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2016 ANY/A Update

Matt Ryan is having a career year, in a not disimilar way from what Carson Palmer did last season. Thanks to a superstar receiver and an offensive coaching staff that is drawing rave reviews, Ryan is having the sort of once-in-a-career year expected from a top-3 pick.  In fact, Ryan is even ahead of Palmer’s pace from last year:

Rk Age Year Lg Tm G W L Cmp Att Cmp% Yds TD Int Rate Sk Yds ANY/A
1 Matt Ryan 31 2016 NFL ATL 6 4 2 143 210 68.10 2075 15 3 117.9 15 98 9.52
2 Carson Palmer 35 2015 NFL CRD 6 4 2 125 193 64.77 1737 14 5 106.9 8 42 8.71

The Falcons ranked 17th in ANY/A last year, and 1st this year; Atlanta’s offensive ANY/A has jumped by 3.34 ANY/A, the biggest leap in the league. You might think the Jets — 14th in ANY/A last year, 32nd this year — would have the biggest decline, but New York’s dip is only the second worst. That’s because Palmer, who had a very lofty perch from which to fall, has been far below-average this season: [click to continue…]


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

Adjusted Points Per Drive

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

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

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

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

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

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

Week 5, 2016 Game Scripts: The 49ers Keep On Running


There were no big comebacks this week, but a few games where the Game Script exceeded the final margin. The Cowboys blew out the Bengals, and led 28-0 entering the fourth quarter; the game ended, 28-14.

With three minutes to go in the Broncos/Falcons game, Atlanta led 23-6. The Falcons were in control for most of the game, leading 20-3 mid-way through the third quarter. Denver scored ten points in the final three minutes, to give a not-as-close-as-it-looked final score of 23-16.

The Packers led 23-9 with a few minutes left in the game, before the Giants scored a touchdown. New York trailed by 8 points at times later in the game, but last trailed by 7 points with the ball with eight minutes left in the second quarter.

Below are the Week 5 Game Scripts data: [click to continue…]



Back in February, Mike Mularkey declared that his vision for Tennessee offense would be something best described as exotic smashmouth.  Then, the Titans passed on 2 out of every 3 plays in a week 1 loss to the Vikings.

Since then, Tennessee has been more run-heavy each week, culminating in a very run-heavy performance in week four. Against Houston, the Titans finished with 32 runs and 30 passes (tho that includes three Marcus Mariota scrambles), despite trailing for most of the game.  Tennessee had a Game Script of -5.8, yet was the only losing team with 30 rushing attempts this week.

Is it working? That’s tough to say: the Titans had 32 carries for 124 yards and 2 touchdowns, which sounds pretty good; meanwhile, Mariota had 196 net passing yards on 30 dropbacks with an interception and no touchdowns, which represents a league average NY/A gain.   So the running game may be a strength for the team, and the passing game may be a weakness; if that holds up, exotic smashmouth makes sense.

On the other hand, taking a big picture look at the Tennessee offense, and it is not good: The Titans are 31st in scoring, and that’s despite ranking 4th in rushing yards and 3rd in yards per carry.

Below are the week 4 game scripts data: [click to continue…]

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Week 3, 2016 Game Scripts: Tampa Bay Turns To Winston


In week 3, only one team won with a negative Game Script. That was early season Game Script favorite Washington, who trailed 21-9 in the first half but came back to win against the Giants, 29-27. In the process, Washington produced its most run-heavy game of the year, with 30 carries (including two kneels) against 37 passes (excluding one spike). Was there a correlation between running more and winning? Washington running backs weren’t very effective — they had 27 carries for 97 yards — but the balance may have helped Kirk Cousins have his best game of the year (9.60 AY/A, 75.1 QBR).

Given that there were no other teams that won with negative Game Scripts, let’s get to the results: [click to continue…]


Previously: Week 1 Game Scripts

The Chargers produced a Game Script of +20.0 in a blowout win over the Jaguars that was worse than the final score indicated; San Diego was up by 21 points before the 21-minute mark of the game! The Cardinals (+15.9 vs. Tampa Bay) and Patriots (+15.6 in a 7-point win over Miami) also had monster Game Scripts in week 2.

Two teams did pull off massive comebacks on Sunday. The first was in Cleveland, where the Ravens came back from a 20-0 deficit to beat the Browns, 25-20. Cleveland became just the 5th team to score 20+ points in the 1st quarter, and then lose while getting shut out for the rest of the game.  The game seemed to turn on a blocked extra point returned by Tavon Young for two points after Cleveland’s final score; that was just the second time an extra point has been returned for two since the rule change was instituted last year.  The other comeback was in Detroit, where the Titans scored 13 4th quarter points to beat Detroit, 16-15, and tank my survivor dreams in the process.

Below are the week 2 Game Scripts: [click to continue…]


538: What Is Wrong With Aaron Rodgers?

Today at 538: What is wrong with Aaron Rodgers?

From 2008 to 2014, Rodgers averaged 7.34 yards per dropback,1 according to ESPN’s Stats & Information Group. Rodgers’s rate was the second-best during that time period and just 0.01 yards per dropback behind Peyton Manning’s. That sort of dominant play earned Rodgers two MVP awards and helped the Packers win a Super Bowl.

