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Manning didn’t have much help during his career

Yesterday, I looked at quarterbacks from 2016 who started at least 8 games and threw at least 150 passes. For those passers, I calculated how many standard deviations above average they were in Relative ANY/A (i.e., how much better they were, statistically, than average) and in winning percentage. I sorted the list by the difference between the two, to find the quarterbacks whose stats and winning percentages diverged by the largest amounts.

What about historically? I performed the same study going back to 1970. And the season that stands out the most is Archie Manning’s 1980 season. That year the Saints were the worst team in the league: New Orleans went 1-15, and every other team won at least 4 games.1 Manning started every game for the team because he actually had a strong season, at least statistically: he ranked 9th out of 30 qualifying passers in ANY/A, and had a Relative ANY/A of +0.53. That, of course, is pretty unusual given his team’s 1-15 record.

That stands out as the biggest example of a divergence of stats being more impressive than team record. The best 100 seasons (although by default, the table only lists the top 20) are below: [click to continue…]

  1. The Saints’ troubles continued into the draft; New Orleans selected George Rogers first overall, when two of the top four, and three of the top eight players went on to be Hall of Famers. []
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Kessler in a losing effort.

In 2016, Browns rookie quarterback Cody Kessler had an uneven year. He went 0-8, but he ranked 24th in ANY/A out of the 31 quarterbacks who started at least 8 games and threw at least 150 passes. His stats weren’t great, but they weren’t 0-8 bad, either. In PFR’s Adjusted Net Yards per Attempt Index, which attempts to adjust for era, Kessler ranked 15th out of the 43 rookie passers to meet the 8 start/150 attempt threshold. It was a pretty good rookie season that came with an 0-8 record.

And then there was Brock Osweiler.  The Texans quarterback — now on the Browns — was dead last with a pitiful 4.34 ANY/A average last season.  But for the second year in a row, Osweiler produced a winning record despite poor play; Houston went 8-6 with Osweiler under center.

I calculated the winning percentage and Relative ANY/A (i.e., ANY/A adjusted for era) for each passer since 1970 to meet the 8 start/150 attempt threshold.  I then calculated the standard deviations above/below average each passer was in each category.  Here are the results for 2016, and here’s how to read the Kessler line: he started 8 games for the Browns and had a 0.000 winning percentage.  His Relative ANY/A was -0.34, so just a hair below league average.  He was 2.53 standard deviations below average in winning percentage, but only 0.28 standard deviations below average in RANY/A.  As a result, he was 2.24 standard deviations better in RANY/A than he was in winning percentage; that was the highest number on the list.  Passers at the top had much better stats than wins; passers at the bottom (highlighted by Osweiler) had better wins than stats. [click to continue…]

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NFL And Regression To the Mean, 1970-2016

Since 1970, you would do a pretty good job estimating any team’s record by regressing that team’s record back to the mean by about 60%. More specifically, if you take 40% of the team’s actual winning percentage the prior year, and 60% of the league average winning percentage, you would get a pretty good estimate of their record the next season (though the R^2 is just 0.17).

That’s over a long period, though, and team variability is on the rise. If you look at the last 20 years, it’s more like 70%, with the best fit formula to project winning percentage being something like 35% plus 30% of the team’s winning percentage the prior year.

Last year was a bit of a weird year, with some notable outliers. The 3-13 Browns would have been expected to progress to the mean, but instead went 1-15. The 49ers went from 5-11 to 2-14. The Bears went from 6-10 to 3-13. And on the positive side of .500, the Panthers dropped from 15-1 to 6-10, while the Jets win total dropped in half, from 10 to five.

The Patriots defied regression to the mean for the umpteenth straight year, improving from 12-4 to 14-2. The Raiders zoomed past the mean, going from 7-9 to 12-4. The Giants similarly went from 6-10 to 11-5.

The correlation coefficient between team winning percentage in 2015 and team winning percentage in 2016 was 0.27, which is pretty low, but not abnormally so. Here are the correlation coefficients for each pair of years since 1970: [click to continue…]

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Dillon was a star with the Patriots, too.

