Every week this season, I’ve posted the Game Scripts and Average Field Position data from the prior week. For new readers, you can read the background and how to calculate Game Scripts here, but the Game Scripts number simply tells us the average points differential for a team throughout a game. There are 3600 seconds in a game that does not go to overtime, and he Game Script is the sum of the score at each of those 3600 seconds, divided by 3600.

This week, the 49ers’ blowout victory against Houston produced the highest Game Script at 18.3, putting it just a hair behind the Seahawks victory over Jacksonville (18.4) on the list of highest Game Scripts in 2013. (We’ll see if Denver/Jacksonville gets the Game Script over 20. The highest Game Script of all time was the Patriots 59-0 blowout in the snow against the Titans, clocking in at 33.7.) Colin Kaepernick completed six passes, but you don’t need to complete many passes when your team is leading by 18 points throughout the game. San Francisco went up 7-0 ninety seconds into the game following a Tramaine Brock pick six of Matt Schaub, and things stayed ugly from there. That reminds me: pick sixes continue to be up this year, an issue I discussed in the dead of March.

Without further ado, the Week 5 Game Scripts:
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Peyton Manning and Tony Romo staged a classic yesterday afternoon in Dalllas. The Broncos won 51-48, and the two quarterbacks put up ridiculous stat lines. Manning went 33 of 42 for 414 yards, and threw four touchdowns and just one interception. He wasn’t sacked, giving him an impressive 10.69 ANY/A average (based on the formula “Passing Yards + 20 * TDs – 45 * INTs – Sack Yards lost” divided by “Sacks + Attempts”). Romo may have been better, completing 25 of 36 passes for 505 yards and 5 scores — along with one fateful interception, and four sacks for -36 yards. That’s an amazing 13.12 ANY/A average.

So far this year, the league average ANY/A is 6.00. Since Manning was at 10.69 over 42 dropbacks, we could say that he provided 197 adjusted net yards of value over average. Meanwhile, Romo produced 285 adjusted net yards of value (40 * 7.12), which means the two quarterbacks combined to produce over 481 yards of value over average. If the league average for the season remains at 6.00, that would make this the fourth best (era-adjusted) quarterback battle since the merger, although only the second best game involving Manning. The table below shows the top 100 quarterback performances in the same game from 1970 to 2012.

Here’s how to read the table: for the top game: In 1972, Joe Namath and Johnny Unitas played a classic. The fourth column provides the team names (listed as QB 1 vs. QB 2) and a hyperlink link to the actual boxscore. The fifth column shows the quarterback stats: Namath completed 15 of 28 passes for 496 yards, 6 touchdowns, and 1 interception, and had 1 sack for -6 yards; Unitas completed 26 of 45 for 376 yards, threw 2 touchdowns and no interceptions, and was sacked 6 times for -44 yards. Namath produced 441 adjusted net yards of value (using the formula from above), while Unitas produced 154 yards, giving the duo a total of 595 yards above average.
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Sometimes, the simplest questions have the most complicated answers. Peyton Manning has thrown 16 touchdowns so far this season, putting him on pace for 64 touchdowns this year. Now, we can be reasonably sure that Manning’s true ability level — even with Wes Welker, Demaryius Thomas, Eric Decker, and Julius Thomas — isn’t four touchdowns per game. But he doesn’t need to keep up that pace to break Tom Brady’s single-season record of 50 touchdown passes: Manning “only” needs to averaged 2.92 touchdowns per game over the final 12 games. But to figure out his odds of averaging nearly three touchdowns per game, we need to figure out his true ability level. So how do we determine that number?

Even for a man who averages four touchdown throws per game over four games, averaging nearly three touchdowns per game going forward is still a tall order. Footballguys.com projected Manning to averaged 2.38 touchdowns per game this year. In 2012, he threw 37 touchdowns, an average of 2.31 touchdowns per game. From 2003 to 2012, excluding games ^[1]That was after removing week 17 of the ’04, ’05, ’07, ’08, and ’09 seasons, and week 16 of the ’05 and ’09 seasons, when Manning left early. Why did I pick the last ten years? I don’t … Continue reading he exited early, Manning averaged 2.17 touchdown passes per game. As a Colt, Manning averaged 1.92 touchdowns per game.

It doesn’t take any advanced math skills to figure out that Manning is likely to average somewhere between 2 and 4 touchdowns per game over the rest of the season. But that doesn’t help us very much: we need to be precise, since the threshold he needs to hit is 2.92 touchdowns per game. I’ll get to the more complicated math in Part II. For now, let’s look at some history.
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Every week, I’m posting the Game Scripts and Average Field Position data for each team. In an effort to get these up a bit quicker, I decided to get these numbers crunched before the Monday Night Game in the result – let me know if you like this method better than waiting until the end of the week. For new readers, you can see the results from the first three weeks here.

For the second week in a row, the Jaguars had the worst Game Script score. And for the second week in a row, Peyton Manning’s Broncos held an average lead of over 15 points. These two teams play in Denver in week six, and the early reports suggest that the line will be 27 points, the highest spread in N.F.L. history. The current record was set in 1976, when the expansion (and winless) Bucs traveled to Pittsburgh to face a Steelers team that had won the past two Super Bowls and had allowed just 28 points in their last seven games. That game had a spread of 26 points, and the Steelers won 43-0.