Recently, things haven’t gone quite so well. Rodgers has averaged 5.79 yards per dropback since the start of 2015. Since November of last year, the Packers are just 5-7. And Rodgers is in the middle of a cold spell prolonged enough to prompt his coach to chip in with a vote of confidence — never a great sign. But what’s to blame for the decline — a change in scheme? Rodgers’s skills? The steady physical destruction of his most trusted receivers? That’s tough to untangle, but we can give it a try.

You can read the full article here.


Regular readers know all about Game Scripts, but you can learn more about them here. Essentially, Game Scripts is the term I’ve used to represent the average margin of lead or deficit over the course of every second of a game.

Last year, I detailed the Game Scripts each week, and I’ll do that again this year.  At the top right of every page, you can see the 2015 Game Scripts, and the dropdown arrow will bring up the 2014 and 2013 results, too.

In week 1, six teams won with negative Game Scripts, including a few big comebacks. The Panthers led 17-7 at halftime in Denver, but the Broncos came back behind two C.J. Anderson touchdowns.  Oakland trailed 24-10 with 20 minutes left in New Orleans, but scored 25 points in the final 20 minutes to pull out a last-minute  win over the Saints.

But the big comeback, of course, was in Kansas City.   With 20 minutes left in that game, the Chiefs trailed 24-3.  With 3:57 left, Kansas City faced a 4th-and-5 at the San Diego 25-yard line; at that time, the Chiefs win probability was less than two percent.  Starting then, Alex Smith went 22 for 29 for 208 yards with 2 TDs and 14 1st downs, along with one interception, and ran three times for 14 yards and a touchdown.

The table below shows the week 1 Game Scripts: [click to continue…]


I highly recommend the Bill Barnwell podcast, and this week’s episode previewing the NFC South was a good one. When hearing about the Saints terrible defense last year, Barnwell noted that it seemed like the Saints defense was always allowing big touchdowns.

Well, that’s true: New Orleans gave up a whopping ten touchdown passes of 40+ yards last season; Washington was second with 7 such touchdowns, and that included three touchdowns of exactly 40 yards. By contrast, the Saints allowed six touchdown passes of 50+ yards! The last pass defense to allow 10 touchdowns of 40+ yards was the 1989 Houston Oilers, a 9-7 team that made the playoffs.

The most long (i.e., 40+ yards) passing touchdowns allowed in a season? That sad place in history belongs to another Oilers team. In 1966, Houston allowed 15 such touchdowns in a 14-game season. The 1961 Bills allowed 14 touchdown passes of 40+ yards, the 1950 Rams allowed 12 such scores, and the ’83 Cowboys, ’68 Dolphins, ’65 Browns, and ’52 Texans allowed 11 long touchdowns.

Last year’s Saints allowed, on average, 7.9 yards on every opposing dropback last year. That’s the largest average gain since the 1981 Colts defense (8.2), and it was obviously inflated by all those long touchdowns. But the good news for Saints fans is that regression to the mean has to help New Orleans… right? [click to continue…]


David Johnson, Receiving Superstar

The great Chris Wesseling at NFL.com published an article this week about Cardinals running back David Johnson.  The piece contained lofty praise about Johnson’s receiving ability, so much so that it made me want to re-evaluate his rookie stats.

One place where Johnson’s receiving ability stands out is in his yards per reception. As a rookie last year, he became the first player (rookie or otherwise) in 16 years to average 12 yards per reception while gaining at least 400+ rushing yards and 400+ receiving yards.   And just the third in the last 25 years:

Games Rushing Receiving
Rk Player Year Age Draft Tm G GS Att Yds Y/A TD Y/G Rec Yds Y/R TD Y/G
1 David Johnson 2015 24 3-86 ARI 16 5 125 581 4.65 8 36.3 36 457 12.69 4 28.6
2 Marshall Faulk* 1999 26 1-2 STL 16 16 253 1381 5.46 7 86.3 87 1048 12.05 5 65.5
3 Garrison Hearst 1998 27 1-3 SFO 16 16 310 1570 5.06 7 98.1 39 535 13.72 2 33.4
4 Gary Anderson 1990 29 1-20 TAM 16 13 166 646 3.89 3 40.4 38 464 12.21 2 29.0
5 Barry Sanders* 1990 22 1-3 DET 16 16 255 1304 5.11 13 81.5 36 480 13.33 3 30.0
6 Albert Bentley 1987 27 2-35 IND 12 4 142 631 4.44 7 52.6 34 447 13.15 2 37.3
7 James Brooks 1986 28 1-24 CIN 16 16 205 1087 5.30 5 67.9 54 686 12.70 4 42.9
8 Gary Anderson 1985 24 1-20 SDG 12 6 116 429 3.70 4 35.8 35 422 12.06 2 35.2
9 Curtis Dickey 1983 27 1-5 BAL 16 16 254 1122 4.42 4 70.1 24 483 20.13 3 30.2
10 Darrin Nelson 1983 24 1-7 MIN 15 9 154 642 4.17 1 42.8 51 618 12.12 0 41.2
11 Joe Cribbs 1981 23 2-29 BUF 15 15 257 1097 4.27 3 73.1 40 603 15.08 7 40.2
12 Billy Sims 1981 26 1-1 DET 14 14 296 1437 4.85 13 102.6 28 451 16.11 2 32.2
13 Billy Sims 1980 25 1-1 DET 16 16 313 1303 4.16 13 81.4 51 621 12.18 3 38.8
14 Wilbert Montgomery 1979 25 6-154 PHI 16 16 338 1512 4.47 9 94.5 41 494 12.05 5 30.9
15 Greg Pruitt 1977 26 2-30 CLE 14 14 236 1086 4.60 3 77.6 37 471 12.73 1 33.6
16 Sherman Smith 1977 23 2-58 SEA 14 14 163 763 4.68 4 54.5 30 419 13.97 2 29.9
17 O.J. Simpson* 1975 28 1-1 BUF 14 14 329 1817 5.52 16 129.8 28 426 15.21 7 30.4
18 Mike Thomas 1975 22 5-108 WAS 14 10 235 919 3.91 4 65.6 40 483 12.08 3 34.5
19 Mack Herron 1974 26 6-143 NWE 14 14 231 824 3.57 7 58.9 38 474 12.47 5 33.9
20 Larry Brown 1973 26 8-191 WAS 14 14 273 860 3.15 8 61.4 40 482 12.05 6 34.4
21 Larry Brown 1972 25 8-191 WAS 12 12 285 1216 4.27 8 101.3 32 473 14.78 4 39.4
22 Cid Edwards 1972 29 SDG 12 12 157 679 4.32 5 56.6 40 557 13.93 2 46.4
23 Carl Garrett 1972 25 3-58 NWE 10 6 131 488 3.73 5 48.8 30 410 13.67 0 41.0
24 Essex Johnson 1972 26 6-156 CIN 14 11 212 825 3.89 4 58.9 29 420 14.48 2 30.0

And while you may remember Johnsons’s game-clinching, 55-yard touchdown catch against the Saints, it wasn’t just one or two catches boosting up his average gain. Consider: there were 40 running backs last year who had at least 25 receptions.  Of that group, only Johnson (58%) converted at least half of his receptions into first downs. To find a player with a better conversion rate, you’d have to go down to Arian Foster, who converted 13 of his 22 catches (59%) into first downs.))

1David Johnson362158.3%
2Danny Woodhead803948.8%
3James White401947.5%
4Dion Lewis361747.2%
5Benny Cunningham261246.2%
6Bilal Powell472144.7%
7Mark Ingram502244%
7Ameer Abdullah251144%
9Charles Sims512243.1%
10T.J. Yeldon361541.7%
11Theo Riddick803240%
11Chris Thompson351440%
11Marcel Reece301240%
14James Starks431739.5%
15Giovani Bernard491938.8%
16Duke Johnson622438.7%
16Dexter McCluster311238.7%
18Matt Forte441738.6%
19C.J. Spiller341338.2%
20Darren Sproles552138.2%
21DeAngelo Williams401537.5%
21LeSean McCoy321237.5%
21Fred Jackson321237.5%
24Shane Vereen592237.3%
25Javorius Allen451533.3%
25Chris Ivory301033.3%
27Frank Gore341132.4%
28Lamar Miller471531.9%
29Jonathan Grimes26830.8%
30Doug Martin331030.3%
31Darren McFadden401230%
31Adrian Peterson30930%
33DeMarco Murray441329.5%
34Devonta Freeman732128.8%
35Rashad Jennings29827.6%
36Shaun Draughn27725.9%
37Melvin Gordon33824.2%
38C.J. Anderson25624%
39Latavius Murray41922%
40Justin Forsett31412.9%

It’s obviously premature to talk about Johnson as an all-time great receiving back, despite the quotes in Wesseling’s article. But this gives us something else to keep an eye on in 2016.


Era-Adjusted TD/INT Ratios

On Saturday, I asked the question: Is TD/INT Ratio Now Meaningless? That question was shorthand for a less hot-takey view, but fortunately I’m lucky enough to have smart readers who engaged in some excellent discussion in the comments. One of those guys, Bryan Frye, brought up the idea of a TD/INT+ Ratio, or an era-adjusted version.

I spent a bit of time playing with different ways to adjust for era, including using Z-Scores. One problem there was that the variance in TD and INT Rates is pretty significant from year to year, which makes the Z-Score heavily influenced more by year-to-year fluctuation that true era adjustments. So here’s what I did, using Milt Plum in 1960 and Tom Brady in 2010 as examples. Those two players rank 4th and 5th in this system despite playing 50 years apart. The raw numbers? Brady had a 9:1 TD/INT Ratio with 36 TDs and 4 INTs, while Plus was at 4.2:1, courtesy of 21 TDs and 5 INTs.

