Odell Beckham is ridiculous. Period.

Mike Evans, in just about any other year, would be considered the best rookie wide receiver in the NFL. Players like Kelvin Benjamin and Sammy Watkins would stand out in most years, too: both have over 25% of their team’s receiving yards.

Jordan Matthews has 767 receiving yards, which is only considered unimpressive against when compared against the above backdrop. Ditto Jarvis Landry and his 79 receptions. Martavis Bryant has seven touchdowns. The Jaguars have three rookie receivers playing well. And on and on we could go (just as I did in late October, and as Bill Barnwell did after week twelve).

Through 16 weeks of the 2014 season, rookies have been responsible for 12.6% of all receptions in the NFL, 12.7% of all receiving yards, and 13.7% of all touchdowns. As it turns out, that does make the 2014 class a very special one. The table below shows the percentage of all receptions, receiving yards, and receiving touchdowns by rookies in each year (other than 1987) since 1970: [continue reading…]

Nine days ago, I looked at the leaders in passing value, measured as the difference between each quarterback’s ANY/A average and league average, multiplied by such passer’s number of dropbacks. This is the conventional method I have used to measure passing value, but that doesn’t make it the best.

Over the summer, Brian Burke of Advanced Football Analytics fame, helped me determine the value of first down. His research concluded that a first down was worth about 9 marginal yards. I was short on time, so I didn’t have the chance to incorporate that into my formula last week. But I will rectify that today.

In addition, I will provide -30 yards for each “net fumble” — defined as fumbles minus fumbles recovered. And since last week I calculated the numbers relative to average, this time around I will compare player production to replacement value, defined as 80% of league average. ^[1]Customarily, I use 75%, but I think with the first down bonus, 80% makes more sense here.

Let’s use Aaron Rodgers as an example. The Packers star has thrown 458 times for 3,837 yards, 35 touchdowns (+700), with 5 interceptions (-225), 9 fumbles, and 5 fumble recoveries (-120). He has also been sacked 27 times and lost 166 yards on those plays. Finally, Rodgers has picked up 188 first downs (+1692), which means he has a total of 5,718 adjusted net yards. Over his 485 dropbacks, that gives him an average of 11.79 “ANY/A”, while the league average is 8.91. That means Rodgers has produced 1,397 yards of value over average, and 2,261 yards of value over replacement. [continue reading…]

The Titans lost to the Jaguars last night, dropping Tennessee’s record to a woeful 2-13. The 2014 season started off nicely for the Titans, who upset the Chiefs in Kansas City, 26-10, on opening day. Since then, not only has Tennessee gone just 1-13 (the sole win being a 2-point home victory against Jacksonville), but the team is a mind-bogglingly poor 2-11-1 against the spread.

Points spread data is not official, of course, and some sources of data are better than others. Using what is available at Pro-Football-Reference, I calculated the worst teams against the spread since 1978. If the Titans fail to cover next week against the Colts, they will end the year at 3-12-1 against the spread. That would make them one of just 13 teams since 1978 to post such a poor ATS record. On the other hand, it would only tie them with another AFC South team from the past two years:

The 2007 Ravens went 5-11 overall and 3-13 against the spread, making them the worst team in recent history when it comes to covering the point spread. That year marked the end of the Brian Billick, Steve McNair, and Kyle Boller eras in Baltimore. And while first-year head coach Ken Whisenhunt is probably safe, Titans fans can rest easy knowing that the Jake Locker era is almost certainly over. As for Zach Mettenberger and Charlie Whitehurst? The door may be about to close on them as well. After losing to the Jets and Jaguars, Tennessee looks to be in great shape once the music stops to land Marcus Mariota or Jameis Winston.

Entering week 15, one of the biggest storylines was that Johnny Manziel was set to make his first start of the season. Manziel’s opening performance was a flop: his -0.56 Adjusted Yards per Attempt average was the second lowest by a quarterback this season, although not the lowest by a quarterback in a Browns/Bengals game. The Bengals won 30-0 in a game that was never in doubt for much of the second half; Cincinnati’s +16.6 Game Script was the highest of the week.

The Patriots, Chiefs, and Saints all posted double digit Game Script scores as well. In the process, New England clinched the AFC East, Kansas City kept their playoff hopes alive and avenged an uglier loss to Oakland, and the Saints? Well, New Orleans still controls its own destiny for the playoffs despite a 6-8 record.

The comebacks were light this week, as only Detroit (-3.3) and the Jets (-1.5) managed to win with a negative Game Script. The table below shows the Game Scripts data from week 15: [continue reading…]

The Jets and the Titans play tomorrow, in a matchup of 2-11 teams that ranks as one of the worst in NFL history. If you’re watching this game, you’re either a diehard fan of both teams or are fascinated by the idea of a Chris Johnson revenge game (which is probably even sadder than being a fan of either team). It’s even worse than the Colts-Jaguars game of a few years ago, when the 2-13 Colts needed a loss in Jacksonville to the 4-11 Jags in order to secure the rights to Andrew Luck. Something similar could be on the line in Tennessee: with the Jets, Bucs, and Titans all 2-11 (not to mention the Jaguars and Raiders), there are three quarterback-needy teams in a draft with two marquee quarterbacks: Jameis Winston and Marcus Mariota. As a result, the loser of the New York/Tennessee game could ultimately be the long-term winner.

