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Positive Air Yards per Attempt: 2017 Update

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


Positive Yards Per Attempt: 2017 Update

If I could only share one thing from my time doing football analytics, it would be the following principle: Positive plays carry more weight than negative plays in determining the winner of a football game. I’ve already written a couple of articles on this subject and hope to further the cause with this update.

Overview

For those of you who don’t feel like reading the previous two posts, I’ll give you the basic gist. Since passing has a far greater impact on winning than running, I’ve focused my research on quarterbacks, but the principle applies to the entire offense (defense, not so sure). Despite everyone constantly harping on turnover avoidance, a potent passing offense is usually able to overcome giveaways. Conversely, avoiding turnovers is normally not enough to overcome a weak passing game. Furthermore, turnovers are highly random and situation dependent, so it follows that turnovers are a very poor method of gauging quarterback performance. Even though sacks are largely the quarterback’s fault, they are also very context dependent and only contribute a small amount in determining game outcomes. More importantly, the majority of signal callers trade sacks for interceptions or vice versa, so it’s no really fair to include one but not the other. [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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Wisdom of Crowds: Quarterback Edition (2017)

Adam Steele is back with some Wisdom of Crowds work. As always, we thank him for that.


 

In 2015 we ran a pair of Wisdom of the Crowd exercises, one for quarterbacks and one for running backs. Participation was high and the ensuing discussions were plentiful, so I decided to bring the idea back this year. First up are quarterbacks, but there will be new rules this time around. The previous edition asked voters to rank their quarterbacks 1-25, with points scored in linear fashion based on the ranking from each ballot. While that method was simple, it left a lot to be desired. Most notably, voters weren’t able to indicate the magnitude of difference between the QB’s on their lists, so the difference between 24th and 25th was worth the same as the difference between 1st and 2nd. That’s just plain wrong.

New Rules

1) Each voter will be allotted 100 Greatness Points to distribute to quarterbacks as he or she wishes, with a few caveats.

2) The maximum points given to a single QB may not exceed 25.

3) Ballots must include a minimum of ten quarterbacks, with a maximum of 40.

4) Points must be assigned as whole numbers.

Just as before, you are free to use whatever definition of Greatness you see fit. If you have trouble getting started, it’s helpful to list every quarterback that you consider Great, then distribute points based on the relative standing among the quarterback you listed. In order for this exercise to work properly, please submit your ballot before reading anyone else’s; we want each opinion to be as independent as possible. Your ballot will not be counted if the points don’t add up to exactly 100, although I will let you know and give you a chance for revision. Here is an example of how I’d like your ballot to look (of course yours may include more quarterbacks): [click to continue…]

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Adam Steele is back for another guest post. You can view all of Adam’s posts here. Adam is now on Twitter, and you follow him @2mileshigh. As always, we thank him for contributing.


In 2014, Football Perspective ran a pair of crowd sourcing exercises to determine the greatest quarterbacks and running backs of all time. These experiments were a lot of fun and generated a great deal of debate amongst the participants, so I thought it would be worthwhile to give crowd sourcing another shot. NFL quarterbacks are the most discussed and analyzed athletes in America, but we can’t properly debate the merits of the league’s famous signal callers without considering the effects of their supporting casts. As of today, there is no mathematically accurate way to measure the strength of a QB’s teammates and coaches, but there are plenty of people around who possess the football knowledge to make educated guesses. Basically, this is the perfect candidate for crowd sourcing. I want to keep things simple to maximize reader participation, so there are just a handful of guidelines I expect participants to follow:

1) Please rate a QB’s supporting cast based on how they affected his statistical performance, not his win/loss record or ring count. The supporting cast umbrella includes the direct effect of skill position teammates, offense lines, coaches, and system, but also the indirect effect of defense, special teams, ownership, and team culture. You’re free to weigh these components however you see fit. The rating for each supporting cast will account for the quarterback’s entire career, using a 0-100 scale. As a rule of thumb, a 100 rating equates to an all star team, 75 is strong but not dominant, 50 is average, 25 is weak but not terrible, and 0 is equivalent to the 1976 Buccaneers.

2) Ratings should be roughly weighted by playing time. The years in which a QB is the full time starter should count more heavily than seasons where he’s a backup or spot starter. And this almost goes without saying, but supporting casts are best evaluated in the context of their respective eras.

3) You may rate as many supporting casts as you wish. Since I will be compiling the results by hand, it doesn’t matter how you order your list, as long as it’s easy to read. I ask that you refrain from rating the supporting casts of quarterbacks you’re not reasonably familiar with; if you don’t know anything about a QB’s career, don’t guess! Any quarterback with at least 1,500 pass attempts is eligible to be rated, and I’ve provided a list of these quarterbacks here. Feel free to break up your ratings into multiple posts on different days, but just be sure to post with the same username each time so I can properly count the results. I plan on keeping the poll open for one week, but reserve the right to extend the duration if interest from new participants remains high enough.

Have fun!

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


Previously, I introduced my new metric — Adjusted Points Per Drive — for measuring team offense. I thought it would be fun to apply the same methodology to quarterbacks, which I what I’m doing today. I highly encourage you to go back and read the previous post if you haven’t already, because I don’t want to clutter today’s post by repeating all of the calculation details.