Here’s how to read the table below. The Colts won in Jacksonville (you can click the boxscore to see the full PFR boxscore), 37-3. Indianapolis won by 34, and held an average lead of 15.5 points throughout the game. Andrew Luck, Trent Richardson, and the rest of the Colts had 41 passes and 29 runs, giving them a 58.6% pass/run ratio. Meanwhile, the Jaguars threw 35 passes and had just 17 runs, giving them a 67.3% pass/run ratio. With few exceptions, the team with the positive game script has the lower pass/run ratio, and that effect is just magnified when the Game Script is large.
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As a rookie, Sam Bradford ranked last in the league in yards per completion. That year, he averaged 9.92 YPC, 1.61 yards per completion lower than the league average of 11.53. In his second and third years — 2011 and 2012 — Bradford was a little better in that metric, but he still finished 0.65 and 0.34 yards per completion below league average in those seasons. So far in 2013, Bradford has earned his reputation as a checkdown artist: with a 10.21 YPC average, he’s averaged 1.43 fewer yards per completion than the average quarterback.

If you take a weighted average (based on his number of completions in each season), Bradford has been 0.98 yards per completion below league average over the course of his 980 career completed passes. In August, I noted that the Rams have experienced constant turnover at offensive coordinator and wide receiver since Bradford entered the league. This year, with Tavon Austin, Jared Cook, and developing talents like Chris Givens, expectations were high for Bradford. So far, we’ve seen more of the same from Bradford, which means lots of checkdowns and few big plays.

For his career, Bradford has averaged just 10.68 yards per completion. The table below shows the 164 quarterbacks since 1950 to complete at least 850 passes. The far right column represents the difference between each quarterback’s career yards per completion average minus the league average (calculated on a weighted-average basis for each quarterback based on his number of completions in each season) rate. As it turns out, Bradford ranks in the “top five” when it comes to the worst era-adjusted yards per completion averages.
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I have some bigger thoughts on the Trent Richardson trade, but I want to first address a statistic I’ve seen cited frequently the past few days. You’ve probably heard some variation on the following, perhaps first reported by ESPN:

Since the start of 2012 with Richardson on the field, Browns quarterbacks were sacked on 4.8 percent of dropbacks. With Richardson off the field, Browns quarterbacks were sacked on 9.4 percent of dropbacks.

Colts quarterbacks have been sacked on 6.2 percent of dropbacks since the start of last season, the ninth-highest rate in the league.

I’ll assume the numbers are accurate, but stats like that don’t mean anything out of context. The conclusion isn’t spelled out, but the reader is asked to connect the dots: Richardson is good at pass protection and Indianapolis could use an upgrade in that department, so this is another reason to like the trade for the Colts. But when is Richardson most likely to be off the field? In obvious passing situations, which happens to be the most likely time the Browns would be sacked.

Brandon Weeden and other Cleveland quarterbacks were sacked 36 times last year and 11 times in the first two games of 2013. First, we should note that Thaddeus Lewis started in week 17 last year against the Steelers — his only start of the year, and the only game Richardson has missed in his pro career. He was sacked four times on 36 dropbacks, an 11.1% rate. So let’s throw that game out, since nobody cares about Lewis’ sack rate.

According to NFLGSIS, Richardson was not on the field for 22 of the 43 sacks of Browns quarterbacks over the last year and two games. But the fine print is the real story: only five of those 22 sacks came on 1st or 2nd down, only seven came when Cleveland had the lead, and only three of those sacks occured when the Browns needed fewer than seven yards for a first down. Take a look:
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As Jason Lisk and I wrote about before the season, Tom Brady and Ben Roethlisberger have become something of the poster children so far this year when it comes to veteran QBs working with inexperienced and otherwise less-than-notable receiving groups. And, lo and behold, each has put up career-low RANY/A marks through 2 games. But how do their receiving corps rank relative to those of other teams so far this year, and how do they stack up historically?

To take a stab at answering these questions, I turned to True Receiving Yards. For each player who debuted in 1950 or later, I computed their Weighted Career True Receiving Yards for every year, as of the previous season, to get a sense of how experienced/accomplished they’d been going into the season in question. Then, I calculated a weighted averaged of those numbers for every receiver on a given team, using TRY during the season in question as the weights. For example, here are the 2013 Patriots receivers:

Player	Age	Debut	TRY	% of Tm	At-the-time WCTRY
Julian Edelman	27	2009	139	38%	615.7
Danny Amendola	28	2009	72	20%	1541.9
Kenbrell Thompkins	25	2013	56	15%	0.0
Shane Vereen	24	2011	44	12%	110.9
Aaron Dobson	22	2013	43	12%	0.0
Michael Hoomanawanui	25	2010	5	1%	278.8
James Develin	25	2013	4	1%	0.0
Weighted Average					560.7

The way to read that is: Julian Edelman has accounted for 38 percent of the Pats’ TRY so far. Going into the season, he had a career Weighted TRY of 615.7, so he contributes to 38% of the 2013 Pats’ weighted average with his 615.7 previous career weighted TRY; Danny Amendola contributes to 20% of the team weighted average with his 1541.9 previous career weighted TRY; etc. Multiply each guy’s previous weighted career TRYs by the percentage of the team’s 2013 TRY he contributed, and you get a cumulative weighted average of 560.7 — meaning the average TRY of a 2013 Pats receiver has been gained by a guy who had a previous career weighted TRY of 560.7.