It’s been an unusually busy offseason for the Patriots. New England signed Buffalo Pro Bowl cornerback Stephon Gilmore to a big contract, and also added former Bengals running back Rex Burkhead.  The Patriots were also active in the trade market, acquiring WR Brandin Cooks from New Orleans, DE Kony Ealy from Carolina, and TE Dwayne Allen from the Colts.

Departing from New England? TE Martellus Bennett went to Green Bay, Jabaal Sheard went to Indianapolis, CB Logan Ryan went to Tennessee, while CB Malcolm Butler and RB LeGarrette Blount, among others, could still be on the move.

Which made me wonder: do the Patriots, as you might suspect, do better adding players from other teams than other teams do when adding players from the Patriots? The table below shows the most productive players (by AV) in year 1 in New England after playing for a different team the prior season. Note that this excludes Dion Lewis in 2015, who was not on an NFL team in 2014. [click to continue…]

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Adam Steele is back to recap his Wisdom of Crowds work. As always, we thank him for that. Football Perspective wouldn’t be what it is without contributions like this from folks like Adam.


I’d like to thank everyone who voted in this year’s Wisdom of the Crowds, and I also appreciate your patience in waiting for the long overdue recap article. I’m not much for small talk, so let’s get right to it.

Originally, my plan was to simply tally the scores and use the totals for the QB ranking. However, it quickly became evident that this wasn’t going to work, as we had very large discrepancies in how voters allocated their points. Some people awarded 25 points to their pick for best ever, while others didn’t give any QB more than six points. It would be just plain wrong for one voter’s GOAT to be weighted four times more than the next voter. My solution (helmet knock to commenter hscer1, since he came up with it) is to tabulate points in proportion to the highest score on each ballot. Thus, a QB who scores five points on a ballot with a 25 maximum receives 0.2 ranking points, while a five-pointer on a ballot with a maximum of six is awarded 0.83 ranking points. This levels the playing field for all ballots, and in my opinion yields a far more honest result than the simple tally method. Since the abstract concept of ranking points is tough to put in proper context, I’ve translated them into Share %, which is the percentage of possible points earned. We had 51 legal ballots submitted this year, so Share % = ranking points / 51.

Results

In order to qualify for a WOC ranking, a quarterback had to be listed on a minimum of three ballots, leaving us with 36 qualifying QB’s. The table below lists the quarterbacks’ Share %, ballot appearances, “pantheon” appearances (ballots where he received at least 0.5 ranking points), and ballots where he received the highest score (including ties). I also included the ranking each QB earned in the 2015 edition of this exercise, as well as the number of positions gained or lost from 2015 to 2017. [click to continue…]

  1. I highly encourage you to check out hscer’s collection of Sporcle quizzes. []
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TD/INT Ratio Leaders

Touchdown/Interception ratio isn’t an official statistic, so here’s something you probably didn’t know. Among qualifying passers (minimum 14 pass attempts per team game), either Tom Brady or Aaron Rodgers has led the league in this category in 6 of the last 7 seasons. The one exception? Nick Foles in 2013.

Brady has lead the NFL in TD/INT ratio four times: 2007, 2010, 2015, and 2016. That’s tied for the most in football history, with Roger Staubach and Charlie Conerly. The table below shows the leaders in pro football (combining the AAFC and AFL with the NFL) in each year since 1946:

[click to continue…]

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Running Back Receiving Data Since 1950

Yesterday, I looked at the amount of rushing yards generated by “quarterbacks” in each year since 1950. I put that term in quotes because what I did was calculated the amount of rushing yards by passers, weighted by the number of passing yards by each passer. So the amount of rushing yards by a passer with 5000 passing yards counts for 50 times as much (when determining how much rushing was generated by “quarterbacks” that season) as a passer with 100 passing yards.