Unfortunately, I don’t have drive stats for individual games, so there’s going to be some approximation here. To calculate a quarterback’s career Adjusted Points Per Drive (AjPPD), I simply take his team’s AjPPD from each of his playing seasons and weight those seasons by games started. This will give us a measure of a quarterback’s scoring efficiency, but it doesn’t account for volume or longevity. That’s where Adjusted Offensive Points (AjPts) comes in handy.

I assign each QB a portion of his teams’ Adjusted Points, then compare that to league average to calculate Points Over Average (POA). The formula for calculating a given season’s POA = (Tm AjPts – 315) * (GS / 16). The 315 figure is derived from multiplying my normalized baselines of 1.75 AjPPD by 180 drives per year, meaning the average team scores 315 Adjusted Points per season.

I’ll use Ben Roethlisberger’s 2015 season as an example: Pittsburgh scored 400 Adjusted Points and Ben started 11 games, so his 2015 campaign is worth (400 – 315) * (11 / 16) = 30 POA. Do this for every season and we have Career POA, which is the primary metric I’ll be using here. However, some people prefer to rank quarterbacks based on their peak years rather than their entire career, so I added the “Peak” column which is the sum of each quarterback’s three best POA seasons.

This study includes all QB’s who started their first game in 1997 or later, and made at least 40 starts between 1997 and 2015 (partial numbers from 2016 are not included). These criteria leaves us with 56 quarterbacks. Before we dig into the results, it’s worth noting that the correlation between Career POA and ANY/A+ is a healthy 0.92. We all know that the NFL is a passing league, but drive efficiency is even more dominated by the passing game than I thought. According to r2, 85% of the variance in Adjusted Points Per Drive is explained by a basic measure of passing efficiency. That doesn’t leave much room for the running game to have an impact. In fact, I’ll go as far to say that rushing efficiency has no appreciable impact on scoring for the majority of teams. That’s not to say running the ball is useless; offenses must run occasionally to keep the defense honest, and running comes in handy for converting short yardage and bleeding the clock. But, to quote Ron Jaworski, “Points come out of the passing game!”

Time for the rankings… [click to continue…]

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


Adjusted Points Per Drive

I love drive stats. There’s no better method, in my opinion, of measuring the performance of offensive and defensive units. However, raw points per drive has a couple of glaring flaws – it doesn’t account for field position or adjust for league offensive efficiency. In this post, I am going to correct those issues and rank every offense in the drive stat era (1997-2015).1 To accomplish this, I created a simple metric called Adjusted Points Per Drive. Here’s how it’s calculated:

Step 1: Calculate total offensive points for each team. OffPts = PassTD * 7 + RushTD * 7 + FGAtt * (LgFGM / LgFGA). I chose to use the average value of a field goal attempt rather than made field goals, as I want to minimize the effect of special teams. In 2015, for example, the average FGA was worth 2.535 points, so I plug that number into each team’s number of attempts.

Step 2: Calculate points per drive (PPD). All drives ending with a kneel down are discarded. PPD = OffPts / Drives.

Step 3: Adjust for starting field position. The expected points value of each yard line is a bit noisy, so I smoothed it out into a simple linear formula. Every yard is worth 0.05 expected points, and PPD is normalized based on an average starting field position at the 30 yard line. I call this field position adjusted points per drive, or fPPD for short. fPPD = PPD – ((AvgFP – 30) *0.05). With this step, we can accurately compare the scoring production of all teams within a given season.

Step 4: Adjust for league scoring efficiency. I normalize each season’s fPPD to a baseline of 1.75 to calculate adjusted points per drive. At the team level, AjPPD = fPPD / LgfPPD * 1.75. Now, at last, we can compare the scoring production of every team since 1997. To make AjPPD more intuitive, I also translate it into adjusted offensive points (AjPts) using a baseline of 180 drives per team season. AjPts = AjPPD * 180. [click to continue…]

  1. Drive Stats provided by Jim Armstrong of Football Outsiders, and expected points data courtesy of Tom McDermott. []
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Resting Starters Database

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


In the same vein as Bryan Frye’s kneel, spike, and first down data and Tom McDermott’s adjusted SRS ratings, I want to contribute some corrections in data distortion. From a stat geek’s perspective, there’s nothing more annoying than strong teams resting their starters in the final week of the season, as it pollutes season long statistics with a game’s worth of junk data. In a 16 game season, even one meaningless outlier can have a big impact on season totals and averages. The most egregious example is the 2004 Eagles, who stormed out to a dominant 13-1 start only to mail in their final two games by a combined score of 58-17. Philly’s season totals look far better (and far more accurate) once those two meaningless games are removed from the sample. I went back to 1993 and noted every game where one team sat their starters and/or played vanilla football with no intention of trying to win. In some instances, a team was clearly going full bore in the first half, then waved the white flag after halftime. In these games, I pulled out the junk data from the second half only.

There are obviously going to be some judgment calls in deciding whether or not a team was really trying to win a given game. For example, this past season’s week 17 matchup between Seattle and Arizona could be viewed two different ways – Arizona was trying to win (at least in the first half) and Seattle just stomped them, or the Cards weren’t really trying even though their starters played the first half. I chose the latter. The one notable game I purposely left out was the week 17 Packers/Lions shootout from 2011. The game was technically meaningless for both teams, and Green Bay kept Rodgers on the bench, but otherwise all the starters played and were clearly playing to win. If the Packers didn’t care, Matt Flynn would not have thrown six TD passes. If you dispute any of the games I’ve listed, I’m happy to discuss and reconsider!