Is that a low number? Well, here are the numbers for all of the 2013 team receiving corps (not including Thursday night’s Eagles-Chiefs tilt), inversely sorted by weighted average (asterisks indicate rookies):

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About a month ago, Chase & I developed a stat called True Receiving Yards, which seeks to put all modern & historical receiving seasons on equal footing by adjusting for the league’s passing YPG environment & schedule length, plus the amount the player’s team passed (it’s easier to produce raw receiving stats on a team that throws a lot), with bonuses thrown in for touchdowns and receptions. It’s not perfect — what single stat in a sport with so many moving parts is? — but it does a pretty good job of measuring receiving productivity across different seasons and across teams with passing games that operated at vastly different volumes.

Anyway, today’s post is basically a data dump to let everyone know we’ve extended TRY data back to 1950 (before, it was only computed for post-merger seasons). Here are the new all-time career leaders among players who debuted in 1950 or later (see below for a key to the column abbreviations):
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In week 1, records were set for the most passing touchdowns (63) and team passing yards (8143) thrown in a single week in league history. Is this a sign that 2013 will represent the next stage in the evolution in the passing game, or are offenses typically ahead of defenses early on?

To compare week 1 of this season to other weeks in NFL history, we need to examine all statistics on a per team game basis to account for bye weeks and number of teams. But after calculating the per team game numbers, I multiplied the result by 32 to make the numbers easier to digest. The first graph shows the average number of passing touchdowns in week N of each season since 1970. As you can see, week 1 doesn’t stand out as typically being passer-friendly. On average, there were roughly 41.5 touchdowns per team game (after multiplying by 32) in week 1 of each season from 1970 to 2012, which is nearly identical to the average (40.9) the rest of the year:

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Regular readers know all about Game Scripts, the term I’ve used to represent the average margin of lead or deficit over the course of every second of a game. Let’s use the Seahawks-Panthers game to explain how to calculate the Game Script score.

Steven Hauschka’s field goal with 9:40 left in the second quarter was the first score of the game; that means for the first 20 minutes and 20 seconds, the score was tied. Cam Newton responded with a touchdown drive, hitting Steve Smith for a three-yard score with 3:20 left in the half. So for six minutes and twenty seconds, the Panthers trailed by three. It wasn’t until 2:26 left in the third quarter that the next score occurred, courtesy of Hauschka’s second field goal of the day. This means the Panthers led by four for fifteen minutes and fifty-four seconds. Russell Wilson threw the game-winning touchdown with 10:21 remaining, the final score of the day. This means for 7:05, the Seahawks trailed by a point, and then for 10:21, Seattle led by five points (following an unsuccessful two-point conversion attempt).

As it turns out, that gives us a Game Script of exactly 0.00. In other words, on average, this game was tied. Here’s how to do the math:

By comparison, the Jacksonville-Kansas City game was much more one-sided:

• With 12:32 left in the first quarter, J.T. Thomas blocked Dustin Colquitt’s punt, which resulted in a Jaguars safety.
• That lead lasted all of three minutes and twenty-three seconds, which is how long it took for Alex Smith to find Donnie Avery for a five-yard score with 9:09 left in the first.
• Next, Smith connected with Junior Hemingway for a three-yard touchdown with 1:40 left in the first quarter.
• With 6:29 left in the second quarter, Jamaal Charles punched it in for a short touchdown, bringing the score to 21-2.
• The final score of the day was a Tamba Hali pick six of Blaine Gabbert, with 12:51 left in the fourth quarter.

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I’ve written a couple of times about “yards per carry” as a key statistic to grade running backs. The usual argument in favor of using YPC is that a running back who rushes for 1200 yards on 300 carries is less valuable, all else being equal, than one who rushes for 1200 yards on 250 carries. But when it comes to running backs and yards per carry, “all else” is is never equal. Two players come to mind whenever yards per carry is cited for running backs: Eddie George and Curtis Martin.

From 1996 to 2002, George led the league with 2,421 carries. Only Martin (2,236) was within 300 carries of George. In NFL history, Eric Dickerson is the only player to ever record more carries during a player’s first seven seasons. But some would have you believe that George wasn’t very good during those seven years, because he averaged just 3.71 yards per carry. During that stretch, the Titans went 68-44, giving them the fourth best record in the NFL and the second-best mark in the AFC during that span. But, the yards-per-carry proponents would argue, Jeff Fisher didn’t know what he was doing when he kept handing the ball off to George, play after play, game after game, year after year.

In 1998, the Jets went 12-4 and earned a first-round bye; New York went 12-1 in Vinny Testaverde’s thirteen starts, and finished in the top five of the league in points, yards, and first downs. That season, Curtis Martin received 369 carries despite missing one game due to injury. Martin rushed 25+ times in seven games and recorded at least 17 carries in every game that year… and averaged only 3.49 yards per carry. There are some who would have you believe that the Hall of Fame head coach didn’t quite know what he was doing that year, and the Jets would have been even better had the team called Martin’s number less frequently.
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The 2012 Raiders weren’t very good. Oakland finished last season 4-12, with the third-worst SRS rating (-10.8) in the NFL. And the outlook isn’t any better for 2013, either. Oakland has fifty million dollars of dead cap space; as a result, the players on the current roster cost the team only 67 million salary cap dollars, nearly $30M less than the next-lowest team. According to Pro Football Focus, Oakland’s five best defensive players last season were Lamarr Houston, Desmond Bryant, Philip Wheeler, Richard Seymour, and Rolando McClain. Only Houston returns in 2013, as Bryant is in Cleveland, Wheeler signed with the Dolphins, Seymour remains a free agent, and McClain has gracefully retired.