What if we do the same thing but with receiving yards generated by “running backs”? The peak year was 2002, when there were a lot of players who both were their team’s workhorse back and were great receivers. Priest Holmes rushed for 1,615 yards and had 672 receiving yards, Charlie Garner was at 962/941, Tiki Barber had 1387/597, and LaDainian Tomlinson was at 1683/489. On average, the league “running backs” produced an average 292 receiving yards. That may not mean much in the abstract, but think of it this way: if each team gave all of its rushing yards to one player, that player would have averaged 292 receiving yards, too. Using the methodology from yesterday, here are the results if we replace passing yards with rushing yards, and rushing yards with receiving yards: [click to continue…]

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Last year, Tyrod Taylor led all quarterbacks with 580 rushing yards. Colin Kaepernick, in 12 games, ranked 2nd with 468 rushing yards, and no other quarterback had even 400 rushing yards. But Aaron Rodgers, Blake Bortles, Cam Newton, Marcus Mariota, and Andrew Luck all had at least 300 rushing yards, so 7 out of 32 teams had a quarterback with at least that many yards.

How does that compare historically? Two years ago, in one of my favorite posts/methodologies, I looked at how to measure quarterback rushing yards. Here’s what I did.

1) Calculate the percentage of league-wide passing yards by each player in each season. For example, Tyrod Taylor was responsible for 2.3% of all passing yards in 2016.

2) Calculate the weighted average league-wide rushing yards for each season. So we take the result in step 1 and multiply that by each player’s number of rushing yards. For Taylor, this means multiplying 2.3% by 580 for a result of 13.4 rushing yards. Perform this calculation for each player in each season and sum the results to obtain a league-wide total. For 2016, this total was 150.9 rushing yards (obviously Taylor was the biggest contributor among quarterbacks).

3) For non-16 game seasons, pro-rate to 16 games.

Perform this calculation for each season since 1950, and you get the following results: [click to continue…]

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2016 Team AV and Draft Value

Last year, I looked at each team’s “average” draft value, with average being defined as the AV-weighted average of the team’s roster. And yesterday, I did the same thing for every Super Bowl champion. Today, we look at the draft value for each team in 2016.

One thing that’s interesting, if not surprising: there’s not a ton of turnover from year to year in this stat. The top five teams in this metric last year were also the top five teams this year, although the order switched around notably. The Falcons were fifth last season, but were first this year, and would have been in the top 10 among all Super Bowl champions had they won. That’s what happens when Matt Ryan (3rd overall), Julio Jones (6th), Alex Mack (21st), Jake Mathews (6th), and Vic Beasley (8th) were the team leaders in AV.

The table below shows all teams in 2016: [click to continue…]

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Super Bowl Champions, by Draft Value

The New England Patriots won Super Bowl LI, but it wasn’t because the team was packed full of high draft picks. Of the five Patriots who had more than 10 points of AV, only one was drafted in the first four rounds. Regular readers know that I created an AV-based draft value chart, which assigns points to each draft pick based on the expected marginal production produced by that pick.

Well, you can calculate a team’s weighted average draft value by doing the following:

  • Calculate the draft value spent on each player on the roster who produced at least 1 point of AV that season.
  • Calculate the percentage of team AV produced by each player. This is key, otherwise Chris Long would skew the results in the wrong direction.
  • Multiply the results in steps 1 and 2, and then sum those values.

Here’s how it would work with the 2016 Patriots, who had an average draft value (as a roster, and weighted by AV) of 6.72.