How to read the table: The first five rows are self-explanatory; “Type” designates whether the whole game should be discarded or just the second half; Points, PaTD, and RuTD indicate the points and offensive touchdowns scored during junk time (the stats I believe should be removed from the season data). Defensive numbers can be found by simply looking at the offensive numbers from the team’s opponent.

Team-OppYearWkTypePtsPaTDRuTD
TEN@IND201517Full2412
INDTEN201517Full3020
SEA@ARI201517Full3631
ARISEA201517Full610
WAS@DAL201517Half1010
DALWAS201517Half910
BUF@NE201417Full1711
NEBUF201417Full900
DEN@OAK201317Half300
OAKDEN201317Half1420
BAL@CIN201217Full1702
CINBAL201217Full2310
TB@ATL201117Full2420
ATLTB201117Full4523
OAK@KC201017Full3112
KCOAK201017Full1001
TB@NO201017Half1310
NOTB201017Half600
CIN@NYJ200917Full000
NYJCIN200917Full3704
GB@ARI200917Full3312
ARIGB200917Full710
IND@BUF200917Full701
BUFIND200917Full3030
NYJ@IND200916Half2601
INDNYJ200916Half601
TB@NO200916Half1701
NOTB200916Half000
NE@HOU200917Half1401
HOUNE200917Half2112
NO@CAR200917Full1001
CARNO200917Full2311
ARI@NE200816Full710
NEARI200816Full4732
TEN@IND200817Full000
INDTEN200817Full2310
TEN@IND200717Full1601
INDTEN200717Full1010
SEA@ATL200717Half2421
ATLSEA200717Half2730
IND@SEA200516Full1310
SEAIND200516Full2822
CIN@KC200517Full300
KCCIN200517Full3713
ARI@IND200517Full1310
INDARI200517Full1720
SEA@GB200517Full1711
GBSEA200517Full2311
MIA@NE200517Half1510
NEMIA200517Half1620
PHI@STL200416Full710
STLPHI200416Full2011
ATL@NO200416Full1301
NOATL200416Full2611
ATL@SEA200417Full2620
SEAATL200417Full2822
IND@DEN200417Full1420
DENIND200417Full3321
PIT@BUF200417Full2910
BUFPIT200417Full2402
NYJ@STL200417Full2910
STLNYJ200417Full3231
CIN@PHI200417Full3813
PHICIN200417Full1010
DEN@GB200317Full300
GBDEN200317Full3112
PHI@TB200117Full1720
TBPHI200117Full1301
TEN@PIT199917Half1610
PITTEN199917Half2921
STL@PHI199917Half1420
PHISTL199917Half2110
SF@SEA199717Full900
SEASF199717Full3841
PIT@TEN199717Full600
TENPIT199717Full1601
DEN@SD199617Full1001
SDDEN199617Full1610
SF@MIN199417Full1420
MINSF199417Full2101
DAL@NYG199417Full1001
NYGDAL199417Full1510
PIT@SD199417Half2121
SDPIT199417Half2002
PHI@SF199318Full3730
SFPHI199318Full3422

My plan is to eventually do this all the way back to 1970, then publish the “real” points scored and allowed for each team by prorating the pristine data to a full season.

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Positive Yards Per Attempt (Updated)

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


Last year, I introduced a simple alternative to ANY/A called Positive Yards Per Attempt. Today I’m going to update the formula with a few tweaks and more years of data. For those who don’t feel like reading the rationale behind PY/A provided in the link, it basically boils down to this: The magnitude of a QB’s positive plays are a better indicator of skill than the frequency of his negative plays, and positive plays contribute to winning more than negative plays contribute to losing. With this in mind, PY/A only counts yards and touchdowns while ignoring sacks, interceptions, and fumbles. In the updated version, I split air yards and YAC in the years where data is available. Here is the formula:

1992 – Present
PY/A = (Air Yards + YAC/2 + TD Pass *20) / Attempts

1950 – 1991
PY/A = (Pass Yards * 0.8 + TD Pass *20) / Attempts

The next step is to measure PY/A in relation to league average, which I call Relative PY/A or RPY/A. This is simply PY/A – LgPY/A. After calculating RPY/A for every season back to 1950, I noticed a pattern of dome-playing passers rating higher than they should, so I built a weather adjustment. Based on the conditions of each quarterback’s home stadium, I assigned him a bonus or penalty applied on a per play basis. The weather adjustment is not split by attempts at each stadium during a season, as that would be way too much work. These adjustments are arbitrary and almost certainly wrong, but still better than no adjustment at all. You can see the weather adjustment for each QB in the “Wthr” column of the tables.

Now comes the issue of balancing volume and efficiency. This is handled by adding 200 attempts of replacement level ball to each QB’s season total, with replacement level being LgPY/A – 0.5. I must give credit to Neil Paine for this idea, as it’s based on his method of adding 11 games of .500 ball to a team’s record to estimate their “true” winning percentage. After applying the 200 attempt regression to every QB season, I stumbled onto another problem – early AFL and older NFL seasons were rated too highly. I decided to use the regression step as a double for a depth of competition adjustment. The AFL from 1960-64 and NFL from 1950-59 are hit with a sharper regression than the -0.5 used for modern seasons, with the most severe being -2 for the 1960 AFL.

