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Pass Identities Through Seven Weeks

I’ve published the Game Scripts data from every game this year at the 2014 Game Scripts page, available here. What would it look like if we plotted Game Script score (on the X-Axis) against Pass Ratio (on the Y-Axis) for every game this year? Something like this:

game scripts
As you move from a more negative Game Script to a more positive one, the expected Pass Ratio decreases. But the relationship is not purely linear: in extreme cases, the Pass Ratios tend to move a bit more towards league average, and I think that trend is probably even stronger than it might appear on this graph. In any event, you can derive a best-fit polynomial equation from that data, which could give us an expected Pass Ratio.

Luck's Colts have been very pass-heavy in 2014

Luck’s Colts have been very pass-heavy in 2014

For example, with a Game Script of +3.0, teams should be expected to pass on 56.3% of all plays. But in the Eagles/49ers game, Philadelphia passed on 78.6% of all plays. At the time, I thought it was an oddly pass-happy performance, as it turns out, it was the most pass-heavy game of the year, with a pass rate 22.3% higher than expectation.

If we perform that calculation for every game this year, we can derive season grades. Let’s look at the Colts line in the table below. In 7 games this year, Indianapolis has an average Game Script of +7.9, which happens to be the highest in the NFL (the table is fully sortable). Based on how each game has unfolded, Indianapolis would be expected to pass on just 52.7% of all plays if it was an average team; however, the Colts have passed on 59.4% of all plays. That means the Colts have passed 6.7% above expectation, the second highest rate in the NFL this year. The table below lists that data for each team through week 7: [click to continue…]

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Passing Kings, From Friedman to Manning

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


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

[click to continue…]

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Week 6 (2014) Game Scripts: Bucs Blown out Again

It was only back in week 3 when the Falcons posted a Game Script of 32.5 against the Bucs. In week 6, the Ravens nearly duplicated that effort in Tampa Bay!

Joe Flacco threw two touchdowns to Torrey Smith in the first 6 minutes of the game. He would hit Kamar Aiken and Michael Campanaro before the quarter was over, becoming just the second quarterback in NFL history with four first-quarter touchdown passes. The other? Tommy Kramer in 1986 against the Packers.

Baltimore’s Game Script produced the 2nd best Game Script of the year; meanwhile the Eagles’ 27-0 shutout against the Giants came with a Game Script of +17.1, the 7th highest mark this season.

The table below lists the Game Scripts data from each game in week 6. As is customary around these parts, I’ve highlighted the Bengals/Panthers game in blue as a result of their tie (you can move your cursor over that row to see it more clearly, not that I know why you would want to). [click to continue…]

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Brian Football

Brian Football.

Last week in this space, we bemoaned the large number of blowouts and the lack of exciting comebacks. Apparently, bemoaning works.

After falling behind against Atlanta by a score of 20-10, the Giants scored the final 20 points of the game to steal the win. The Saints jumped out to a 13-0 lead against Tampa Bay, but the Bucs responded by going on a 31-7 run. With the season teetering on the edge, New Orleans responded by scoring 17 straight points to pull off the rare come from ahead comeback.

In Detroit, the Lions jumped out to a 14-0 lead. But the Bills scored 17 straight, and won with a Game Script of -6.4. In Carolina, the Bears took an early 21-7 lead, but the Panthers scored 24 of the game’s final 27 points, winning with a -3.8 Game Script. But by far the biggest comeback of the day came in Tennessee, when the 2014 edition of the Kardiac Kids pulled off the largest road comeback in NFL history.

With 2:55 left in the first half, the Titans led the Browns, 28-3. But from that point forward, Brian Hoyer completed 16 of 27 passes for 259 yards and 3 touchdowns, while Ben Tate, Isaiah Crowell, and Terrance West rushed 24 times for 107 yards. By the end of the day, Cleveland had won 29-28 despite a Game Script of -10.5. That checks in as the worst Game Script by a winning team since the Colts won with a -11.0 against the Texans in week 9 of last season.

