field position. [click to continue…]

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As I did last year, I want to analyze the rushing stats for each team in 2014 using a metric known as Adjusted Rushing Yards per Carry. Thanks to the help of Brian Burke of Advanced Football Analytics (formerly Advanced NFL Stats), we were able to conclude that the value of a first down was about 9 yards. And since we’ve previously determined that the marginal value of a touchdown is 20 yards, this means Adjusted Rushing Yards per Carry is pretty easy to calculate. Also, since Bryan Frye crunched the numbers, we might as well exclude all kneels from the process, too.

One thing to keep in mind (which I have forgotten in the past): since the NFL records-keeping process labels touchdowns as first downs, you should only assign 11 yards per touchdown if you are already giving 9 yards to all 1st downs. And since kneels are marked down as runs, you must back those out, too. As a result, here’s the formula to use:

Adjusted Rushing Yards per Carry = (Rush Yards + 11 * Rush TDs + 9 * Rush First Downs – Kneel Yards Lost1 ) / (Rushes – Kneels)

If we use this metric to analyze the 2014 season, how would it look? Seattle was by far the top rushing team in the NFL last year, rushing for 2,762 yards and 20 touchdowns on 525 carries, good for a 5.26 yards per carry average. But 19 of those 525 carries were kneels, and they went for -20 yards. In addition, Seattle not only led the league with 144 rushing first downs, the Seahawks gained a first down on 28.5% of non-kneel carries, also the highest mark in the NFL. Seattle averaged 8.49 Adjusted Rushing Yards per Carry, while the NFL average was 6.63. Since the Seahawks averaged 1.86 ARY/C over average for 506 non-kneel carries, that means Seattle rushed for 941 rushing yards (1.86 * 506) above average.

The full list for all 2014 teams, below: [click to continue…]

  1. Since this is a negative number — i.e., 10 kneels for -11 yards — we need to subtract kneel yards to turn those yards into an add back in the numerator. []
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Back in December 2009, Jason Lisk wrote about a recent trend in the NFL: quarterbacks throwing for 300 passing yards and actually winning. Jason wondered whether that was something fluky, or a sign of the shifting nature of the NFL. With the benefit of hindsight, I think the answer is…. well, I think it’s pretty clear.

Including playoffs, quarterbacks who threw for 300+ yards in a game during the 2009 season won an incredible 63.3% of games. And that mark remains the highest in modern history. Over the last five years (2010 to 2014), quarterbacks have won 52% of games when cracking that mark; during the decade of the ’90s, quarterbacks won 53% of their games when throwing for 300+ yards.

Of course, the likelihood of a quarterback throwing for 300+ yards has increased significantly. Over the last four years, quarterbacks have thrown for 300+ yards in 25% of all games, an enormous increase relative to most of NFL history. The graph below shows both pieces of information: in blue, and measured against the left Y-Axis, shows winning percentage by year when a quarterback throws for 300+ yards; in red, and against the right Y-Axis, is the percentage of all games where a quarterback hit the 300+ yard mark: [click to continue…]

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Data Dump: Defensive Points Allowed SRS

Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work.


In a previous post, I provided SRS-style ratings for all offenses since 1970, using only points scored by the actual offense (including field goals). Today, I’ll do the same thing for defenses – meaning, of course, our “metric” will be points allowed only by the actual defense.1

Here’s how to read the table below: in 1970, the Vikings allowed 10.2 points per game, 8.2 of which came from touchdowns and field goals allowed by the defense. This leaves 2.0 PPG scored by their opponent’s defense or special teams (i.e., due to Minnesota’s offense or special teams).2 Their 8.2 Def PA/G was 9.5 points better than league average; after adjusting for strength of opponent, their rating remains at 9.5. Their overall points allowed SRS rating (DSRS) is 9.2, meaning PFR’s defensive SRS rating undersells them by 0.3 points. [click to continue…]

  1. To quickly recap: SRS ratings for offense (OSRS) and defense (DSRS) on PFR’s website include points scored by the defense and special teams. To get a more accurate points-based evaluation of offenses and defenses, I weeded these scores out and reran the iterations. I didn’t note this last time, but for those interested: the numbers used do not include any home field advantage adjustment or a cap on blowout point differentials. []
  2. In this case, it was the result of three touchdowns off of offensive turnovers and one on special teams, as highlighted by Chase in this post on estimated points allowed per drive. []
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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work.


When looking at teams’ offensive SRS ratings (OSRS) on PFR, we know that those number also include points scored by the defense and special teams – punt and kick return touchdowns, interception and fumble return touchdowns, return scores on blocked punts and field goals, and safeties. This makes OSRS not as accurate a point-based rating of the offense “proper” as it could be. But, considering those “non-offense” types of scores make up a small fraction of a team’s overall points scored in a season (the average is around 8% since 1970), we can generally ignore this “hiccup” in the system.

Well, most of us can ignore it; for some reason, I cannot! My curiosity has gotten the better of me, so I decided to run offensive and defensive SRS ratings for each team since the merger, using only points that we would normally credit the offense for scoring (or the defense for allowing) – passing and rushing touchdowns, and field goals.1

As the title states, this is a data dump; I’m hoping that readers of this site will find the info useful for their own research or general interest. Today, we’ll just look at the offense, I’ll post the numbers for defense in a follow-up post. [click to continue…]

  1. I have to assume that at some point Chase or one of the guys at PFR has run the numbers for “SRS without special teams/defense scores”, but I have yet to find it. []
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You remember the November 20th game between the Bears and Lions in 1960, right? If you look at the boxscore on PFR, you will see that Detroit quarterback Jim Ninowski was 10 for 26 for 121 yards with 0 touchdown passes and 2 interceptions. You’ll also see that the Lions as a team went 10 for 26 for 121 yards with 0 touchdown passes, 2 interceptions, and 12 sacks for 107 yards. But the PFR boxscore does not indicate how many sacks Ninowski took that game, because the individual game log data wasn’t kept on that metric.

But, you know, I’m a pretty smart guy. I have a feeling that Ninowski was probably sacked 12 times in that game for 107 yards. I could be wrong, of course — maybe a backup came in and took two dropbacks, and was sacked on both of them — but it seems like making a good faith effort here is better than ignoring it completely. [click to continue…]

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Yesterday, I looked at which receivers produced the most Adjusted Catch Yards over the baseline of the worst starter. Today, I want to use that data to help identify which receivers put up their numbers in the most pass-happy offenses.