With all the adjustments factored in, we arrive at the final product – True Relative PY/A (abbreviated with the alphabet soupy TRPY/A). The table below shows the top 200 seasons since 1950: [click to continue…]

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Guest Post: Adam Steele on True QB Talent

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


Introduction: QB True Talent

One of the mandates for being a football analytics guy is to create a quarterback rating system, so today I’m going to throw my flag onto the field. However, I’m taking a different approach by asking a different question. I’m not trying to measure individual or team accomplishments, nor am I calculating value or attempting to predict the future. My goal is to answer a simple question: At a fundamental level, how good was he? As far as I’m aware, nobody has made a systematic attempt at answering this. Before we go any further, I need to add a vital disclaimer. My formula is statistically derived, and does not account for supporting cast, coaching, or anything else we can’t measure directly. So when I use the label “True Talent”, it really means “A rough estimate of true talent, based solely on statistics.”

I’ll save the gory micro details of True Talent’s calculation for another post, but today I want to outline how it works and ask for feedback on how to improve it. First, I’ve attempted to isolate what I believe are the four pillars of QB play: Passing Dominance, Passing Consistency, Ball Protection, and Rushing Ability. These categories are weighted by a) their importance within the framework of the overall QB skillet, and b) the level of control a QB has in converting the skill into results. The score for each category is era-adjusted, balanced by volume and efficiency, and scaled so zero is equal to replacement level. The overall True Talent score is simply the sum of the four category scores, minus five (the replacement level bar is higher for overall QB play compared to each of the pillars on their own). The overall score is expressed as percentage above or below replacement level. I’ve purposely rounded all figures to whole numbers to remind readers that these numbers are estimates, not precise measurements.

My plan is to eventually apply True Talent back to the 1940s, but for now we’re going to look at the last twenty seasons (1996-2015). I want to nail down the methodology before I go all the way back through history. Normally I wouldn’t subject readers to an arduously long table, but in this case I think it’s warranted. I want you to see how all levels of QB fare in my system, not just the best and worst. This table includes every QB season since 1996 with at least 100 dropbacks. I encourage you to sort by each category, by season, and by player to really get a feel for True Talent. [click to continue…]

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


There have been countless attempts at deducing the clutchiness of NFL quarterbacks, most of which involve tallying playoff wins and Super Bowl rings. Today I’m going to take a stab at the clutch conundrum using a different approach: Pythagorean win projection. If a quarterback’s actual win/loss record diverges significantly from his Pythagorean estimated record, perhaps we can learn something from it. I began this study having no idea how it would turn out, so there were definitely some surprises once I saw the end results. This study evaluates the 219 quarterbacks who started at least 32 games since 1950, including playoffs but excluding the 1960-64 AFL (lack of competitive depth).

Here’s how to read the table, from left to right: points per game scored by the QB’s team in games he started, points per game allowed in his starts, total starts, total wins (counting ties as a half win), Pythagorean projected wins based on the points scored and allowed in his starts (using a 2.37 exponent), and the difference between his actual win total and Pythagorean win projection. [click to continue…]

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Last offseason, Adam Steele helped administer a Wisdom of Crowds experiment on running backs and quarterbacks. Today, an update from Adam, along with some news. Below are Adam’s words:


Thanks to the opportunities Chase has given me at FP to publish my research and writing, I’ve decided to branch off and start my own website, quarterbacks.com. Ultimately, my mission for this site is to build the most complete database of NFL quarterbacks on the internet, a resource for statistics, history, opinion pieces, and FP-esque engagement among the readership. However, I can’t do this alone, so consider this an open invitation to the FP faithful to collaborate with me for this admittedly ambitious project. I welcome all types of submissions, including custom stats you’d like to publish and op-eds about anything related to NFL quarterbacks. If you think a certain QB is overrated or underrated and want to make a case for him, send it to me! At this juncture, the site is still under construction, and it will be a month or two before anything is published, so consider this the foundation building stage. For any aspiring writers out there, I’d like to help give you the same opportunity Chase gave me. Please email all inquiries and submissions to quarterbacks1031@gmail.com.


Wisdom of the Crowds: Ideas

Last offseason, Football Perspective ran crowdsourcing experiments to determine the greatest quarterbacks and running backs of all time. Given the amount of interest the community showed in WotC, I will be running more crowdsourcing projects this offseason! Before any votes are cast, I want your feedback on what you’d like to see in this year’s iterations. I definitely want to run a WotC for wide receivers (didn’t happen last year) and quarterbacks again (draws by far the most interest), but I’m certainly open to doing more if the readership desires. What other positions or units would you like to see crowdsourced?

Last year there were three main problems that I’d like to address and fix before the next go-around:

1) Lack of precision from ordinal rankings. An ordered list may be the simplest method to evaluate players, but it’s not the most accurate. Ordinal rankings don’t allow the voter to show the magnitude of difference between players. For example, if you think two players are head and shoulders ahead of the pack, that won’t be reflected in the linear gap between #2 and #3. My proposed solution is to switch from rankings to ratings, most likely on a 1-10 scale.

2) Difficulty comparing players across eras. It’s hard to compare a modern player with someone from the 50’s, and a number of participants last year voiced their struggle in dealing with this. I think the best solution is to separate players into groups based on their era, then rate all the players from each era together. This would help voters put players in proper context, knowing that we’re evaluating them only in relation to their direct peers. I would then take the winner from each era and put him in a pool for the overall GOAT title, which would involve a re-vote.

