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.

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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]AFL numbers were not included. 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%.

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.

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?

There’s no more enjoyable community than the Football Perspective community. Football Perspective has always encouraged guest submissions, but it’s worthwhile reminding folks of that every now and again.

If you ever want to submit a guest post, all you need to do is write it and email it to me at chase[at]footballperspective[dotcom]. I don’t need a bio or an explanation for why you should be considered for a guest post: at Football Perspective, content trumps all.

Last year, I wrote a post on the plays that had the biggest impact on the eventual Super Bowl champion. These were the plays that affected the Super Bowl win probability by the biggest amount among teams that did not win the title. At the time, the Buffalo Bills were on the short end of the most influential play in the Super Bowl era. When Frank Reich put the ball down for Scott Norwood, I estimated that the Bills had a 45% chance on winning the Super Bowl. ^[1]Recent research by Chase suggests something similar. After the kick went wide right, the Bills’ win probability fell to zero. The 45 percentage point fall was the biggest change for a non-champion of any play in the Super Bowl era. Over 48 years, a bunch of plays fell in that range, but no team could point to a single play as having lowered its championship chances by so large an amount.

A couple weeks ago, that long-held record got broken kind of like Michael Johnson broke the 200-meter record in the Atlanta Olympics. Malcolm Butler’s pick obliterated the old mark. My estimate has the Butler interception as increasing the Patriots’ chances of winning by 0.87. There is no doubt that what some have called the Immaculate Interception is on an island by itself as the most influential play in NFL history.

To get that change in win probability from Butler’s play, I am going to assume that the Seahawks would have run on third and fourth down. I am going to give a run from the one a 60% chance of working. That might seem high, but the Patriots were the worst team in football in stuffing the run in important short-yardage situations either on third or fourth down, or down by the goal line. And their limited success mostly came against terrible running teams. It is not a huge sample, but against teams outside the worst quarter of rushing teams by DVOA, the Patriots had allowed opponents to convert 16 of 17 times with two yards or less to go for a first down or touchdown. If we add the playoffs, they actually had three more stops against good running teams (Baltimore and Seattle), albeit in games where the opponent had a good amount of success on the ground. ^[2]Note that the stop against Baltimore should not even count. In an otherwise great game for Gary Kubiak, he called for a reverse to Michael Campanaro on third-and-1 in the second quarter. The run was … Continue reading With Seattle being the best rushing team in football by a mile and the Patriots being at best not great in run defense in that situation, it seems hard to think that Seattle had anything less than a 0.60 chance of scoring on a run. [continue reading…]

Bryan Frye is back with another fun guest post. Bryan, as you may recall, owns and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history. You can view all of Bryan’s guest posts at Football Perspective at this link. You can follow him on twitter @LaverneusDingle.

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Fans familiar with the history of the NFL know that Fritz Pollard and Bobby Marshall were the first black NFL players, playing in the league’s inaugural season of 1920. ^[1]For its first two seasons, the NFL was known as the American Professional Football Association, or APFA. It didn’t become the National Football League until 1922. However, often lost in history is the story of the first recorded black professional football player: Charles Follis.

Follis’ name rarely comes up because he played well before the inception of the NFL, before Americans had even heard of Jim Thorpe. When Follis first played professionally, Pollard was only ten years old, and Marshall was just a freshman at Minnesota. The year was 1904 – when Teddy Roosevelt was more interested in negotiating treaties between Japan and Russia than he was in saving football – and the twenty-five year old “Black Cyclone” inked a meager deal with the Shelby Blues of the Ohio League. However, Follis was more than a footnote in football history, and his story merits another telling.

Follis was born in Virginia in 1879, the oldest of seven children. His father was a farm laborer, which effectively meant Charles was, too. He worked long hours with his father, developing great strength at a young age. ^[2]This reminds me of the well-known story of Jerry Rice working countless hours with his bricklaying father, catching brick after brick after brick that his father tossed to him. It is unclear when the family left Virginia for Wooster, Ohio, but interviews suggest that it was when Follis was still a small child. ^[3]From Milt Roberts’ 1975 interview with Follis’ sister-in-law, Florence Follis.

As a junior in high school in 1899, he not only led the effort to establish Wooster High School’s first football team, but he was also subsequently elected team captain by his white teammates. He was the team’s star player as they went undefeated, not allowing a point all season. So great was his impact on Wooster High School that the school’s football stadium was named Follis Field in 1998. His prowess in both football and his best sport, baseball, were so easily recognizable that he was eagerly recruited by the local college. [continue reading…]

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.

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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]A big thanks to Chad Langager at sportingcharts.com for helping me compile this data.

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]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 … Continue reading 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. [continue reading…]

Munir Mohamed, a reader of Football Perspective, is back for another guest post. And I thank him for it.

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Methodology: I looked at the expected touchdown rate on each yard line from inside the five-yard line in goal-to-go situations, then assigned touchdowns over expectation based on the number of scores above or below average the expected rate. For example, passes from the 1-yard line score a touchdown 50% of the time. Therefore, if a quarterback throws a touchdown when the line of scrimmage is the one, he receives +0.5 touchdowns over expectation. If he didn’t throw a touchdown, he is credited with -0.5 touchdowns over expectation. The numbers in this article are from 1998-2014. Note that touchdowns, like nearly every other statistic in football, is a reflection of not just a player, but his team. A player who scores highly in this metric may simply be great at scoring touchdowns, may have played with coaches or teammates who significantly aided his production, may be lucky over a small sample size, or a combination of several of those factors. But we can discuss the reasons behind the data in the comments: let’s get to the data, and begin with passing touchdowns. [continue reading…]

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

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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 110 ^[1]Which means the player was at least 0.67 standard deviations better than league average at avoiding interceptions. and a NY/A+ of 90 or below (min 224 attempts). ^[2]Which means the player was at least 0.67 standard deviations worse than league average in net yards per attempt. These guys avoided picks but failed to move the ball efficiently, the hallmark of a conservative playing style.