1) First, we convert to rates. Plum threw 250 passes, while Brady had almost exactly double, with 492 attempts. Plum averaged 8.40 TD/100Att, while Brady was at 7.32 TD/100Att. On the other hand, Plum was at 2.00 INT/100Att, vs. 0.81 INT/100Att for Brady.

2) Next, we adjust for era, using only players who had enough pass attempts to qualify for the league passing crown, and taking a simple average of the rates of those players. Therefore, for the 1960 NFL season, the TD/100Att average was 5.34, while the INT/100Att average was 7.13. So Plum was at 157% of league average at throwing touchdowns and 357% of league average at avoiding interceptions.

Brady? Well, in 2010, the qualifying passers averaged 4.47 touchdowns and 2.78 interceptions per 100 pass attempts. This means he was at 164% of league average in touchdowns and 342% at avoiding interceptions

3) Finally, we multiply the two rates. So Plum’s Adjusted TD Rate times his Adjusted INT Rate was 5.61, while Brady was at 5.60. [click to continue…]


Right now, three of the top 20 running backs in career receptions are active: Matt Forte, Darren Sproles, and Reggie Bush. Note that for these purposes, players like Bobby Mitchell, Charley Taylor, and Eric Metcalf — who all entered the league as running backs but then converted to wide receiver — were excluded.

Games Rushing Receiving
Rk Player From To Draft G GS Att Yds Y/A TD Y/G Tgt Rec
Yds Y/R TD Y/G Ctch% Y/Tgt
1 Larry Centers 1990 2003 5-115 198 108 615 2188 3.56 14 11.1 1044 827 6797 8.22 28 34.3 6.51
2 Marshall Faulk* 1994 2005 1-2 176 156 2836 12279 4.33 100 69.8 1013 767 6875 8.96 36 39.1 75.7% 6.79
3 LaDainian Tomlinson 2001 2011 1-5 170 155 3174 13684 4.31 145 80.5 868 624 4772 7.65 17 28.1 71.9% 5.50
4 Keith Byars 1986 1998 1-10 189 160 865 3109 3.59 23 16.4 428 610 5661 9.28 31 30.0 13.23
5 Marcus Allen* 1982 1997 1-10 222 168 3022 12243 4.05 123 55.1 241 587 5411 9.22 21 24.4 22.45
6 Tiki Barber 1997 2006 2-36 154 109 2217 10449 4.71 55 67.9 814 586 5183 8.84 12 33.7 72.0% 6.37
7 Ronnie Harmon 1986 1997 1-16 181 27 615 2774 4.51 10 15.3 462 582 6076 10.44 24 33.6 13.15
8 Roger Craig 1983 1993 2-49 165 133 1991 8189 4.11 56 49.6 62 566 4911 8.68 17 29.8 79.21
9 John Williams 1986 1995 1-15 149 133 1245 5006 4.02 18 33.6 316 546 4656 8.53 19 31.2 14.73
10 Eric Metcalf 1989 2002 1-13 179 77 630 2392 3.80 12 13.4 635 541 5572 10.30 31 31.1 8.77
11 Herschel Walker 1986 1997 5-114 187 137 1954 8225 4.21 61 44.0 296 512 4859 9.49 21 26.0 16.42
11 Earnest Byner 1984 1997 10-280 211 131 2095 8261 3.94 56 39.2 275 512 4605 8.99 15 21.8 16.75
13 Warrick Dunn 1997 2008 1-12 181 154 2669 10967 4.11 49 60.6 710 510 4339 8.51 15 24.0 71.8% 6.11
14 Walter Payton* 1975 1987 1-4 190 184 3838 16726 4.36 110 88.0 492 4538 9.22 15 23.9
15 Tony Galbreath 1976 1987 2-32 170 73 1031 4072 3.95 34 24.0 490 4066 8.30 9 23.9
16 Matt Forte 2008 2015 2-44 120 120 2035 8602 4.23 45 71.7 636 487 4116 8.45 19 34.3 76.6% 6.47
17 Curtis Martin* 1995 2005 3-74 168 166 3518 14101 4.01 90 83.9 606 484 3329 6.88 10 19.8 79.9% 5.49
18 Darren Sproles 2005 2015 4-130 153 23 577 2867 4.97 20 18.7 631 473 4156 8.79 28 27.2 75.0% 6.59
19 Thurman Thomas* 1988 2000 2-40 182 160 2877 12074 4.20 65 66.3 416 472 4458 9.44 23 24.5 10.72
20 Reggie Bush 2006 2015 1-2 121 96 1274 5493 4.31 35 45.4 652 470 3508 7.46 18 29.0 72.1% 5.38

Sproles just turned 33, and entered the league back in 2005.  He was a rookie at 22, but as a late 4th round pick, he had just 42 career receptions before turning 26.

Bush was the second overall pick in ’06, of course, and he entered the NFL at just 21.  He got off to a blazing start, tying the NFL record for receptions through two seasons set by Larry Fitzgerald.1 But Bush has not maintain that level of play, and the future isn’t all that bright. He turned 31 in March, and just signed with the Bills, his 5th NFL team.  Bush had just 4 catches  in five games last year, before an ACL injury in St. Louis ended his season.