3) Voters accidentally leaving players off their ballot. Even for a football historian, it’s a daunting task to pick out X number of players from everyone in history who’s ever played the position. With an open ballot, it’s easy to forget a few players by accident, which several participants lamented in last year’s edition. This year, I’d strongly prefer to use a ballot with a predetermined pool of players for each participant to rate. I’m thinking maybe 15-20 players per era depending on the position. This solves the issue of forgetting players, forces voters to think about players they might not otherwise have, and provides statistical symmetry since every player will receive the same number of ratings.

Now I’ll open the forum to the FP readership. What do you think of my proposed changes? For those of you who participated last year, what did you like and dislike about it? I welcome any suggestions to make Wisdom of the Crowds a better experience for all!

Oh, and one note from Chase: does anyone have any recommendations on how to automate this process? That would obviously save us lots of time on the back end.

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Guest Post: QB Playoff Support: Part II

Adam Steele is back, this time with some playoff support stats for eight more quarterbacks. You can view all of Adam’s posts here.


Two weeks ago, I published a study detailing the playoff supporting casts of Tom Brady and Peyton Manning. Today I’m going to look at eight more notable quarterbacks under the same microscope. Below are the career tables for each QB, in no particular order. [click to continue…]

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Guest Post: Brady vs. Manning and Playoff Support

Adam Steele is back, this time throwing his hat into the never-ending Brady/Manning debate. Fortunately, this isn’t your typical Brady/Manning post, as Adam brings some new stats to the table. You can view all of Adam’s posts here.


By any statistical measure, Tom Brady and Peyton Manning have performed at a nearly identical level in the postseason. Of course, many observers don’t care about passing statistics, and prefer to judge quarterback based on playoff W/L record alone. And as we all know, Brady has a significant edge over Manning in this regard. But if we’re going to judge quarterbacks by the performance of their entire team, it’s only fair to also evaluate the parts of the team the QB has no control over – defense and special teams.

Using PFR’s expected points estimations, I recorded the defensive and special teams EPA for Brady’s and Manning’s teams in each of their playoff games. The “Support” column is the total EPA contributed by defense and special teams. Brady first: [click to continue…]

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Guest Post: Marginal YAC, 2015 in Review

Adam Steele is back to discuss Marginal YAC, this time in the context of the 2015 season. You can view all of Adam’s posts here.


Marginal Air Yards: 2015 Year In Review

Today I will be updating my Marginal Air Yards metric for the now completed 2015 season. New readers who aren’t familiar with Marginal Air Yards can get up to speed by reading my three part intro-series and 2014’s year in review.

There were 44 quarterbacks who threw at least 100 passes in 2015, and they are ranked by mAir below: [click to continue…]

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Guest Post: Questioning ANY/A

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


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

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

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

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

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

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

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

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

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

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

  1. Note that in calculating league average, I excluded the player in question from the league average totals. So each player is compared to a slightly different definition of league average. []
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Guest Post: Touchdown Pass Vultures

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


 

During the 2014 season, Chase noted that the league-wide touchdown pass rate was the highest it had been since the NFL merger. The final few weeks of the season dragged down the average a little bit, but 2014 still checks in as the most touchdown pass friendly year in NFL history. In response, a few commenters cited the possibility that teams were tallying more TD passes by sacrificing TD runs, which is a logical conclusion considering the very low rate of rushing touchdowns in 2014 (teams averaged 0.74 per game, the lowest since 1999). Today, I’m going to look into this further and see if teams really are inflating their passing TD numbers at the expense of the run.

First, we have to establish a historical baseline, and I did this by looking at every NFL season since 1950.1 In that time frame, teams averaged 2.26 offensive touchdowns per game, with 1.35 of those coming via the pass and 0.91 via the run. Translated into a ratio, offensive touchdowns have historically been 59.6% passing and 40.4% rushing. That 59.6% is the key number here, as it will be the baseline ratio for expected passing touchdowns. Below is a chart containing relevant information for each year since 1950. The “PaTD %” column represents the percentage of offensive touchdowns in a given year that were scored via the pass, and the “Inflation” column compares that year’s passing TD ratio with the historical average of 59.6%.