Let’s use Calvin Johnson as an example. He’s been with the Lions for each season of his career, and Detroit has been very pass-happy throughout his career. Last year, Detroit averaged averaged 40.56 dropbacks (pass attempts plus sacks) per game, while the league average was 37.29 dropbacks per game. So Detroit passed 108.8% as often as the average team.

In 2013, Detroit’s ratio to the league average was 108.2%, but it was 129.8% in 2012. To measure pass-happiness as it pertains to Johnson, we can’t just take Detroit’s average grade from ’07 to ’14; instead, we need to assign more weight to Johnson’s best years. Johnson gained 1,358 ACY over the baseline in 2012, which represents 29% of his career value of 4,721 ACY over the baseline. As a result, Detroit’s 129.8% ratio in 2012 needs to count for 29% of Johnson’s career pass-happy grade.

If we do this for each of the players in yesterday’s top 100, here are the results. [click to continue…]

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Brown stuck the lanning.

Brown stuck the lanning.

Adjusted Catch Yards are simply receiving yards with a 5-yard bonus for each reception and a 20-yard bonus for each receiving touchdown. In 2014, Antonio Brown led the NFL with 2,603 Adjusted Catch Yards, the 5th highest total in NFL history. That was the result of a whopping 129 receptions for 1,698 receiving yards (both of which led the league) and 13 touchdowns.

Brown was dominant in 2014, and he led the NFL in more advanced systems, too. But today, I wanted to do something relatively simple. How do we compare Brown’s 2014 to say, three Packers greats from years past?

In 1992, Sterling Sharpe had 108 catches for 1,461 yards and 13 touchdowns. Those are pretty great numbers for 1992, although they don’t leap off the page the way Brown’s 2014 stat line does. If we go back farther, Billy Howton in 1956 had 55 receptions for 1,188 yards and 12 touchdowns. Like Brown, that was good enough to lead the NFL in two of the three major categories, and rank 2nd in the third. And 15 years earlier, Don Hutson caught 58 passes for 738 yards and 10 touchdowns. How do we compare that statline to Brown’s?

Here’s what I did.

1) Calculate each player’s Adjusted Catch Yards. For Brown, that’s 2,603. For Sharpe, Howton, and Hutson, it’s 2,261, 1,703, and 1,228, respectively.

2) Next, calculate the Adjusted Catch Yards for every other player in the NFL. Then, determine the baseline in each year, defined as the number of ACY by the Nth ranked player, where N equals the number of teams in the league. For Brown, that means using 1,398 Adjusted Catch Yards, the number produced by the 32nd-ranked player in ACY in 2014. For Sharpe, we use 1,078 ACY, the number gained by the 28th-ranked player in ’92. For Howton, it’s just 797, the number of ACY for the 12th-ranked player (keep in mind that ’56 was a very run-heavy year). And finally, for Huston, we use the 10th-ranked player from 1941, who gained only 413 Adjusted Catch Yards.

3) Next, we subtract the baseline from each player’s number of Adjusted Catch Yards. So Brown is credited with 1,205 ACY over the baseline, Sharpe gets 1,183 ACY over the baseline, Howton is 906 ACY over the baseline, and Hutson is 815 ACY over the baseline.

4) Finally, we must pro-rate for non-16 game seasons. For Brown and Sharpe, we don’t need to do anything, so Brown wins, 1,205 to 1,183. Howton played in a 12-game season, so we multiply his 906 by 16 and divide by 12, giving him 1,208 ACY, narrowly edging Brown. And in 1941, the NFL had an 11-game slate; multiply 815 by 16 and divide by 11, and Hutson is credited with 1,185 ACY.

As you can see, it wasn’t a coincidence I chose those three Packers seasons to compare to Brown. Those four seasons are the 19th-through-22nd best seasons of all time by this metric, and stand out as roughly equally dominant for their eras (both Sharpe and Hutson won the triple crown of receiving in their years).

This is not my preferred method of measuring wide receiver player, but it’s my favorite “simple” one. I put simple in quotes, of course, since there’s a lot of programming power behind generating these numbers. But at a high level, it’s simple: we combine the three main receiving stats into one, we adjust for era because the game has changed so much, and we pro-rate for years where the league didn’t play 16 games. Nothing more, nothing less. [click to continue…]

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In 2012 and 2013, I looked at which passers were most effective on third and fourth downs; today, we examine those numbers for 2014. Throughout this article, when I refer to “third downs” or “third down performance”, note that such language is just shorthand for third and fourth downs.

To grade third down performance, I included sacks but discarded rushing data (in the interest of time, not because I thought that to be the better approach). The first step in evaluating third down performance is to calculate the league average conversion rate on third downs for each distance. Here were the conversion rates in 2014, along with the smoothed (linear) best-fit rates: [click to continue…]

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On Sunday, I calculated the average number of pass attempts (including sacks) per game for each season since 1950, and then looked at which were the highest era-adjusted passing games in football history. On Monday, I looked at the single seasons that were the most and least pass-happy, from the perspective of each quarterback and after adjusting for era. Today, career grades.

How much do you know about Frank Tripucka? Probably not that much. If you’re a younger fan, you might know him because Denver “unretired” his #18 when Peyton Manning came to town, or because his son Kelly played in the NBA.

If you’re a Football Perspective regular, you may recall that he was the first quarterback in pro football history to throw for 3,000 yards in a season.1 Well, after today, you’re never going to forget about Tripucka.

I looked at all quarterbacks who started at least 48 regular season games since 1950.2 As a reminder about the methodology, I then calculated the league average dropbacks per game (i.e., pass attempts + sacks) in each season. Then, I determined the number of dropbacks by each quarterback’s team in each game started by that quarterback.