The team’s top two offensive players in 2012 were left tackle Jared Veldheer and quarterback Carson Palmer. Palmer is now in Arizona, while Veldheer is out indefinitely with a torn triceps. Brandon Myers (Giants) and Darrius Heyward-Bey (Colts) are also gone, and they combined for 29 starts last season. The big free agent signings were S Charles Woodson (Green Bay), LB Kevin Burnett (Miami), QB Matt Flynn (Seattle), CB Mike Jenkins (Dallas), CB Tracy Porter (Denver).

As a result, there’s little optimism in Oakland entering the season. The Raiders are one of the favorites to land the first pick in the 2014 draft, so the 2013 season will likely be used to see what building blocks actually exist in Oakland. After Terrelle Pryor outplayed Flynn in the preseason, many now think the Raiders going to start Pryor in week 1 because, well, why not? If that’s the case, we’ll have another example to test out a theory that’s widely-accepted by conventional analysts.
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In March, Miami signed deep threat extraordinaire Mike Wallace from Pittsburgh. Also this offseason: general manager Jeff Ireland allowed left tackle Jake Long to leave and sign with the Rams, and the Dolphins have filled his position by moving right tackle Jonathan Martin to the blind side.

Much has been made of Martin’s inability to handle that role. Leading up to the 2012 draft, much was made of how Martin only projected as a right tackle in the pros, even though (or perhaps because of what evaluators saw when) Martin played on the left side at Stanford with Andrew Luck. Miami played Martin on the right side of the line for the first eleven games of the season last year, but switched him to the blind side after Long suffered a season-ending injury. Pro Football Focus graded Martin as a terrible pass blocker in his rookie season, and he was even worse in the five starts he made at left tackle: In those games, he allowed an incredible 17 hurries, two hits, and two sacks.

I thought it might be interesting to run some tests on left tackles, building off a study that Jason Lisk ran on offensive linemen:

To do this, I looked at all offensive linemen since 1978 who made at least 1 pro bowl and started 80 or more games in their careers, and then found any season in which that player played in fewer than 10 games before age 34, after starting more than that amount the previous year at the same position. I then compared the team performance in things like points, yards per attempt, sack rate, and rushing yards per carry. As it turns out, we don’t have very many cases that allow us to look at this (19 players). If I had reliable game by game data for offensive linemen participation, I could broaden the study, but for now, I can’t tell which games a lineman missed, like I can with a quarterback or running back. My goal in setting this up the way I did was to find pretty good tackles and try to see if there were any differences the following year when they missed a large chunk of the season.

Our 19 offensive tackles averaged 15.1 games the year before the injury, and only 6.3 games played during the injury season. That’s a difference of 8.8 games played, or over half a season.

In his conclusion, Lisk noted that “The likely effect on a per game basis when playing versus when out with an injury was somewhere between 0.7 to 0.8 Net Yards per Attempt dropoff.” Well, what if we run the same study but (1) limit ourselves to left tackles ^[1]I changed the cutoffs to 14 starts in Year N and fewer than 9 starts in Year N+1 and (2) look at the yards-per-reception average of the team’s leading receiver? There are only 12 situations to examine, a sample size far too small to really use, but here are the results:

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Running back workload is a very difficult topic to tackle, and I don’t expect to make much of an indent into the subject today. But I do want to take a few minutes and look at some ways to measure heavy workloads. One school of thought is that the effect of carries is cumulative: Not only is a 25-carry game more likely to cause a running back trouble than a five-carry game, but it’s more than five times as likely to shorten a player’s shelf life. The cumulative effect of taking hit after hit means that carries 16, 17, and 18 hurt a runner more than carries 1, 2, and 3.

I don’t know if that’s true, but let’s investigate. First, I’m only giving a running back credit for his additional carries after his 15th carry of the game. So an 18-carry game goes down as a “3” and a 25 carry game is a “10.” Using this scoring system, Arian Foster had the highest number of “Carries over 15” from last season, with 117. Such a list mostly corresponds to the number of overall rushing attempts for a player, but the exceptions could be revealing.
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When I wrote my ode to Steve Smith earlier this week, I discovered an incredible stat. In 2008, Smith played in 14 games and gained 1,421 receiving yards. But the more incredible part is that the Panthers passed only 352 times in 2008, meaning Smith averaged an absurd 4.04 yards per team pass attempt. At this point, we all know that passing yards is a meaningless way to measure quarterbacks: yards per attempt is a much better indicator of talent. It’s much easier to throw for 4,000 yards on 600 attempts than it is on 500 attempts, and it’s silly to pretend otherwise. But most analysts don’t make the same adjustment when dealing with the man catching those passes. But it’s much easier to gain 1,500 receiving yards on a team that passes 700 times than it is to do it on a team that passes only 500 times. Hence my interest in Yards per Team Pass Attempt as a statistic.

