There are a few ways to get a lot of receiving yards.

One way is to play with a good quarterback, assuming that is defined as a quarterback who averages a high number of yards per attempt. This is pretty self explanatory.

Another way, though, is to play with a quarterback who throws a lot of passes. For example, we know that Russell Wilson is a much better quarterback than Derek Carr. The stats back this up, too: Wilson has averaged 7.82 Y/A for his career; by comparison, Carr has averaged just 6.54 Y/A for his career. But Carr has averaged 36.2 pass attempts per game, while Wilson is at just 29.5. As a result, Carr has averaged 237 passing yards per game, while Wilson has averaged only 231 passing yards per game. Since every passing yard is a receiving yard, it’s actually been better to play with Carr than Wilson if your goal was to get a lot of receiving yards.

So, you might realize, if receiving yards just equals gross passing yards, then having a good quarterback is only half the equation.

Receiving Yards = (Yards/Attempt) x Attempts

So if you are a receiver, you want to play on a team that has a good passer, or passes a lot, or better yet — both! On the other hand, a wide receiver can’t control these things.  We would naturally expect that the same wide receiver would gain fewer yards if he suddenly played for a team with a worse passer and if that team passed less often.

So can we control for this?  You might think that we should focus more on percentage of team receiving yards, rather than raw receiving yards.  For example, this might mean a receiver with 1,000 receiving yards on a team that threw for 3,000 yards was “better” than a receiver with 1,200 receiving yards on a team that threw for 4,000 yards.  After all, the first receiver had 33.3% of his team’s passing game, while the second receiver had just 30% of his team’s passing game.

But there are issues with that, too.  Let’s assume that both teams threw 500 passes, so the team that threw for 3,000 yards averaged just 6.0 yards per attempt, while the team that threw for 4,000 yards averaged 8.0 yards per attempt.  A team that averages 6.0 yards per attempt is a very bad passing team, while a team that averages 8.0 yards per attempt is a very good passing team.  But here’s the question: is it “better” or “more impressive” to be responsible for 33.3% of a very bad passing game or 30.0% of a very good passing game? [continue reading…]

On Monday, we looked at the AV-adjusted average age of each team’s offense in 2017.

On Tuesday, we did the same for defense.

Today, let’s look at the average age of each team overall in 2017. For reference, here are last year’s results. You won’t be surprised to see Cleveland grade out as the youngest team in 2017 by over a full year. The Browns also failed to win a game, so youth didn’t work out for that team.

But the second-youngest team in football was the Jacksonville Jaguars, who nearly won the AFC. The Jaguars held a lead in the AFC Championship Game, before losing to the third-oldest team in the NFL… the Patriots.

The table below shows the average age of each team last season. [continue reading…]

Yesterday, we looked at the age-adjusted offenses from 2017. Today we do the same for defenses, just like we did last year. Here’s how I opened that column:

Being young isn’t by itself a virtue: the Browns ranked in the bottom 5 in points allowed, yards allowed, net yards per attempt allowed, net yards per rush allowed, turnovers forced, and first downs allowed. But Cleveland was, by far, the youngest defense in the NFL last season.

In 2016, the Browns defense had an average AV-adjusted age of just 25.2; the Falcons were the second-youngest defense at 25.8. In 2017, the Falcons again had an average AV-adjusted defense that was just 25.8 years old. But the Browns? That number dropped to just 24.5! The Browns defense was even younger than the Browns offense, and was by far the youngest unit in all of football: [continue reading…]

After each of the last six years, I’ve presented the AV-adjusted age of each roster in the NFL. Measuring team age in the NFL is tricky. You don’t want to calculate the average age of a 53-man roster and call that the “team age” because the age of a team’s starters is much more relevant than the age of a team’s reserves. The average age of a team’s starting lineup isn’t perfect, either. The age of the quarterback and key offensive and defensive players should count for more than the age of a less relevant starter. Ideally, you want to calculate a team’s average age by placing greater weight on the team’s most relevant players.

My solution has been to use the Approximate Value numbers from Pro-Football-Reference.com, and to calculate age using each player’s precise age as of September 1 of the year in question.  Today, we will look at offenses; tomorrow, we will crunch these same numbers for team defenses. The table below shows the average AV-adjusted age of each offense, along with its total number of points of AV. In 2017, the Browns, Jaguars, and Texans were the three youngest offenses, with Cleveland really standing out. [continue reading…]

On Friday, I looked at each team’s offensive pass identity. Today, the reverse: a look at defensive pass identities.

The Jacksonville Jaguars were one of the best teams in the NFL last year. Jacksonville had the 3rd-best points differential in the NFL in 2017 after 1 quarter (+41), the 5th-best after 2 quarters (+86), the 4th-best through 3 quarters (+109), and tied for the 3rd-best points differential overall. Unsurprisingly, Jacksonville had the 4th best average Game Script last season, which means you should expect the Jaguars to be run-heavy and Jacksonville’s opponents to be pass-heavy.