Then, I compared that number to league average to determine the ratio. Do this for every game of a quarterback’s career, and viola, career ratings! Here’s how to read the table below. Tripucka started 50 games in his career since 1950. In those games, his teams averaged 38.5 dropbacks per game, while the league average was 31 dropbacks. As a result, Tripucka’s teams in games he started finished with 124% as many pass attempts as the average team, or 7.5 more attempts per game. That makes him the most pass-happy quarterback ever. The final column shows whether the quarterback is in, or very likely to wind up in, the Hall of Fame.3 [click to continue…]

  1. And by first, I mean that in the most literal sense: in 1960, Tripucka, playing in the AFL and a 14-game season, crossed the 3,000 yard mark in the final game of the season. For Denver, that happened to be a Saturday. The next day, another AFL quarterback, Jack Kemp, crossed the 3,000-yard threshold with the Chargers. The AFL opened with a 14-game schedule to get a jump on the NFL, which was still playing a 12-game schedule in 1960. The NFL’s regular season ended at the same time, and Johnny Unitas became the first NFL passer to hit 3,000 yards on the same day as Kemp. []
  2. For quarterbacks who played prior to 1950, like Tripucka, they are included, but only their post-1950 stats are counted. []
  3. Note that I have included Peyton Manning, Tom Brady, Drew Brees, Brett Favre, Kurt Warner, and Aaron Rodgers as HOF quarterbacks for these purposes. This is not based on my subjective opinion of those players, but based on my subjective opinion of their likelihoods of enshrinement. If one was to sort by the HOF category, I thought it would be more useful to have them as a “Yes” than as a “No.” Your mileage may vary. []
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On Saturday, we looked at the top passing performers against each franchise. Yesterday, we did the same thing but with rushing statistics. Today, we revive a post from two years ago and complete the series with a look at the top receiving producers against each franchise (all data beginning in 1960).

Let’s begin with receptions. In the past two seasons, Jason Witten has emerged as the number one franchise nemesis for both Washington and New York, eliminating Art Monk and Michael Irvin, respectively, from the tops of those record books. Witten was already the top guy against the Eagles, making him the career leader in receptions against each of the Cowboys three NFC East rivals.

Other non-surprising news: Jerry Rice is the top man against the Falcons, Saints, and Rams, with his numbers against Atlanta being particularly mind-blowing. Tim Brown is number one against his old AFC West teams, and was also number one against the Seahawks until Larry Fitzgerald just passed him. Andre Reed takes the top spot against the Dolphins/Colts/Jets (Marvin Harrison is #1 against the Patriots), Hines Ward has more catches than anyone against the Browns/Bengals/Ravens, while Cris Carter is number one against all four of his old NFC Norris rivals. [click to continue…]

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Franchise Nemeses: Rushing Metrics

Yesterday, we looked at the top statistical passers against each franchise. Today, we revise a post from a couple of years ago and look at the top rushing producers against each franchise.

Only two players have emerged as a franchise’s top rushing nemesis over the last two years. One of those situations involves the Rams. Only five players have ever rushed for 1,000 yards in their careers against the Rams franchise: Shaun Alexander, Jim Taylor, and Tony Dorsett each finished with between 1,008 and 1,032 rushing yards against the Rams. As of two years ago, Roger Craig’s 1,120 was the most, but since then, Frank Gore has upped his career total to 1,191 rushing yards against St. Louis (and he’s done it in three fewer games than Craig).

With the Saints, it’s even trickier. For a long time, Lawrence McCutcheon was the career rushing leader against New Orleans with 966, but Eric Dickerson (984) passed him before Dickerson retired. Then, Warrick Dunn took over the top spot with 1,135 yards. But in 2013, DeAngelo Williams passed Dunn for most career rushing yards against the Saints. Otherwise, the list below remains pretty similar to how things were last time, although note that this time around, I’m including the playoffs. That’s enough to cause Eddie George to leapfrog Jerome Bettis for the top spot against the Ravens.

Oh, and for the second day in a row, you have to go back to the ’60s to find the man who has been the number one nemesis for the 49ers: [click to continue…]

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A couple of years ago on the July 4th holiday, I looked at each team’s franchise nemesis in a number of statistics. Let’s revisit that, beginning today with passing yards and passing touchdowns.

You won’t be surprised to know that John Elway has thrown for more yards against the Chiefs, Chargers, Raiders, and Seahawks — his four division rivals — than any other player has gained against those four teams. Similarly, Dan Marino has thrown for more yards against the Bills, Jets, Patriots, and Colts than any other quarterback. Brett Favre threw for more yards than anyone else against the Lions, Bears, and Vikings (but not the Bucs), and Peyton Manning is the top nemesis for the Oilers/Titans franchise, the Jaguars, and the Texans.

Drew Brees is the big enemy of the Bucs, Panthers, and Falcons, while Ben Roethlisberger is the top passer against the Ravens, Bengals, and Browns. Perhaps more surprising is that Eli Manning has already thrown for more yards against Philadelphia, Washington, and Dallas than any other quarterback: that’s particularly surprising since he wasn’t #1 against any of those teams two years ago.

One that always kind of surprises me is seeing Johnny Unitas as number 1 against the 49ers, but it does make some sense. My guess is you could win quite a few bar bets with that one. Here’s the full list, which includes all passing yards thrown by each quarterback against each of the 32 teams (and includes playoff games): [click to continue…]

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Guest Post: Questioning ANY/A

Adam Steele is back for another guest post. And, as always, we thank him for that. You can view all of Adam’s posts here.


Within the analytics community, we seem to have reached a consensus that ANY/A is the best box score metric for measuring passing efficiency. Over at the Intentional Rounding blog, Danny Tuccitto tested the validity of ANY/A using a technique called Confirmatory Factor Analysis. You can read his three part analysis here, here, and here. Essentially, he discovers that Y/A and TD % are valid statistics for measuring QB quality, while sack % and INT % are not. At first I was skeptical, but after some pondering I came up with a half-baked theory of why this might be true:

As we evaluate the potential for an athlete to succeed in professional sports, there are two kinds of statistics: Qualifying and Disqualifying. In the case of quarterbacks, I define a qualifying statistic as a minimum threshold the player must meet to even be considered NFL worthy. If we deconstruct ANY/A into its four components, Y/A and TD % emerge as qualifying statistics. In today’s NFL, I estimate that a QB must possess a true talent level of at least 6.0 Y/A and 2.5 TD % to deserve a roster spot. There are very few people in the world who can reach those thresholds against NFL caliber defenses (my best guess is around 100). With these two simple statistics, we’ve already weeded out the vast majority of quarterbacks from ever playing in the NFL.