This will be the first matchup of 2-11 teams since a 2008 game between the Rams and Seahawks. That game turned out to be much less exciting for draftniks with the benefit of hindsight: St. Louis selected Jason Smith with the second overall pick, while the Seahawks drafted Aaron Curry fourth overall.

So what’s the worst matchup of teams in NFL history? You can’t use just winning percentage, and it’s hard to compare teams who have played a different number of games. One solution is to add 11 games of .500 ball to each team. For the Jets and Titans, that would make both teams 7.5-16.5, which translates to an adjusted winning percentage of 0.313. That would be tied for the 19th worst game in NFL history.

The worst? There’s a tie there, too, involving a pair of Colts teams a decade apart. In 1981, the 1-14 Colts defeated the 2-13 Patriots. Baltimore had an adjusted (after adding 11 games of .500 play) winning percentage of 0.250, while New England was at 0.288, for an average of 0.269. The win swung the first overall pick to the Patriots and dropped the Colts to second overall, although Kenneth Sims and Johnie Cooks didn’t change the fate of either franchise. Ten years later, the Colts were again 1-14 and were scheduled to play the 2-13 Bucs. The twist here: Tampa Bay had already traded the team’s first round pick in 1992 to Indianapolis in exchange for Chris Chandler in 1990. The Bucs defeated the Colts, and Indianapolis selected Steve Emtman and Quentin Coryatt with the first two picks. Spoiler alert: that didn’t change the fate of the franchise, either. [continue reading…]

Over the prior two weeks, we were short on comebacks. Things took a big turn towards exciting in week 13:

• The Bengals trailed the Bucs 3-0 for most of the first quarter, and then 10-0 for the majority of the second. Cincinnati would ultimately take the lead by the end of the third quarter, but the Bengals still finished with a -3.0 Game Script.

• On Monday Night Football, the Jets also jumped out to a 10-0 first-half lead before ultimately falling to Miami, 16-13. But more to come on this game later in the post.

• Another team that fell behind 10-0 early was San Diego. In fact, the Chargers didn’t take their first lead against the Ravens until the final minute, winning 34-33 despite posting a Game Script of -5.9.

• But the biggest “comeback” of the week was in Jacksonville, where the Jaguars ruined Tom Coughlin’s homecoming. New York stormed out to a 21-0 lead, but imploded in the second half, allowing Jacksonville to steal the win, 25-24. Jacksonville won with a Game Script of -6.8, the fourth largest of the year and the worst Game Script by a victor since the Lions 21-point comeback in London against the Falcons.

Below are the Game Scripts data for each game in week 13: [continue reading…]

The NFC South has been miserable this season: the Falcons, Saints, Panthers, and Bucs are now 6-23-1 (0.217) in games outside of the division. If Atlanta, New Orleans, Carolina, and Tampa Bay combine to go 3-7 in their ten remaining non-division games, they would eclipse the 2008 NFC West and become the worst division in modern history (at least, by record).

The NFC South has been particularly bad against the AFC North, going 1-10-1 this year. The two non-losses were the shocking upset by Tampa Bay in Pittsburgh in week 4, and the tie between the Bengals and Panthers in week 6 (which ended, you may recall, with Cincinnati kicker Mike Nugent missing a 36-yarder as time expired).

The remaining games between these two divisions in 2014 are: Cincinnati at Tampa Bay, Cleveland at Carolina, Pittsburgh vs. New Orleans, and Pittsburgh at Atlanta.  If the AFC North can go 3-1, that would up its record to 13-2-1, which would set the post-2002 mark for the best record by one division against another in a single season.

The current record? A mark of 13-3, set five times in the current era.  It was most recently done by the NFC West last year, when the only losses it had against the AFC South came versus the Colts (in the case of the 49ers and Seahawks) or the Titans (Rams).

What if the AFC North went on a clean sweep the rest of the way, finishing 14-1-1? That would be the best mark since the merger, but not the best mark of all time.  That honor belongs to the 1965 NFL West: that year, the division went 13-1 against the NFL East.  That’s going to be a tough mark to ever eclipse, as it would require a 15-1 mark given the current format. How about the best mark of the post-merger era by one division against another?

The honor belongs to the AFC West, which went 31-9 outside of its division in 1984. The division really beat up on the NFC Central, going a collective 15-2 in such games. Not surprisingly, the two losses were against the Bears (by Denver and Los Angeles).

Measuring success by one division against another across eras is complicated due to differing number of games. One tweak we can make is to use True Winning Percentage, which adds 11 games of 0.500 ball to any record. If your record is 1-0, True Winning Percentage will strongly regress that 1.000 winning percentage to the mean; if your record is 90-10, not so much: we add 5.5 wins and 5.5 loss regardless of your record. Using that methodology here would translate the AFC North’s record against the NFC South in 2014 from 10-1-1 to 15-6-2 (or 16-7), equivalent to a 0.696 winning percentage. That would be the 5th best in NFL history, and the 3rd best since the merger:

The 1991 NFC East was what TV executives apparently think that division will always be. That year, teams from the NFC East went 14-2 against the NFC Central, with both losses coming by three points (Giants at Bengals), with one going to overtime (Dallas at Houston).

A 3-1 finish would give the AFC North a 13-2-1 record, good enough for a 0.704 true winning percentage.  One more loss would knock it behind the five 13-3 teams of the post-2002 era, but so far this year, the AFC North has been dominating the NFC South at a historic level.

