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Team Pass Identities Through 10 Weeks (2015)

I’ve published the Game Scripts data from every game this year at the 2015 Game Scripts page, available here. What would it look like if we plotted Game Script score (on the X-Axis) against Pass Ratio (on the Y-Axis) for every game this year? Something like this:

avg game script

(Note that this looks pretty similar to how it was through seven weeks last year, although the constant (i.e., league-average pass ratio) has increased by nearly a full percentage point.)
[click to continue…]



Rob Gronkowski is in a scoring slump. It’s one of the worst scoring slumps of his career. But more on that in a bit.

Jerry Rice once1 caught 67 touchdowns over a 57-game period. This stretch was during all of 1987, 1988, and 1989 (including playoffs) and the start of the 1990 season. That pro-rates to an insane 19-touchdown per-season average for three-and-a-half seasons. Then again, the weirder thing is when Rice doesn’t top a receiving category.

Lance Alworth once caught 55 touchdowns2 over one 57-game stretch from 1963 to 1967.

Only three other players since 1960 have ever had more than 50 touchdowns in any 57-game stretch (including playoffs): Randy Moss, Terrell Owens, and Art Powell, each of whom topped out at 53 touchdowns in 57 games. Cris Carter, Sonny Randle, Sterling Sharpe were at 49, Larry Fitzgerald was at 58, and Gary Collins, Anthony Freeman, Marvin Harrison, and Andre Rison were at 47.  Calvin Johnson topped out at 46 at one point in 2013.  Dez Bryant hit 46 in his last 57 after the Lions playoff game, but then went three straight games without a touchdown catch. [click to continue…]

  1. Well, he actually did it three times, although the same 55 games were in all three stretches. []
  2. Three times, like Rice, with 55 of the same games. []

The Jets, and Rushing in Wins and Losses

The Jets are 4-3, and it would not be an exaggeration to say that the team’s success is heavily tied to New York’s ability to control games on the ground. The Jets running backs have rushed for over 150 yards in three games this year, wins over Washington (190), Miami (173), and Cleveland (155). The Jets have also had three games with 60 or fewer rushing yards, losses against New England (60), Philadelphia (34), and Oakland (28). The seventh game, a win over the Colts, saw the Jets running backs operate at a reasonably effective pace of 26 carries for 95 yards.

In other words, when the Jets run well, they win, and when they don’t, they lose. That sounds simplistic, and it is: it’s a bit of an over exaggeration, although one grounded in some truth. In general, teams run more when they win — or, more precisely, when they have favorable Game Scripts — and run less often when they have negative Game Scripts. And the Jets games have had pretty strong Game Scripts in the four wins, scoring a +6.1 against Washington, +6.5 against Cleveland, +7.3 against the Colts, and +11.9 against Miami. Those are the sorts of games where it’s easy to produce good numbers, and Jets running backs1 have averaged 153.25 rushing yards in these four wins.

The losses to Philadelphia (-10.6) and Oakland (I haven’t calculated it yet, but it will certainly be in the double digits) were ugly; the Patriots game (-0.6) was the only game that unfolded with a neutral Game Script. Still, in three losses, the Jets running backs have rushed 54 times for just 122 yards. So the causation arrow isn’t pointing only one way here: the Jets are winning when they run more effectively, and losing when they aren’t, on top of whatever bonus the raw totals get out of Game Scripts. [click to continue…]

  1. This is meant to exclude Ryan Fitzpatrick and other non-running backs. []

Running Back Class of 2008 Still Going Strong

Jamaal Charles, Matt Forte, Chris Johnson, Justin Forsett, Jonathan Stewart, Danny Woodhead, Mike Tolbert, Darren McFadden, Marcel Reece, and Jerome Felton all entered the NFL in 2008. So did Steve Slaton, the rookie rushing leader that year, and Ray Rice, Rashard Mendenhall, Michael Bush, Peyton Hillis, and Felix Jones. Analyzing where the ’08 class ranks in NFL history is a project for the offseason, but today, I thought it would be fun to look at rushing yards by running backs by class year.