Forte, despite being only nine months younger than Bush, he entered the NFL two years later. Forte has been a mix of Bush and Sproles when it comes to the age curve: he started off strong, like Bush, but has aged well, like Sproles.  Forte had 223 receptions in his first four seasons in 60 games; In his last 4 years, he has also played in 60 games, and caught 264 passes.

Despite being the youngest of the three, Forte has the most career receptions.  Bush had more receptions last year, but given the age difference, Forte seems like the better bet to become the 5th running back — and only 3rd non-fullback — to hit the 600-reception mark.

  1. By the end of that season, his teammate Marques Colston broke that record, and A.J. Green, Odell Beckham, and Jarvis Landry have all since broken that Bush’s mark. []

Positive Yards Per Attempt (Updated)

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

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

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

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

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

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

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

Antonio Brown is the Steelers leader in touchdown celebrations

Antonio Brown is the Steelers leader in touchdown celebrations

Is Antonio Brown already the best wide receiver in Steelers history? That depends on how you define “best”, of course. But from at least one statistical standpoint, Brown already stands out as the most dominant.

One of my favorite simple methods to measure dominance is to measure receiving yards above the worst starter. For example, the 32nd-ranked player in receiving yards last year gained 922 receiving yards. Brown, meanwhile, had 1,834. As a result, he had 912 receiving yards above the “worst starter” last year.

In 2014, the 32nd-ranked receiving yards leader gained 916 yards; Brown had 1,698, so that’s +782. In 2013, Brown’s 1,499 yards were 603 yards above the baseline of 896, i.e., the amount of yards gained by the 32nd-ranked receiver.

In 2012, the baseline was 855 receiving yards; Brown, with 787 in 13 games, did not rank in the top 32 in receiving yards. Therefore, he gets a 0 for 2012. Finally, in 2011, Browns’ 1,108 receiving yards were 221 receiving yards above the threshold of 887 yards.

As a result, Brown’s six-year career looks like this: +912, +782, +603, 0, +221, 0. That sums to 2,518 yards above worst starter.

Last year, I looked at the leaders in Adjusted Catch Yards over worst starter using the same formula. I re-ran that methodology using receiving yards and pro-rating non-16 games to come up with a career list. The table below shows the top 200 players in football history using this methodology; Brown checks in at #31: [click to continue…]


You probably aren’t too surprised by this headline: until 2014, no team had ever repeated as NFC South champions (then the Panthers three-peated last year). From ’03 to ’06, all four teams won the division; then, all four teams won a division title from ’07 to ’10, too. It’s been an inconsistent division, but Carolina is now bringing some stability to the top of the NFC South.

That’s ironic, though, because since realignment and expansion in 2002, no team has been as inconsistent as Carolina. Consider the 2009-2010 Panthers; in ’09, Carolina went 8-8 but had an SRS of +3.9 (the .500 record was the product of an SOS of +3.5). But in 2010, the Panthers went 2-14, with an SRS of -13.2. That’s a change of 17.1 points, which is pretty significant. And over the last three years, Carolina has made two big changes: from +9.2 in ’13 to -3.1 in ’14 to +8.1 last year.

In fact, let’s take a look at how Carolina’s SRS changed in every year since realignment. That means starting in 2003, using the ’02 season as the N-1 year: [click to continue…]


Not a reach to call Nuk one of the best players in the NFL

Not a reach to call Nuk one of the best players in the NFL

Last year, Antonio Brown and Julio Jones were the best wide receivers in the NFL. But DeAndre Hopkins was was in a small group of receivers after those two vying for the title of third best wideout. And when it comes to relying on one player, well, Hopkins really stands out among the pack.

Last year, Jones had 40.7% of all Falcons receiving yards, highest rate in the league. That was followed by Brown at 38.0%, and then Hopkins at 37.3%. After him, Brandon Marshall was at 36.0%, and Odell Beckham was a distant fifth at 32.2%. And at just 23 years old, Hopkins obviously has a very bright future ahead of him.

Since 1970, there have been 132 player seasons where a player had at least 35.0% of his team’s receiving yards. But as you’d suspect, it’s rarely done by a player as young as Hopkins. The bar graph below shows how many players at each age have hit that mark since the Merger: [click to continue…]


Least-Conforming Games of 2015

The Buccaneers were not very good last year. Tampa Bay finished with the worst SRS in the NFC, and the second-worst in the NFL ahead of only Tennessee. But that doesn’t mean the Bucs season was predictable; in fact, Tampa Bay had arguably the two weirdest games of the year.

The Bucs opened the season with the least-conforming game of the first half of the season: Tampa lost, at home, to Tennessee, by 28 points! That’s incredible: the Titans only other two wins were by 3 points against Jacksonville and in overtime against the Saints.

But, amazingly, that wasn’t even the least-conforming game of the Bucs season. In week 11, in Philadelphia, Tampa Bay beat the Eagles 45-17. The same team losing at home by 28 points to Tennessee and winning by 28 points on the road in Philadelphia? That’s pretty weird.