YearPassTD/GRushTD/GOffTD/GPaTD %Inflation
20141.580.742.3268%14.1%
20131.570.82.3766.2%11.1%
19651.571.012.5760.9%2.2%
19631.540.972.5161.4%3%
19621.531.092.6258.5%-1.9%
19521.511.022.5359.7%0.2%
20121.480.782.2665.4%9.7%
20101.470.782.2565.3%9.6%
19581.471.222.6954.5%-8.5%
19541.471.062.5258.1%-2.5%
20111.460.782.2465.1%9.2%
19871.450.862.3162.9%5.5%
19611.451.052.558.2%-2.4%
19671.450.982.4259.7%0.1%
19691.440.882.3262.1%4.2%
20041.430.812.2463.8%7%
19641.420.992.4158.9%-1.2%
19601.421.032.4557.9%-2.9%
19501.411.332.7451.4%-13.7%
20071.410.752.1665.1%9.2%
19831.40.982.3758.9%-1.2%
19511.391.222.653.3%-10.5%
20091.390.842.2262.3%4.6%
19951.380.82.1863.3%6.2%
19841.370.922.2960%0.7%
19981.370.792.1663.5%6.5%
19681.370.92.2760.4%1.4%
19591.371.12.4755.3%-7.1%
20021.360.92.2560.1%0.9%
19801.350.962.3158.4%-1.9%
19991.340.732.0764.7%8.6%
19851.330.992.3257.4%-3.6%
19661.330.952.2958.3%-2.1%
19811.320.982.357.3%-3.9%
19531.311.172.4952.8%-11.4%
19861.310.92.259.4%-0.4%
19961.30.762.0663.2%6.1%
19941.30.762.0663.2%6%
19891.30.872.1760%0.7%
19971.290.82.0961.6%3.4%
19901.280.842.1360.4%1.4%
20011.280.742.0263.5%6.6%
20001.280.832.1160.6%1.7%
20031.280.832.1160.5%1.5%
19821.270.922.1958.1%-2.5%
20061.270.832.0960.4%1.4%
20081.260.932.1957.6%-3.4%
20051.260.842.159.9%0.5%
19551.251.152.452%-12.7%
19881.240.952.1956.7%-4.8%
19791.21.092.2952.5%-11.9%
19751.191.132.3251.2%-14.1%
19571.181.12.2851.7%-13.3%
19701.170.81.9859.3%-0.5%
19931.150.681.8363%5.7%
19921.150.741.960.8%2%
19911.140.81.9458.8%-1.3%
19561.131.242.3747.5%-20.3%
19721.1112.1152.6%-11.7%
19761.11.062.1651.1%-14.3%
19711.070.911.9853.9%-9.6%
19781.041.012.0650.8%-14.8%
19731.040.911.9553.4%-10.4%
19741.0312.0450.7%-14.8%
19770.990.91.8952.4%-12%

As you can see, 2014 really did feature highly inflated passing TD totals, with 68.0% of offensive touchdowns coming through the air. This trend began in 2010, stabilized for four years, then jumped again significantly last season. The most obvious explanation is that teams are now passing more in general, so it would follow that they would also pass more to score touchdowns. But that’s only part of the story, as the rate of passing touchdowns has far outstripped the rate of overall called passes.

The main culprit appears to be goal line play selection, which has heavily favored the pass in recent seasons. Interestingly, from 1997-2009, there was no trend whatsoever, with passing TD ratios jumping around randomly from season to season. From 1980-1994, passing TD ratios were slightly lower, yet still very random. Even during the dead ball era of the 1970s, when the rules made passing far more difficult than it is today, teams still scored more often with passes than they did with runs. In fact, the famous 1956 season was the only time in the last 65 years where teams scored more rushing touchdowns than passing touchdowns.

But here’s what fascinates me the most: Despite the huge increases in total yardage and passing efficiency in recent years, offensive touchdowns have increased very little. In 2014, teams scored only 0.06 more offensive touchdowns than the historical average. In fact, the top 15 seasons for offensive TD production all came before the merger! If the NFL had been playing a 16 game schedule in the ’50s and ’60s, TD pass totals would be very similar to what we see today, and rushing TD totals would be higher.

So how does all this affect touchdown records for various quarterbacks? Since the 16 game schedule began in 1978, there have been 51 teams who scored at least 50 offensive touchdowns in a given season. Of those 51 teams, 33 of them had passing TD ratios above the historical average of 59.6%. In this chart, I list the primary QB, although the numbers represent team totals. The “Adjusted Pass TD” column is calculated by multiplying offensive touchdowns by .596, calculating how many TD passes would have been thrown by sticking with the historical average ratio. The “Change” column represents the difference in adjusted TD passes compared to actual TD passes, basically measuring how many TD pass were vultured from the run game.

YearTeamQBPassTDRushTDOffTDPaTD%Adj PaTDChangeInflation
2013BroncosManning55167177%42-1330%
2007PatriotsBrady50176775%40-1025.2%
1984DolphinsMarino49186773%40-922.7%
2011PackersRodgers51126381%38-1335.8%
2000RamsWarner37266359%381-1.4%
2011SaintsBrees46166274%37-924.5%
2004ColtsManning51106184%36-1540.3%
199849ersYoung41196068%36-514.7%
199449ersYoung37236062%36-13.5%
1981ChargersFouts34266057%362-4.9%
2012PatriotsBrady34255958%351-3.3%
1983RedskinsTheismann29305949%356-17.5%
1998VikingsCunningham41175871%35-618.6%
1998BroncosElway32265855%353-7.4%
2004ChiefsGreen27315847%358-21.9%
2011PatriotsBrady39185768%34-514.8%
2001RamsWarner37205765%34-38.9%
1985ChargersFouts37205765%34-38.9%
2010PatriotsBrady37195666%33-410.9%
1980CowboysWhite30265654%333-10.1%
2006ChargersRivers24325643%339-28.1%
2003ChiefsGreen24325643%339-28.1%
1986DolphinsMarino4695584%33-1340.4%
1999RamsWarner42135576%33-928.1%
2014BroncosManning40155573%33-722%
1991BillsKelly39165571%33-619%
2009SaintsBrees34215562%33-13.7%
199349ersYoung29265553%334-11.5%
1988BengalsEsiason28275551%335-14.6%
2008SaintsBrees34205463%32-25.7%
2005SeahawksHasselbeck25295446%327-22.3%
2012SaintsBrees43105381%32-1136.2%
2014CowboysRomo37165370%32-517.2%
2009VikingsFavre34195364%32-27.7%
198449ersMontana32215360%3201.3%
2004ChargersBrees29245355%323-8.2%
2002ChiefsGreen27265351%325-14.5%
2014PackersRodgers38145273%31-722.6%
1983CowboysWhite31215260%3100%
2014ColtsLuck4295182%30-1238.2%
2013EaglesFoles32195163%30-25.3%
2007ColtsManning32195163%30-25.3%
1985BengalsEsiason31205161%30-12%
1991RedskinsRypien30215159%300-1.3%
199249ersYoung29225157%301-4.6%
2000RaidersGannon28235155%302-7.9%
2011LionsStafford4195082%30-1137.6%
2007CowboysRomo36145072%30-620.8%
2003PackersFavre32185064%30-27.4%
1985DolphinsMarino31195062%30-14%
2009PackersRodgers30205060%3000.7%