So, how absurd was Smith’s 4.04 Yards per attempt average? We have individual player game logs going back to 1960, so I went through and measured every game each receiver “played” in each season since then. I use that word in quotes, because it’s possible that a receiver was active in a game but did not record any statistics, and therefore he may not show up in our game logs. That caveat aside, the table below shows the single-season leaders since 1960 in Receiving Yards per Team Pass Attempt (minimum 170 passes, so I could bring in Paul Warfield), with only the games in which the player “played” counting in the attempts column. As always, the table is sortable and searchable, and you can click the arrows at the bottom to scroll to the next names on the list (the table contains the 138 receivers to average 2.75 Yd/Att.)
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Yesterday, I set up a method for ranking the flukiest fantasy football seasons since the NFL-AFL merger, finding players who had elite fantasy seasons that were completely out of step with the rest of their careers. I highlighted fluke years #21-30, so here’s a recap of the rankings thus far:

30. Lorenzo White, 1992
29. Dwight Clark, 1982
28. Willie Parker, 2006
27. Lynn Dickey, 1983
26. Robert Brooks, 1995
25. Ricky Williams, 2002
24. Jamal Lewis, 2003
23. Mark Brunell, 1996
22. Vinny Testaverde, 1996
21. Garrison Hearst, 1998

Now, let’s get to…

The Top Twenty

20. RB Natrone Means, 1994

Best Season
year	g	rush	rushyd	rushtd	rec	recyd	rectd	VBD
1994	16	343	1,350	12	39	235	0	103.0
2nd-Best Season
year	g	rush	rushyd	rushtd	rec	recyd	rectd	VBD
1997	14	244	823	9	15	104	0	12.9

Big, bruising Natrone Means burst onto the scene in 1994 as a newly-minted starter for the Chargers’ eventual Super Bowl team, gaining 1,350 yards on the ground with 12 TDs. In the pantheon of massive backs, he was supposed to be the AFC’s answer to the Rams’ Jerome Bettis, but Means was slowed by a groin injury the following year and never really stayed healthy enough to recapture his old form. The best he could do was to post a pair of 800-yard rushing campaigns for the Jaguars & Chargers in 1997 & ’98 before retiring after the ’99 season.

19. WR Braylon Edwards, 2007

Best Season
year	g	rec	recyd	rectd	VBD
2007	16	80	1,289	16	107.7
2nd-Best Season
year	g	rec	recyd	rectd	VBD
2010	16	53	904	7	15.4

The 3rd overall pick in the 2005 Draft out of Michigan, Edwards seemingly had a breakout 2007 season catching passes from fellow Pro Bowler Derek Anderson. But both dropped off significantly the next season, and Edwards was sent packing to the Jets in 2009. He did post 904 yards as a legit starting fantasy wideout in 2010, but he has just 380 receiving yards over the past 2 seasons, and it’s not clear he’ll ever live up to those eye-popping 2007 numbers again.
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There’s nothing like a truly great fluke fantasy season. Because they can help carry you to a league championship (and therefore eternal bragging rights — flags fly forever, after all), a random player who unexpectedly has a great season will often have a special place in the heart of every winning owner. And even if you only use their jerseys as makeshift aprons to cook in, fluke fantasy greats are a part of the fabric of football fandom. That’s why this post is a tribute to the greatest, most bizarre, fluke fantasy seasons of all time (or at least since the 1970 NFL-AFL merger).

First, a bit about the methodology. I’m going to use a very basic fantasy scoring system for the purposes of this post:

• 1 point for every 20 passing yards
• 1 point for every 10 rushing or receiving yards
• 6 points for every rushing or receiving TD
• 4 points for every passing TD
• -2 points for every passing INT

I’m also measuring players based on Value Based Drafting (VBD) points rather than raw points. In a nutshell, VBD measures true fantasy value by comparing a player to replacement level, defined here as the number of fantasy points scored by the least valuable starter in your league. For the purposes of this exercise, I’m basing VBD on a 12-team league with a starting lineup of one QB, two RBs, 2.5 WRs, and 1 TE. That means we’re comparing a player at a given position to the #12-ranked QB, the #24 RB, the #30 WR, or the #12 TE in each season. If a player’s VBD is below the replacement threshold at his position, he simply gets a VBD of zero for the year.
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Most of you know that counting stats mean little for quarerbacks, but most of you also know that nothing beats meaningless trivia. Last July, I wondered who would lead the NFL in rushing yards from 2012 to 2021? It took a year, but now the natural follow-up question: Who will lead the NFL in passing yards over the next ten seasons?

Did you know that Joe Montana passed for the most yards in the ’80s? Or that Y.A. Tittle passed for the most yards from ’48 to ’57 (AAFC included) and then ’53 to ’62? The table below shows each leader in passing yards for every ten-year period, along with their age and NFL experience during their first season of that decade.
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Every year at Footballguys.com, I publish an article called Rearview QB, which adjusts quarterback (and defense) fantasy numbers for strength of schedule. I’ve also done the same thing using ANY/A instead of fantasy points, and today I revive that concept for the 2012 season.

Let’s start with the basics. Adjusted Net Yards per Attempt is defined as (Passing Yards + 20 * Passing Touchdowns – 45 * Interceptions – Sack Yards Lost) divided by (Pass Attempts plus Sacks). ANY/A is my favorite explanatory passing statistic — it is very good at telling you the amount of value provided (or not provided) by a passer in a given game, season, or career.

Let’s start with some basic information. The league average ANY/A in 2012 was 5.93. Peyton Manning averaged 7.89 ANY/A last year, the highest rate in the league among the 39 passers with at least 75 attempts. Since the Broncos star had 583 pass attempts and 21 sacks in 2012, that means he was producing 1.96 ANY/A over league average on 604 dropbacks. That means Manning is credited with 1,185 Adjusted Net Yards above average, a metric I simply call “VALUE” in the table below. Manning led the league in that category, with Tom Brady, Drew Brees, Aaron Rodgers, and Matt Ryan rounding out the top five. Remember, the ANY/A and VALUE results aren’t supposed to surprise you, so it makes sense that the best quarterbacks finish near the top in this category every year.
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Scott Kacsmar recently wrote about how Daunte Culpepper had the worst performance of his career in his biggest game. In his only NFC Championship Game appearance, Culpepper went 13-of-28 for 78 yards with three interceptions and four sacks. Which made me wonder: how many other great quarterbacks have had their worst game in the playoffs?