Last year, Jeff Cumberland finished #2 in DVOA among all tight ends.  This really happened.  Of course, that required some digging, so I wrote the following about Cumberland in the 2014 Football Outsiders Almanac:

What’s going on here? How did Cumberland produce such strong numbers, and wind up second in DVOA among tight ends? Among the 52 tight ends with at least 20 targets, Cumberland ranked fifth in yards gained through the air (per reception) and seventh in yards gained after the catch (per reception). Incredibly, [Ladarius] Green ranked first in both of those metrics, but there’s generally an inverse relationship between those two statistics: you either catch passes downfield, or you gain a lot of yards after the catch, but rarely both. In fact, Green and Cumberland were the only two tight ends to rank in the top 15 in both categories, which underscores just how impressive Cumberland’s efficiency numbers were in 2013.

So is Cumberland coming off a sneaky strong season and about to break out? There’s no denying that his efficiency numbers were great, but sometimes, the best course of action is to take a step back and look at the bigger picture. In 2012, Cumberland finished second on the team with 53 targets. In the offseason, New York allowed Dustin Keller to head to Miami, but instead of handing the job to Cumberland, signed Kellen Winslow. As a result, Cumberland wound up seeing only 40 targets in 2013. If the Jets were as high on Cumberland as his numbers would suggest, he would have managed to pick up more than 2.5 targets per game in one of the league’s most anemic passing attacks. Then, New York drafted Jace Amaro in the second round of the 2014 draft. Efficiency numbers are fun to look at, but the revealed preference of the Jets organization would seem to trump those metrics. And it appears as though the organization views Cumberland as a role player and little more.

Cumberland split time with Winslow, and his low target numbers were a strong indicator that he was an average talent. ^[1]In his defense, Cumberland was tied for 11th in yards per route run, but that’s (1) still a far cry from #2 and (2) more a reflection of the weakness of the 2013 Jets supporting cast. Yards per target is not a good stat because it is not very sticky; yards per route run is quite a bit better.  After all, a route run is more the analog of a “pass attempt” than a target, so YPRR is really the receiver’s version of yards per attempt.

This year, as he did in 2013, and 2012, and nearly in 2011, Rob Gronkowski leads all tight ends in yards per route run. He is averaging 2.67 yards per route run on his 304 routes, and the only receivers with a higher yards per route run average on over 225 routes are Demaryius Thomas (2.77) and Jordy Nelson (2.84). In short, Gronkowski is the man.

But, assuming you read the title to this post, you know that today we want to focus not on Gronk, but on baby Gronk. Kansas City’s Travis Kelce is second among tight ends in yards per route run, with a 2.49 average over 218 snaps. Those are incredible numbers, and a reflection that Kelce is already one of the top playmaking tight ends in football.

The Chiefs star hase has 542 yards on 52 targets, and his 10.4 yards/target average is the best among all tight ends.  But remember, Y/T is not a good stat: Cumberland ranked 3rd last season in yards per target, with a 10.2 average. Cumberland’s issue was that he wasn’t targeted very much despite being on the field.  As a result, his yards/target average overstated his value. Let’s throw some math into this equation: in 2013, Gronkowski was targeted on 30% of his routes, the best rate among all tight ends; Cumberland was targeted on 16% of his routes, the 35th best rate among tight ends.

Kelce isn’t the best receiving tight end in football because he leads with a 10.4 yards/target average.  It’s not just about what you do per target, it’s how often you get targeted. As he did last year, Gronk leads all tight ends in targets per route run, as he has been targeted on 28% of his pass routes.  But Kelce ranks 4th — and just a hair behind Jimmy Graham for 3rd place ^[2]Jordan Reed is second. — in targets per route run! He’s not having a fluky season at all, or at least, not in the way Cumberland did. The Chiefs are throwing to Kelce very often when he’s running routes, which is a very good sign that he’s the real deal.

So why is Kelce “only” 6th in receiving yards among tight ends? Because he’s just 28th in pass routes run by tight ends this year. And that’s the real conundrum: he simply isn’t getting much playing time. For the 2013 Jets, Cumberland was on the field for more offensive plays than any Jets player other than the quarterback and offensive linemen. Kelce simply doesn’t get the same level of playing time, as he ranks 4th among non-OL/QBs for Chiefs offensive players in snaps.

The other problem for him is that Kansas City ranks just 31st in pass attempts this year, which is going to depress his raw totals. But the good news is his playing time is on the rise — he was on the field for 63 of 67 snaps in week 12 and 50/52 in week 11. He can’t do anything about how often the Chiefs pass, but in his case — unlike Cumberland’s — the organization seems to be buying into the numbers. Kelce’s been dominant on a per-route basis this year, and now, Kansas City keeps giving him more playing time. The next big question is whether he can maintain his level of production as a full time starter, but the hunch here is that he can. And hey, maybe we just identified the first undervalued fantasy player of 2015.

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…]

Last week, the Game Script winners went 14-0. In week 12, there were two moderately-sized comebacks:

• In New York, the Giants jumped out to 14-3 and 21-10 leads, fueled in part by Odell Beckham being ridiculous. But then the Cowboys offensive line got ridiculous, and Dallas went on a 21-7 run to win the game despite posting a Game Script of -4.0.

• In Denver, the the Dolphins controlled the game for most of the first three quarters. Miami led 21-10 with two minutes left in the first half, and later took a 28-17 lead into the fourth quarter. But the Broncos scored three straight touchdowns to put the game out of reach, despite finishing with a Game Script of -4.3.