I have plenty of thoughts about this chart, but I’m more interested to see what the readers think. Does this analysis change your opinion of any of these great QB seasons?

  1. AFL numbers were not included. []
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The GOAT.

The GOAT.

On February 20th, Football Perspective hosted a “Wisdom of Crowds” election with respect to the question: Who is the Greatest Running Back of All Time?™ Well, Football Perspective guest commenter Adam Steele offered to count the ballots, and I’ll chime in with some commentary.

There were 41 ballots entered, with each person ranking his or her top 20 running backs. The scoring system was simple: 20 points for a 1st place vote, 19 for a 2nd place vote, and so on. As it turns out, the race for the top spot was heated, with three players running away from the pack.

This chart is sortable by total points, points per ballot (using 41 as the denominator), GOAT votes, top 5 votes, and top 10 votes. In the interest of statistical significance, a player needed to appear on at least five ballots in order to be ranked in the table below. [click to continue…]

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Beginning on Friday the 6th, Football Perspective hosted a “Wisdom of Crowds” election with respect to that age old question: Who is the Greatest Quarterback of All Time?™ Well, Football Perspective guest commenter Adam Steele offered to count the ballots and provide a summary. What follows are his words, and the results from the contest.


Two of the greatest  quarterbacks of all time

Two of the greatest quarterbacks of all time

First, I want to offer my sincere appreciation to all the readers who participated in this project, as it wouldn’t have been possible without your contributions. We generated over 300 comments and lots of great discussion. And, as you’re about to see, every vote really did matter.

After tallying 80 ballots, 2,000 votes, and 26,000 ranking points, the difference between first and second place was just eight points. That’s insane. Well, I won’t tease you any longer, so here are the results:

This chart is sortable by total points, points per ballot (using 80 as the denominator), GOAT votes, top 10 votes, and top 25 votes. In the interest of statistical significance, a player needed to appear on at least five ballots in order to be ranked in the table below. [click to continue…]

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[Update: You can view the results from our 80 ballots here.]

Regular guest contributor Adam Steele has offered to administer a Wisdom of Crowds edition of the GQBOAT debate. And we thank him for that.


Who is the Greatest Quarterback of All Time? This is a fun question to debate because there is no absolute right answer. In recent years, the practice of crowdsourcing has gained momentum in the analytics community, in some cases yielding more accurate results than mathematical models or expert opinions. For the uninitiated, here’s the gist: Every human being represents a data point of unique information, as all of us have a different array of knowledge and perspective about the world. Therefore, when you aggregate the observations of a group of people, they will collectively possess a greater and more diverse reservoir of knowledge than any single member of the group.

The readers of Football Perspective are an insightful bunch with areas of expertise spanning the entire football spectrum; we are the perfect group for crowdsourcing an age old football question. If you’d like to participate in this experiment, there are just a few guidelines to follow:

1. Create a list of the top 25 quarterbacks of all time, in order, using any criteria you believe to be important. I encourage readers to be bold in your selections – don’t worry about what others may think.

2. Commentary is not necessary, but most definitely welcome. In particular, I’d enjoy seeing a short blurb explaining the criteria you based your selections on.

3. Please compile your rankings BEFORE reading anyone else’s. Crowdsourcing works best when each source is as independent as possible.

4. Please DO NOT use multiple screen names to vote more than once.

I’ll give readers a week or so to cast their ballots, then analyze the results in a follow-up article. A first place vote is worth 25 points, second place 24 points, and so on. Let the process begin!

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Guest Post: Marginal YAC, 2014 in Review

Adam Steele is back to discuss Marginal YAC, this time in the context of the 2014 season. You can view all of Adam’s posts here.



Manning is more of a downfield thrower than you think

Manning is more of a downfield thrower than you think

Back in September, I posted a three part series introducing Marginal Air Yards and Marginal YAC. Today, I’m going to update the numbers for 2014 and analyze some interesting tidbits from the just completed season.1

League-wide passing efficiency reached an all-time high in 2014 with a collective 6.13 Adjusted Net Yards per Attempt average. However, this past season was also the most conservative passing season in NFL history; 2014 saw the highest completion rate ever (62.6%), the lowest interception rate ever (2.5%), and also the lowest air yards per completion rate ever (5.91 Air/C). Passing yards were comprised of 51.4% yards through the air and 48.6% yards after the catch, the most YAC-oriented season in history.2 This trend shows no sign of reversing itself, so expect more of the same in 2015.