PlayerPosAVPerc of TmAV%Draft PkDraft ValWt Draft Val
Dont'a HightowerILB145.4%2514.10.76
Malcolm ButlerCB135%udfa00.00
Tom BradyQB135%1990.90.05
Marcus CannonOL135%1383.20.16
Julian EdelmanWR114.2%23200.00
Devin McCourtyDB103.8%2713.60.52
Nate SolderT103.8%1716.60.64
Alan BranchDT93.5%3312.30.43
LeGarrette BlountRB83.1%udfa00.00
David AndrewsC83.1%udfa00.00
Shaq MasonC83.1%1313.60.11
Joe ThuneyOG83.1%786.90.21
Malcom BrownDT83.1%3212.50.38
Chris HoganWR72.7%udfa00.00
James WhiteRB72.7%1303.60.10
Trey FlowersDE72.7%1015.20.14
Martellus BennettTE72.7%618.40.23
Rob NinkovichDE62.3%1353.40.08
Jabaal SheardDL62.3%3711.60.27
Patrick ChungDB62.3%3412.10.28
Chris LongDE62.3%230.20.70
Nate EbnerDB51.9%19710.02
Logan RyanCB51.9%836.50.13
Jamie CollinsOLB51.9%529.40.18
Rob GronkowskiTE51.9%4210.80.21
Elandon RobertsILB41.5%2140.40.01
Cameron FlemingOT41.5%1403.10.05
Malcolm MitchellWR41.5%1124.60.07
Shea McClellinDE41.5%1915.80.24
Dion LewisRB31.2%1492.70.03
Stephen GostkowskiK31.2%1184.20.05
Vincent ValentineDT31.2%965.50.06
Eric RoweCB31.2%4710.10.12
Danny AmendolaWR20.8%udfa00.00
Jonathan FreenyDE20.8%udfa00.00
Ryan AllenP20.8%udfa00.00
Ted KarrasOG20.8%2210.20.00
Duron HarmonFS20.8%915.90.05
Jacoby BrissettQB20.8%915.90.05
Jimmy GaroppoloQB20.8%628.30.06
Kyle Van NoyOLB20.8%4011.10.09
Barkevious MingoOLB20.8%623.20.18
Jonathan JonesDB10.4%udfa00.00
Anthony JohnsonDT10.4%udfa00.00
Justin ColemanCB10.4%udfa00.00
Brandon KingDB10.4%udfa00.00
Woodrow HamiltonDL10.4%udfa00.00
Joe CardonaLS10.4%16620.01
Geneo GrissomOLB10.4%975.50.02
Jordan RichardsSS10.4%648.10.03
Cyrus JonesCB10.4%608.50.03
Total260100%6.72

[click to continue…]

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Ben Roethlisberger takes a lot of sacks. Since entering the league in 2004, he has nearly 100 more sacks than any other quarterback. I thought it would be interesting to look at the leaders in sacks taken over rolling 5-year periods.

Over the last 5 years, the most sacked quarterback is Ryan Tannehill, who may be going down a Roethlisberger-like path. Tannehill has surprisingly only missed 3 games in his 5-year career, but I’d say the odds of him continuing to take sacks at a high rate and playing 16 game seasons is pretty low.

As for Roethlisberger, he had the most sacks taken from ’04 to ’08, ’05 to ’09, ’06 to ’10, ’07 to ’11, and ’09 to ’13; he was 2nd from ’08 to ’12, just four sacks behind Aaron Rodgers. That was the only thing stopping Ben from leading for a whopping 6 straight 5-year periods.

Here’s a list of the leader for every 5-year period beginning in 1969, which is as far back as PFR’s sack data goes: [click to continue…]

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Guest Post: Alternative Super Bowl MVPs

Today’s guest post comes from Damon Gulczynski, a longtime reader, Seattle sports fan, and part-time writer. He also wrote this book on baseball names. As always, we thank our guest posters for contributing.


White runs for a score against the Falcons

James White was fantastic in Super Bowl LI, setting records in receptions (14) and total points (20), but he did not win the MVP Award.  Instead the voters bestowed that honor on a player who reduced his team’s chances of winning by nearly 15% on a single play (Robert Alford’s pick-six).  That, of course, is a misleading statement — Tom Brady went on to finish the game with over 450 passing yards in leading his team to the greatest comeback in Super Bowl history — but it is completely accurate to say James White was fantastic.  It would not have been unreasonable in the least to pick him over Brady for game MVP.  Super Bowl LI was a case where it would have been more representative of the story of the game to give out two MVP awards — or better yet to have a “three stars” of the game system, like hockey, so that Trey Flowers (2.5 sacks) could have been recognized along with Brady and White.