Denver had posted a Game Script of at least +5.0 in 6 of the team’s first 7 games, after doing so in 10 of 16 games in 2013. But things have changed drastically in Denver over the last month: the Broncos have had Game Scripts of -11.5, -7.7, and now -4.3 in three of the team’s last four games.

On the positive Game Script side, the Eagles (+14.3) and Bills (+14.2) were the big producers this week, although New England’s +11.2 against Detroit might have been the most impressive when you consider strength of schedule. The table below shows the week 12 Game Scripts data: [continue reading…]

Some weeks, the NFL is filled with comebacks. Some weeks there are teams that wind up winning with strongly negative Game Scripts. And then there was week 11. There were only three comebacks last weekend, and none of them were last-minute comebacks:

• Seattle led 20-17 entering the 4th quarter, but the Chiefs scored the game’s final points — a Knile Davis touchdown — with over 13 minutes left in the quarter.

• Pittsburgh technically trailed 24-13 entering the 4th quarter, before Le’Veon Bell scored on the first play of the final frame. The Steelers scored the go-ahead score with just over nine minutes left, and since Pittsburgh led for most of the first half, the Steelers finished with a Game Script of +0.3.

• Carolina took a 1-point lead with just over 6 minutes left in the game against Atlanta, but the Falcons responded with a field goal on the ensuing drive to win the game. The kick came with just over 2 minutes remaining, but a 1-point 4th quarter deficit doesn’t move the Game Script needle.

The table below shows the Game Scripts for each game in week 11. As you can see, despite some shocking upsets, week 11 was as straightforward as it gets: all 14 teams with positive Game Scripts were victorious. For the second straight week, the Packers provided the biggest Game Script of the week, while the Bucs (!) were the only other team with a Game Script in double digits. [continue reading…]

It was another week of blowouts in the NFL, particularly in prime time. On Thursday night, the Browns shocked the Browns with a 24-3 blowout, as Andy Dalton produced a historically bad performance. Cleveland had a Game Script of +12.8, which turned out to be the third largest of the week.

On Monday Night Football, the Eagles dominated the Panthers. Darren Sproles scored two first quarter touchdowns, Jordan Matthews chipped in with two touchdown catches from Mark Sanchez later in the game, and Philadelphia won, 45-21. That score is a bit misleading, as Kelvin Benjamin caught two late touchdowns: the Eagles had a Game Script of +20.0, which is more in line with about a 40-point win.

But the biggest Game Script of the week came in the other primetime game, Bears at Packers on Sunday Night Football. Aaron Rodgers was an insane 18/24 for 315 yards and 6 touchdowns… in the first half! Green Bay produced a Game Script of +28.7. The Packers took a 42-0 halftime, lead, and finished with the second best Game Script of 2014.

The table below shows the Game Scripts from every game in week 10: [continue reading…]

Last night, Andy Dalton was not very good. completed 10 of 33 passes for 86 yards, while throwing 0 touchdowns and 3 interceptions. Add in his two sacks for 14 yards, and Dalton averaged -1.80 ANY/A. That’s terrible, of course, but how bad does that measure historically? Let’s use the same methodology we did when calculating how good Ben Roethlisberger was against the Colts. Against every other passer this year, the Browns had allowed 5.58 ANY/A to opposing players. As a result, Dalton finished 7.38 ANY/A below expectation.

Over the course of his 35 dropbacks, that means Dalton provided -258 adjusted net yards of value relative to expectation. That’s bad — really bad — but it only ranks as the 83rd worst performance since the merger. The table below shows the 150 worst games since 1970, although for 2014, I have only included Dalton’s game (so Geno Smith and his bad performances could make the list, but I didn’t have time to calculate — feel free to do so in the comments!). [continue reading…]

Andrew Luck and Tom Brady are pretty good. And it appears as though their coaches know it.

The Colts were were the 2nd strongest pass identity of any team in the NFL through seven weeks. Since then, Indianapolis has kicked it up another notch: in week 8, the Colts threw on over 80% of their plays while playing under a Game Script of -11.6 against the Steelers. Well this week, Indianapolis posted a Game Script of +13.0… and still managed to throw on 67% of all plays! Combine the team’s fast tempo with its pass-heavy nature, and you can see why Luck easily leads the league in pass attempts (oh, and pass completions, passing yards, and passing touchdowns). Luck now has 393 attempts; in NFL history, only Drew Bledsoe (401 in 1994) has thrown more passes through 9 games.

In the case of Brady, his name may as well be a proxy for Rob Gronkowski, who has transformed this offense over the past month. New England passed on 68% of plays against the Broncos, despite the Patriots having a Game Script of +11.5. With Stevan Ridley out for the year and Gronkowski playing some of the best football of his career, New England seems destined to stick with this pass-heavy approach. In each of the past three weeks, the Patriots have passed at least 10% more than one would project from the team’s Game Scripts. ^[1]Based on the formula: 0.000005*GameScript^3 – 0.00003 * Game Script^2 – 0.0082 * Game Script +0.5875.

The largest Game Script of the weekend was also the most surprising: Miami blew out San Diego, 37-0, and posted a Game Script of +19.8 along the way. Miami led 7-0, 20-0, and 37-0 after each of the first three quarters in a total annihilation of of the Chargers. Miami was more pass-happy, and San Diego more run-happy, than you might expect: as a result, despite the one-sided nature of the game, the Chargers only passed on 8.5% more plays than the Dolphins. The table below shows the Game Scripts data from each game in week 9: [continue reading…]

The Jets passing offense being bad does not qualify for news.  However, the Jets passing offense and passing defense combining for historically inept numbers? Sure, that qualifies.