Next, we turn to sack % and INT %, which are disqualifying statistics. By themselves, neither of these skills qualify a QB to play in the NFL. Anybody can avoid sacks or interceptions if they’re not worried about gaining yards. However, the inability to avoid sacks or interceptions will disqualify a QB from the NFL, regardless of how high his Y/A and TD % might be. I estimate these limits as roughly a true talent 12% sack rate and 4.5% INT rate. The population of quarterbacks who can stay under these limits AND perform above the minimum Y/A and TD % is very small. In most years, there aren’t enough of these QB’s to fill the 32 NFL starting spots. Among quarterbacks who receive significant NFL playing time, there is a strong survivorship bias for the disqualifying statistics of sack % and INT %, as the quarterbacks who make too many negative plays have already been weeded out of the sample. Given that Y/A and TD % are far rarer skills with no upper limits, these two statistics are the true measuring stick at the NFL level.

To test this theory, I created a very simple metric called Positive Yards Per Attempt (PY/A). It’s just passing yards plus a 20 yard bonus for touchdowns, divided by pass attempts (which does not sacks). I then converted PY/A into a value metric by measuring it relative to league average (RPY/A)1 and VALUE above average by multiplying RPY/A by attempts. We already have these variations of ANY/A (that is, RANY/A and VALUE), so comparing the two metrics is very straightforward. Since the merger, there have been 1,423 QB seasons of with least 200 dropbacks. This table lists the top 100 seasons of PY/A VALUE, as well as the ANY/A VALUE and rankings for these players. The “Diff” column signifies the gap in ranking between the the two metrics, with a positive number indicating a QB who is favored by PY/A and negative number favoring ANY/A.

RankQuarterbackTeamYearDpbkRPY/AVALUERANY/AVALUERankDiff
1Peyton ManningIND20045103.2916374.27217621
2Dan MarinoMIA19845772.8315984.0923591-1
3Aaron RodgersGNB20115383.0815443.6193652
4Kurt WarnerSTL20015842.714722.2913392521
5Kurt WarnerSTL19995282.914483.22170161
6Tom BradyNWE20075992.4314023.4820834-2
7Peyton ManningDEN20136772.0513483.1121043-4
8Kurt WarnerSTL20003673.6512652.8210357668
9Lynn DickeyGNB19835242.5512321.71898112103
10Steve YoungSFO19944922.6712302.961454155
11Steve YoungSFO19934932.6412182.5212413524
12Ken StablerOAK19763103.912123.4811504533
13Daunte CulpepperMIN20045942.1611812.471468130
14Boomer EsiasonCIN19884183.0411782.8511924026
15Chris ChandlerATL19983723.5511612.35876119104
16Drew BreesNOR20116811.7511472.4216507-9
17Randall CunninghamMIN19984452.6911433.32147912-5
18Tom BradyNWE20116431.8611332.4315628-10
19Bert JonesBAL19763723.0811283.8415259-10
20Philip RiversSDG2010579210842.112173818
21Drew BreesNOR20095342.1110842.74146514-7
22Daunte CulpepperMIN20005082.2410612.1310836240
23Philip RiversSDG20095112.1510462.73139519-4
24Philip RiversSDG20085032.1510252.412093915
25Joe MontanaSFO19894192.589963.161322272
26Tony RomoDAL20075441.849551.79279973
27Aaron RodgersGNB20145481.839512.59142118-9
28Mark RypienWAS19914282.249443.25139120-8
29Steve YoungSFO19985651.829412.0511564314
30Steve YoungSFO19924312.39253.33143617-13
31Jim KellyBUF19915051.959231.8392510069
32Ben RoethlisbergerPIT20095561.829211.5284513098
33Nick FolesPHI20133452.869083.371162429
34Peyton ManningIND20054701.999042.76130029-5
35Brett FavreGNB19956031.568911.911474611
36Tony RomoDAL20144652.028781.9891910670
37Drew BreesNOR20086481.388761.92124234-3
38Steve BeuerleinCAR19996211.538741.6410197941
39Dan FoutsSDG19823422.038573.07134223-16
40Roger StaubachDAL19733292.778462.09735169129
41Aaron RodgersGNB20095911.548321.8811115918
42Ken AndersonCIN19754092.058253.04132526-16
43Matt SchaubHOU20096081.418221.861130529
44Dan FoutsSDG19854481.918192.229938440
45Ken StablerOAK19743282.458093.231128549
46Jeff GeorgeMIN19993572.458081.88672193147
47Boomer EsiasonCIN19864951.728072.1410606922
48Peyton ManningIND20005911.418052.08123236-12
49Dan MarinoMIA19866401.298022.12135522-27
50Peyton ManningDEN20126041.378012.02122237-13
51Jim EverettRAM19895471.547971.9810826312
52Eli ManningNYG20116171.357961.69868735
53Warren MoonHOU19906201.367952.08128732-21
54Donovan McNabbPHI20045011.687902.3115444-10
55Peyton ManningIND20065711.417872.63150310-45
56Philip RiversSDG20135741.447841.98113651-5
57Joe NamathNYJ19723352.237712.2179114689
58Tom BradyNWE20105171.567682.59133924-34
59Vinny TestaverdeBAL19965831.397651.27743163104
60Steve YoungSFO19973912.157642.3692210444
61Drew BreesNOR20136871.177631.7116541-20
62Aaron RodgersGNB20105061.617631.8291910745
63Joe MontanaSFO19844541.767593.02137021-42
64Brett FavreGNB19975381.477551.7292310238
65Drew BreesNOR20126961.127511.2989511449
66Steve YoungSFO19912922.677463.1692210337
67Steve McNairTEN20034191.857412.67111958-9
68Trent GreenKAN20045881.337371.4585612658
69Brett FavreMIN20095651.387352.03114447-22
70Terry BradshawPIT19783891.997322.0881114171
71Drew BreesNOR20065721.327292.28130428-43
72Tony RomoDAL20095841.327281.95114049-23
73Brett FavreGNB20015321.437281.881003818
74Neil LomaxSTL19846091.297251.76107166-8
75Tony RomoDAL20063582.147231.85662200125
76Peyton ManningIND20095811.267221.93112057-19
77Aaron RodgersGNB20126031.37191.4487112144
78Peyton ManningIND20075361.397151.839798911
79Ben RoethlisbergerPIT20052912.647082.22647208129
80Ken AndersonCIN19743642.027072.459519414
81Dan FoutsSDG19816281.157022.37148611-70
82Dan FoutsSDG19806211.197011.69104871-11
83Jeff GarciaSFO20005851.257012.21129031-52
84Ben RoethlisbergerPIT20074511.736991.06476299215
85Trent GreenKAN20024961.476931.7285412742
86Peyton ManningDEN20146141.156881.59979882
87Ben RoethlisbergerPIT20146411.126841.75112156-31
88Craig MortonDEN19814301.86751.06455318230
89Peyton ManningIND20035841.176642.22129430-59
90Trent GreenKAN20035431.256541.95105670-20
91Peyton ManningIND19995471.226511.94106267-24
92Brett FavreGNB19965831.196481.5288911624
93Dan FoutsSDG19833541.96452.3884313239
94Greg LandryDET19712902.316442.13660201107
95Carson PalmerCIN20055281.266441.97104174-21
96Roger StaubachDAL19712342.866433.9799185-11
97Carson PalmerCIN20065561.236421.4681214043
98Joe MontanaSFO19874201.41640295893-5
99Tom BradyNWE20055561.216391.5686912223
100Dan FoutsSDG19784031.676362.1285212828