[continue reading…]

Today’s guest post comes from Adam Harstad, who is also part of the Smitty Fan Club. You can follow Adam on twitter at @AdamHarstad.

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IS STEVE SMITH THE GREATEST POSTSEASON WR IN HISTORY?

Prior to this last weekend’s slate of games, I remarked to several friends what a treat it was that we got to watch Calvin Johnson, Larry Fitzgerald, and Steve Smith all playing on the same weekend. In addition to being three of the best receivers of the last decade, all three could lay claim to the best per-game postseason numbers in history, depending on where you set the cut-offs.

Calvin Johnson had only appeared in one postseason game prior to this season, but he made it count with 12/211/2 receiving in a losing effort. Calvin was actually the fourth player in history to top 10 receptions, 200 yards, and 2 touchdowns in a single playoff game, ^[1]Oddly, all four receivers to reach those marks were active this past weekend; in addition to Calvin Johnson, they were Reggie Wayne, Steve Smith, and T.Y. Hilton. but each of the three previous have played additional games to bring their per-game numbers down. Among players who appeared in at least one playoff game, Calvin’s 211-yard “average” was the best by a mile.

If you moved the cutoff to 6 games, Larry Fitzgerald’s postseason averages took over the spotlight. Following the 2008 NFL season, Fitzgerald had arguably the greatest postseason run by a wide receiver, hauling in 6/101/1, 8/166/1, 9/152/3, and 7/127/2 in his four games, including what would have been the Super Bowl-winning touchdown and a likely MVP performance if not for some heroics from Ben Roethlisberger and Santonio Holmes. Fitzgerald followed that up with a strong showing in the 2009 playoffs, catching 12/159/2 over two games. All told, Fitzgerald had 53/705/9 receiving in just six postseason appearances, for a per-game average of 8.8/118/1.5. [continue reading…]

Munir Mohamed, a reader of Football Perspective, has agreed to write this guest post for us. And I thank him for it.

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Regular readers are familiar with Adam Steele’s three–part series here on Marginal YAC; today, I want to look at drops, and marginal drops.  As Adam noted, Sportingcharts.com keeps track of dropped passes. ^[1]Some fine print: Unfortunately, that data is only recorded on a team level, not at the individual passer level.  As a result, I gave each quarterback his pro rata portion of his team’s dropped … Continue reading

Her’s how to read the table below, which is sorted by career Marginal Drops.  Over the course of this data set, Eli Manning completed 2,929 of his 5,008 passes, for a completion percentage of 58.5%.  Manning’s Giants dropped an estimated 299.4 of his passes; if we add those to his 2,929 completions, Manning was therefore “On Target” with 64.5% of his throws.  Relative to league average, Manning had 44 more drops than we would expect. Manning’s drop percentage — i.e., his number of drops divided by his total number of completions and drops, was 9.3%, which represents his percentage of catchable balls that were dropped. Manning lost 516.5 yards from his marginal drops, or 52.9 yards last from marginal drops per 300 completions. [continue reading…]

Bryan Frye is back with another fun guest post.  Bryan, as you may recall, owns and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history.  You can view all of Bryan’s guest posts at Football Perspective at this link.

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Reggie Wayne has been in the news recently because Chuck Pagano called a pair of late-game pass plays in order to stretch Wayne’s streak of consecutive games with at least three receptions to 81 games. ^[1]That number has since grown to 82. Frankly, I don’t care to criticize either of them for that. What I do want to do is acknowledge an impressive record from a great player and discuss whether or not he is likely to join fellow greats in the Pro Football Hall of Fame. ^[2]And yes, it is a very impressive streak, regardless of how it was achieved. According to Pro Football Reference, the second longest such streak is Cris Carter’s 58 from 1993-1997.

Hall of Fame voters don’t seem to care too much about advanced stats, so I won’t bother covering anything beyond simple box score numbers. ^[3]However, if you do want a more in depth look at receiving stats, check out Chase’s series on the greatest wide receivers of all time. What voters do seem to care about are counting stats and a good story, or a combination thereof. Without any more ado, let’s get into the stats and the narrative.

The Stats

Currently ranks 7th all-time in receptions, 8th all-time in receiving yards, and 22nd all-time in receiving touchdowns. I am making the assumption that he will play a few more years at a diminishing level until he retires. That will leave us with a few questions about his statistical merits.

[continue reading…]

Bryan Frye is back with another fun guest post.  Bryan, as you may recall, owns and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history.  You can view all of Bryan’s guest posts at Football Perspective at this link.

Oh, and Happy Thanksgiving to all the loyal Football Perspective readers!

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Win Contribution Rating

It’s Thanksgiving. I don’t have a ton of time to write; you don’t have a ton of time to read. Let’s make this snappy.

A few months ago, I began using a rating that I feel better describes a quarterback’s contributions to helping his team win. I am terrible at coming up with names for stuff like that, but Football Guy Adam Harstad swooped in like a guardian angel and suggested the name “Win Contribution Rating.” I liked it, and I began using it without delay.

I used three metrics that correlate highly with future wins: Brian Burke’s EPA/P, Football Outsiders’ DVOA, and my Adjusted Yards per Play (AYP). ^[1]Please note that the difference between the 45 yard penalty Chase uses and the 50 yard penalty I use for interceptions is based on this article by Brian Burke. I chose 50 as a compromise between the … Continue reading  The correlation coefficients with future wins (i.e., Year N+1 wins) for the individual metrics are .273 for EPA/P, .265 for DVOA, and .256 for AYP. ^[2]This includes all quarterbacks for which data is available from both FO and AFA, from 1999-2012. I did not include 2013 because I didn’t know year N + 1 wins; I’m not in the fortune telling … Continue reading When I ran those in a multiple regression, I got the following best fit equation (rounded):

Win% = .5 + EPA/P *.39 + DVOA * .13 + AYP * .008

Because the basis of this regression is win percentage, the equation spits out small decimals that I find aren’t relatable to most of the casual fans I know. To transform this into a number that resembles the NFL passer rating that people already know, I simply multiply by 140 to find the Win Contribution Rating. ^[3]This may seem strange, but keep in mind that the NFL multiples by 16.67 to achieve its final passer rating.