New York has thrown 8 touchdown passes this year against 11 interceptions. That’s a -3 differential which is pretty bad.  Only two other teams have negative ratios this year: the Jaguars, also at -3 (11 TDs, 14 INTs), and the Vikings at -5 (6/11).  But the Jets pass defense has allowed 24 touchdowns while forcing just 1… ahem, ONE… interception.  That +23 ratio for opposing quarterbacks is better than any offense this year (the Broncos are at +19 (24/5), and the Patriots and Steelers are both at +20 with matching 23/3 TD/INT ratios).

From the perspective of the Jets defense, though, that +23 reverses to a -23.  Add to that the -3 from the offensive side of the ball, and New York’s combined TD/INT ratio from both units is an incredibly bad -26.

How bad? It’s tied for the 2nd worst number through 9 games since 1970, just narrowly behind the 1975 Cleveland Browns. Those Browns began the year with 3 passing touchdowns and 17 interceptions through nine games. Okay, that was even bad for the dead ball era, but what about the defense? Cleveland allowed 19 passing touchdowns while forcing just six interceptions during that stretch! Those numbers led to an 0-9 start under first-year head coach Forrest Gregg.

The table below shows all teams to start the season with at least a -20 ratio in this statistic I just made up. Here’s how to read the line from the famous 1944 Card/Pitt combination, forced together due to World War II. Through nine games, that team threw 8 touchdowns and 40 interceptions (-32), while allowing 19 passing touchdowns and intercepting just 15 passes (-4), for a total score of -36. [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…]

In 2013, Kenny Stills saw his average reception come 13.9 yards past the line of scrimmage, the farthest amount of yards in the air per catch of any receiver in the NFL. He’s the deep threat in the Saints offense, and he’s being utilized in a similar way this year, with his average catch from Drew Brees coming 12.8 yards downfield. When it comes to the top deep threats in the NFL, Stills and Arizona’s Michael Floyd stand out. Cardinals head coach Bruce Arians loves the vertical passing game, and Floyd has been the perfect weapon: he averaged a healthy 11.7 air yards per catch in 2013, but that number has spiked to 16.5 in 2014!

But not every player’s role is so static. In 2013, the Bengals used A.J. Green (average reception 10.5 yards in the air) and Marvin Jones (9.6) as deep threats, while Tyler Eifert (5.6), Mohamed Sanu (4.3), and Jermaine Gresham (4.2) were used on short/intermediate routes. But Jones will miss all of 2014 due to a foot injury, while Green has been limited to just 43% of the Bengals offensive snaps to date (and he was playing injured for a percentage of those plays, too). As a result, Sanu’s air yards per catch has jumped from 4.3 to 8.4, and his yards per reception has increased from 9.7 to 15.2.

Similarly, Emmanuel Sanders has seen his role change in 2014, as a result of switching teams. Last year, in Pittsburgh, Todd Haley’s offense called for lots of short routes for his wide receivers, but even among the wide receiver group, Sanders (6.3) had the shortest air yards per catch. Eric Decker, meanwhile, had his average reception come 10.8 yards downfield while playing with Peyton Manning. This year, Sanders — taking over Decker’s role — has averaged 10.3 yards in the air per catch.

The graph below shows wide receiver air yards in 2014 (on the X-axis) and 2013 (on the Y-axis): [continue reading…]

Let’s start with the SRS ratings for every team in the NFL. The SRS ratings are generated based off of the points scored, points allowed, home field, and opponent for each game. In its simplest form, the SRS is just an SOS-adjusted version of points differential, although the devil is in the details. After running hundreds of iterations to get the ratings to converge (and awarding 3 points to the home team), below are the ratings through week 8: [continue reading…]

Against Indianapolis in week 8, Ben Roethlisberger was close to perfect. He completed 40 of 49 passes for 522 yards. He threw six touchdowns, and didn’t throw an interception or take a sack. That’s a magnificent performance: in fact, among players with an 80% completion percentage in a game, he set a record for completions. It goes without saying that 500+ yard games are rare, and 6+ TD games are rare, and the combination of both are really rare.

But was it the best passing game ever? Not so fast. Let’s start by calculating his Adjusted Net Yards per Attempt, which gives a 20-yard bonus for touchdown passes, a 45-yard penalty for interceptions, and deducts sack yardage from the numerator (and adds sacks to the denominator). Roethlisberger averaged 13.10 ANY/A, a sparkling number. That’s an outstanding number that needs no qualifier, but it’s even more impressive when you consider the opponent. Entering the day, the Colts were allowing just 5.52 ANY/A to opposing passers.

Therefore, the Steelers star averaged 7.58 more ANY/A against the Colts than the average passer in 2014. Over the course of 49 dropbacks, this means Roethlisberger produced a whopping 372 Adjusted Net Yards above average, with average being defined as what all other passers did against Indianapolis.

That number may not mean much in the abstract. But if the Colts defense continues to allow just 5.52 ANY/A to all other passers year, that would give Roethlisberger the 7th best passing game since 1960. [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…]

With just under five minutes left in last Sunday’s game against the Giants and his team trailing 27-20, Mike Smith went for it on 4^th and 1 from his own 29 yard line. As was the case on repeated 4^th down attempts the last time his team visited MetLife Stadium to face the Giants, the decision to be aggressive did not work out well. Matt Ryan was sacked for a nine-yard loss that effectively ended the game. If his previous behavior is any guide, Smith may learn the wrong lesson from that outcome and choose not to go for it again when the next similar opportunity arises. Smith illustrates better than any other coach the potential for fourth down failure to lead to future fourth down timidity.