The table below shows all 512 regular season games from 2015, and how it differed from expectations.  Here’s how to read the first line. The biggest outlier game was Tampa Bay against Philadelphia, which came in week 11.  You can click the Boxscore link to go to that game’s boxscore on PFR.  Tampa Bay had an SRS rating of -7.7, while Philadelphia’s rating was -4.7.  As a result, given that the game was in Philadelphia, the Expected Margin of Victory for Tampa Bay was -6.0.  In reality, Tampa Bay scored 45 points and allowed 17, for a 28-point Margin of Victory. That exceeded expectations by 34.0 points. [click to continue…]


Last year, Denver had a pretty tough schedule. In only five games did they face an opponent that an average team would have been favored to win:1 home games against San Diego, Baltimore, and Oakland, and road games against the Colts and Browns. In those games, Denver went just 3-2, with all five games being decided by one score.

The Broncos had six games against top-8 teams by the SRS: two games against the Chiefs, and games against Cincinnati, Minnesota, New England, and Pittsburgh. In those games, Denver went even better at 4-2, with five of those games being decided by one score.

The middle five games of the schedule by SRS standards was where the Broncos really dominated: the Broncos went 5-0 in road games against Oakland, San Diego, Chicago, and Detroit, and a home game against Green Bay, with three of those five wins coming by double digits.

As it turns out, Denver had the third “strangest” season in the NFL last year. How did I define strange? I measured the correlation coefficient between two variables: the actual margin of victory in a game, and the opponent’s SOS (after adjusting for home field advantage). The Broncos had a CC of 0.18, which means (in case you couldn’t figure it out above) that there wasn’t a big relationship between results and expectation. [click to continue…]

  1. I.e., home games against teams with SRS ratings of 3.0 or worse, or road games against teams with SRS ratings of -3.0 or worse. []

Joe Gibbs Inherited a Very Underachieving Team

theismannYesterday, we looked at the teams that overachieved their projected wins total by the largest amount based on the strength of their offensive and defensive passing games. Today, the reverse: the biggest underachievers. And that starts in Washington, D.C., the year before Joe Gibbs arrived.

The head coach was Jack Pardee, who was in Washington for three years, going 8-8 in 1978, then 10-6, and then 6-10 in 1980. Pardee was fired after the season, and you can see why: Washington didn’t just have a good pass defense in 1980, but a great one. It ranked as the 12th best pass defense from 1950 to 2013. Both corners, Lemar Parrish and Joe Lavender, made the Pro Bowl. Both safeties, Mark Murphy and Tony Peters, were in the primes of their careers, and would make a Pro Bowl under Gibbs.

Washington had an absurd 8.4% interception rate and a 9.9% sack rate, which helped the defense allow just 2.4 ANY/A, nearly a full yard better than any other team and 2.51 ANY/A better than league average. And the team went 6-10! The offense had Joe Theismann, Art Monk, and Wilbur Jackson; Theismann ranked 17th out of 30 qualifying passers in ANY/A, but that shouldn’t have been enough to keep the team out of the playoffs, let alone below .500.

Washington’s offense finished with a Relative ANY/A of -0.33, and its defense had a RANY/A of +2.51. The team had a 0.375 winning percentage, but “should” have had a 0.704 winning percentage. That means the team underperformed by 5.3 wins, the most of any team in the Super Bowl era. [click to continue…]


The 1968 Cardinals and Outlier Teams

Hart had a great career, but was still developing in '68

Hart had a great career, but was still developing in ’68

In 1968, the St. Louis Cardinals did not have a very good passing offense. The Cardinals averaged 3.9 ANY/A, good enough for 11th place in a 17-team league where the league average was 4.5. The main issue? St. Louis finished dead last with an anemic 44% completion rate. That was mostly due to the second-year starter, 24-year-old Jim Hart. His 44.3% completion rate remains the lowest by any Cardinals quarterback in history with a minimum of 300 pass attempts, and no quarterback with even 160 pass attempts for the Cardinals has dipped below 45% since Hart in ’68. The defense was also below-average against the pass: the Cardinals allowed 6.2 ANY/A, 5th worst in the NFL.

Teams that are below-average at passing and stopping the pass are usually not very good. In the Super Bowl era, each additional yard of ANY/A (on either offense or defense) relative to league average increases a team’s winning percentage by about nine percent. The Cardinals, at -0.5 Relative ANY/A on offense and -1.7 RANY/A on defense, would therefore be expected to win about 30% of their games. Instead, the Cards won 68% of games, going 9-4-1.

The table below shows the 100 teams of the Super Bowl era that have most exceeded expectations based on Offensive and Defensive RANY/A. In general, using ANY/A and RANY/A will get you most of the way there with a team’s record, but not for these teams. The Cardinals had an Off RANY/A of -0.52, a Def RANY/A of -1.72, and therefore, an expected winning percentage of 0.296. By having an actual winning percentage of 0.679, the Cardinals exceeded expectations by 6.1 wins per 16 games. [click to continue…]


538: Front- and Back-loaded Schedules

Today at 538, a look at which teams have front-loaded (the Jets) and back-loaded (the Ravens) schedules. The methodology will be familiar to regular readers: I created implied NFL ratings based on Vegas point spreads, and then calculated general and then weighted strength of schedule ratings. The weight, of course, was based on how late in the season a particular game occurred.