The highest score since 1999 belongs to Peyton Manning in his virtuoso 2004 performance. Let’s take a look at his rating:

EPA/P: .38
DVOA: 58.9%
AYP: 9.1
WCR = (.5 + .38 * .39 + .589 * .13 + 9.1 * .008) * 140 = 111.7 [continue reading…]

Adam Steele is back for his third guest post in his Marginal YAC series.

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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. [continue reading…]

Friend-of-the-program Bryan Frye has contributed a fantastic guest post for us today. Bryan lives in Yorktown, Virginia, and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history. Be sure to check out Bryan’s site, and let him know your thoughts on today’s posts in the comments.

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Last Sunday, Peyton Manning broke the record for career touchdown passes. You may have heard about it. Rather than add more flotsam and jetsam to the vast sea of internet articles dedicated to Manning, I thought I would instead focus on the rich history of the record itself.

[continue reading…]

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?

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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:: [continue reading…]

Just above these words, it says “posted by Chase.” And it was literally posted by Chase, but the words below the line belong to Bryan Frye, a longtime reader and commenter who has agreed to write this guest post for us. And I thank him for it. Bryan lives in Yorktown, Virginia, and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history.

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With six weeks behind us, we should be at the point where we can figure out who teams are. ^[1]For a counter view, see this post by Chase. However, this season seems to be a parity lover’s dream. Although many teams near the poles are who we thought they were, others (such as New Orleans and Dallas and perhaps San Diego) are far from their preseason projections. The middle ranks are a jumble of average and indiscernible teams, and no team was even able to make it to 4-0. ^[2]Think that’s crazy? In 1961, the Cowboys, Lions, and Eagles were the last undefeated teams in the NFL, at 2-0. With half of the NFL’s teams lingering around 1-2 losses, how can we tell the petty tyrants from those with legitimate claims to the throne? I recently began working on a model to do just that.

[continue reading…]

Just above these words, it says “posted by Chase.” And it was literally posted by Chase, but the words below the line belong to Bryan Frye, a longtime reader and commenter who has agreed to write this guest post for us. And I thank him for it. Bryan lives in Yorktown, Virginia, and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history.

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In August, I introduced a concept on my site to better adjust the NFL’s passer rating for the league passing environment. I love Pro Football Reference’s use of the Advanced Passing Index for passer rating (Rate+), but it still bothered me that the internal math of the NFL’s formula remained the same.

The NFL’s official passer rating formula is based on four variables: completion percentage, yards per attempt, touchdown percentage, and interception rate. Each of those variables are then used to determine four different variables, as seen below:

A = (Cmp% – .3) * 5
B = (Y/A – 3) * .25
C = TD% * 20
D = 2.375 – Int% * 25

Passer rating is then calculated as follows, provided that each variable is capped at 2.375 and has a floor of zero:

(A + B + C + D)/(0.06)

For each component, a score of 1 represents the ideal average passer. Because the formula is based on a league average completion rate of 50%, modern passers significantly exceed that; pre-modern passers rarely reached it. Similarly, the NFL’s model is based on a 5.5% interception rate and a 5% touchdown rate. Thanks to a Greg Cook injury (and Bill Walsh’s genius reaction to it), those numbers have also changed significantly. Last year, the league interception and touchdown rates were 2.8% and 4.4%, respectively. [continue reading…]

Which team will be the biggest surprise in 2014? Last year, the Houston Texans shocked Vegas and analytics fiends alike. Before the season, the Texans’ over/under win total was 10.5. Football Outsiders Almanac projected them to have 9.3 wins and gave them a 67% chance of making the playoffs. Basically nobody saw the 2013 Texans’ implosion to 30th in DVOA coming. Interestingly, though, the Texans are part of a larger trend in the kinds of teams that have been having enormous drop-offs in performance.

Consider the graph below. It looks at the change in DVOA for good-but-not-great teams, those that ranked between 6th and 15th in the previous year. ^[1]Before 1989, I use Andreas Shepherd’s estimated DVOA. I thank him for sharing his data.

Historically, the good-but-not-great teams have regressed a little bit. From 1985 to 2010, those teams dropped on average between two and four points of DVOA. The trend was relatively stable for each five year period. While we would expect some regression from good teams, the size of that regression has changed since 2010. Over the last four years, the good-but-not-great teams have dropped an average of ten points of DVOA, the biggest regression by far since the merger. Note that if we drop 1983 to account for regression coming out of the strike-shortened 1982 season, we get a DVOA change for 1980-1984 of about four points of DVOA, making 2010-2013 even more clearly on its own island. This idea leads into my first prediction for the season. [continue reading…]

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.

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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]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 … Continue reading 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: [continue reading…]

In 1995, Football Outsiders graded the Eagles special teams as the worst in the NFL. The next two years, Philadelphia ranked 20th and 26th, respectively. In 1998, after hiring a new special teams coordinator, the team still finished just 25th. But, over the next eight years, the Eagles’ special teams flipped dramatically, ranking as the second-best in football during that period. In fact, from 2000-2004, Philadelphia ranked in the top five in the Football Outsiders’ special teams ratings each season.

When the Ravens hired the coordinator of those special teams, John Harbaugh, as their head coach in 2008, Baltimore turned one of the more surprising coaching hires in recent history into one of the best. Based on where the team was when it hired him, Harbaugh’s first three years were about the best since 1990 of any coach not named Harbaugh, at least according to DVOA. The Ravens made the playoffs in Harbaugh’s first five seasons, winning the Super Bowl in the last of those. Harbaugh’s success even caused Chase to wonder whether it would change the way teams hired head coaches.

Since Harbaugh was so successful as a coordinator, does that mean he was a good bet to be a successful head coach? At first glance, you might think just about every coordinator who gets promoted or poached to become a head coach was very successful in his previous job. As it turns out, that’s not always the case. Once we correct for expectations, a little more than one in four hired head coaches actually underperformed in their previous jobs, at least according to DVOA.