With this in mind, for fun, I decided to go through each of the 51 Super Bowls and retroactively select the three stars of the game.  In making these selections I relied on box scores, play-by-play logs, news articles, and video clips from past Super Bowls.  My full list is given below.  The actual Super Bowl MVPs are denoted with a + sign after their name; players on the losing team are denoted with a ~ after their name.  In 30 of the 51 cases the MVP was my first star of the game, which means I think the voters “got it wrong” 21 times.  And in six cases I think they really got it wrong, as the player they chose for MVP did not even qualify as my third star of the game. [click to continue…]

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Net Interceptions and the Jets

The Jets threw 25 interceptions last year, which was bad because no other team threw more than 21. Also not good: the Jets defense forced just 8 interceptions last year, tied for second-fewest in the league behind the Jaguars (7).

That means New York was at -17 net interceptions last year, which is bad. But the Jets have generally been pretty bad in this category over the last decade, with -59 net interceptions from ’07 to ’16. Take a look at the yearly totals, with interceptions throwing by Jets passers in white, and interceptions forced by the Jets defense in green. [click to continue…]

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Two days ago, I looked at the median age of defensive players based on interceptions. And yesterday, I looked at the median age of players based on sack totals.

Today, we will do the same thing but by position, using AV as our tool of measurement.

First, let’s start with defensive linemen. On average, from 1970 to 2016, 50% of all AV came from players 27.0 years of age or younger as of September 1st of the season in question. That number rose to as high as 27.6 in 1999, dropped to just 26.1 in 2013, and has been around 26.5 over the last three seasons.

[click to continue…]

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Median Age of Sacks (By Defensive Players)

Yesterday, I looked at the median age of interceptions by defensive players. Today, using the same methodology, I will be looking at sacks by defensive players.

Since 1982, the median sack age has been rising, although it did drop from the high levels we saw 5-10 years ago. Take a look:


[click to continue…]

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Median Age of Defensive Interceptions

Good instagram post by Tony Khan yesterday: Since 2001, there have been more interceptions made by defensive players aged 26 than by players of any other age.

That was true for this past season, too, where 70 of the interceptions came from players that were 26 years old in 2016. Perhaps more interesting: the median age for interceptions, by defenders, was 26 years. What do I mean by that?

Well, 2% of all interceptions in 2016 came from players 21 years of younger; 7% came from players 22 or younger, 14% from 23 years or younger, 28% from 24 or younger, 39% from age 25 or younger, and 56% from age 26 or younger. So if you sort all interceptions by the (ascending) age of the defender, you need to go up to age 26 to cross the 50% mark.

I looked at the September 1st age of every player who recorded an interception in each year from 1940 to 2015 (I haven’t updated by database for 2016 just yet). The graph below charts the median 9/1 age in each season (i.e., what is the youngest 9/1 age you need to use to make sure you capture at least 50% of all interceptions from player that age and younger): [click to continue…]

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Brad Oremland is a longtime commenter and a fellow football historian. Brad is also a senior NFL writer at Sports Central. There are few who have given as much thought to the history of quarterbacks and quarterback ranking systems as Brad has over the years. What follows is Brad’s latest work on quarterback statistical production.

Author’s Note: This is a very long post, but I discourage you from skimming it. Wait to read it until you can go over it without feeling distracted.

Two years ago, I wrote an exhaustive series on the greatest quarterbacks of all time. That was a subjective ranking, but I also discussed the formula for Quarterback Total Statistical Production, QB-TSP. This post concerns that stat, QB-TSP, so you may want to read that link if you haven’t already.

I’ve made three minor adjustments to the formula since that writing: [click to continue…]

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Dez Bryant had a “down” year by most standards last season. He ranked 51st with just 796 receiving yards, and only moves up to 33rd in receiving yards per game (the Cowboys star missed three full games). But Dallas ranked 31st in team pass attempts last season, which significantly impacted Bryant’s ability to produce strong receiving numbers.