Here are the 2014 Marginal Air Yards (mAir) and Marginal YAC (mYAC) for quarterbacks with at least 100 pass attempts. The 2014 leader in Marginal Air Yards is…Peyton Manning? Yes, the noodle-armed, duck-throwing, over-the-hill Peyton Manning averaged 4.54 Air Yards per pass Attempt; given that the average passer on this list averaged 3.70 Air Yards per pass Attempt, this means Manning averaged 0.84 Air Yards per Attempt over average. Over the course of his 597 attempts, this means Manning gets credited with 500 marginal Air Yards, the most of any quarterback in the NFL. [click to continue…]

  1. A big thanks to Chad Langager at sportingcharts.com for helping me compile this data. []
  2. Even though YAC data only goes back to 1992, I feel safe in using the phrase “all-time” with regard to YAC dependency. The offensive schemes of yesteryear emphasized downfield passing, which generated far less YAC than the short passing games of today. []
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Guest Post: Are Interceptions Overrated?

Guest contributor Adam Steele is back again. You can read all of Adam’s articles here.


Are Interceptions Overrated?

There’s nothing worse than throwing an interception. Everyone seems to agree on this, from fans to media to advanced stats guys. But is it really true? In this quick study, I looked at the tradeoff between interception avoidance and aggressive downfield passing to see which strategy has a larger impact on winning. To measure this, I created two categories of quarterbacks: Game Managers and Gunslingers.

First, the Game Managers, which includes all post-merger quarterback seasons with an INT%+ of at least 1101 and a NY/A+ of 90 or below (min 224 attempts).2 These guys avoided picks but failed to move the ball efficiently, the hallmark of a conservative playing style.

[click to continue…]

  1. Which means the player was at least 0.67 standard deviations better than league average at avoiding interceptions. []
  2. Which means the player was at least 0.67 standard deviations worse than league average in net yards per attempt. []
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Adam Steele is back for his third guest post in his Marginal YAC series.


In my two previous two posts, I introduced Marginal YAC and Marginal Air Yards. Today, I’m posting the career mYAC and mAir for the 96 quarterbacks with at least 1,000 pass attempts from 1992-2013. There’s a lot of data here, so I’ll let the readers do most of the commentary.

Here is a table of career Marginal YAC. The “Per 300” column is the rate of mYAC per 300 completions, or roughly equivalent to one full season. And on a “per season” basis, no quarterback benefited more from YAC than Steve Young, who also had four top-40 seasons. [click to continue…]

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In early September, Adam Steele, a longtime reader and commenter known by the username “Red” introduced us to his concept of Marginal Yards after the Catch. Today is Part II to that post. Adam lives in Superior, Colorado and enjoys digging beneath quarterback narratives to discover the truth; hey, who can blame him?


Introducing Marginal Air Yards

There are three components of Y/A: Completion %, Air Yards/Completion, and YAC/Completion. In my last post I looked at YAC, so today, let’s look at the other two components. By multiplying completion percentage and air yards per completion, we get air yards per attempt, which we can then modify to create Marginal Air Yards (mAir):

mAir = (Air Yards/Attempt – LgAvg Air Yards/Attempt)*Attempts

Here are the yearly Air Yard rates since 1992, with the table sorted by Air Yards per Attempt:: [click to continue…]

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Just above these words, it says “posted by Chase.” And it was literally posted by Chase, but the words below the line belong to Adam Steele, a longtime reader and commenter known by the username “Red”. And I thank him for it. Adam lives in Superior, Colorado and enjoys digging beneath quarterback narratives to discover the truth; hey, who can blame him? One other house-keeping note: I normally provide guest posters with a chance to review my edits prior to posting. But due to time constraints (hey, projecting every quarterback in the NFL wasn’t going to write itself!), I wasn’t able to engage in the usual back and forth discussion with Adam that I’ve done with other guest posters. As a result, I’m apologizing in advance if Adam thinks my edits have changed the intent of his words. But in any event, sit back and get ready to read a very fun post on yards after the catch. When I envisioned guest submissions coming along, stuff like this is exactly what I had in mind.



Introducing Marginal YAC

A quarterback throws a two yard dump off pass to his running back, who proceeds to juke a couple defenders and run 78 yards into the endzone. Naturally, the quarterback deserves credit for an 80 yard pass. Wait, what? Sounds illogical, but that’s the way the NFL has been keeping records since 1932, when it first began recording individual player yardage totals. The inclusion of YAC — yards after the catch — in a quarterback’s passing yards total can really distort efficiency stats, which in turn may distort the way he is perceived.

In response, I created a metric called Marginal YAC (mYAC), which measures how much YAC a quarterback has benefited from compared to an average passer. Its calculation is very straightforward:

mYAC = (YAC/completion – LgAvg YAC/completion) * Completions

I have quarterback YAC data going back to 1992 for every quarterback season with at least 100 pass attempts.1 That gives us a healthy sample of 965 seasons to analyze, and includes the full careers of every contemporary quarterback. But first, let’s get a sense of what’s average here. The table below shows the league-wide YAC rates since 1992: [click to continue…]

  1. This data comes courtesy of sportingcharts.com. It’s obviously unofficial, but there doesn’t seem to be any noticeable biases from one team to another. Some unofficial stats, such as passes defensed or quarterback pressures, can vary wildly depending on the scorekeeper, but Sporting Charts’ YAC stats seem pretty fair, from what I can tell. Here is a link to the 2013 data. Chase note: I have not had the chance to compare these numbers to what is on NFLGSIS, but that’s a good idea. []
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