Consider one man who performed particularly poorly as a coordinator: Eric Mangini. The 2005 New England defense had a DVOA that was 15.2 points lower than we would have predicted based on the Patriots’ performance in the preceding seasons. He was not so much of a (Man)genius to have a good defense in 2005, and that may have given some hint that he was not the greatest bet to succeed as a head coach, either. ^[1]Always a bonus when painful Jets memories come up organically. There are always other coaching greats like Joe Walton for Jets fans to remember fondly, at least for epic nasal invasions.

This leads to an obvious question: on average, have teams done better when they have hired head coaches who were actually good in their previous jobs (either as coordinators or head coaches)? Let’s take this to the data. [continue reading…]

I think I’m one of eight billion people who love “The Wire” and “Breaking Bad.” Those are the two best TV shows I’ve seen and it isn’t particularly close. ^[1]“Seinfeld” is all alone in third with a pretty big gap after that, too. “The Wire” had an amazing volume of unforgettably vivid characters. Below is my list of memorable “Wire” characters. To be a real test of unforgettableness, it’s got to be off the top of my head, so I’m sure I’m going to forget somebody, but here goes and I’ll include the first thought that jumps to mind:

Omar (“man’s gotta have a code”), Bunk (“f***”), McNulty (“f***”), DeAngelo (library), Stringer (mastermind), Avon (winner), Brother Mouzone (bow tie), Cedric (good posture), Garcetti (that’s actually the mayor of LA, I mean the Baltimore mayor), Clay Davis (“sheeeeeet”), Bunny (“New Hamsterdam”), Keisha (car chase scene), Lester (wood carving), Bodie (corner), Prop Joe (large), …

Ah, Prop Joe. Prop Joe was a very large and very reasonable drug kingpin. His name apparently stemmed from saying “I’ve got a proposition for you,” so we could certainly see him getting into prop bets. So, in honor of Prop Joe, I’ll cover some intriguing season prop bets. ^[2]The actor who played Prop Joe, Robert F. Chew, sadly passed away in January 2013. Most of these bets are only available online, which continues to be a legal gray area. Like Prop Joe, I would never directly touch anything slightly questionable, so I will be referring to bets made by my good friend Rawls. ^[3]Definitely not this Rawls who is the enemy of all that is good. We’ll start with his favorite prop bet for 2014 and go from there in descending order.

Rawls’s Prop Bet #1: $76 On Any Team To Win at Least 14 Games (Odds: 3/1)

At first glance this bet seems to have a lot of merit. Since the 1987 strike, at least one team won 14 games 15 out of 26 times (57.7%). In the last 15 years, it’s even better, hitting 10 out of 15 times (66.7%). The bet only needs to win 25% of the time to break even, so this looks fantastic.

But Chase brought up a point that Rawls missed: schedule strength. The years without a 14-game winner in the last 15 years include 2012 and 2013. Rawls dismissed that as a blip, but it comes in part from two of the best teams in football playing in the same division. Moreover, the last run of years without a 14-game winner (1993-1997) also happened during a time of NFC dominance, at least until ‘97. The Cowboys played the Packers and Niners every year during that span, for example. This season, the best teams in football may have it even tougher. The Niners and Seahawks have to play each other twice, and each has one of the four hardest schedules in football this year. The Broncos get the NFC West, the Saints have the sixth-hardest schedule, and the Packers have above-average schedule strength. Only the Patriots have an easy schedule amongst the main threats to win 14 games. [continue reading…]

Just above these words, it says “posted by Chase.” And it was literally posted by Chase, but the words below the line belong to Bryan Frye, a longtime reader and commenter who has agreed to write this guest post for us. And I thank him for it. Bryan lives in Yorktown, Virginia, and operates his own great site at http://www.thegridfe.com/, where he focuses on NFL stats and history.

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In February, Chase used a regressed version of Football Outsiders’ DVOA metric to derive 2014 expected wins. If you are reading this site, you probably have some familiarity with Football Outsiders and DVOA, FO’s main efficiency statistic. Given the granularity of DVOA, it is no surprise that Year N DVOA correlates more strongly with Year N + 1 wins (correlation coefficient of .39) than Year N wins does (correlation coefficient of .32).

By now, even casual NFL fans probably have at least heard of Pythagorean wins, and regular readers of this site are certainly familiar with the concept. Typically, an analyst uses Pythagorean records to see which teams overachieved and underachieved, which can help us predict next year’s sleepers and paper tigers. Well, I wondered what would happen if we combined the two formulae to make a “DVOA-adjusted Pythagorean Expectation” (or something cooler sounding; you be the judge).

Going back to 1989, the earliest year for DVOA, I used the offensive, defensive, and special teams components of DVOA to adjust the normal input for Pythagorean wins (points). Because DVOA is measured as a percentage, I adjusted the league average points per team game accordingly (I split special teams DVOA between offense and defense). Let’s use Seattle, which led the league in DVOA in 2013, as an example.

In 2013, the league average points per game was 23.4. Last year, Seattle had an offensive DVOA of 9.4% and a defensive DVOA of -25.9% (in Football Outsiders’ world, a negative DVOA is better for defenses).  The Seahawks also had a special teams DVOA of 4.7%.  So to calculate Seattle’s DVOA-adjusted points per game average, we would use the following formula:

23.4 + [23.4 * (9.4% + 4.7%/2)] = 26.15 DVOA-adjusted PPG scored

And to calculate the team’s DVOA-adjusted PPG allowed average, we would perform the following calculation: [continue reading…]

By now, you know about guest blogger Andrew Healy, an economics professor at Loyola Marymount University and author of today’s post. There’s now a tag at the site where you can find all of his great work. He’s back with a cap to his excellent series about playoff performance, and today’s post will not disappoint:

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We know the teams that have experienced consistent heartbreak at the altar. But is it the Vikings, Eagles, or Bills that are the most unlikely to have never won a Super Bowl? On the flip side, we know the teams that stacked championships on top of championships. But is it the Packers, Steelers, or 49ers that have made the most of their chances?