You can read the article here.


On average, passing yards is a pretty meaningless measure of quarterback play.  Consider that the winning team and the losing team in a game both generally throw for about the same number of yards. Last year, for example, winning teams averaged 258 gross passing yards per game, while losing teams averaged 259. In 2013, it was 253 for the winners, 251 for the losers. In 2012, it was 246 for the winners, 248 for the losers. Since 2000, winning teams have averaged about 5 more passing yards per game, thanks mostly to 2009 (244 for winning teams, 222 for losing) and 2014 (261/242) as big outliers.

Joe Flacco, for example, has averaged 233 passing yards per game in wins and 231 in losses. But just because the averages are close together doesn’t mean every quarterback follows this same formula. And two of the best examples of that are Nick Foles and Blake Bortles.

Foles has lost 17 games where he was the starting quarterback; in those games, his average stat line was 21/38 for 214 passing yards, 0.7 TDs and 1.1 INTs. He also has started and won 19 games; in those games, his average stat line was 19/30, for 258 passing yards, 2.1 TDs, and 0.4 INTs. That paints the picture of a guy who is much better in wins than losses, which makes a lot of sense.  (Also, 7 of his 17 losses have come during his ugly time with the Rams, compared to just 4 of 19 wins.) [click to continue…]


Guest Post: Bryan Frye on Adjusted Drive Yards

Friend of the program Bryan Frye is back for another guest post. As regular readers know, Bryan operates his own fantastic site, http://www.thegridfe.com. You can view all of Bryan’s guest posts here, and follow him on twitter @LaverneusDingle.

For some time, I have wanted to create a new metric that used elements from Total Adjusted Yards (TAY) in order to quantify a team’s production on each drive. Past work from both Chase and Brian Burke has given us insight into the value of touchdowns, interceptions, fumbles, and first downs, translated into yards. This work has been fundamental in the development of stats like Adjusted Net Yards per Attempt, Adjusted Rushing YardsAdjusted Catch Yards, and TAY.

Those metrics have given us valuable insight regarding statistical measurement of individual player performance. I’ve also used TAY to measure the output of offenses and defenses.

However, I wanted to attach generic values to every way a drive can end.1 This is not a rigorous study, and it is meant to be a starting point for future research rather than a conclusive formula to govern the way anyone interprets on-field action.

With that in mind, I’ll briefly cover the generic yardage values for various drive outcomes. [click to continue…]

  1. With the exception of kneel down drives to end halves or games, as those don’t demonstrate an offense’s (or defense’s) ability to actually play the game. []

Adjusted Completion Percentage

In 1991, Dave Krieg led the NFL in completion percentage. He completed a career-high 65.6% of his passes, and while that mark was very good for that era, it doesn’t mean Krieg was great that season. In fact, he arguably wasn’t even good: Krieg actually finished just 24th in ANY/A that year.

One reason, I think, that Krieg was able to lead the NFL in completion percentage is because Krieg “ate” a lot of his incomplete passes. What do I mean by that? Krieg took a ton of sacks — he was sacked every ten times he dropped back to pass. When under duress, some quarterbacks eat the ball, to avoid an interception; that’s bad (well, it’s better than n interception) but it doesn’t get graded that way when calculating completion percentage. Other quarterbacks will throw the ball away; that’s good (assuming it isn’t intercepted) because no yards are lost, but it does hurt the quarterback’s completion percentage.

Even ignoring the yards lost due to sacks, fundamentally, a sack is no better than an incomplete pass. So why are quarterbacks who take sacks rather than throw the ball out of bounds given an artificial boost when it comes to completion percentage? Well, that’s largely just an artifact of how the NFL always graded things. The NFL was not always good at recording metrics, and somewhere along the way, sacks were either included as running plays, ignored, or included as pass plays. I don’t think a lot of thought went into it, but in my view, it makes the most sense to include sacks in the denominator when calculating completion percentage. Otherwise, we give undue credit to quarterbacks that take a lot of sacks, and penalize quarterbacks who throw the ball away when under pressure. [click to continue…]


The 1978 Patriots, Part II

The 2001 Rams had Kurt Warner, Marshall Faulk, Isaac Bruce, and Torry Holt.

The ’92 and ’93 49ers have prime Steve Young and prime Jerry Rice, along with the first two years of Ricky Watters’ great career.

The ’88 Bengals had MVP Boomer Esiason, Pro Bowler Eddie Brown, HOFer Anthony Munoz and Pro Bowler Max Montoya on the offensive line, and a running back tandem of James Brooks and Ickey Woods. Two years earlier, the ’86 Bengals had those players save Woods, but also had Cris Collinsworth in the prime of his career.

The ’51 Rams had Norm Van Brocklin and Bob Waterfield — two HOFers — at quarterback, along with Elroy Hirsch, Dan Towler, Dick Hoerner, and Tom Fears.