On the offensive side, things held to form: Jacksonville rushed on 49.4% of plays, the highest ratio in the NFL last season. But on defense, that wasn’t the case: teams passed on only 56.4% of plays against the Jaguars last year! Consider that opponents passed on 65.4% and 62.3% of plays against the Eagles and Vikings, teams that finished 3rd and 5th in Game Script last season.

On the other side, the Tennessee Titans.  Last season, the Titans were an average team, finishing with a slightly negative Game Script. And yet teams passed on them like they were the Patriots! In fact, opponents passed against New England on 61.6% of plays and against the Titans on 61.7% of plays.  The Titans Game Script was 0.27 standard deviations below average, while the opponent pass ratio was 1.42 standard deviations above average. As a result, the Titans have a Defensive Pass Identity of +1.69, making them the defense teams were most likely to pass against. [continue reading…]

Tonight, the 2018 Pro Football Hall of Fame class will be inducted. It’s a great class, with Ray Lewis, Randy Moss, Brian Urlacher, Terrell Owens, and Brian Dawkins as the five modern-era selections.,joined by Jerry Kramer and Robert Brazile from the senior’s committee and Bobby Beathard as the Contributor selection.

Here’s what I wrote about Lewis when he was announced:

You won’t be on an island if you suggest that Lewis is the best inside linebacker in NFL history. Lewis scores well in pretty much every metric possible. When it comes to Approximate Value, what Ray Lewis did was unbelievable. He made 13 Pro Bowls, which is also absurd. The Ravens went on a magical run to win the Super Bowl in his final year, and at the time he retired, he was arguably the best player to retire after winning the Super Bowl.

[continue reading…]

In June, I published the Game Scripts results from every game of the 2017 season. A team’s Game Script is simply the average points differential over every second of every game. The largest Game Script of the season came in the Rams/Seahawks game in Seattle last year that the Rams won by a score of 42-7. Just as impressive as the final score was how Los Angeles got up on Seattle early, which is how a team gets a very high Game Script: LA was up 13-0 after the first quarter and 34-0 at halftime, and finished with a Game Script of +23.4. This means, on average, the Rams lead by 23.4 points over the course of the 3600 seconds in that game.

Not surprisingly, LA only passed on 36.8% of plays in that game. In general, as Game Script goes up, Pass Ratios go down. In 2017, for every point of Game Script, a team would be expected to pass about 0.72% less often. A team with a Game Script of 0 would be expected to pass on 57.6% of plays; if the Game Script was -10, it would be expected to pass on 64.8% of plays, at -5, 61.2%, at +5, 54.0%, and at +10, 50.4%.

Last year, the Dolphins were the most pass-happy team in the NFL. Miami finished the year with 602 pass attempts, 33 sacks, and just 360 runs; in other words, the Dolphins passed on 63.8% of all plays last year. If you take the calculate the average pass ratio for the Dolphins in each game, Miami has a 63.9% pass ratio (I have decided to use an average of the average approach for this post, rather than an average of the gross). That was 1.74 standard deviations above average last year, since the average pass ratio was 57.6% and the standard deviation was 3.6%. However, the Dolphins had an average Game Script of -5.27, which was 1.65 standard deviations below average. The average Game Script, by definition, is 0, and the standard deviation last year was 3.20.

So Miami wasn’t particularly pass-happy once you account for Game Script. But you know who was? The team with the Hall of Fame quarterback, Hall of Fame tight end, and star young wide receiver. And in addition to Tom Brady, Rob Gronkowski, and Brandin Cooks, three of New England’s top four running backs are known just as much (if not more) for their receiving prowess than their rushing ability: Dion Lewis, who caught 91% of his targets last year, Rex Burkhead, who had 264 rushing yards and 254 receiving yards, and James White, who had 56 receptions and just 43 carries.

By traditional numbers, New England ranked 16th in the league in pass ratio. But the Patriots are the Patriots, a team that generally plays with the lead. New England finished the year with a Game Script that was +1.66 standard deviations above average and a pass ratio that was 0.29 standard deviations above average. Add those two numbers, and New England’s Pass Identity was +1.95, easily the strongest in the league. The table below shows the results from every team last season. [continue reading…]

The 1991 Eagles had a very bad passing offense. Philadelphia was one of four teams to finish with an ANY/A below 4.00, wasting a legendary defense along the way. You may have already known that, but here’s something you may not have known: Jim McMahon was Philadelphia’s starting quarterback that season, and he had a good season, ranking 12th in ANY/A and 13th in passer rating.