The table below shows all the Game Scripts data from week 5:

[click to continue…]

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Thursday Night Football. New York and Washington. Can you feel the excitement? Probably not. Despite being 3-point underdogs, the Giants won in a snoozer, 45-14, while posting a Game Script of +12.9.

Okay, what about Sunday Night Football? Dallas and New Orleans. Tony Romo and Drew Brees. Can you feel the excitement? Probably not. Despite being 3-point underdogs, the Cowboys won in a snoozer, 38-17, while posting a Game Script of +14.4.

The week ended with Monday Night Football and Tom Brady! Can you feel the excitement? Probably not. Despite being 3-point underdogs, the Chiefs won in a snoozer, 41-14, while posting a Game Script of +14.5.

In between, two other teams — Miami and Indianapolis — also finished with Game Scripts of 13-14 points. Green Bay and San Diego won by a combined 40 points, although the Game Scripts indicated slightly more competitive action against the Bears and Jaguars than that final score. In fact, just two games were won by teams with negative Game Scripts, and those were the only two real comebacks of the week.1

Team
H/R
Opp
Boxscore
PF
PA
Margin
Game Script
Pass
Run
P/R Ratio
Op_P
Op_R
Opp_P/R Ratio
KANNWEBoxscore41142714.5283842.4%331667.3%
DALNORBoxscore38172114.4303446.9%451278.9%
INDTENBoxscore41172413.8414150%311567.4%
MIA@OAKBoxscore38142413.5313547%461773%
NYG@WASBoxscore45143112.9403851.3%351767.3%
BALCARBoxscore38102811.9313050.8%362559%
GNB@CHIBoxscore3817217.1281959.6%364146.8%
SDGJAXBoxscore3314196.2411968.3%402561.5%
DET@NYJBoxscore241775.9382758.5%352657.4%
MINATLBoxscore4128135.6304440.5%422265.6%
HOUBUFBoxscore231761.5392263.9%462366.7%
TAM@PITBoxscore27243-1.3442068.8%462663.9%
SFOPHIBoxscore26215-3344244.7%441278.6%

The two teams to win with negative Game Scripts were San Francisco and Tampa Bay. The 49ers trailed for most of the first half, and the Eagles extended their lead to 21-10 in the 2nd quarter. That means that in every Philadelphia game this year, the first team to obtain a 10-point lead has wound up losing. And the 49ers, after blowing a 17-point lead to the Bears and an 8-point lead to the Cardinals, finally found themselves on the positive side of a comeback. In Pittsburgh, the Bucs jumped out to a 10-0 lead, Pittsburgh responded with a 24-7 run, and then Tampa Bay scored the final 10 points of the game.

For the Patriots, this was the 3rd worst Game Script of the Tom Brady era. The worst performance came in the 31-0 loss to the Bills on opening day 2003, when the Patriots had a Game Script of -18.0. The only other game with a lower Game Script was a -16.6 in the playoff loss to the 2009 Ravens.

Finally, let’s look at some of the unusual pass/run ratios from week 4:

  • Against the Packers, the Bears became the first team since 1976 to run 40+ times despite losing by at least three touchdowns. To some extent, there was a perfect storm of events to make that happen: the Packers scored the final 24 points of the game, and the 21-point margin was much worse than the -7.1 Game Script number indicates. But Chicago still was very run-happy in this game: consider that the Bears ran more than they passed, while the Packers threw on about 60% of their plays. That stat line is typically associated in a game where the Bears would be posting the +7.1 Game Script, not the other way around. Of course, Chicago rushed for 235 yards and averaged 5.7 yards per carry, which might explain the run-heavy offensive game plan.
  • The Chargers are known as a run-oriented team, but injuries to Ryan Mathews and Danny Woodhead may change things. Donald Brown and Branden Oliver rushed 19 times for just 42 yards against the Jaguars. As a result, San Diego threw on about twice as many plays as it ran, which is out of character for a team (especially the Chargers) with a +6.2 Game Script. Jacksonville actually ran more frequently, although without much success (to be fair, five of the Jaguars runs were by Blake Bortles). Were the Jaguars trying to protect their rookie quarterback? Probably. But giving Toby Gerhart, Denard Robinson, and Jordan Todman 20 carries isn’t worth much if they can only muster 61 yards. Another sign of the team’s conservative attack: Other than a 44-yard bomb to Allen Hurns, Bortles averaged 7.6 yards per completion on his other 28 completions.
  • The Jets and Lions had nearly identical pass/run ratios, with Detroit passing slightly more often. That is only unusual because the Jest trailed by an average of 5.9 points throughout the game on Sunday. As we’ve said just about every week, the Jets like to run the ball, and teams do not like to run the ball against the Jets. By the end of the year, expect New York to rank in the bottom three in both pass identity and in opponent’s pass identity.
  • The Eagles had an incredible 78.6% pass rate against San Francisco. Nick Foles did not have a very good day, completing just under half of his pass attempts.  So why did the Eagles abandon the run? LeSean McCoy couldn’t do much against the 49ers front: he had just 10 carries for 17 yards, with Darren Sproles chipping in with only one rush.  The Eagles offensive line has been decimated, although it’s not clear that the response to that circumstance is a very pass-happy attack. There’s nothing wrong with passing so often, but it’s always worth noting when the team that was the most pass-happy of the week was in one of the more competitive games. The Eagles had been passing on around 60% of their plays through the first three weeks, with a consistent ratio each week.  Perhaps Sunday’s result says more about the opponent than it does the Eagles.
  1. Technically, the Vikings had a 4th quarter comeback against the Falcons, but Minnesota took the lead for good with about 11 minutes left in the game. []
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Ryan posted his 2nd monster game in three weeks on TNF.

Ryan posted his 2nd monster game in three weeks on TNF.

In 2013, the largest Game Script was 23.8, courtesy of the Chiefs 45-10 blowout in Washington. But that game was child’s play compared to the NSFW game that was Atlanta/Tampa Bay on Thursday Night.

The Falcons finished with a Game Script of +32.5, the sixth highest in NFL history. Matt Ryan finished the day 21 of 24 for 286 yards and 3 touchdowns. Incredibly, Atlanta turned it over 4 times, although that didn’t stop the Falcons from finishing +1 in the turnover margin.

In a normal week, Indianapolis would stand out for its thrashing of the Jaguars: the Colts posted a Game Script of 19.8, which is even large by Indianapolis/Jacksonville standards. Last year, the Colts finished with Game Scripts of 15.5 and 17.8 against the Jags. What’s weird, though, is that Indianapolis — which has a tendency to get very conservative at times — has thrown on about 60% of its plays in the team’s last three games against the Jaguars, despite monster leads. Andrew Luck fantasy owners, take note, although I’m not quite sure what this says about the Colts mindset.

The Bengals continued their dominant ways in week 3, holding an average margin of victory of 14.8 points against the Titans. Cincinnati had a Game Script of +8.5 in week 2, while the Titans had -8.5 Game Script in week 2, so I guess 8.5 + -8.5 = 14.8? Leave the math to the professional bloggers, folks.

The table below shows the Game Scripts data from each team in Week 3: [click to continue…]

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Thoughts on the value of a fumble vs. an interception

In the late ’80s, The Hidden Game of Football determined that an interception was worth -45 yards and a lost fumble was worth -50 yards. Why was a fumble five yards worse than a pick? That’s because Carroll, Thorn, and Palmer found that, on average, the team gained possession via the turnover was five yards closer to their opponent’s end zone when that turnover was a fumble.