I’ll define worst game using the methodology from my Greatest QB of All-Time series ^[1]For pre-2008, I will be using estimated sack data based on the following formula: Team Sacks allowed X (QB pass attempts / team pass attempts). This means I will calculate each quarterback’s ANY/A ^[2]I will also include rushing touchdowns and count them the same as passing touchdowns, but will not include any other rushing data. and compare it to league average for that season.

For example, Peyton Manning‘s worst game came in November 2007 against the Chargers. He threw for 328 yards and 2 touchdowns, but that came on 58 dropbacks and with six interceptions. He averaged 1.44 ANY/A that game, in a season where the league average was 5.52 ANY/A. That means Manning was 4.08 ANY/A below average on 58 pass plays, which gave him 239 Adjusted Net Yards below average. The table below shows some of the top quarterbacks in NFL history and which games were their worst. For quarterbacks like Johnny Unitas and Bart Starr, the records are incomplete, as our individual games database only goes back to 1960. The “VALUE” column shows how many Adjusted Net Yards each quarterback was below average.
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Scott Kacsmar recently wrote about Robert Griffin III’s struggles on third downs last season. Despite Griffin’s otherworldly rate stats, that was one area where he really struggled in 2012. I thought it would be interesting to take a look at how all quarterbacks fared on “third downs” last season. I put that in quotes because I’m including fourth down data, but don’t want to write third and fourth down throughout this post. Regular readers may recall I did something similar last November, but now we can work with full 2012 season numbers.

To grade third down performance, I included sacks but threw out all rushing data (not for any moral reason, just in the interest of time). The first step in evaluating third down performance is to calculate the league average conversion rate on third downs for each distance. Next, I came up with a best-fit smoothed line based on the data, which is based off the following formula:

Conversion Rate = -0.0001 * Distance^2 – 0.0224 * Distance + 0.5301

Take a look at the table below. For example, there were 309 passes (i.e., pass attempts or sacks — scrambles are not included) and the league-wide conversion rate was 51.1%. Using the best-fit formula, the smoothed rate is 50.8%. There is nothing groundbreaking here — the conversion rates drop as the “to go” number increases, but it helps to quantify what we already know.

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I hate passer rating. So do you. Everyone does, except for Kerry Byrne. Passer rating is stupid because it gives a 20-yard bonus for each completion, a 100-yard penalty for each interception, and an 80-yard bonus for each touchdown. In reality, there should be no (or a very small) weight on completions, a 45-yard weight on interceptions, and a 20-yard weight on touchdowns.

But let’s ignore those issues today. Reading Mike Tanier’s recent article inspired me to make see what passer rating would look like if we make three tweaks. I’m not going to change any of the weights in the formula, but just redefine the variables.

1) There’s no reason to exclude sack data from passer rating. I’ve stopped writing about how sacks are just as much (if not more) on the quarterback than other passing metrics, because I think that horse has been pretty well beaten by Jason Lisk and me.

2) Scrambles should be treated like completed passes. If Russell Wilson is about to be sacked, but escapes and run for 7 yards, why should that be treated any differently than if Peyton Manning is about to be sacked, but throws a seven-yard pass at the last second?

3) Lost Fumbles should be counted with interceptions. One could make a few advanced arguments here — we should use all fumbles instead of lost fumbles, or fumbles should be given an even stronger weight than interceptions (although consider that in light of this post), or that we should limit ourselves to just fumbles lost on passing plays. I’m going to play the simple card here, and just use lost fumbles data on the season level.

Passer rating consists of four metrics, all weighted equally: completions per attempt, yards per attempt, touchdowns per attempt, and interceptions per attempt. I will use the same formula with the same weights and the same variables, but redefine what those variables are. Here are the new definitions, with the additions in blue.

Completion percentage is now (Completions plus Scrambles) / (Pass Attempts plus Sacks plus Scrambles)

Yards per Attempt is now (Passing Yards plus Yards on Scrambles minus Sack Yards Lost) / (Pass Attempts plus Sacks plus Scrambles)

Touchdown Rate is now (Passing Touchdowns plus Touchdowns on Scrambles) / (Pass Attempts plus Sacks plus Scrambles)

Turnover Rate will replace Interception Rate in the formula, and is calculated as (Interceptions plus Fumbles Lost) / (Pass Attempts plus Sacks plus Scrambles)

The table below lists all of those metrics for the 32 quarterbacks who had enough pass attempts to qualify for the passer rating crown, along with Alex Smith and Colin Kaepernick, who just missed qualifying. Let’s look at the Robert Griffin III line.

He completed 258 of 393 pass attempts for 3200 yards, with 20 touchdowns and five interceptions. Those are the standard stats that make up passer rating, but he also took 30 sacks and lost 217 yards on those sacks. That makes Griffin’s numbers worse, but he also had 38 scrambles for 302 yards (which gets recorded as 38 completed passes for 302 yards), with no scramble touchdowns. Finally, he lost two fumbles. His new completion percentage is 64.2%, his new yards per attempt is 7.13, his new touchdown rate is 4.3%, and his turnover rate (which includes fumbles) is 1.5%. The final two columns show each quarterback’s passer rating under the normal system and their passer rating using these metrics, which I’ll call the FPPR for short.
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The Washington Redskins were powered by a pair of rookie stars in 2012. We all know about Robert Griffin III, but sixth round pick Alfred Morris finished second in the league in rushing. Griffin’s efficiency numbers were unmatched — he led the NFL in both yards per pass and yards per run — en route to 3200 passing yards and 815 rushing yards. Morris gained1,690 yards from scrimmage; add in Kirk Cousins‘ 466 passing yards and 22 rushing yards, and Washington rookies produced 6,193 “yards” in 2012. I put that in quotes because it’s not customary to sum players’ passing, rushing, and receiving yards, but I think that’s the right idea for the point of this post: figuring out which teams have most relied on rookies.