So yes, Philadelphia’s starting quarterback was an above-average passer despite Philadelphia’s passing attack being terrible. How did that happen? Well, it’s pretty simple: McMahon was responsible for 61% of the Eagles pass attempts that year, but also 66% of the team’s completions, 71% of the passing yards, 71% of the passing touchdowns, and only 47% of the sacks and just 41% of the interceptions. McMahon averaged 5.59 ANY/A; the rest of the Eagles passers averaged 0.77 ANY/A! That’s not a typo:

Thought of another way, non-McMahon passers had 226 dropbacks.  Using McMahon’s average of 5.59 ANY/A, we would “expect” McMahon to have produced 1,263 Adjusted Net Yards on those dropbacks.  In reality, other Eagles passers produced just 175 Adjusted Net Yards on those dropbacks, in large part due to 16 interceptions on 202 pass attempts.  This means McMahon “would have” produced 1,088 more Adjusted Net Yards than his backups.

That’s… a lot.  In fact, it’s the second-most in history using the following methodology:

• 1) Calculate the ANY/A for each passer on each team.  So for 1991 McMahon, it’s 5.59.
• 2) Calculate the ANY/A for the rest of that team’s passers for each passer.  For 1991 McMahon, it’s 0.77.
• 3) Subtract the result in step 2 from the number in step 1. This leaves us with 4.82 in the case of ’91 McMahon.
• 4) Multiply the result in step 3 by the smaller number of (a) that passer’s number of dropbacks and (b) the total number of dropbacks by the rest of the team.  In ’91 McMahon’s case, we use (b), which is 226, to get a result of 1,088.

The biggest difference in NFL history wasn’t McMahon, but Dan Fouts on the 1983 Chargers.   He averaged 7.32 ANY/A and threw 347 passes.  His backup, Ed Luther, averaged 3.77 ANY/A and threw 287 passes.  Fouts threw 20 TDs and 15 INTs, while Luther had 7 TDs and 17 INTs!  Non-Fouts passers in 1983 on San Diego had 308 dropbacks and averaged 3.66 ANY/A, exactly half of Fouts’ average!  Therefore, Fouts had 1,130 Adjusted Net Yards above expectation that season.

Three players from 2017 make the top 75 using this methodology, and my hunch is you could guess them pretty easily.  Then again, this formula isn’t supposed to shock you: it’s just a way of measuring which teams had a really good passer play about half a season, and really bad passers the rest of the season.

Yesterday, I looked at the leaders in Gray Ink in Receiving Yards per adjusted Team Pass Attempt. There was a pretty notable name absent from that list: Larry Fitzgerald, who currently ranks 3rd on the all-time receiving yards list. So what gives? The graph below shows Fitzgerald’s relevant statistics from every season of his career.

For example, in 2004, playing for Arizona, he had 780 receiving yards and his team had 533 Team Pass Attempts (excluding sacks). The Cardinals ranked 13th Team Pass Attempts that season. Fitzgerald averaged 1.46 Receiving Yards/Team Pass Attempt, and 1.46 Receiving Yards/adjusted Team Pass Attempt (meaning he played a full season). He ranked 46th in RY/TPA and 55th in RY/aTPA. He also ranked 42nd in raw receiving yards.

[continue reading…]

Yesterday, I wrote about how Michael Irvin was dominant in Receiving Yards per adjusted Team Pass Attempts.  From 1991 to 1996, he ranked 1st in that category three times, and 2nd in the other three seasons.  From a Gray Ink perspective, that would mean he would get 10 points each for his three first place finishes and 9 points each for his three second place finishes, for a total of 57 points.  He also ranked 10th in 1998, which would give him one more point.

The fact that I wrote about Irvin yesterday wasn’t a coincidence.  I calculated the Gray Ink for each receiver in NFL history in RY/aTPA, and Irvin’s 58 points (which turns out to be 55 points after you adjust for the number of teams in the league) was the third best in NFL history. Here are the top 75 receivers by this metric. You can read the table below as follows. Jerry Rice played from 1985 to 2004, and accumulated 92 points of Gray Ink, where a 1st-place finish in Receiving Yards/adjusted Team Pass Attempt is worth 10 points, a 2nd-place finish 9 points, a 3rd-place finish 8 points, and so on. The final column is a pro-rated value number, which lowers the value gives in seasons where there were fewer than 32 teams. This most clearly impacts older players like Don Hutson, who drops from 102 points to 66.9 points. [continue reading…]

Michael Irvin ranks 27th on the list of career receiving yards. At the time he retired, Irvin ranked 9th in that metric, so some of his “poor” ranking is due to the passing inflation of the last two decades.

Another reason is because he had a relatively short career. Irvin also ranks tied for 11th in receiving yards per game, and was tied for 5th when he retired. How short? His prime was from ages 25 to 32, an 8-year stretch that encompassed 86.5% of his career receiving yards.

During those years, covering the 1991 to 1998 seasons, Irvin led all players in receiving yards.  Yes, that’s all players, even Jerry Rice, although Rice missed 9 more games than Irvin due to a torn ACL.  Even still, Rice averaged 85.5 yards per game, Irvin 83.7, and the two receivers were far ahead of the rest of the NFL.  But it’s worth noting that the Cowboys were a run-heavy team during this era: Dallas ranked 24th in pass attempts during this time, while the 49ers ranked 12th.