In terms of pro-rated Adjusted Catch Yards per Team Pass Attempt, Bryant ranked 9th (he ranked 11th in pro-rated receiving yards per Team Pass Attempt). What do I mean by that? Well, Adjusted Catch Yards are simply receiving yards with a 5-yard bonus for receptions and a 20-yard bonus for receiving touchdowns. Team pass attempts are just passes plus sacks for each team (the Cowboys had 511; the Dolphins were last with 507). And pro-rated? That multiplies the number of team pass attempts by a player’s number of games played, divided by 16.

Here were the leaders in this metric last season. You won’t be too surprised to learn that the leaders were Julio Jones, A.J. Green, and Antonio Brown, arguably the three best receivers in the NFL in 2016. [click to continue…]

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On the surface, Kenny Britt didn’t have a remarkable season. He had just 1,002 yards, to go along with 68 receptions and 5 touchdowns. But then again, every receiver is playing in a different environment, and Britt’s environment was very, very bad.

The three teams with the worst passing stats in 2016 — from a cumulative perspective — were Buffalo, San Francisco, and Los Angeles. The Bills had a below-average passing offense but rank at the bottom because the team threw the fewest passes in the league, which makes is tough for a receiver to produce good stats. The 49ers were in the bottom 10 in ANY/A and were one of just four teams that didn’t hit the 500-attempt mark. And the Rams were — by a large margin — the worst passing team in the NFL from an efficiency standpoint, thanks in part to Jared Goff having one of the worst rookie seasons ever.

Green grabbing a bunch of ACY

Britt had 1,422 Adjusted Catch Yards — calculated by giving 5 yards for every reception and 20 yards for every touchdown — which ranked just 26th last season. But the Rams offense as a whole had just 5,153 total ACY, so Britt had 28.0% of all Los Angeles Adjusted Catch Yards. And Britt missed one game: on a pro-rated basis, he had 29.8% of all Rams ACY, calculated as 1422 * (Games Played * 5153/16).

That’s good enough for 5th best in the NFL last year. The leader by this metric was A.J. Green of the Bengals, who took over as options 1, 2, 3, and 4 in the Cincinnati passing attack with Mohamed Sanu gone, and Tyler Eifert limited to just eight games. Green had 1,374 ACY in 10 games, but more impressively, he had 34.4% of the Bengals team ACY on a pro-rated basis. [click to continue…]

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Draft Capital Used By Position: DB

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for defensive backs: [click to continue…]

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Draft Capital Used By Position: LB

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for linebackers: [click to continue…]

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Draft Capital Used By Position: DL

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for defensive linemen: [click to continue…]

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Draft Capital Used By Position: OL

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for offensive linemen: [click to continue…]

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Draft Capital Used By Position: TE

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for tight ends: [click to continue…]

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Draft Capital Used By Position: WR

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for wide receivers: [click to continue…]

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Draft Capital Used By Position: RB

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s look at the graph for running backs: [click to continue…]

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Draft Capital Used By Position: QB

Regular readers know all about the Football Perspective Draft Value Chart, which is derived from the approximate value actually produced by draft picks at each draft slot. Over the next week, I will be showing how much draft capital has been used to select players at certain positions in every draft since 1990. This will allow us to see how much the league’s view on the value of a position has changed, while also giving us a visual insight into the volatility among the talent in draft classes is from year-to-year.

I will be staying out of the commentary for now, so I encourage you to post your thoughts. To make comparison across positions easier, I will be using the same scale for each position. Let’s start with the graph for quarterbacks: [click to continue…]

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Just like yesterday, I’m very short on time, so Bryan Frye agreed to help keep the streak alive here by asking me to reproduce his work on the career rushing touchdown kings. What follows is a reproduction of his work here. As always, thanks so much to Bryan for contributing.