Before those two failed Ryan fourth down sneaks against the Giants in that 2011 playoff game, Smith actually was one of the more enlightened coaches on fourth down strategy. From 2008-2011, Smith was the third-most aggressive coach of the last twenty years, at least according to Football Outsiders’ Aggressiveness Index. Dating Smith’s turning point is a little tough. He got burned going for it in Week 10 of the 2011 regular season, when he tried a sneak on 4^th and inches from his own 29 in overtime against the Saints. He punted in a couple of situations where he usually went for it late in the 2011 season, but then was aggressive closer in against the Giants. By the 2012 regular season, Smith hadn’t just abandoned his prior tendency for aggressive strategy. He entirely reversed it. In 2012, he was the least aggressive coach in football, only going for it once in 91 qualifying fourth-down tries. He was similarly passive in 2013. His fourth down decision last Sunday was surprising given that trend.

To see Smith’s evolution on fourth down strategy, consider his decisions on 4^th and 3 or less when between the opponent’s 10- and 40-yard lines. To consider only situations where there was a real choice while keeping things as simple as possible, I look only at first-half decisions along with third-quarter decisions where the margin was ten points or less. [continue reading…]

Allow me to present to you Atlanta running back Antone Smith’s 2014 play-by-play log in its entirety:

Week 1 vs. NO
Qtr	Time	Score	Down/Dist	Yardline	Description
2	11:40	0 - 13	2nd-and-10	own 20	rushed for 2 yards
2	09:16	0 - 13	1st-and-10	opp 31	rushed for 5 yards
3	00:33	17 - 20	2nd-and-9	own 46	caught pass for 54 yards TOUCHDOWN
Week 2 vs. CIN
Qtr	Time	Score	Down/Dist	Yardline	Description
2	14:49	41701	1st-and-10	own 28	caught pass for 4 yards
2	01:14	41708	3rd-and-4	own 38	target of incomplete pass
4	10:23	41722	1st-and-10	opp 35	caught pass for 15 yards (first down)
4	00:54	41936	1st-and-10	own 41	target of incomplete pass
Week 3 vs. TB
Qtr	Time	Score	Down/Dist	Yardline	Description
1	04:21	36708	1st-and-9	opp 9	rushed for 4 yards
2	09:00	28 - 0	1st-and-10	opp 11	rushed for 10 yards (first down)
3	02:36	49 - 0	1st-and-10	opp 36	rushed for -2 yards
3	01:59	49 - 0	2nd-and-12	opp 38	rushed for 38 yards TOUCHDOWN
Week 4 vs. MIN
Qtr	Time	Score	Down/Dist	Yardline	Description
1	05:23	0 - 7	3rd-and-2	opp 29	rushed for 2 yards (first down)
1	04:47	0 - 7	1st-and-10	opp 27	rushed for 3 yards
2	14:55	41834	2nd-and-10	own 31	rushed for 9 yards
3	01:40	21 - 27	1st-and-10	opp 48	rushed for 48 yards TOUCHDOWN
Week 5 vs. NYG
Qtr	Time	Score	Down/Dist	Yardline	Description
1	03:42	0 - 7	1st-and-10	opp 23	rushed for 2 yards
2	14:59	41827	3rd-and-4	opp 4	caught pass for 1 yards
2	12:33	41919	1st-and-10	own 25	caught pass for 8 yards
3	05:51	13 - 10	3rd-and-4	own 26	caught pass for 74 yards TOUCHDOWN

That’s four long touchdowns on 17 offensive touches.  On his four scoring plays, Smith has gained an incredible 214 yards.  That’s the most in the NFL so far, with Steve Smith (162 yards) and Jordy Nelson (160) rounding out the top three.  Perhaps even more incredible is that Smith has gained 214 yards on scoring plays despite gaining only 63 yards on non-scoring plays.  Here’s a chart I tweeted a couple of days ago, showing yards gained on TDs on the X-axis and yards gained on all other plays on the Y-axis: [continue reading…]

In the third quarter on Monday night, I texted my Patriots fan buddy Matt, “Is it possible that we suck? Maybe the run is finally over.” Bill Barnwell mused on this, and Aaron Schatz also wrote about it. It was hard not to think that, given the way the Patriots were manhandled by a mediocre team playing without several key players. It looked every bit as bad as the 41-14 score and maybe worse.

I remember the last time I wondered if the Pats were done. In a 34-14 loss to the Browns in 2010, the Patriots looked pretty impotent. In that game, as in the Chiefs one, the Pats had just under 300 yards of offense. Peyton Hillis ran over the Patriots. Of course, that wasn’t the end. Maybe this time is different, though. If anything the Chiefs game was even worse, so it’s possible this time really is the end. ^[1]And those Pats were 6-1 at the time of the loss to the Browns.

Will the Patriots offense be good later this year? To provide a little insight into this, I went back and looked at performance trends for quarterbacks who have had long careers. The first table looks at quarterbacks since 1969 who have the biggest single-season drops in adjusted net yards per attempt (ANY/A) from the previous five year trend. I look just at quarterbacks with at least 100 attempts in a season and I weight by the number of attempts when calculating the average ANY/A over the previous five years.