Irvin was a dominant player on a team that wasn’t pass-happy; he was particularly dominant in games his team won, when he averaged 85 receiving yards per game.  Those Cowboys won a lot of games, and didn’t pass often, but when they did, they passed to Irvin.  And it usually worked.

Over the weekend, I wrote about the year-by-year leaders in receiving yards per team pass attempt (RY/TPA) and Receiving Yards per Adjusted Team Pass Attempt (RY/aTPA), which are two simple but effective ways to measure receiver play. RY/TPA simply takes a player’s number of receiving yards and divides it by his team’s number of pass attempts; RY/aTPA adjusts for missed games. [continue reading…]

Yesterday, we looked at the leaders in receiving yards per team pass attempt. Today, I want to look at the leaders in receiving yards per adjusted team pass attempt.

What do I mean by adjusted team pass attempts?  Let’s look at three players from last year: Julio Jones, DeAndre Hopkins, and Antonio Brown.  Those three had 1444, 1378, and 1533 yards, respectively, in 2017. And the Falcons, Texans, and Steelers had 530, 525, and 590 pass attempts, respectively, last year. This means Jones averaged 2.72 receiving yards per team pass attempt, Hopkins 2.62, and Brown 2.60. Pretty straightforward.

Except Jones played in 16 games, Hopkins 15, and Brown 14. If we assume Hopkins therefore only saw (15/16) * 525 pass attempts — or 492 pass attempts — then Hopkins averaged 2.80 receiving yards per adjusted team pass attempt. And if we assume Brown only saw (14/16) * 590 pass attempts — or 516 pass attempts — then Brown averaged 2.97 RY/aTPA. [continue reading…]

In 2017, Julio Jones had 1,444 receiving yards playing for the Falcons.  Last year, Atlanta had only 530 pass attempts (excluding sacks); this means Jones recorded 2.72 receiving yards per Atlanta team pass attempt.  That was the most in the NFL last year, followed by DeAndre Hopkins (2.62) and Antonio Brown (2.60), although it’s worth noting that both Hopkins and Brown missed games.

This was the second season in a row that Jones led the NFL in receiving yards per team pass attempt. In 2016, he had 1,409 yards while Atlanta had 537 pass attempts; that’s a 2.62 RY/TPA average that is even more impressive when you consider that Jones missed two games that season. Jones is already in select company, and one more year would put him in exclusive territory: only four players have led the league in RY/TPA in more than two seasons. Don Hutson did it 7 times, Jerry Rice 4 times, and Steve Smith and Michael Irvin each did it three times. [continue reading…]

You’re going to want to sit down for this one: Antonio Brown and Julio Jones were the two best receivers in the NFL last year.

Houston star DeAndre Hopkins was responsible for 37.8% of all Texans receiving yards last season, the highest rate in the league in 2017 (Hopkins also did this as a rookie in 2014).  But Brown and Jones weren’t too far behind him: Brown had 34.8% of all Steelers receiving yards despite missing nearly three full games.  And Jones had 33.8% of all Falcons receiving yards, the third highest ratio in the NFL.  But Hopkins played on a mediocre Texans passing attack that ranked 20th in ANY/A (more precisely, he spent 40% of his time on a great passing attack led by Deshaun Watson, and 60% of his time on a terrible pass offense with Tom Savage and T.J. Yates under center).  Jones and Brown played on passing offenses that averaged 7.0 ANY/A, ranking 7th and 8th in the NFL in 2017.

One of my favorite things to do at Football Perspective is to look at receiving production in the context of two stats: percentage of team yards and team passing efficiency (highlighted here when looking at Gary Clark’s production on the ’91 Skins). Why do I like looking at this? In some ways, these are counter forces.  Put a great wide receiver on a good passing attack and he might not have a huge share of the offense, but the passing attack should be outstanding.  Put him on a bad passing attack, and the pass efficiency may not be great, but he should have a huge share of the pie.  It is hardly perfect, but it’s fun to look at.

So how do we quantify this? Let’s use Keenan Allen as an example for the table below. He had 30.6% of all Chargers receiving yards last season and Los Angeles averaged an impressive 7.48 ANY/A. He ranked 6th in percentage of Team Receiving Yards, and the Chargers ranked 3rd in ANY/A. Allen was 1.03 standard deviations above average in percentage of team receiving yards – the % of TRY Z-Score — and the Chargers were 1.47 standard deviations above average in ANY/A (the ANY/A Z-Score). If you add those two numbers together, Allen was 2.50 standard deviations above average, the metric by which the table below is sorted. [continue reading…]

Among players with at least 8,000 career receiving yards, there are just seven players who averaged fewer than one receiving touchdown per 200 receiving yards: Jeff Graham was by far the biggest outlier, with just one touchdown catch per 272 receiving yards. Henry Ellard is the second biggest outlier because (1) he’s Ellard, a borderline Hall of Famer and (2) he had just one touchdown per 212 receiving yards. Next up is Julio Jones at one score for every 211 receiving yards, followed by Eric Moulds, Johnnie Morton, another HOF candidate in Andre Johnson, and finally Terry Glenn.