This list makes a strong case for the validity of PY/A. It’s populated by the greatest QB seasons of all time at the top, and filled out by a number of other notably great and very good seasons. There are a few head scratchers (most notably Lynn Dickey at #9), but for the most part it’s a very credible list that closely mirrors the ANY/A rankings. That’s the point, really. When we remove sacks and interceptions from ANY/A, it doesn’t lose much accuracy, if any. At first glance, I was concerned that PY/A systematically overrates certain quarterbacks and underrates others. That’s probably true to a certain degree. However, I would argue that ANY/A has the same issue, except it’s a different set of quarterbacks who are over- and underrated by it. The true balance almost certainly lies somewhere in between the two metrics. FWIW, the correlation between RPY/A and RANY/A is a robust 0.877, with an r-squared of 0.769.

Now lets look at the other end of the spectrum – the 100 worst PY/A VALUE seasons since 1970.

Rank TeamYearDpbkRPY/AVALUERANY/AVALUERankDiff
1423Derek CarrOAK2014623-2.02-1209-1.36-8481395-28
1422Drew BledsoeNWE1995659-1.71-1086-1.09-7161366-56
1421Jon KitnaCIN2001606-1.67-972-1.48-8981408-13
1420Chris WeinkeCAR2001566-1.79-964-1.5-8481396-24
1419Joey HarringtonDET2003563-1.67-928-1.38-7791380-39
1418Kyle BollerBAL2004499-1.93-894-1.51-7551374-44
1417Blaine GabbertJAX2011453-2.16-894-2.28-103214192
1416Jack TrudeauIND1986446-2.14-893-1.96-8741405-11
1415Vince EvansCHI1981459-2.02-883-1.78-8181391-24
1414Ryan FitzpatrickCIN2008410-2.23-828-2.18-8921407-7
1413Archie ManningNOR1975387-2.23-803-2.76-113914229
1412Sam BradfordSTL2010624-1.36-801-1.04-6461349-63
1411Mark RypienWAS1993335-2.47-788-2-6711354-57
1410Bobby HoyingPHI1998259-3.46-775-3.94-102014188
1409Kordell StewartPIT1998491-1.68-769-1.78-8731404-5
1408Kyle OrtonCHI2005398-2.05-753-2.19-8721403-5
1407Jimmy ClausenCAR2010332-2.51-749-2.8-93014136
1406Blake BortlesJAX2014530-1.57-745-2.39-1268142317
1405Colt McCoyCLE2011495-1.59-736-1.19-5911329-76
1404Mark MalonePIT1987354-1.91-734-2.24-90714106
1403A.J. FeeleyMIA2004379-2.06-732-2.25-8511399-4
1402Joey HarringtonDET2002437-1.66-711-1.4-6131337-65
1401Akili SmithCIN2000303-2.65-708-2.44-7381371-30
1400Bruce GradkowskiTAM2006353-2.07-679-1.76-6211342-58
1399Jake PlummerARI2002566-1.27-676-1.54-87014023
1398Rusty HilgerDET1988337-2.19-672-2.38-8021386-12
1397Gary MarangiBUF1976254-2.62-649-3.14-85214014
1396Joe FlaccoBAL2013662-1.05-648-1.42-942141620
1395Matt CasselKAN2009535-1.27-627-1.43-7631378-17
1394Dan PastoriniHOU1973320-2.02-626-2.39-8161390-4
1393Steve SpurrierTAM1976343-1.84-610-1.3-4771265-128
1392Joe FergusonBUF1983535-1.2-609-1.06-5701321-71
1391Jeff GeorgeIND1991541-1.25-608-1.37-7431372-19
1390Sam BradfordSTL2011393-1.69-604-1.44-5651318-72
1389Jake PlummerARI1999408-1.57-600-2.65-1079142031
1388Joe NamathNYJ1976246-2.44-598-2.91-7631377-11
1387John FrieszSDG1991519-1.22-596-0.89-4601254-133
1386Mark MalonePIT1986438-1.38-588-0.78-3441159-227
1385Mike PhippsCLE1975341-1.76-587-2.01-7311368-17
1384JaMarcus RussellOAK2009279-2.37-584-3.39-945141733
1383David CarrHOU2002520-1.31-583-2.17-1127142138
1382Brad JohnsonTAM2001603-1.04-580-0.4-2381041-341
1381Bernie KosarCLE1990460-1.37-580-1.27-5851327-54
1380Ryan LeafSDG1998267-2.31-566-3.44-918141131
1379Phil SimmsNYG1980438-1.41-565-1.41-6161338-41
1378Mark BrunellWAS2004252-2.35-558-1.85-4671261-117
1377Steve DeBergSFO1978319-1.84-554-2.25-7191367-10
1376Christian PonderMIN2012515-1.14-551-0.97-4991285-91
1375Browning NagleNYJ1992414-1.42-549-1.45-5981332-43
1374Rick MirerSEA1993533-1.13-547-1.27-6761356-18
1373Joe KappBOS1970246-2.34-546-3.55-933141441
1372Josh FreemanTAM2011580-0.99-543-1.18-6851359-13
1371Chad HenneJAX2013541-1.08-543-1.04-5651319-52
1370Steve DilsMIN1983481-1.22-542-0.68-3281142-228
1369Alex SmithSFO2007210-2.79-539-2.43-5111290-79
1368Chuck LongDET1987433-1.13-535-0.93-4611257-111
1367Todd BlackledgeKAN1984308-1.82-535-1.14-3521169-198
1366Joe FergusonBUF1984379-1.55-532-2.13-807138721
1365Donovan McNabbPHI1999244-2.45-530-2.8-6841358-7
1364Stan GelbaughSEA1992289-2.08-529-2.63-759137511
1363Jack ConcannonCHI1970409-1.29-528-0.68-2951104-259
1362Jim HartSTL1979403-1.4-528-1.39-5591315-47
1361Josh McCownARI2004439-1.29-525-1.06-4671260-101
1360Tommy KramerMIN1979602-0.93-524-0.43-2581062-298
1359Jim ZornSEA1976464-1.11-521-1.09-5411305-54
1358Mike LivingstonKAN1978308-1.79-520-1.08-3321144-214
1357Kerry CollinsCAR1997408-1.36-518-2.28-930141255
1356Rick MirerSEA1994408-1.35-515-0.71-2911100-256
1355Trent DilferTAM1996510-1.07-515-1.18-6011333-22
1354Donovan McNabbPHI2000614-0.9-514-0.44-2681071-283
1353Craig WhelihanSDG1998335-1.6-511-2.38-798138532
1352Brady QuinnCLE2009275-1.99-509-1.75-4811268-84
1351Boomer EsiasonCIN1992297-1.82-505-2.23-66213521
1350Dan PastoriniHOU1972336-1.57-502-1.35-4831270-80
1349Joey HarringtonMIA2006403-1.29-501-1.23-4941279-70
1348Kordell StewartPIT1999297-1.82-501-1.83-5451308-40
1347Doug PedersonCLE2000227-2.38-500-2.55-5781325-22
1346David KlinglerCIN1993383-1.45-499-1.37-5241297-49
1345Kelly StoufferSEA1992216-2.62-497-3.39-732136924
1344John HadlGNB1975388-1.32-496-1.55-64213484
1343Cleo LemonMIA2007334-1.6-496-1.17-3921212-131
1342Vince FerragamoBUF1985306-1.71-491-2.04-62313431
1341Danny KanellNYG1998321-1.63-488-1.6-5131291-50
1340Steve FullerKAN1979307-1.8-485-2.28-701136323
1339Steve DeBergSFO1979595-0.83-4800.32193537-802
1338Tony BanksSTL1998449-1.17-479-1.32-5921330-8
1337Bobby DouglassCHI1971255-1.98-474-2.6-708136427
1336Joey HarringtonDET2004525-0.97-474-0.52-2741075-261
1335Marc BulgerSTL2008478-1.07-472-1.36-652135015
1334Steve BonoKAN1996460-1.07-470-0.66-3051120-214
1333Mark SanchezNYJ2012487-1.02-462-1.61-786138350
1332David WoodleyMIA1980344-1.4-459-1.3-4461244-88
1331Matt HasselbeckSEA2009520-0.94-458-1.07-5581314-17
1330Mike PagelBAL1982237-1.62-458-0.83-2511055-275
1329Brett FavreGNB2006634-0.74-456-0.18-114901-428
1328Roman GabrielPHI1974373-1.26-456-0.59-2341036-292
1327Neil LomaxSTL1986473-1.08-454-0.92-4361241-86
1326Ken DorseySFO2004239-2-451-2.1-5011286-40
1325Brandon WeedenCLE2012545-0.87-449-0.98-5361301-24
1324Mike BorylaPHI1976275-1.71-448-2.07-608133511