For the latter question, it turns out that it’s option D, none of the above.  One mystery team has won four championships despite having had a pretty decent chance of never winning a single Lombardi.  The most unlikely team never to win a Super Bowl turns out to be a team that lost “only” two Super Bowls, but that has led the NFL in DVOA four times since 1979.

To figure this stuff out, I’ve utilized DVOA ratings and estimated DVOA ratings to rerun the NFL playoffs. In the simulations, the slate is wiped clean, which means there’s no reason The Fumble or The Helmet Catch or The Immaculate Reception have to happen this time around.

In last week’s post, I went decade by decade to look at the best teams, and also those that most let opportunity slip through their fingers. Today, I bring it all together. I compare what might have been with what actually was for the NFL from 1950 to 2013. I’ll also hand out awards for the teams that were the most unlikely winners and the most unlikely losers of all time. [continue reading…]

Andrew Healy is back with a sequel to his popular post. As always, we thank him for his generous contributions. Andrew Healy is an economics professor at Loyola Marymount University. He is a big fan of the New England Patriots and Joe Benigno.

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A couple of weeks ago, I went decade-by-decade since the 1970 AFL-NFL merger to identify the teams that were the best of their eras and the teams that nearly became the teams we remember most instead. In those rankings, I used Pro Football Reference’s Simple Rating System to estimate team strength. Today, I use Football Outsiders’ DVOA ratings and go back an additional twenty years. Using DVOA produces some pretty notable differences that were bigger than I would have guessed.

What are some of those changes?

• The Steelers have been supplanted as the true team of the ‘70s.
• The best team to win no titles changes for three of the decades.
• The ‘70s Vikings get replaced by a more recent what-might-have-been team as the best to win nothing in the Super Bowl era.

Before we get to that, I cover the 1950s and 1960s, identifying the true teams of those decades and the what-might-have-been teams. In a follow-up post, I’ll bring it all together and identify the franchises that have maximized their championship potential the most, and those that have left the most money on the table. [continue reading…]

A couple of weeks ago, Andrew Healy contributed a guest post titled, “One Play Away.” He’s back at it today, and we thank him for another generous contribution. Andrew Healy is an economics professor at Loyola Marymount University. He is a big fan of the New England Patriots and Joe Benigno.

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What teams do we remember the most? Going back to the merger, the 1970s Steelers, the 1980s 49ers, the 1990s Cowboys, and the 2000s Patriots seem to stand above the rest. Each of these teams earned that place in our collective memory by winning the most Super Bowls in the decade.

How different could it have been? In other words, were the dynasties that happened by far the most likely ones? Or were there others that were equally, or even more likely? Think of teams that have suffered unusually cruel sequences of defeats (cue nodding Vikings, Bills, and Browns fans). We all know that those teams could have won Super Bowls. But maybe the more interesting question is whether those teams realistically could have won multiple Super Bowls, or even have become the dominant team of the era.

Today, I estimate the chances that different teams had of becoming the Team of the Decade (the TOD) for the ’70s, ’80, ’90s, and ’00s. Some of the results are surprising. One of the teams that became the TOD was actually much less likely than another to dominate that decade. Only two of the four teams truly stand out as being clearly the single most-likely team to be the TOD.

Even more interesting are the teams that might have been dynasties instead of the ones we’ve come to know. In most cases, these teams won at least one Super Bowl. In one case, though, a team that became famous for losing easily could have been not just a one-time winner, but a team that became a dynasty and dominated the decade.

To come up with the estimates of a team’s chances of winning Super Bowl, I simulated the playoffs 50,000 times. I used the actual playoff brackets and then created win probabilities for each game based on team strength. In tables that follow below, I’ll describe the probabilities that teams won multiple titles in a decade. I’ll also pick a True Team of the Decade (most expected Super Bowl wins), a What-Might-Have-Been-Dynasty that Won Nothing, a Team that Wasn’t as Good as We Remember, and a A Bottom-Feeder Team(s) for each decade.

First, a brief description of how I performed the simulations before getting to the rankings:

• The playoffs were run under the rules in a given year: All rules relating to seeding, home field, and number of teams were used. If there was a rule in place preventing matchups between divisional opponents in a given round, I also applied that rule. To some extent, the fewer teams in earlier years helped make dynasties more likely in those decades.

• Pro Football Reference’s Simple Rating System was used to measure team strength: I used PFR’s for all years to be consistent. It’s worth noting that their ratings and DVOA usually match up closely. Another possibility is to try to simulate DVOA ratings, but it seems simpler to just use SRS throughout. In some cases, there are some differences, such as for the 1998 Broncos and 1999 Titans.

• I used the beginning of the NFL season to define the decades: So 1970-79 means Super Bowls V-XIV. An interesting thought experiment is to consider Super Bowl time instead of calendar decades. Then the Raiders would have been the team of Super Bowls XI-XX. Anyway, I’ll stick with the convention. It’s worth noting that my results suggest the Raiders were not as good as we might remember.

1970s

The table below shows each franchise’s probability of having won 0, 1, 2, 3, 4, 5, or 6 Super Bowls during the decade according to the methodology described above. The final column shows the expected number of Super Bowl wins for the decade.

The True Team of the Decade: Pittsburgh Steelers
The Steelers had only a 14.5% chance of winning no Super Bowls in the ’70s and a 4.9% chance of winning the four that they did. The expected value of SB wins for Pittsburgh was 1.67, the highest value for any team in any decade.

The What-Might-Have-Been-Dynasty that Won Nothing: Minnesota Vikings
The Vikings are not too far away from the Steelers and Cowboys. There was only a 23.6% chance the Vikings would have won nothing in the ’70s. And they certainly could have won multiple championships. There was over a 35% chance the Vikings would have won at least two titles and a 9.6% chance they would have won at least three. Of all the teams that won nothing, the 1970s Vikings are the best candidate for the team that could have been the TOD.