So why does Jones have so few touchdowns as a percentage of his astronomical receiving yardage? To dig in a little deeper, I wanted to compare the length of his touchdowns to that of the other best receivers in today’s game. Who are the top 10 wide receivers of this current era? That’s subjective, of course, but in addition to Jones, a pretty good list would include Antonio Brown, Odell Beckham, DeAndre Hopkins, Mike Evans, A.J. Green, Larry Fitzgerald, Demaryius Thomas, Dez Bryant, and T.Y. Hilton. [continue reading…]

A random thought experiment for you guys today. Suppose you are the owner of an expansion team, with a below-average offense line, a below-average set of skill position players, and a below-average defense. Your only goal is to get to at least .500 in your first season.

By luck, you do have one wish. You get to have as your quarterback either Cam Newton or Joe Montana; in either case, you would get your quarterback in his prime, so you’re getting the best Newton or Montana possible.

Which would you choose? The obvious answer, of course, is Montana: a man always in discussion for the title of best quarterback of all time, and an AP All-Pro three times in four years during (one stretch of) his prime.  But in this example, there’s no Jerry Rice, Dwight Clark, Freddie Solomon, Roger Craig, John Taylor.  And, perhaps most importantly, no Bill Walsh.

With Newton, you get arguably the best running quarterback in football history, but a player whose passing efficiency stats have been average at best in four of the last five seasons.  But Newton has a lot more experience carrying below-average teams to success than Montana.

So who would you pick, and why?

Last week, I looked at how many players were active for at least one game by class year, both before and after the current CBA was adopted in 2011. I also analyzed the production of players (by class year) from 2005 to 2010 and from 2012 to 2017 using Approximate Value and looking at just defensive players.

I’m short on time today, but wanted to do a quick search. I looked at all players who were in years 5-9 of their career and played in a given season. Then, I looked to see how many of those played in a single game the next year. The results are consistent with the other studies I’ve done: players in years 5-9 are losing jobs the following year. About 25% of players who were in their 5th, 6th, 7th, 8th, or 9th years in 2016 were not on an active roster in 2017. That rate used to be about 20%.

More time to come, but let me know if you have other thoughts on how to measure this.

Since Bill Belichick began his reign of terror on the AFC East in 2001, it’s been a rough time to be a Jets, Dolphins, or Bills head coach.  It’s been a rough two decades for Buffalo — Marv Levy was the last Bills coach to win the division back in 1995 — but it hasn’t been much better for New York or Miami. Belichick has won 15 of the 17 division titles since 2001, with Herm Edwards leading the Jets to the division title way back in 2002.

The other man to do it was Tony Sparano, who passed away unexpectedly yesterday. In one of the great turnarounds in NFL history, taking the 1-15 Dolphins from the worst record in football to AFC East champions in one season.

But even Sparano left Miami with a losing record. In fact, since 2001, Dave Wannstedt (31-26) and Todd Bowles (2-1 as interim head coach) are the only Dolphins coaches with winning records. Adam Gase (16-16) is the next most successful Miami head coach since 2001, followed by Nick Saban (15-17), Tony Sparano (29-32), Joe Philbin (24-28), and Cam Cameron (1-15). Along with Bowles, Dan Campbell (5-7) and Jim Bates (3-4) spent time as interim coaches in Miami.

The Bills have had 9 head coaches since 2001: Sean McDermott (9-7) has the only winning record, followed by Rex Ryan (15-16), Doug Marrone (15-17), Mike Mularkey (14-18), Dick Jauron (24-33), Gregg Williams (17-31), and Chan Gailey (16-32); Perry Fewell (3-4) and Anthony Lynn (0-1) also spent time as interim head coaches. [continue reading…]

On Tuesday, I looked at the best passing seasons in NFL history. On Thursday, I looked at the worst passing seasons in NFL history, but by measuring quarterbacks against average.  There are “problems” with that to the extent there are ever problems with anything presented as trivia, and Adam suggested a fix in the comments: use 75% of league average as replacement level, and compare the worst passers to replacement level rather than average.

Let’s look at Bobby Hoying in 1998. He was horrible that year: he averaged a league-worst 1.43 ANY/A, even worse than the dread 1.93 ANY/A by a rookie Ryan Leaf. In fact, since 1979, Hoying/Leaf ’98 (not to be confused with Sosa/McGwire ’98) are the only two players to average less than 2.0 ANY/A on 200+ pass attempts in a season. That is not likely to be seriously challenged anytime soon: JaMarcus Russell, who averaged 2.31 ANY/A in 2009, is the closest since.