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As a lover of football history, I enjoy writing about the evolution of the game and examining statistical achievements from a historical perspective. I’m also fond of looking at the progression of career records, as doing so can often give us a glimpse into the progression of the game itself. I’ve previously written about the history of the passing touchdowns, passing yards, and receptions records. Today, I’ll focus on the storied history of the carer rushing touchdowns record.

The first ever Football Sunday in league history occurred on September 26, 1920.1 Rock Island Independents back Eddie Novak scored the first touchdown on a ten yard rush, but that was in the day before official stats. We have to move twelve years from that point till we find a full season with officially recorded stats. Note that, while the NFL does not recognize as official most records prior to 1932, it does recognize touchdowns; whether or not the league is correct in doing so is beyond the scope of this article. My aim is to begin in 1932, but to use the numbers the league recognizes for its first rushing touchdown king, Ernie Nevers. Without further ado…

Running Backs to Hold the Career Rushing Touchdowns Record

Ernie Nevers (16 years, 1 month as official record-holder) [click to continue…]

  1. This is in dispute, however, because that game, between the Rock Island Independents and the St. Paul Ideals, only featured one official NFL team – the Independents []
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I’m very short on time, so Bryan Frye agreed to help keep the streak alive here by asking me to reproduce his All-Time 53 Man NFL Roster. What follows is a reproduction of his work here on his all-time 53 man roster. Given that I am short on time, maybe you are long on time (is that how time works?), in which case — get ready for a great read.

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Sometimes when I am bored, I make football lists or rosters in my head (what is the all-time Steelers team, what is the current all-NFC South team, what is the all-time Hispanic team, etc.). Of all the whimsical thought experiments in which I have engaged, the one with the most decisions and revisions has been my all time 53 man NFL roster (with coaching staff).

The purpose of building an all time 53 man NFL roster is not to simply pluck the best 53 players out of history. If I did that, I’d end up with an unbalanced roster, with as many as seven quarterbacks. Having seven Hall of Fame passers would be nice, but it’s completely unnecessary. The important thing to me is depth, which means I value versatility from the players on the roster. Yes, Jan Stenerud was a great kicker, but why put him on the team when I can have Gino Cappelletti kick, return kickoffs and punts, take handoffs, and catch passes? You get the idea. I will make exceptions for most starters, but I want most of my backups to contribute in more than one area.

Having read the comments sections in some popular sports sites, I feel that it is necessary to make the following disclaimer: Players will be picked, in large part, based on how they performed in their respective eras. Danny Fortmann was one of the great interior offensive linemen of his generation, but it would be insane to posit that he could be plucked out of 1941 and be a star guard today at 6’0” and 210 pounds. That’s smaller than RG3. [click to continue…]

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Top Running Backs Rarely Play For Just One Team

With the Vikings and Chiefs moving on from Adrian Peterson and Jamaal Charles, respectively, it’s a reminder that even the best running backs rarely finish their careers with one team.

Currently, there are 31 running backs in NFL history who have at least 7,000 rushing yards and have rushed for at least 70 yards per game.  Among that group, Peterson and Charles are two of just six players to spend their entire careers with one team — and that is likely going to change.  Can you guess the first 4?  Take a second.

Those thresholds aren’t insanely high.  To put some scope on those thresholds, drop them to 6500/65 and you bring in Robert Smith of the Vikings, and a pair of Giants in Tiki Barber and Rodney Hampton.

Who are those four?  They are all Hall of Famers: Barry Sanders, Jim Brown, Walter Payton, and… Terrell Davis.

For comparison’s sake, there are 30 wide receivers who have at least 6,000 receiving yards and a career average of at least 60 receiving yards per game. Even excluding the six receivers who are still active, Don Hutson, Charley Hennigan, Steve Largent, Sterling Sharpe, Michael Irvin, Marvin Harrison, Reggie Wayne, Marques Colston, and Calvin Johnson all retired and played for just one team; they represent nearly half — 9 of 19 — of the retired players in the 6000/60 club.

I don’t have much to add to that, other than I find it kind of interesting.

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