[continue reading…]

I am getting some well-deserved crap from people about just how bad my predictions have been so far. The Arizona Cardinals have already somehow outperformed the number of wins I gave them. The Jacksonville Jaguars, my pick to win the AFC South at 8-8, at one point in the game against the Colts had been outscored 112-13 over a stretch of about nine quarters. And my pick to win the NFC North at 14-2 could be 0-3 if Marty Mornhinweg let his head coach call the timeouts. ^[1]We are talking about the Jets here so they probably would have blown that game, anyway.

But I did win my first Stone-Cold Mega-Lock of the Week with my very comfortable tease of the Bengals and Falcons. So things are looking up and I’m taking that as license to check out some historical betting data for anything that might seem appealing after three weeks.

Last year’s Carolina Panthers are the inspiration for the analysis here. After three weeks, they were a 1-2 team with a big positive point differential. The Panthers last year lost 12-7 to Seattle and 24-23 to Buffalo before annihilating the Giants 38-0. Despite VOA liking the Panthers even after just three games, the betting market came around later in at least one way. The Panthers were at 3/1 to make the playoffs last year after three weeks, even though Football Outsiders had their playoff odds at over 50% at that time.

Is it possible that teams like the 2013 Panthers have historically been undervalued? It seems likely that Carolina was a little undervalued last year after three weeks. By looking at point spread data, we can see if teams that have likely been better than their records have been good bets in the early part of the season. Specifically, I’m going to look at whether betting on early-season underachievers (teams with deceptively poor records) or against overachievers has been profitable now and in the past.

Data and Methods

Feel free to skip this part, but here’s the background for those interested. I have put together Pro Football Reference’s point spread data for all games from 1979 to 2012. This sample is good enough for the tests of long-term and recent betting strategies that I want to do.

I’m going to look at betting outcomes in games 4-8 for teams that are either losing teams (winning percentage below 0.5) with strong Pythagorean records or winning teams with weak Pythagorean records. I will keep things simple and define Pythagorean wins here as:

Pythagorean Wins = (Previous Points Scored ^2.53)/(Previous Points Scored^2.53 + Previous Points Allowed^2.53)

In a continuing effort to avoid unnecessary complications, I’m just going to split the data up over time, looking separately at results before and after 2000.

Betting On and Against Pythagorean Outliers

Below is how you would have done over time if you bet on or against two kinds of teams:

• Overachievers: Teams with winning records with bad point differentials for their records
• Underachievers: Teams with losing records with good point differentials for their records

An overachiever is more specifically a team with a winning record that has a Pythagorean winning percentage at least 25 percentage points worse than their actual winning percentage. An underachiever has a Pythagorean winning percentage at least 25 percentage points better than actual.

The results show that, before 2000, you would have won most of the time betting on overachieving teams, teams that were not as good as their records would suggest. I was surprised by that and it even made me wonder if I made a coding mistake. I certainly expected that any tendency away from an even split would have been in favor of betting against teams with good records and relatively poor point differentials. Note that the even split occurred in the past for the underachievers, the teams with good point differentials and poor records.

More recently, the data come pretty close to an even split for betting both on the overachievers and the underachievers. Betting on the overachievers and the underachievers has been successful about 52% of the time since 1999.

So the overall message is that there is little value now or in the past in identifying Pythagorean outliers and either riding the teams with deceptively poor records or fading the teams with misleadingly good ones. In fact, the only pattern from the past suggested it was a good idea to ride the teams with misleadingly good records. I tried to check this out a bit to just see if it was just betting on teams with good records that was profitable, but betting on all teams with winning percentages over .750 has gone almost exactly dead even over time. It would be great to hear any thoughts you might have in the comments for this pattern. I feel like I’m missing something.

Overall, the message here is the one that we get most of the time if we try to find patterns that might lead to a consistently profitable and simple betting strategy. It just ain’t there. That doesn’t make this a bad post, though: as Chase once noted, an answer of “not useful” is often just as meaningful as any other answers.

The Stone-Cold (I Think There May Be a 60% Chance This Bet Will Win) Mega-Lock of the Week

So I am now 33% on my Stone-Cold Mega Locks of the Week. If I get the next two, I will be at 60%. If I get the next two after that, I’ll be at 71%. I kind of think I should be able to claim extra points already, Chris Berman-style, for my tease last week, since the Falcons and Bengals won by a combined 89-21 score that wasn’t that close. But I will instead put my faith in the always reliable larger sample size that will bear out these predictions living up to their title. ^[2]Note that no mega-lock promises were made on the season predictions.

Two-team teaser: Pittsburgh down to -1.5 and Indianapolis down to -1.5

This week, I like another two-team teaser of two home teams, this time down to 1.5 points. I particularly like the Steelers down to 1.5 points. I do not understand how they could be the same offense for quarters 3-8 of this season as they were for the other high-efficiency ones. Still, I like the Steelers (#10 in DVOA) at home against the Buccaneers (#32).

I’m a little less sure about the other side of the tease, where I have Indianapolis (#21) over Tennessee (#25). In fact, I mainly just wanted to get the Pittsburgh end of the tease. I may be getting that queasy-knees feeling come Sunday. It’s hard to feel that way about Andrew Luck, but I didn’t imagine I’d ever be going into the water tethered to a Ryan Grigson-led team.

Season record: 1-2

Last week, Neil Paine wrote that while the Eagles were 2-0, it was not all sunshine and rainbows in Philadelphia. The Eagles posted Game Scripts of -7.1 against Jacksonville and -4.8 against Indianapolis; based on Neil’s research, the Eagles had the worst Game Scripts of any team to start 0-2 since at least 1978.