I actually find the Worst list even more validating of PY/A than the Best list. When we think of bad quarterbacks, most us reflexively focus on quarterbacks who make a lot of mistakes and sink their teams in obvious and memorable ways. And this list is filled with conventionally terrible quarterbacks. But remember, nearly all of their negative plays have been removed, so it’s not their mistakes putting them on the list. It’s their impotence. These guys couldn’t make plays or move the ball down the field, killing their teams slowly and agonizingly. At the very top (err, bottom), we find Derek Carr’s rookie year. A lot of fans and pundits classify Carr as a budding franchise QB who showed “flashes of potential”. Actually no, he showed the exact opposite. While the younger Carr avoided sacks and interceptions at a reasonable rate, his Y/A was absolutely pathetic. Even accounting for his lousy supporting cast, that is a major red flag. It’s much easier for a young QB to reign in his mistakes than it is for him to suddenly learn how to make positive plays down the field. Blake Bortles fits precisely the same troubling profile, so I don’t have much hope for the class of 2014.

Does this change your feelings about ANY/A? Do you think Danny and I are wasting our time? If anyone else has created their own passing metric using basic stats, I’d love to hear about it.

  1. Note that in calculating league average, I excluded the player in question from the league average totals. So each player is compared to a slightly different definition of league average. []
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NVB loved to go deep

NVB loved to go deep

During the 2013 season, I crunched the numbers to determine that Chris Johnson was the career leader in average length of rushing touchdown (since then, his average has dropped to 25.8, allowing Robert Smith to regain the top spot). Last year, I did the same analaysis to show that Homer Jones is the career leader in average length of receiving touchdown. Today, we look at the average length of passing touchdowns for over a hundred quarterbacks.

The table below shows the average and median length of touchdown passes for each quarterback with at least 125 career passing touchdowns. Playoff touchdowns are included in this data set. Norm Van Brocklin is your career leader, although it is Otto Graham who is the leader in median touchdown length; as such, the Van Brocklin/Graham debate must rage on. [click to continue…]

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1-Yard TD Passes

The 2nd touchdown of Ken Stabler’s career came in mop up duty at the end of a blowout in 1972 against the Oilers. With the Raiders up 27-0 on Monday Night Football, Stabler threw a one-yard touchdown off of play-action late in the fourth quarter.1

A month later, Mike Ditka caught a 1-yard touchdown pass from Craig Morton to put the Cowboys up 24-0 against the Chargers in the first half.2 In December, Denver’s Charley Johnson found Haven Moses for a 1-yard touchdown in the first half against the Chiefs.