How bad was Hoying that year? He threw a record 224 pass attempts without a touchdown, also unlikely to be seriously challenged ever again.  The league average ANY/A that year was 5.31, and Hoying had 259 dropbacks.  As a result of being  3.88 ANY/A below average, he produced -1,004 yards of value over average, as noted last week.  That was the 10th-worst season of all time.

If we use replacement level, then Hoying gets compared to 75% of 5.31 ANY/A, or 3.98 ANY/A, which gives him -660 yards below replacement.  That’s actually the lowest of any season in NFL history, although if you pro-rated older seasons, it drops down to 15th. The worst season by this method comes from Cardinals quarterback Ronnie Cahill in 1943. Cahill was a star running back at Holy Cross, and he played exactly how you would expect a running back at Holy Cross would play quarterback in the NFL. He threw 3 TDs against 21 INTs, had a passer rating of 33.5, and averaged -2.5 AY/A. He had the second-worst season against league average, and jumps to the lowest season once we pro-rated for the fact that 1943 was a 10-game season. This means the war-ravaged ’43 season produced the best and worst passing seasons in NFL history. [continue reading…]

Yesterday, I looked at how many players were active for at least one game by class year, both before and after the current CBA was adopted in 2011. Today, I want to do the same thing — analyze the production of players (by class year) from 2005 to 2010 and from 2012 to 2017. But instead of using games, I’ll use Approximate Value, and instead of using all players, I will use just defensive players.

On the X-Axis is all defensive players by class year, with the 2012-2017 players in blue and the 2005-2010 players in blue.  On the Y-Axis is percentage of AV by all players.  In general, second, third, and fourth year players produce the most AV, and that was true prior to the current CBA.  But the new CBA imposes an artificially low cap on the amount that can be paid to players in their first four seasons (with few exceptions).  As a result, teams seem to be giving more playing time and roster spots to players in their first four years, with 7th-8th-9th year players losing playing time: [continue reading…]

From 2005 to 2010, 42% of all players (minimum one game) in the NFL were in their 5th year or later. Meanwhile, playing in their first three years made up just 47% of all players in the NFL during that time. In other words, there were only a few more of the youngest players than the oldest players (5th plus years) in the NFL.

In 2011, there was a lockout and then the new CBA was instituted. The collective bargaining agreement instituted a very low wage scale for rookies and a higher floor for veterans; as a result, all other things equal, players on rookie contract were much more valuable than veterans due to significantly lower salaries. The 2011 year is a complicated year to analyze because teams scrambled to put together rosters and they didn’t have a chance to fully comprehend the new CBA. But let’s look at 2012 to 2017: during the last six years, less than 36% of all players to play one game in a season was in their 5th year or later, and over 53% of all players were in their first three years.

The graph below shows what percentage of players were in their 1st, 2nd, 3rd, 4th, etc., years of playing status over two sets of eras: 2005 to 2010, and 2012 to 2017.

[continue reading…]

On Tuesday, I looked at the best passing seasons in NFL history. What about the reverse? Blake Bortles had one of the worst seasons in 2014 in NFL history, in terms of quality combined with quantity.  There are a lot of bad seasons, but what makes that Bortles year stand out was that he played nearly the full season.  This was his rookie year, and Bortles started 13 games; he averaged 3.81 ANY/A when the league average was 6.14 ANY/A.  Bortles therefore produced -1,234 Adjusted Net Yards of Value above average, since he had 530 dropbacks.  That’s actually the worst in NFL history, which isn’t too surprising; most of the bad years with a large number of dropbacks came in recent history.

The table below shows the 500 “worst” seasons in passing history in terms of Adjusted Net Yards of Value below average. Note that the 500th-worst passing season (pro-rated, as on Tuesday) is -365. This means that a season that is 0.70 ANY/A below average on 550 attempts will make this list. That’s not a really bad season, of course. So just a reminder when you view these lists that quantity is a key component here: [continue reading…]

This is the final article in a seven-part series. Below is an update to my 2015 ranking of the greatest quarterbacks of all time. I won’t be offering detailed player stories or explanations of the rankings, because [1] not much has changed in the last three years, and [2] I’ve spent the last month and a half writing about the top 100 QBs of the Modern Era, as ranked by by QB-TSP. If you haven’t read that series already, I’d really encourage you to do so before continuing here. At least read the posts on the quarterbacks ranked 1-40.

Best Statistical QBs: 81-100
Best Statistical QBs: 41-60
Best Statistical QBs: 21-40
Best Statistical QBs: 1-20
Best Statistical QBs: HOF Data [continue reading…]

In 1984, Dan Marino averaged 8.94 Adjusted Net Yards per Attempt (ANY/A, defined as passing yards + 20*Pass TDs – 45*INTs – sack yards, all divided by pass attempts plus sacks) while the league average was 5.00 ANY/A.   Marino had 577 dropbacks (pass attempts plus sacks), so he had 3.94 ANY/A over average over 577 dropbacks; that means he provided 2,271 Adjusted Net Yards of Value over average.