Makes sense, but is that still true? Courtesy of Mike Kania of Pro-Football-Reference, here are some data on turnovers since 1999:

  • Ignoring interceptions returned for touchdowns, the team recording the interception loses about 4.41 yards of field position, on average, on each interception. So let’s assume the Patriots are playing the Jets, the Patriots have the ball at their own 40, and New England throws an interception. On average, the Jets will (ignoring pick sixes) have 1st and 10 at the Patriots 44.4-yard line on the next play.
  • If, instead, the Jets gained possession via a fumble, New York would, on average, start on the Patriots 39.2-yard line. That’s because following a fumble by an offense that is not returned for a touchdown, the line of scrimmage moves about 0.8 yards closer to the offense’s end zone.
  • In other words, teams gain about 5.2 yards of field position when recovering a fumble rather than an interception. That’s kind of remarkable, considering it matches the results found from researchers in the ’80s. However…
  • We still have to consider turnovers that are returned for touchdowns.  Roughly 10.7% of interceptions were returned for touchdowns during this period, compared to only 7.9% of recovered fumbles. Remember, interceptions are now much more likely to be returned for touchdowns than they were in the mid-’80s.

Thirty years ago, the penalty was 45 yards for an interception and 50 yards for a lost fumble.  We haven’t shown today whether those numbers in the abstract were correct, but the five yard relative difference still seems supported by current data, with one notable exception.  But as more interceptions are returned for touchdowns1, interceptions are becoming about as bad for offenses as lost fumbles.

  1. I’ll note that fumbles are also being returned for touchdowns at higher rates — that’s probably worth its own post — but it is not increasing at the same rate. []
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Here is graphic video of a famous football player performing an act of cowardly violence against a defenseless victim. The offender did not receive any penalty for his actions. After committing that crime, the assailant showed no remorse at the condition of the victim, who lay prostrate on the ground. Not disciplined for earlier acts of violence, that player struck again, this time paralyzing his defenseless victim. That victim would eventually die far too young, in part as a consequence of that attack.

For this perpetrator, the response was much worse than insufficient punishment or radio silence. Jack Tatum was celebrated for many of his hits, perhaps most notably the one on Sammy White in Super Bowl XI. The Ray Rice punch makes all of us cringe, but the hit on White―and even more so the one on Darryl Stingley ― should also make us cringe. [click to continue…]

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Marshall wonders why the Bears Game Script was so poor.

Marshall wonders why the Bears Game Script was so poor.

It was a week for comebacks in the NFL. Chicago trailed San Francisco 17-0 with just 30 seconds left in the first half, but won 28-20. With 20 minutes left, the Eagles trailed the Colts 20-6, but came back to win 30-27. Midway through the 2nd quarter, the Jets led the Packers 21-3, but Green Bay came back to win, 31-24.

All three games produced Game Scripts by the winning team of between -4 and -7 points. Game Scripts, regular readers know, measure the average points differential over the course of the entire game. Week 2 brought a pair of games with very large game scripts, with Oakland (Game Script of -15.9) and Jacksonville (-15.3) failing to look competitive in losses to houston and Washington, respectively. Minnesota (-11.7) wasn’t much better. Not surprisingly, the Raiders, Jaguars, and Vikings all passed significantly more often than their opponents. [click to continue…]

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Regular readers are familiar with the concept of 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 Washington/Houston game (since it featured just four scoring plays) to explain how to calculate the Game Script score.

The first score of the game came with 6:11 left in the second quarter, when Darrel Young rushed for a touchdown (the extra point was blocked, of course, by J.J. Watt).  This means for the first 23 minutes and 49 seconds, the score was tied.  On Houston’s ensuing drive, Ryan Fitzpatrick hit DeAndre Hopkins for a 76-yard touchdown with 4:28 left in the half.  That means Washington held a 6 point lead for only one minute and 43 seconds.

After a three-and-out, Washington’s punt was blocked, and Alfred Blue recovered, giving Houston a 14-6 lead with 2:09 left in the half.  This means that Houston held a 1-point lead for two minutes and 19 seconds.

Then, the Texans held that 8-point lead for just over 30 minutes: Houston kicked a field goal right at the two minute warning, and ultimately won, 17-6.

Now, to calculate the Game Script, all you need to do is average the Texans’ margin over the course of the 3600 seconds in the game. As you can see in the table below, that number is 4.3. [click to continue…]

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


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

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

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

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

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

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

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Gronk can catch, block, and spike. But can he do all that without getting injured?