Why am I reviewing some random 1-yard touchdown throws from 1972? Well, I’m not reviewing some random 1-yard touchdown passes from 1972; I just finished reviewing all of them. That’s right: there were just three touchdown passes of one yard in the entire 1972 season. [click to continue…]

  1. That was probably not the most memorable part of that broadcast. []
  2. Dallas would go up 31-0 before John Hadl (!) led a spirited second-half comeback that fell just short. []
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On Thursday, I looked at yards per attempt and outlier teams. Today, we use the same methodology but look at yards per attempt allowed (or, more specifically, Relative Yards per Attempt, which subtracts the league average from each team’s Y/A allowed).

In 2014, the best-fit linear formula to correlate relative yards per attempt allowed and winning percentage was 0.5019 – 0.1646 * Relative Y/A allowed. In the picture below, each team’s Relative Yards/Attempt allowed is on the X-Axis, while their winning percentage is on the Y-Axis. Since a negative RY/A is better — it means a team has allowed fewer yards per attempt than league average — you would expect the best teams/pass defenses to be on the top left of the chart. [click to continue…]

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In 2002, Rich Gannon, a former 4th round pick, led the NFL in passing yards. That year, Tom Brady (6th round), Trent Green (8th round), Aaron Brooks (4th round), and Jeff Garcia (undrafted) were in the top 11 in passing yards, while Jon Kitna (undrafted), Matt Hasselbeck (6th), and Brad Johnson (9th) all gained at least 3,000 passing yards, too.  You can find all that information here.  So in a year where only 17 quarterbacks threw for 3,000 yards, nearly half of them were drafted in the 4th round or later.

Ten years later, the quarterback landscape was very different. Other than Tony Romo, Brady, and Matt Schaub, all of the top 17 leaders in passing yards were drafted inside the top 35. Last year, Brady, Romo, and Russell Wilson were the only quarterbacks in the top 20 in passing yards not taken inside the first 36 picks (#36 was the draft slot for both Bay area quarterbacks, Colin Kaepernick and Derek Carr).

But those are just three isolated years.  How does the trend look over time? Here’s what I did.

1) Convert each player’s draft pick selection to its draft value.

2) For each player with passing yards in a season since 1970, calculate their percentage of league-wide total passing yards.

3) Multiply that number by each player’s draft value. Then sum those values to get a weighted-average of the draft value for each quarterback.

Here are the results: the number on the Y-Axis may not mean much to you in the abstract (it’s the weighted average draft value), but it’s the shape of the curve that’s important.

draft val QBs

As a general rule, the modern passing attack barely resembles what was going on in the early ’70s, but there is at least one exception: an emphasis on quarterbacks that were highly drafted.  For example, an overwhelming number of early draft picks are at the top of the passing charts from 19721  That trend didn’t hold for very long, though.  Then, in the early ’90s, things peaked again for highly drafted quarterback.  In 1994, five of the top seven passers were former top 3 picks, with the other two going in the top 33 selections.

My hunch is that this trend is going to stick around this time: once Brady and Romo retire, there may not be much out there other than Wilson (and perhaps Nick Foles) when it comes to quarterbacks drafted outside of the top 40.  This year, Buffalo, Houston, and Cleveland may be going with quarterbacks that were not highly drafted, but those appear to be short-term solutions, anyway.   And, at least for 2015, we have four top-2 picks that should boost the average. Carson Palmer should be back in Arizona after starting just 6 games last year, while Sam Bradford is a projected starter after missing all of 2014.  And we should also see Jameis Winston and Marcus Mariota helping to bring up last year’s average.

  1. Note that for players who went in both the AFL and NFL drafts, I assigned the better pick to them. []
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Andrew Healy, frequent contributor here and at Football Outsiders, is back for another guest post. You can also view all of Andrew’s guest posts at Football Perspective at this link, and follow him on twitter @AndHealy.


For a stats guy, the Wells Report is gripping reading, particularly the appendices provided by the consulting firm Exponent. The conclusion there is pretty simple. Compared to referee Walt Anderson’s pregame measurements, the Patriots’ footballs dropped significantly further in pressure than the Colts’ footballs did. Therefore, even if Tom Brady’s involvement is unclear, a Patriots’ employee probably deflated the balls.

At first glance, that evidence seems pretty convincing, maybe even strong enough to conclude more definitively that tampering occurred. And it is kind of awesome that the officials even created a control group. But there is a problem with making firm conclusions: timing. As Exponent acknowledges, the measured pressure of the balls depends on when the gauging took place. The more time that each football had to adjust to the warmer temperature of the officials’ locker room at halftime, the higher the ball pressure would rise.

And, not surprisingly given the Colts’ accusations, the officials measured the Patriots’ footballs first. This means that the New England footballs must have had less time to warm up than the Indianapolis footballs. Is that time significant? We will get to that, but it does make for a good argument that the Indianapolis footballs are not an adequate control group for the New England footballs. Given the order of events, we would expect the drop of pressure from Anderson’s initial measurements to be lower for the Colts’ balls that had more time indoors at halftime. As the Wells report notes, the likely field temperature was in the 48-50 degree range, compared to the 71-74 degree range for the room where the footballs were measured.

So, how much lower? Here it gets a little fuzzy. The report is clear that the Patriots footballs were gauged first during halftime, but it is unclear about whether the second step was to reinflate the Patriots’ balls or to measure the four Colts’ balls. In Appendix 1 (see p. 2 of the appendix), Exponent notes “although there remains some uncertainty about the exact order and timing of the other two events, it appears likely the reinflation and regauging occurred last.” If events unfolded this way, it would make the Indianapolis footballs at least a better sort of control group. [click to continue…]

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Bryan Frye, owner and operator of the great site nflsgreatest.co.nf, is back for another guest post. You can also view all of Bryan’s guest posts at Football Perspective at this link, and follow him on twitter @LaverneusDingle.


Last week, I posted a quarterback performance metric that accounts for both passing and rushing. The base stat, Total Adjusted Yards per Play, is easy to comprehend and easy to figure out yourself with basic box score data. My original post only included performance that occurred during or after the 2002 season, because I don’t have spike and kneel data going back further than that. For the sake of consistency, I wanted to maintain the same parameters when calculating career values.

Before we get into the tables, I’d like to first briefly talk about what these numbers are and what they are not.

The formula, in case you forgot: [click to continue…]

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Bryan Frye, owner and operator of the great site nflsgreatest.co.nf, is back for another guest post. You can also view all of Bryan’s guest posts at Football Perspective at this link, and follow him on twitter @LaverneusDingle.