The graph below shows that data through six weeks of the 2015 season. As you can see, players in their 8th NFL season — those who entered the league in 2008 — are doing quite well.

wk6 2015 rushing yards class year

The class with the most rushing yards so far in 2015 are the rookies. That class is currently led by Thomas Rawls, but has also received strong production from higher picks like Todd Gurley, Melvin Gordon, and T.J. Yeldon. After the class of ’15, there’s a gradual decline with respect to production by older classes. And then, there’s the class of 2008. [click to continue…]


WP: Pre-Week 6 – 4th Down Aggressiveness

This week at the Washington Post, a look at some of good and bad fourth down decisions in both week 5 and this season.

The Atlanta Falcons are 5-0, and are quickly becoming one of the major stories of the 2015 NFL season. With a win tonight in New Orleans, the Falcons will match the team’s entire win total from the 2015 season. But without some aggressive coaching from Dan Quinn last weekend, the Falcons likely wouldn’t be among the league’s five remaining unbeaten teams.

With just under five minutes left in the third quarter, the Falcons faced a 4th-and-6 from the Washington 40-yard line, trailing by four points. Given that Atlanta had thrown incomplete passes on the previous two plays, most coaches would have punted or tried a long field goal. Instead, Quinn played to his team’s strengths, and Matt Ryan connected with Julio Jones for a nine-yard gain. Atlanta wound up scoring a field goal on that drive, which put the team in a position to tie the game late in the fourth quarter, and eventually win in overtime.

You can read the full article here.


I’m short on time this week, so I will present the data and leave the commentary to you guys. Here are the Game Scripts data from week 4.

Well, okay, allow me one comment. Under Joe Philbin, the Miami Dolphins have been incredibly pass-happy, despite the fact that the team has often been more effective on the ground than through the air. Well, in Philbin’s last game as head coach, Miami passed on 81% of dropbacks, the highest rate of any team in week four. And, of course, while some of that was due to the team’s poor Game Script, note that Tampa Bay had nearly the same Game Script and passed on only 61% of all plays.

Miami rushed 11 times for 59 yards, so it was not as though the Dolphins rushing attack mandated a pass-happy approach. And Ryan Tannehill averaged 2.49 ANY/A on 47 dropbacks. You can probably figure out why Philbin was fired. [click to continue…]


In week 3, Arizona picked off two Colin Kaepernick passes and returned them for touchdowns… in the first six minutes of the game. The Cardinals led 28-0 before we were halfway through the second quarter! On average, Arizona led by 24.3 points during every second of game play, the most dominant Game Script so far in 2015 (it would rank 5th last year).

But while the Cardinals provided the biggest blowout of week three, it was hardly the only one. A full half of all 16 games had a double digit Game Script, and only the Jets managed to finish within one score of their opponent. Three other games finished with double-digit margins; there simply weren’t that many nail biters last weekend.

The Falcons, though, did pull off an impressive upset: Atlanta trailed 14-0 midway through the first quarter against the Cowboys, and then 21-7 midway through the second. Atlanta even went into halftime down 11, but scored three second half touchdowns while shutting out the Cowboys to pull away with the victory. In the process, the Falcons became just the 11th team since 1990 to trail by at least 11 at halftime and still win by at least 11 points.

Below are the week 3 game scripts: [click to continue…]


It’s safe to say that no team has exceeded expectations through two weeks quite like the Jets. In week 1, New York was a 3.5-point home favorite against the Browns, but won by 21 points (a 17.5-point cover). In week 2, the Jets won 20-7 in Indianapolis, despite being 7-point underdogs (a 20-point cover). The Jets are the only team to cover by 17+ points in each of the first two weeks; in fact, Arizona (+10 against New Orleans, +23 against Chicago) is the only other team to even cover by at least five points in both games so far.

The last team to pull off this feat? The 2007 Patriots. Yes, another day, another Tom Brady/Ryan Fitzpatrick comparison. From 1978 to 2014, there were 19 teams that covered by at least 17 points in each of their first two games. How did those teams do the prior year, and during the rest of that season?