The What-Might-Have-Been Dynasty that Won Nothing, Part 2: Los Angeles Rams
A little bit behind the Vikings are the Rams. Los Angeles had only a 39% chance of winning no Super Bowls in the ’70s and a 20.3% chance of winning multiple titles.

The Team that Wasn’t as Good as We Remember: Oakland Raiders
When I starting working with the data, I expected the Raiders to challenge for the TOD. Five losses in the AFC championship to go with the one title. Seven playoff appearances. Despite all that, the Raiders only had the sixth-most expected titles in the decade. In fact, they didn’t really underperform at all in terms of titles. They had a 39.5% chance of winning none at all. The Raiders’ SRS ratings explain this. Oakland was never really great, only passing +10.0 in a year (1977) where they finished second in the division.

Bottom-Feeder Teams: New York Giants, New York Jets
Only two teams played the entire decade and missed the playoffs every single year. They happened to be the two teams that played in New York. The chance that two teams would miss the playoffs every year and New York would happen to miss playoff football entirely: about 0.2%.

1980s

The True Team of the Decade: San Francisco 49ers
Unlike the 1970s, the ’80s weren’t close. The Niners were similar to the ’70s Steelers with an expectation of 1.64 Super Bowl wins in the decade. The ’80s 49ers had about a 4.2% chance of winning the four Super Bowls they did and 51.7% chance of winning at least two. And, while not shown in the table above, it’s exciting to note that the Niners had a 0.004% chance of winning seven Super Bowls in the 1980s.

The What-Might-Have-Been-Dynasty that Won Nothing: Miami Dolphins
I was really surprised by this one. The Dolphins come in third in the 1980s in expected SB wins with 0.81. Based on their consistency in the first half of the decade, the Dolphins had an 18.6% chance of winning multiple Super Bowls in the 1980s. That’s substantially higher than the 12.4% chance for their nearest competitor: the much better-remembered Denver Broncos who were annihilated in three Super Bowls.

The Team that Wasn’t as Good as We Remember: Oakland/LA Raiders
Despite never being close to dominant, the Raiders won two Super Bowls in the 1980s. According to the number of SB wins we would have expected them to have, the Raiders actually rank 11th, behind six teams that won none in the decade. They had about a 5.5% chance of winning multiple titles in the decade.

A Bottom-Feeder Team: Houston Oilers
For teams that played every season since the merger, the Oilers had the least hope of winning a title over the 1970s and 1980s combined. That’s a little surprising given that they had at least one memorable moment in the playoffs during that stretch, unlike some of the teams ahead of them.

1990s

The True Team of the Decade: San Francisco 49ers
This one almost leaps off the page. Not only were the Niners on top in the 1990s in terms of expected SB wins, they were way on top. Given the Cowboys’ relatively short run, it’s not surprising that they would do worse here, but they’re closer to the 10th place Rams on this list than they are to the 49ers. Even though they only won one in the decade, the Niners had the same number (1.64) of expected titles in the ’90s as they did in the ’80s, and a 51.7% chance of multiple titles.

The What-Might-Have-Been-Dynasty that Won Nothing: Buffalo Bills
The Bills actually do worse on this list than I would have expected. They were about even money to win the zero titles that they did in the ’90s. They had an 11.0% chance of winning multiple titles, making them the top-ranked no-title team of the ’90s, but ranking them well behind the ’70s Vikings, the ’70s Rams, and the ’80s Dolphins.

The What-Might-Have-Been-Dynasty that Won Nothing, Part 2: Kansas City Chiefs
On the field, the ’90s Chiefs only went to one AFC Championship game and no Super Bowls. Nevertheless, they’re about even with the Bills in terms of the Super Bowls they could have won. They had a 10.4% chance of winning multiple titles in the ’90s.

The Team that Wasn’t as Good as We Remember: Pittsburgh Steelers
I’m not sure there’s a great candidate in this category, so I was tempted to just pick the Raiders again to keep the pattern. You could go with Broncos here, but the 1998 Broncos are one case where there’s a clear gap between SRS and DVOA, which gives them more credit. The ’90s Steelers had four playoff byes in a run of six straight playoff appearances. Still, they had a 59.3% chance of winning no Super Bowls and only a 7.9% chance of winning multiple titles.

A Bottom-Feeder Team: Phoenix/Arizona Cardinals
The worst team in two consecutive decades. Over twenty years, the Cardinals had 0.003 expected titles. That’s only 0.003 more expected titles than the Houston Texans and they weren’t even in the league yet.

2000s

The True Team of the Decade: New England Patriots
Less dominant than the other True TODs, the Patriots of the aughts still have a healthy gap over their closest rival, the Colts. There was only a 17% chance the Patriots would have gotten shut out in the ’00s. There was a 41.7% chance that the Pats would win multiple titles in the decade, more than double the chance of any other team.

The What-Might-Have-Been-Dynasty that Won Nothing: Philadelphia Eagles
The Eagles rank third in expected titles in the ’00s with 0.78, just a hair behind the Colts for second. They also look similar to the 1970s Rams and 1980s Dolphins in terms of multiple-title potential. They had about a 17.4% chance of winning multiple titles in the aughts.

The Team that Wasn’t as Good as We Remember: Tampa Bay Buccaneers
Hopefully, it’s not too hard to remember a decade that ended with President Obama in the White House, but the Bucs come in lower here than I might have guessed. They made the playoffs five times, but still are only 14th in expected SB wins. They actually had a 71% chance of winning no titles in the decade. Even in their best year, 2002, where they ranked #2 in SRS and #1 in DVOA, they were far from dominant and so had only about a 21% chance of winning the title.

Bottom-Feeder Teams: Buffalo Bills, Detroit Lions
Neither team made the playoffs in the decade, a more impressive accomplishment than the ’70s Giants and Jets in an era of expanded playoffs. Both cities also suffered through deindustrialization and so seemed to deserve better football as a compensating differential.