Against Washington in week 3, the Eagles fell behind 17-7 before coming from behind and again emerging victorious. As a result — and after trailing the Jags 17-0 and the Colts 20-6 — Philadelphia became the first team since at least 1940 to start a season 3-0 despite trailing by at least 10 points in each game.

In fact, only three teams had ever overcome a deficit of a touchdown or greater in each of their first three games: the 2000 Rams, the 2000 Jets, and the 1960 Giants. Those teams finished the season 10-6, 9-7, and 6-4-2 respectively, which means they went just 16-17-2 the rest of the season after starting 9-0.

In general, teams that have started 3-0 despite constantly falling behind have not been as successful over the rest of the season as other 3-0 teams. In fact, if you add up the worst margin for each 3-0 team in each game, 25 teams have trailed by an “aggregate” of 21+ points in those three games. On average, those teams won just 53.5% of the remainder of their 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 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…]

Since 1940, there have been 616 times where a team rushed for at least 125 yards and completed at least 75% of its passes. On Sunday, when Washington pulled off that feat against the Texans, they became the first team to fail to score double digit points in the process.

In the second half, both RG3 and Niles Paul lost fumbles inside the Houston 10-yard line; that obviously contributed to the team failing to score more than 6 points. But Griffin’s 78.4% completion percentage was also pretty misleading. Griffin’s average throw went just 5.8 yards in the air, and his average completion covered just 3.9 yards before including his receiver’s yards gained after the catch. Both of those averages put ranked 30th among 32 qualifying passers. But while short throws can be part of an effective offense, on Sunday, that wasn’t the case for Washington. Consider:

• A 4th and 10 completion to Roy Helu for 6 yards
• A 3rd and 16 completion (on the Washington 15) to Helu for 9 yards
• A 3rd and 13 completion to DeSean Jackson for 0 yards
• A 2nd and 25 completion to Jackson for 0 yards
• A 2nd and 19 completion to Pierre Garcon for 3 yards
• A 2nd and 14 completion to Logal Paulsen for -3 yards
• A 2nd and 8 completion to Garcon for 3 yards
• A 2nd and 1 completion to Jackson for 0 yards
• Four 1st and 10 completions to Jordan Reed, Paulsen, Paul, and Darrel Young for 4, 3, 2, and 1 yard(s), respectively.

Sure, Griffin completed 29 of his 37 passes, but 12 of his completions did little or nothing to help his offense.  He also was sacked three times.  As a result, just 17 of his 40 dropbacks — or 42.5% — were successful completions.

To be fair, this isn’t as much a knock of Griffin as the Washington offense as a whole, or perhaps just a counter to those who like to rely on completion percentage or its brother, passer rating.  If Griffin’s targets could have gained more yards after the catch, things would have looked a lot different.  And against the frightening pass rush of J.J. Watt and company, ^[1]While Jadeveon Clowney went out early, Whitney Mercilus, Brooks Reed, and Brian Cushing all got to Griffin several times. short passes make some sense.  But looking at Griffin’s completion percentage and concluding he had a good game is kind of silly. Again, more a knock on the misuse of statistics than the player.

Football Outsiders considers a completion that fails to gain a first down on 3rd or 4th down, a completion that fails to gain at least 60% of the distance needed on 2nd down, or a completion that fails to gain at least 45% of the needed yards on 1st down to all be failed completions. Those cut-offs seem reasonable enough to use for theses purposes. Looking at the numbers, Griffin led the NFL in failed completions in week one.

Here’s how to read the table below. In week 1, Griffin completed 29 of 37 passes, producing a completion percentage of 78.4%. However, 12 of his completions were failed completions, as identified above. That means 41.4% of his completions were failed completions. He also took 3 sacks; as a result, just 42.5% of his dropbacks were successful completions. The difference between his raw completion percentage and his SCmp/DB average was 35.9%. [continue reading…]

Is week 1 a window into a team’s soul? Or is week 1 best left ignored by analysts, since results are skewed by teams that are still shaking off the rust from the summer? As it turns out, week 1 isn’t just like any other week: it’s more like any other week than, uh, any other week. What do I mean by that?

Let’s begin with a hypothesis. The best teams in the league are [more/less] likely to win in week 1 than they are normally. This is because the best teams are [at their best/rusty] in week 1. How would we go about proving this to be true?

One method would be to take a weighted average winning percentage of teams in week one, with the weight being on the team’s actual season-ending winning percentage. For example, the Patriots went 16-0 in 2007, which means New England was responsible for 6.25% of all wins in the NFL that season. That year, the Colts went 13-3, so Indianapolis was responsible for 5.1% of all wins that year. If we want to know whether good teams play [better/worse] in week 1, we care a lot more about how teams like the ’07 Patriots and Colts fared than the average team.

By using weighted average winning percentages, we place more weight on the results of the best teams, which is exactly what we want to do. So when the ’07 Patriots and ’07 Colts won in week one, rather than being responsible for 6.25% of the league, they are now are responsible for over 11% of the NFL’s weighted week 1 winning percentage. Of course, you can probably figure out pretty quickly that by using this methodology, we are ensuring that the “average” winning percentage over the course of the season will be quite a bit over .500, since the best teams will win more often than not. And that’s exactly what we see: the average weighted winning percentage across all weeks, using this methodology, was 0.574. As it turns out, that’s exactly what the average is in week 1, too. [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…]