That 6,193 figure is the second most amount of “yards” produced by a group of rookies in NFL history. The leader in the clubhouse? The 2012 Indianapolis Colts, behind Andrew Luck, Vick Ballard, T.Y. Hilton, Dwayne Allen, Coby Fleener, LaVon Brazill and Dominique Jones. The third place spot belongs to the 2012 Cleveland Browns: Brandon Weeden, Trent Richardson, Josh Gordon, Travis Benjamin, Josh Cooper, and Brad Smelley combined for over 6000 yards.

What if we instead look at percentage of team yards (defining yards as the sum of all passing, rushing, and receiving yards)? Expansion teams would begin to dominate the list — some AFL teams in 1960, the ’68 Bengals, and the ’02 Texans, for example. But what if we look at only non-expansion teams since 1950?

In that case, the 2012 Colts come in second, behind another Colts team.
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Just about every article about Trent Richardson references his unimpressive 3.6 yards per carry average from last season. That stat is pretty meaningless, in my opinion. I suppose if you took a random running back from NFL history, and his YPC average in one random season was 3.6, and I knew nothing else about the player, I suppose I would probably assume that the running back was not (or was not going to become) a star. But Richardson isn’t a random running back from a database, because…

• His 3.6 YPC average came on 267 carries, which represented 77% of all carries by Cleveland running backs
• He was a rookie last year
• He was a high first round pick

Since 1970, only 13 first round rookies have recorded 70% of all running back carries by their team. Two of those players were Richardson and Tampa Bay’s Doug Martin last year. Of that group, Richardson did post the lowest YPC average, but he was within 0.1 YPC of LaDainian Tomlinson. The next two lowest averages belong to Robert Edwards and Emmitt Smith; the former suffered a career-debilitating injury in a beach football game after his rookie season, while the latter ran for the most yards in NFL history.

Yeah, Richardson’s yards per carry average was well below average. But the universe of first round running backs who became workhorses right away as rookies and had a low YPC average consists of a HOF running back, a future HOF running back, and a player who suffered the flukiest of injuries. Richardson has something else in common with Emmitt Smith: after both of their rookie seasons, Norv Turner came on board as offensive coordinator.

But let’s say you don’t want to give Richardson any credit for his draft status. And you’re not in the mood to give him a pass just because he was a rookie. OK. Since 1990, 48 running backs have averaged fewer than 3.8 yards per carry while recording at least 70% of all running back carries for their team. Twenty-six of those players were at least 27 years old, and on the back half of their careers. Here are the other 22 running backs:
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Kenny Britt’s story is hardly a secret. As a freshman at Rutgers in 2006, his 64-yard catch turned the tide in the biggest win in school history. The next year, he was part of a dynamic offense: Ray Rice rushed for 2,012 yards, Tiquan Underwood caught 65 passes for 1100 yards and 7 touchdowns, and Britt was the big play threat, gaining 1,232 yards and 8 touchdowns on 62 catches. In 2008, Britt caught 87 passes for 1,371 yards and 7 touchdowns. After his junior year, the dynamic college receiver made the obvious move and declared for the NFL Draft. He then watched his hometown Giants take Hakeem Nicks one pick before the Titans made Britt the first Rutgers player ever to be selected in the first round.

Before analyzing his NFL career, I think it’s important to remember that Britt is young for his class year: he played in his first NFL game before he was old enough to legally drink. Despite the young age, Britt didn’t disappoint, producing 701 receiving yards as a rookie in ’09. He even produced a signature moment, catching the game-winning touchdown pass from Vince Young in what was one of the greatest comeback drives of all time (no, really — I swear).

Britt’s 2010 season looks like modest improvement on the surface, but his 775 yards and 9 touchdowns don’t tell the full story. According to Pro Football Focus, Britt only ran passing routes on 253 snaps that season, but averaged a whopping 3.1 yards per route run, easily the highest rate in the league. The obvious follow-up question is why didn’t he run more routes? Well, the 2010 Titans were a run-heavy team centered around Chris Johnson; Tennessee finished 30th with just 474 pass attempts. Britt also missed nearly five full games with a hamstring injury, and Tennessee tended to place Nate Washington on the field in their 1-WR sets. Those seem like reasonable explanations for overlooking why a 22-year-old would play a limited number of snaps. The impressive part is his insane production.
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Chris Brown of buffalobills.com is not happy with the way the Bills’ schedule shakes out. Again. Last year, this is what Brown wrote after examining the 2012 schedule:

After playing what is expected to be a physical contest with the 49ers in San Francisco in Week 5 [Chase: In retrospect, not that physical], the Bills then face the Cardinals in Arizona in Week 6. The Cards will have the benefit of three extra days of prep time for Buffalo as their Week 5 game is on Thursday night.

The very next week when the Bills play host to Tennessee, the Titans will also have three extra days of prep time for Buffalo because they’re playing on Thursday night the previous week (Week 6) as well.

The Bills look to get a break as they’ll have a bye week in Week 8 to get two weeks to prep for the Texans in Houston. But that extra prep time will be a wash because Houston also has their bye in Week 8.