Those are 6 of the 7 teams since 1950 to lead the NFL in both average yards per rush and average yards per pass. Can you guess the 7th team? You have three guesses, but the first two don’t count. [click to continue…]


The 1978 Patriots, Part I

Here’s what I wrote in my first post at Football Perspective:

I’ll be blogging about everything football-related, from Jerry Rice to Bobby Douglass, and from the 1978 Patriots to who is the greatest quarterback of all time.

The New England Patriots rushed for 3,165 yards, an NFL record that still stands. Take a look at the individual players on that team:

Games Rushing
No. Age Pos G GS Att Yds ▾ TD Lng Y/A Y/G A/G Fmb
39 Sam Cunningham* 28 FB 16 14 199 768 8 52 3.9 48.0 12.4 4
23 Horace Ivory 24 rb 15 3 141 693 11 28 4.9 46.2 9.4 5
32 Andy Johnson 26 RB 15 13 147 675 3 52 4.6 45.0 9.8 4
14 Steve Grogan 25 QB 16 16 81 539 5 31 6.7 33.7 5.1 9
44 Don Calhoun 26 rb 14 2 76 391 1 73 5.1 27.9 5.4 1
37 James McAlister 27 16 0 19 77 2 16 4.1 4.8 1.2 3
86 Stanley Morgan 23 PR/WR 16 16 2 11 0 6 5.5 0.7 0.1 6
29 Harold Jackson 32 WR 16 13 1 7 0 7 7.0 0.4 0.1 0
30 Mosi Tatupu 23 16 0 3 6 0 3 2.0 0.4 0.2 0
4 Jerrel Wilson 37 P 14 0 1 0 0 0 0.0 0.0 0.1 1
83 Don Westbrook 25 16 0 1 -2 0 -2 -2.0 -0.1 0.1 0
Team Total 26.2 16 671 3165 30 73 4.7 197.8 41.9 35

[click to continue…]


He gained 120+ yards pretty frequently

He gained 120+ yards pretty frequently

Yesterday, I posted a list of the career leaders in receiving yards after removing “junk” yards gained on an individual game basis. I’ve defined junk games as somewhere between 32 and 40 yards in 2015, and a lower threshold in less pass-friendly eras. You can view the Justin Blackmon example here.

While I presented the career list yesterday, I thought it would make sense to plot the career yards after removing junk yards (using 2.5x as the baseline) against each receiver’s plain career receiving yards (in both cases, since 1960). That’s what I’ve done in the graph below, with actual career receiving yards on the X-Axis and career yards after removing junk yards on the Y-Axis. Jerry Rice is literally off the chart (22,895; 13,786) because including him would require using a much broader (and less helpful) chart. Let’s just ignore Rice and focus on the other 99 receivers: [click to continue…]


[Note: Due to a scheduling blunder, you may have missed yesterday’s post on single-season leaders.]



On Sunday, I explained one methodology to modify receiving yards in a way to give more value to top receivers while devaluing junk games. You can read that explanation here, and see the Justin Blackmon example.

Jerry Rice, of course, will rank as the top receiver by this or any other methodology, especially if that system excludes Don Hutson (today’s data only goes back to 1960). In fact, using a 3X baseline, Rice still gained 15,314 receiving yards after removing junk yards, more than every wide receiver in NFL history has gained including junk yards other than Terrell Owens. Rice was just incredible.

Perhaps the first real surprise on the list is Don Maynard, who ranks 6th among all players since 1960 by this methodology. The Jets Hall of Famer currently ranks 26th in career receiving yards, but 30 years ago, he was the all-time leader in that category. Maynard benefits here for some era adjustments — his 14-game seasons get prorated, the baseline for junk seasons was lower in the ’60s and ’70s — and his dominant play for a long stretch is rewarded.

The table below shows the top players by this methodology since 1960. Here’s how to read the table, using the Owens line. Using a 2.5X baseline, he ranks 2nd all-time. His career began in 1996 and ended in 2010, and he had 9,386 receiving yards above that junk baseline. Using a 3X baseline, he still ranks 2nd, and had 10,493 non-junk receiving yards. [click to continue…]


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

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

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


Brad Oremland noted in his last post that Stanley Morgan is the only player in history to average more than 19 yards per catch in a career with at least 500 receptions, and that such distinction will probably stand forever. Brad’s likely right: given today’s environment, Vincent Jackson and Calvin Johnson are the two preeminent deep threats of the last decade with at least 500 catches, and Jackson (16.97) and Johnson (15.89) were far shy of that mark.

That’s a fun bit of trivia, but let’s expand it. You can use reception cut-offs to come up with lots of Yards per Catch Kings. Here’s an exhaustive one:

  • Jerry Rice is the all-time leader in yards per reception (14.78) among players with at least 1,079 receptions.
  • Terrell Owens (14.7811 to Rice’s 14.7805) is the all-time leader in yards per reception among players with at least 1,025 receptions.
  • Isaac Bruce is the career leader in YPR, at 14.85, among players with at least 983 receptions.
  • Randy Moss (15.57) is the only player to average 15 yards per reception and record 820+ receptions.

[click to continue…]

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