Gronk can catch, block, and spike. But can he do all that without getting injured?

In the 2011 AFC Championship Game against the Ravens, Bernard Pollard happened to Rob Gronkowski. And the Patriots offense ground to a halt for the rest of the game before being held to just 17 points in the Super Bowl.1 In 2012, it was a freak injury on an extra point and then a reinjury in the divisional playoffs against the Texans. After that, the Patriots offense put up only 14 against the Ravens in the 2012 AFC Championship Game. Last year against the Browns, he took one of those horrible hits that make you cringe and want to keep him away from running seam routes in any regular season game.2 And the Pats put up 16 points against a mediocre and banged-up Broncos defense in the AFC Championship game.3

The Gronkowski injuries provide a tantalizing set of what-ifs. The Patriots have been within two games of a title the last three years. A healthy Gronkowski could have made the difference in any of those years. The Football Outsiders’ Almanac shows that the Pats’ offense was actually pretty good late in the season without Gronk, but they were terrible early in the year―they actually had a negative DVOA without him. Over the last two regular seasons, the Pats have averaged 34 PPG with Gronkowski, but six points fewer in New England’s 14 Gronk-less games.

And as much as I believe in stats, I’m not sure we really need them to tell us that Gronkowski is one of the most important non-quarterbacks in football. If he’s healthy through the playoffs, the Patriots seem likely to be neck-and-neck with the Broncos. With a defense that may be one of the best in football, I’d argue that the Pats should be a little better than the Broncos, even.4 Regardless, the Pats offense has been uniformly excellent with a healthy Gronkowski since 2010. Taking just the games where Gronk played, the Pats have ranked 1st, 3rd, 1st, and 2nd in offensive DVOA over the last four years.

That means one of the most important questions in the NFL in 2014 is whether we’ll see a healthy Gronkowski through the end of the season and into the playoffs. At this point, I think the reflexive answer is to assume that the answer is “no.” It certainly doesn’t feel like he’s going to be healthy. But previous examples of players getting hurt can provide some insight into Gronkowski’s actual chances.

Recovery for Injured Young-and-Excellent Players

In his second year, Gronkowski had an Approximate Value (AV) of 14. He then played only parts of the next two seasons due to injury. Considering players who started their careers since 1970, there have been 34 who had an AV season of at least 13 in their first two years and who then did not start at least 25% of the games in the following two years. This is a reasonable list of young-and-excellent players who then missed significant time in years 3 & 4. Most of these players missed time due to injuries, although some of those cases were a bit debatable.5 Regardless, the conclusions are pretty much the same if we drop some of those cases. [click to continue…]

  1. Yes, a very limited Gronk played in SB XLVI, but he had only two catches and jumped like me when battling Chase Blackburn on Brady’s underthrown fourth quarter pick. []
  2. The link is of Gronk shopping for groceries instead of the hit, because who wants to see that again? []
  3. The only two games all season where the Broncos gave up fewer points were against Houston and Oakland. []
  4. Unless Manning is just much better than Brady, I guess. I’m not seeing that. Denver’s only other big advantage is at receiver. Fine, but a healthy Gronkowski seems to even up a fair bit of that. And then there’s Brandon LaFell’s impending record-breaking season. I’m about to get shouted down. [Chase note: I don't know how much longer I can stomach Andrew writing for Football Perspective.] []
  5. In addition, I omitted two players who were obviously benched for other reasons: Shaun King and Derek Anderson. And Joe Cribbs, who went to the USFL for the fifth year of his pro career. []
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Yesterday, I looked at how long it took the best quarterbacks to break out. Today, I want to apply what we learned from that post to 15 current NFL quarterbacks with fewer than 50 starts, all of whom were 26 years old or younger during the 2013 season.

Bradford looks to check down

Bradford looks to check down.

Sam Bradford (49 career starts): Career Relative Adjusted Net Yards per Attempt of -0.68.