Finally while the Bills are battling the Texans in Houston, the Patriots will be on their couches watching at home while their head coach grinds tape for two weeks to prepare for the Bills who travel to New England in Week 10 as the Pats have their bye in Week 9.

Brown’s claims were accurate: Buffalo did face a team coming off extra rest (i.e., more than eight days) four times in five weeks. Of course, those were the only times all season the Bills played a team coming off extra rest. Still, if we look at the 2012 season, it’s fair to say the Bills got the short end of the scheduling stick.

But they don’t have the biggest beef. Philadelphia faced four teams coming off bye weeks last year, tying the ’09 Falcons, ’05 Chargers, ’03 Cowboys, and ’99 Chargers for facing the most teams coming off a bye week since 1994.
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Last year’s article on the Lions was somewhat negative. Detroit went 4-12 in 2012, but the Lions are going to win more games this year. This is the type of article that Jason Lisk, Bill Barnwell, Brian Burke, or Aaron Schatz could write in their sleep. But I’m writing it while watching TV, writing a more complicated article, working out, taking out the trash, and tweeting. Let’s see Lisk do that!

The Lions went 3-9 in games decided by 8 or fewer points last year, giving them the most losses and the worst winning percentage of all teams in one-possession games. While this might imply that the Lions lack the mental fortitude to win close games, you might recall that in 2011, the Minnesota Vikings (2-9) and the Indianapolis Colts (1-7) were the worst two teams in such situations and then made the playoffs last year.

Another way to convey similar information is to look at each team’s Pythagorean record, which is calculated based on a team’s points scored and points allowed and is a better predictor of future winning percentage than past winning percentage. The table below shows each team’s number of wins, points scored and allowed, and number of Pythagorean wins for 2012, using 2.57 as my exponent(which produced the best fit for recent years). The table is sorted by the difference between actual wins and Pythagorean wins:
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Yesterday, Joe Fortenbaugh canonized Mike Lombardi for discovering and emphasizing one of the game’s great hidden stats: the number of rushing attempts plus completions a team has in a game. If you hit 50, you’re in great shape. Fortenbaugh reminds us that Lombardi, whose last team went 2-14, “possesses a vast range of knowledge spanning from management to game theory.” Fortenbaugh does the math for us, noting that the “top-10 teams in rushing attempts + completions combined to post a record of 101-59 (.631) in 2012, with seven of those ten organizations advancing to the postseason. On the opposite end of the spectrum, the bottom-10 teams combined for a 62-97-1 (.387) mark, with zero total playoff berths.” Then, he blows us away with the prize-winning line:

If you take only the teams that averaged 50.0 or more rushing attempts + completions per game over the last five years, you get a combined regular season record of 339-189 (.642), with 22 of 33 (66%) teams qualifying for the postseason. That winning percentage puts a team in between 10 and 11 wins per season.

The headline to the article reads: Average a combined total of 50 rushing attempts and completions per game and a winning season will likely follow. I’ll do the article one better: From 2008 to 2012, including playoffs, teams with 50+ rushes + completions have a record of 819-325-3, giving them a .715 winning percentage.

After reading that article and getting an inside look into Lombardi’s wisdom, I had considered the code to producing a winning season cracked. But I’ve got a robust database, so I thought maybe I could do even better than that .715 winning percentage Lombardi’s stat produces. The following information is based on the results from every game, regular and postseason, since 2008:
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Consider the following example. The Colts gain possession at the 20-yard line. Andrew Luck is in shotgun and throws a strike to Reggie Wayne, who catches it at the 30, runs 15 yards, and gets tackled at the 45-yard line. Luck gets credited with 25 passing yards and Wayne records 25 receiving yards. Wayne is also credited with 15 yards after the catch, a statistic that you’ll occasionally see discussed or cited.

But you rarely see Luck’s completion split into (a) 10 yards through the air, and (b) 15 yards after the catch by his receiver. Brian Burke calls those 10 yards “Air Yards” and I think that is a pretty useful moniker. The question is, what do you do with Air Yards? Luck led the NFL in Air Yards per completed pass last year (8.0), but that doesn’t make the statistic an indicator of quality. Tim Tebow’s 2011 performance produced the highest single-season Air Yards per completion average since 2006 (8.9), while Jake Delhomme (2008) and Derek Anderson (2010) each have led the league in that metric, too. Air Yards per completed pass is a very useful way to describe a player’s style, but you can’t use it alone to determine a player’s quality.

One question I have: Are Air Yards more repeatable for a quarterback than the yards he gains via his receivers’ YAC? It’s important to keep that question separate from this one: Is a quarterback who has a high number of air yards and a low YAC better than a quarterback in the opposite situation? Today, I plan to focus on the first question, but let’s take a second to address the second one.

According to ESPN’s research, yards after the catch is more about what the receiver does than the quarterback. As a result, a completion that is in the air for 40 yards is better for a quarterback’s ESPN QBR than a pass that is in the air for 5 yards on which the receiver runs for 35 yards after the catch. That makes sense, I suppose, and I suspect that’s probably true more often than not. The easiest counterargument is to point to Joe Montana, and say that what made Montana great was his pinpoint accuracy that enabled players like Jerry Rice to rack up big YAC numbers.I’m going to put off any further analysis of how much of YAC should be attributed to the quarterback and how much to the receiver, because it’s pretty complicated. One thing that is a bit easier to analyze is how “sticky” Air Yards are from year to year.
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