I spent a few weeks this offseason parsing out quarterback spike and kneel numbers from post-2002 play by play data. Chase published the findings, which I believe are a useful resource when trying to assess a QB’s stats.1 Since I have the data available, I thought it would be good to use it.

Regular readers know Chase uses Adjusted Net Yards per pass Attempt as the primary stat for measuring quarterback performance.2 I am going to do something similar, but I am going to incorporate rushing contribution as well. This is something Chase talked about doing awhile ago, but we didn’t have the kneel or spike data available.3 I’ll call the end product Total Adjusted Yards per Play (TAY/P). The formula, for those curious:4

[Yards + Touchdowns*20 – Interceptions*45 – Fumbles*25 + First Downs*9] / Plays, where

Yards = pass yards + rush yards – sack yards + yards lost on kneels
Touchdowns = pass touchdowns + rush touchdowns
First Downs = (pass first downs + rush first downs) – touchdowns
Plays = pass attempts + sacks + rush attempts – spikes – kneels [click to continue…]

  1. For instance, 180 of Peyton Manning’s 303 rush attempts since 2002 have been kneels. He has lost 185 yard on those plays. Why in the world should we include those in his total output? Similarly, Ben Roethlisberger has spiked the ball 44 times, by far the most in the league since 2002. Why count those 44 “incomplete passes” in his completion rate? []
  2. It’s not perfect, but it’s at least easy to understand and calculate, and is not proprietary like DVOA, ESPN’s QBR, or PFF’s quarterback grades. []
  3. For another thing Chase wrote on combining rushing and passing data — while (gasp) analyzing Tim Tebow — click here. []
  4. I use 25 as the modifier for fumbles based on the idea that a QB fumble is worth roughly -50 yards, and fumble recovery is a 50/50 proposition. []
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Cornerback Targets

According to Pro Football Focus, Richard Sherman was targeted just 65 times last season. That number is even more remarkably low when you consider that Sherman was in on 552 pass plays for the Seahawks last season.

We all know that Sherman generally sticks to the defense’s left side of the field; as a result, offenses tend to put their best wide receiver on the offense’s left, in order to avoid having to throw at Sherman. But that’s what I want to look at today: which cornerbacks are targeted the least?

Based on data from Pro Football Focus, the average cornerback was targeted on 16.4% of his pass snaps last year. That means an average cornerback would be expected to see about 90.5 targets on 552 snaps; in other words, Sherman saw 25.5 fewer targets than we would expect.

That’s the most impressive number of any cornerback in the league last year, with “impressive” here being a synonym for not being targeted. The second largest number belongs to Darrelle Revis, which perhaps isn’t much of a surprise, either. While with the Patriots, Revis was targeted 79 times on 606 pass snaps, or 20.4 fewer targets than we would expect.

The table below shows that data for each cornerback that was in on at least 175 snaps last season: [click to continue…]

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On Tuesday, I looked at the fantastic rookie class of wide receivers that entered the NFL last year. But in that post, I focused on receiving yards; in fact, the group was even more incredible when it comes to receiving touchdowns.

Rookie wide receivers caught an astounding 92 touchdowns last year, highlighted by Odell Beckham and Mike Evans each snatching a dozen scores. In addition, Kelvin Benjamin (9), Martavis Bryant (8), Jordan Matthews (8), Sammy Watkins (6), Allen Hurns (6), John Brown (5) and Jarvis Landry (5) each caught at least five touchdowns.

Let’s put that number in perspective. Second-year wide receivers caught just 43 touchdowns last year, while third-year and fourth-year wideouts each caught 59 touchdowns. Players from the class of 2010 caught 72, the second highest amount of any class last year. Take a look: [click to continue…]

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The 2014 Class of Rookie Wide Receivers

In December, I provided a quick look at rookie receiving production, and noted that an unusually large amount of receiving yards had come from first-year players. In that study, I lumped all rookies together, but today, the focus will be on only wide receivers.

And the 2014 season was an incredible one for rookie wide receivers. Odell Beckham was unsurprisingly named the Offensive Rookie of the Year by the AP, with a rookie-high 1,305 receiving yards. Tampa Bay’s Mike Evans and Carolina’s Kelvin Benjamin each topped 1,000 yards, while Sammy Watkins (982), Jordan Matthews (872), and Jarvis Landry (758) all had seasons that would stand out as special in many other years.

The depth of the class was impressive, too: John Brown (696), Allen Hurns (677), Taylor Gabriel (621), Brandin Cooks (550), Martavis Bryant (549), Allen Robinson (548) all topped 500 yards, while Davante Adams, Donte Moncrief and Marqise Lee all hit the 400-yard mark.

Collectively, rookie wide receivers recorded 12,611 receiving yards last year, the most of any class year in the NFL in 2014. The graph below shows the number of receiving yards from wide receivers from each class (i.e., 1st year, 2nd year, 3rd year, etc.) in the NFL in 2014: [click to continue…]

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Career RANY/A Rankings

Adjusted Net Yards per Attempt is my preferred basic measurement of quarterback play. ANY/A is simply yards per attempt, but includes sacks and sack yardage lost, and provides a 20-yard bonus for touchdowns and a 45-yard penalty for interceptions.

RANY/A, or Relative ANY/A, measures a quarterback’s ANY/A average to league average. Let’s use Aaron Rodgers as an example. This past season, he threw 520 passes and gained 4,381 yards and 38 touchdowns, while throwing five interceptions and being sacked 28 times for 174 yards. That translates to an 8.65 ANY/A average, best in the NFL in 2014.

The league average rate in 2014 was a record-high 6.14 Adjusted Net Yards per Attempt; as a result, this means that Rodgers averaged 2.52 ANY/A above average, or had a RANY/A of +2.52.1 But that is just for one season. To measure Rodgers’ career RANY/A, we need to do that for every season of his career, and weight his RANY/A in each season by his number of dropbacks.

For example, Rodgers had 14.7% of his career dropbacks come in 2014, which means 14.7% of his career RANY/A is based off of the number +2.52. During his other MVP season in 2011, Rodgers had a RANY/A of 3.49 on just 10 fewer dropbacks; as a result, 14.4% of his career RANY/A is based off of +3.49. If you multiply his RANY/A in each year by the percentage of dropbacks he had in that season relative to his entire career, and sum those results, you will get a player’s career RANY/A. Here, take a look: [click to continue…]

  1. Difference due to rounding. []
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