Closing Thoughts

I was excited to check this out because I wanted to compare teams like the ’90s Bills and the ’70s Rams. That comparison makes it pretty clear that the ’70s Vikings are hands-down the clearest What-Might-Have-Been-Dynasty that Won Nothing. This is all post-merger, so arguably the best Vikings team of that era (the ’69 edition) doesn’t even count in the calculation. If you count the 1969 Vikings, there was only about a 1-in-6 chance that those Vikings would end up with no Super Bowls.

Maybe the most remarkable regularity over the years is how the Cardinals have been so bad for so long. Even though Arizona came close in 2008, the Cardinals had only an 11.2% chance of winning any of the last 44 Super Bowls. In fact, they were lucky just to make the one Super Bowl that they did (in more ways than one).

Finally, a couple of thoughts about this decade. While we’re only four years in, this decade could wind up resembling the 1990s. The Patriots right now are playing the role of the ’90s Niners, while the Seahawks may be the best candidate to be the Cowboys. So far, the Patriots have been (perhaps surprisingly) dominant. There’s only about a 27% chance that New England would have no titles in the 2010s and there was even a 28.5% chance that the Patriots would have already won multiple titles; that likelihood is more than four times more as any other team. Despite having none on the field through four seasons, the ’10s Patriots are on pace through four years to have the most expected SB wins for any decade. They already have 1.07 expected wins, more than double their nearest competitor.

Three weeks ago, Vegas released the first set of 2014 win totals for all 32 teams. I immediately wondered how those win totals compare to the estimated wins I created based on 2013 DVOA ratings. I tweeted a request for someone to write such an article, and Warren Sharp (@SharpFootball) was kind enough to oblige. Warren runs SharpFootballAnalysis.com, where he provides handicapping analysis.

One other note before I let Warren take over. If you missed the post on estimating team wins using DVOA, I included this disclaimer:

Even Football Outsiders won’t use these [projections] for more than a starting point — their preseason projections will have the customary tweaks for things like teams getting new quarterbacks, injuries (or the lack thereof) in 2013, rookies, offensive line continuity, etc.

Please keep that note in mind. So when you see “Football Outsiders projects Green Bay to win 7.8 games this year,” that’s just shorthand for “Green Bay’s 2013 offensive, defensive, and special teams ratings, when regressed based on historical data, project a 7.8-win season.” I’m sure with a healthy Aaron Rodgers, Football Outsiders expects more than 7.8 wins in 2013, but the regression formula is ignorant of that fact. And now, I’ll let Warren take over.

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On March 7, CG Technology, formerly Cantor Gaming, became the first Las Vegas book to set win totals. For eight teams (25%), the win totals were within one half-game of the estimated DVOA projections: The two sources see eye to eye on Washington, Chicago, Cincinnati, Miami, Detroit, Dallas, Cleveland, and the New York Giants.

For 11 teams (34%), Las Vegas was more enthusiastic than DVOA was, i.e., the books projected higher win totals. The biggest outliers here were Green Bay (10 projected wins by CG vs 7.8 by DVOA) and Houston (8.5 vs 6.5). For Green Bay, we can presume that injuries were the biggest reason for the discrepancy: in addition to Rodgers, Randall Cobb, Clay Matthews, and Casey Hayward all missed significant action. As for the Texans, my guess is that Vegas sees the Colts dropping back two wins from 2013, and the AFC South remains pretty poor.  Houston won 10 games in 2011 and 12 games a year ago, and now faces a pretty easy schedule (the rest of the division, Buffalo, Oakland, the AFC North and NFC East; note that last year, Houston had to face the AFC West, NFC West, New England, and Baltimore.) The Texans were also just 2-9 in games decided by seven or fewer points, a trend that is unlikely to continue.

For the remaining 13 teams (41%), Las Vegas projected fewer wins than DVOA. The two standouts in this category were Arizona (7.0 vs 8.6) and St. Louis (6.5 vs 8.0). As we’ll get to later, CG and Football Outsiders are in considerable disagreement about the fortunes of the NFC West teams.

In some cases, the borderline playoff teams are the most interesting to analyze. There were four teams that DVOA had at sub-.500 that the linemakers have in playoff contention: Green Bay, Houston, Pittsburgh (9.0 vs 7.7) and Baltimore (8.5 vs 7.3). Arizona was the only team in the opposite situation, where Las Vegas projects a losing record despite the DVOA estimates pointing towards a winning record.

The table below shows the numbers for all 32 teams. The Packers had 8.5 wins in 2013, Vegas has set Green Bay’s 2014 wins total at 10.0, and DVOA projects the Packers at 7.8 wins. Therefore, Vegas is 2.2 wins higher on Green Bay than DVOA, Vegas is 1.5 wins higher on Green Bay than the Packers’ 2013 result, and DVOA expects 0.7 fewer wins from Green Bay this year. [continue reading…]

Football Perspective accepts guest posts, and Andrew Healy submitted the following post. And it’s outstanding. Andrew Healy is an economics professor at Loyola Marymount University. He is a big fan of the New England Patriots and Joe Benigno.

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The Catch. The Immaculate Reception. The Fumble. We remember all these plays, but which mattered the most? More specifically, what plays in NFL history had the biggest impact on who won the Super Bowl?

The answer to this question is kind of surprising. For example, two of those famous plays are in the top 20, but the other wasn’t even the most important play in its own game. Going all the way back to Lombardi’s Packers, the memorable and important plays overlap imperfectly.

Here, I try to identify the twenty plays that shifted the probability of the eventual Super Bowl winner the most. According to this idea, a simple win probability graph at Pro-Football-Reference.com identifies a not-surprising choice as the most influential play in NFL History: Wide Right. What is surprising is that they give Buffalo a 99% chance of winning after Jim Kelly spiked the ball to set up Scott Norwood’s kick. Obviously, that’s way off. ^[1]I think it happens because their model basically gives you credit for your expected points on the drive, which is enough to win since Buffalo was down by a point.