In 2004, Peyton Manning averaged 9.78 ANY/A while the league average was 5.63 ANY/A. Manning had 510 dropbacks, so he is credited with 2,113 yards of Value that season.  This was Manning’s best year: even better than what he did in 2013, when averaged 8.87 ANY/A, the league average was 5.87, and Manning had a whopping 677 dropbacks. So he had 2,031 yards of Value that season.

The table below shows the top 500 passing seasons by this metric. For non-16 game seasons, the final column (ProRt value) prorates the value as if it was a 16-game season.For AFL seasons 1960 to 1964, in the pro-rated column, I assigned only 50% credit in 1960, 60% in ’61, 70% in ’62, 80% in ’63, 90% in ’64, and then full credit in the remaining seasons. I also assigned full credit for all AAFC seasons. [continue reading…]

On Saturday, I wrote about Gray Ink for rushing yards, and showed the career leaders using the methodology described in that post. Today, we do the same but for Yards from Scrimmage.

One interesting player is LeSean McCoy, who continues to look like a Hall of Famer. He ranked 15th in Rushing Yardage Gray Ink, which puts him comfortably in the group of Hall of Fame rushers. This is due to a number of strong seasons: he led the NFL in rushing in 2013, ranked 3rd in 2014, 4th in 2011 and 2017, and 6th in 2016. He also has a good chance (he needs 1700 more rushing yards with the Bills) of becoming just the third player with 5,000 rushing yards with two different teams.

Well, McCoy also ranks 15th in Yards from Scrimmage Gray Ink. He led the NFL in YFS in 2013, and ranked 4th in 2010 and 2017 and 5th in 2011 and 2016. He leads the NFL in yards from scrimmage since entering the league, too.

The table below shows the top 100 players in Gray Ink in terms of Yards From Scrimmage: [continue reading…]

Yesterday, I looked at Gray Ink for running backs. You can read the details there, but today I wanted to delve into the specifics behind each player’s grade.

The table below shows the results for each running back in each season where he finished in the top 10 in rushing. It’s fully sortable and searchable. Let’s use Edgerrin James as an example.  If you type his name into the search box, you’ll see the following: In 2000 and 1999, he led the league in rushing, and received 9.8 points for each year. In 2004, he ranked 4th in rushing, and gets 7 points for that finish. In ’05, he ranked 5th and in ’07 he ranked 7th; he gets 6 and 4 points for those performances.  In total, James receives 36.7 points of Gray Ink, the most of any non-HOFer among eligible running backs. [continue reading…]

On Thursday, I looked at a measure of passing efficiency and gray ink: where a first place finish rewards a player with 10 points, a second-place finish 9 points, a third-place finish 8 points, and so on. Today, I’m going to do the same thing but with rushing yards, so a rushing crown is worth 10 points, a runner-up title is worth 9 points, and so on. You can read a few twists in this footnote. ^[1]I want to give more credit to seasons where there were more teams. So when Kareem Hunt led the 32-team NFL last season, that was worth 10 points. What about when Pug Manders led the 9-team NFL in … Continue reading

Let’s use Barry Sanders as an example. In four seasons, he led the NFL in rushing (+40 points), and in three other seasons, he was the runner up (+27 points). He also had two seasons where he ranked 4th in rushing yards (+14) and one season where he ranked 5th (+6), for a total of 87 points. Using the pro-rated method explained in the footnote, since there were fewer than 32 teams during his career, Sanders actually gets credit for 82.7 points. That is still the most of any player in history: [continue reading…]

Yesterday’s post about ANY/A and the HOF Gray Ink Test turned out to be very popular, with over 65 comments in 24 hours. So to facilitate further debate and discussion, I’m going to post the year-by-year ranks for each passer in the table below.

The table is fully sortable and searchable, but here is how to read it, using Manning’s line. He had a Pro-rated score of 110.8, the column the table is sorted by. The next 10 columns show his top 10 seasons. He had four seasons where he finished 1st in ANY/A — 2005, 2004, 2012, and 2006. In the parentheses in each cell I have included the number of pro-rated points. The only reason ’05 was Manning’s best year was because there were more qualified passers that year.

If you type ‘Aikman’ into the search box, you’ll see that his best season was a 2nd-place rank where he received 8.7 points; this was in 1995. His next best year was another 2nd-place finished where he received 8.6 points in 1993. He ranked 3rd in 1992 (7.1 points), 4th in 1994 (6.7 points), 6th in 1998 (4.8 points), and 10th in 1999 (1.0 point). If you type in Manning, you’ll see that both Archie and Eli topped out with a 5th-place ranking, although Eli’s was worth slightly more points because there were more qualifying passers that season. Archie had a 7th and 9th place finish, while Eli only has three other 10th-place finishes. [continue reading…]

A few years ago, I looked at Eli Manning and the HOF in the context of an ANY/A Gray Ink test. What do I mean by that? Here’s what I did:

• Step 1) Calculate each quarterback’s ANY/A for each season of his career where he had enough pass attempts to qualify for the passing title (14 attempts per team game). ANY/A, of course, is calculated as follows: (Passing Yards + PassTDs * 20 – INTs * 45 – Sack Yards Lost) / (Pass Attempts + Sacks).