I’ve included the relevant data for each team in the table below. Here’s how to read the line of the ’06 Chargers. San Diego covered by 24 points in week 1, and 21 points in week 2. The Chargers won 9 games in 2005, but the hot start in ’06 was a sign of things to come, as San Diego won 14 games. That was an improvement of 5 wins, although the Chargers season ended in the Division round of the playoffs. [click to continue…]


On Sunday, New England defeated Buffalo by the misleading score of 40-32. The Patriots may have won by only one score, but New England held an 11-point lead at halftime and a 24-point lead after three quarters. The Patriots were in control of the game for most of the contest, and held an average lead of 9.8 points during each second of game play (the “Game Script”).

Teams with large leads don’t pass very often; in general, you’d expect a team with a Game Script of +10.0 to pass around 50% of the time. But New England threw on 80% of all snaps! That even includes three Tom Brady kneels, and one run each by wide receivers Julian Edelman and Danny Amendola. Excluding those plays, New England passed on 61 of 71 plays, an astonishing 86% pass rate. Much of that number owes to a stout Buffalo run defense, but that’s a remarkable pass-happy performance regardless of Game Script or opponent; given that it came in a game where New England dominated, it was even more noteworthy. By comparison, Minnesota had a Game Script of +10.4 against Detroit, and passed on just 31.7% of plays. In fact, none of the other 31 teams passed as often as New England in week two. [click to continue…]


What Can We Learn About The 49ers Defense From Week 1?

Yesterday, we looked at what Tennessee’s offensive explosion in week 1 might mean for the rest of the year. Today, let’s do the same but for the 49ers defense. The 49ers were 2.5-point underdogs against Minnesota in week one, and the Over/Under in the game was 41.5 points. This translates to a projected a final score of 22-19.5 in favor of Minnesota. As it turns out, San Francisco won the game, 20-3, which means the Vikings were held 19 points below their expected total. That’s the 4th best performance by a team by this methodology since 2002.

The most impressive game? That came in 2003, in the Lawyer Milloy game. The Bills shut out New England, 31-0, while the pre-game spread projected New England to score 21.75 points. That wasn’t a sign that Buffalo was about to break through (the team finished 6-10), but it did provide some insight into a Bills defense that jumped from 27th (in 2002) to 5th (in 2003) in points allowed. [click to continue…]

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The Titans were 3-point underdogs against Tampa Bay in week one, and the Over/Under in the game was 41 points.  This translates to a projected a final score of 22-19 in favor of Tampa Bay. Of course, Tennessee scored 42 points, outscoring its projection by a whopping 23 points, tied for the fourth biggest number in all week 1 games since 2002.  In the graph below, I’ve plotted each team’s expected points scored in week 1 on the X-axis, and their actual week 1 score on the Y-axis. [click to continue…]


Six years ago, I took my first crack at analyzing field goal kickers. I have been meaning to update that article for each of the last three offseasons, and with the sun setting on the 2015 offseason, I didn’t want to let this slip yet again.

Ranking field goal kickers is not difficult conceptually, but it can be a bit challenging in practice. One thing I’ve yet to refine is the appropriate adjustments for playing surface, stadium, time of game, temperature, and wind. That’s a lot of adjustments to deal with, all on top of the most obvious adjustment: for era.

But as I understand it, Rome was not built in a day; further, I believe that a journey of a thousand miles begins with one step. As a result, I’m okay with only getting part of the way there for now, and punting (which is very, very different from kicking) the rest of this process to next offseason.

Let’s begin with the obvious: era adjustments are really, really, important.  To provide some examples, I looked at the field goal rates at four different increments: 22-24 yard kicks, 31-33 yard attempts, kicks from 40-42 yards away, and finally, field goal attempts from 49-51 yards.  In the graph below, I’ve plotted the success rate at those four distances for each year since 1960, along with a “best-fit” curve at each distance. Take a look: [click to continue…]


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…]


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…]


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…]


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…]


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…]


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…]


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. []

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…]


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. []

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. []

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…]


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…]


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…]


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…]


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. []

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…]


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…]


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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