A better estimate would say him missing the kick lowered the Bills chances of winning from about 45% to about 0%. Norwood was about 60% for his career from 40-49 yards out, and 2 for 10 from over 50. Moreover, he was 1 for 5 on grass from 40-49 before that kick. But the conditions in Tampa that night were close to ideal for kicking. It’s hard to put an exact number on things, but around 45% on that 47-yard kick seems about right.

So that 45 percentage point swing in a team’s chances of being the champ is what I’m going to call our SBD, or Super Bowl Delta, value. I’m going to identify the twenty plays with the biggest SBD values, the ones that swung the needle the most.

Here are the ground rules for making the cut. [continue reading…]

Steve Buzzard has agreed to write another guest post for us. And I think it’s a very good one. Steve is a lifelong Colts fan and long time fantasy football aficionado. He spends most of his free time applying advanced statistical techniques to football to better understand the game he loves and improve his prediction models.

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Last month, I wrote about how to project pass/run ratios using offensive Pass Identities and the point spread. However, this methodology only considers one side of the ball. Can we actually improve our projections model using both offensive and defensive Pass Identities? As it turns out the answer is yes.

First, I started off by creating defensive Pass Identities using the same methodology found here. The first thing I noticed was the standard deviation of pass ratios for defenses was only 3.0% compared to 5.1% for offenses. This led me to believe that offenses control how much passing goes on in a game more than defenses. I was glad to see this as it confirmed most of my previous research as well. Given this, it wasn’t appropriate to use a standard deviation of 3.0% for defenses in my projection while using a standard deviation of 5.1% for offenses. Instead, I used the combined standard deviation of all 64 offensive and defensive pass ratios, which turned out to be 4.17%. This doesn’t change the order of passer identities much but obviously does increase the deviation from the mean for the offensive side of the ball and decrease it for the defensive side. [Chase note: Determining the best way to handle the differing spreads between offensive and defensive pass ratios is a good off-season project; in the interest of time, I advised Steve to split the difference and move ahead with the analysis.]

Now that we have a Pass Identity grades for both sides of the ball, we can add a strength of schedule adjustment, too. To make the SOS adjustment, I simply took the average of the defensive Pass Identities played by each offensive unit and the average of the offensive Pass Identities played by each defensive unit. As expected the SOS adjustments had a much larger impact on the defensive Pass Identities than the offensive Pass Identities.
[continue reading…]

Just above these words, it says “posted by Chase.” And it was literally posted by Chase, but the words below the line belong to Steve Buzzard, who has agreed to write this guest post for us. And I thank him for it. Steve is a lifelong Colts fan and long time fantasy football aficionado. He spends most of his free time applying advanced statistical techniques to football to better understand the game he loves and improve his prediction models.

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The way to win fantasy football games is to have players that score a lot of points.  Players tend to score more points when they get more touches.  One of the most important factors in determining how many touches each player is going to have is to determine the Game Script ahead of time.  As we all know positive game scripts result in more passing attempts and negative Game Scripts result in more rushing attempts.  But I am going to try to project the pass ratio using two key stats, Pass Identity rating and the Vegas spreads. We can use these projected pass ratios to build our own projections or at least look for outliers and figure out how to adjust players from their year to date averages.

Regular readers know what Pass Identity means. For newer readers, you can read here to see how Pass Identities are calculated.  But the quick summary is that Pass Identity grades allow us to predict the pass ratio of any game where the point spread is zero. This is because Pass Identity tries to eliminate the Game Script from the pass ratios.  For example since the Bears/Cowboys game is a pick’em this week, we can predict the pass ratio of the Bears by using the following formula.  League average pass ratio + (A + B) *C, where

(A) = number of standard deviations above/below average the Bears are in Game Script (-0.49);

 

(B) = number of standard deviations above/below average the Bears are in Pass Ratio (+0.53); and

(C) = the standard deviation among the thirty-two teams with respect to Pass Ratio (5.3%)

Of course, the product of (A) and (B) is the Pass Identity grade for each team; once we determine that, we multiply that number by the standard deviation of the pass ratios of all teams to get us a prediction for the pass ratio in a game with a Game Script of 0.0. Since the Bears have a Pass Identity of basically 100, the projected Pass Ratio for Chicago against Dallas is 58.9%.

We can then compare this projection to Chicago’s year-to-date pass ratio of 61.5% and predict that all else equal Jay Cutler and the passing game should score about 4% ^[1]Since 58.9% is 96% of 61.5%. less this week than their average week where as Matt Forte and the run game would score about 4% more.

[continue reading…]

Today you’re in for a treat, as frequent commenter Shattenjager has contributed an absolutely outstanding guest post.

Introduction: Question

Every time I have looked at Marion Campbell’s coaching career, a question has leapt to mind: How on earth did he keep getting jobs?

When Chase Stuart recently revisited Doug Drinen’s Dungy Index to measure coaches’ performances in the regular season compared to expectation, the man who appeared dead last in Wins over Expectation was former Falcons and Eagles head coach Marion Campbell, at a rather staggering -14.9. His teams essentially lost 15 more games than a linear regression based on Pythagorean winning percentage expected.

“So what?” you might think, “He was just a terrible coach.” I wouldn’t blame you for having that reaction. However, here’s what’s really fascinating about Marion Campbell: he had three separate head coaching stints.

Strictly looking at win-loss records is a poor measure of a coach, but it works well enough as a shorthand overview of Campbell’s career. He went 6-19 as the Atlanta Falcons head coach 1974-1976 after Norm Van Brocklin was fired during the 1974 season. Then, several years later, he took the head coaching job of the Philadelphia Eagles after Dick Vermeil famously stepped down due to burnout and promptly went 17-29-1 over the next three seasons before being fired again with one game remaining in the season. Just a year later, the Falcons—yes, the same Falcons who had already hired and fired Campbell as their head coach—decided that Campbell was the man to replace newly-fired Dan Henning. He rewarded them with an 11-32 run that ended with his retirement late in the 1989 season.

Again, how did he keep getting jobs? Well, it’s complicated, but I think an in-depth look at his career can explain it. [continue reading…]