• Step 2) For each quarterback, award him 10 points if he led the league in ANY/A, 9 points if he finished 2nd, 8 points if he finished 3rd, … and 1 point if he finished 10th. A quarterback receives 0 points if he does not finish in the top 10 in ANY/A or does not have enough pass attempts to qualify.

• Step 3) For each quarterback, add his “points” from each season to produce a career grade.

I decided to update that post (written in 2014) and make a few changes.

1) I have included the results from the last four seasons.

2) I included sack data from 1960 to 1968, using estimated individual sack data based on team sack data.

3) I have lumped together the AAFC/AFL with the NFL in each season as if it was all one big league.

4) I have pro-rated the values based on the number of qualifying passers in each season. So let’s say a player ranks 4th in ANY/A in 2017, where there were 32 qualifying passers. He gets the average of 7 points and 7 points * 32 divided by 32, which is of course just 7 points. Now, let’s say a player ranks 4th in ANY/A in a 10-team league. He gets the average of 7 points and 7 points * 10/32 (2.18), which is 4.6 points. This is not a special formula, but a “good enough” metric that helps discredit players in smaller leagues, but not overly so (hence the average idea).

Okay, enough words. Here are the results. I have also included the raw totals, so you can see what happens if I didn’t pro-rate the values. Finally, I subjectively included some HOF labels at the end: actually in the HOF (not very subjective), Future HOFer, Borderline, and then either Not Eligible or a No for the HOF.

The future HOFers rank 1-4-5-13-18 by this litmus test: there’s not much to debate there.

Among the actual HOFers, only George Blanda — who is probably the least qualified quarterback of the “modern” era to make the HOF — ranks outside of the top 50. Bob Waterfield and Ken Stabler are the next lowest quarterbacks, and that’s consistent with how I’d view them. As a litmus test, this does a decent job for being a (somewhat) quick and dirty way to measure HOF play.

Among the Not Eligible guys, only Carson Palmer is in the top 60. He ranks 37th, on the back of a #1 season in 2015 with the Cardinals (+10.3 points, since there were 34 qualifying passers that year), a #3 season with the Bengals in 2005 (+8.25, also 34), and a 6th and 7th rankings in ’06 and ’14 (+5, +4.06). That’s not really a HOF career by any stretch, but it’s a memorable career.

Among the pure no guys — those who have been passed over and didn’t receive my subjective borderline label — you have Charlie Conerly at 26, Trent Green at 27, Boomer Esiason at 29, John Hadl at 31, Tommy Thompson at 33, and Milt Plum at 35. All good quarterbacks, occasionally great ones, who are HOVG (at worst) type players.

And then we get to the borderline guys. I gave 10 players that label, including three guys who entered the league in the last ten years and who are probably too young to really evaluate. Among the other 7…

Philip Rivers, Ken Anderson, and Tony Romo all are in the top 25, and make sense to be discussed together. None of the three won a Super Bowl, all three had fantastic efficiency numbers, and all three are more favored by the analytics crowd than the non-analytics crowd. Statistically, based on regular season efficiency, all three are clear HOF players. But, of course, that’s not the HOF test.

Steve McNair, Donovan McNabb, and Randall Cunningham are all borderline guys, too, and are underrated by an analysis like this that ignores rushing. They rank 68th, 72nd, and 104th. McNair (2003) and Cunningham (1998) each have a first place finish in ANY/A, while McNabb has a 2nd (2006) and 3rd (2004) place spot. McNair also has a 5th (2001) and a 9th (1999), while Cunningham has a 10th (1990), but that’s it, and that’s the problem. McNabb and McNair each have three finishes at 11 or 12 (worth zero) and McNabb has three more at 13/14; this analysis ignores solid seasons and rushing, which is going to hurt these guys a lot.

And then, of course, we get to Eli Manning. He was the inspiration for this post three years ago, and not much has changed since.  Manning has a a 5th-place finish from 2011 (+6.1), and three 10th place finishes (2009, 2012, and 2015, each worth 1 point).  He also has a 12th (2014) and a 13th (2010) place finish, but those are his only other top-15 seasons.  Manning performs horribly in the ANY/A Gray Ink test for HOFers.  He’s far behind Blanda, let alone the Stabler/Waterfield floor of quarterbacks.

The graph below shows the data data but with color-coded labels: black for HOF or future HOFer,red for not in, orange for not eligible, and large green dots for the borderline guys.  The running quarterbacks (McNair, McNabb, Cunningham, and Newton) are at the back of the pack with Manning (the second farthest dot to the right); the three “stats stars” are to the left, and then you have Ryan who currently ranks just outside of the top 40 (the X-Axis is rank; the Y-Axis is prorated value).

What do you guys think?