Bradford was overrated after he put up good counting stats but weak efficiency numbers as a rookie; he posted a -1.0 RANY/A in 2010, a -1.4 average in 10 starts in 2011, was at -0.3 in 16 starts in 2012, and then +0.2 in seven starts last year. Yesterday, we noted that great quarterbacks who came to terrible teams (Warren Moon and Drew Brees, in addition to former number one picks like Troy Aikman, Terry Bradshaw, Vinny Testaverde, and Steve Young) struggled initially. Bradford would seem to fit that mold, although he’s now 49 starts into his career. Are there other reasons to give him a pass?

St. Louis had the third-youngest offense in the NFL last year, and the man who has gained the most yards from Bradford over the last four years is Brandon Gibson. The former first overall pick has received very little help, and been saddled with a revolving door of mediocre receivers.

On the other hand, Kellen Clemens posted better numbers than Bradford last year, at least when you adjust for strength of schedule. As Bill Barnwell pointed out last week, Bradford’s big problem is his inability to throw the ball down the field, which jives with some of the work I’ve done Bradford’s historically low yards per completion averages.  If not for Bradford’s first season of above-average work last year, I’d say his odds of ever being a franchise quarterback are very low.  But there has been some progression, and he does fit the mold of number one pick being saddled with bad teammates.  Of course, the presence of Brian Schottenheimer is enough to make me skeptical of Bradford’s ability to put it all together this year.  Perhaps the best case scenario is a Testaverde-like revival with another team years from now.

Cam Newton (48 career starts): Career RANY/A of +0.30.

Not much to see here. Newton’s RANY/A has moved from +0.3 as a rookie to +0.7 in 2012 to -0.2 last year; it went under the radar because #QBWINZ, but Newton did have a down season in 2013.  It’s hard to find any reasons for optimism for the Panthers this year after a mass exodus in the offseason, but that doesn’t say much about Newton’s long-term prospects.   Add in his rushing ability, and Newton has shown enough to say that he’s still in contention (if he’s not already there) to go down as a franchise quarterback.

Andy Dalton (48 career starts): Career RANY/A of -0.01.

Look at that, Dalton is almost perfectly average! Bill Barnwell did a nice job profiling Dalton last week, and it does seem like what you see is what you will get from Dalton.  After posting slightly below-average RANY/A numbers in 2011 and 2012, he was above-average (+0.4) last year.  But the Bengals have one of the most talented offenses in the NFL if you exclude the quarterback position; at this point, you’d be hard-pressed to find many folks who believe Dalton will turn into a future star.

Of the 42 quarterbacks I looked at yesterday, 13 failed to be significantly above-average during any of their first three 16-game samples.  Dalton doesn’t really resemble any of them: Bradshaw/Testaverde/Elway/Vick were former number one picks; Brady/Favre/Krieg/Kelly were on the border of being good enough on to not make the list, and were certainly ahead of where Dalton is now; McNabb and Cunningham were running quarterbacks.  Moon played for a terrible team, and Gannon and Theismann sat for long stretches.  That’s the full thirteen. The best case scenario may be that Dalton turns into a Krieg or a poor man’s Jim Kelly.  Of course, he could also win a Super Bowl by riding the coattails of one of the more talented (and youngest) rosters in the league.

Christian Ponder (35 career starts): Career RANY/A of -1.19.

There are always excuses to be made for bad quarterbacks, and I’m sure that there are still some Vikings fans who believe in Ponder.  He produced a -1.7 RANY/A as a rookie, improved to -0.9 in 2012, but was back at -1.1 in nine starts last year.  Minnesota may not have a ton of talent at wide receiver, but Ponder’s failure to produce even with Greg Jennings is yet another strike against him. The Vikings drafted Teddy Bridgewater at the end of the first round in the 2014 draft, which seems like the beginning of the end for the former Florida State star.

Wilson is watching game tape right now.

Wilson is watching game tape right now.

Russell Wilson (32 career starts): Career RANY/A of +1.15.

Franchise quarterback achievement badge mode: unlocked.

Ryan Tannehill (32 career starts): Career RANY/A of -0.80.

Tannehill was at -0.7 RANY/A in 2012 and at -0.9 RANY/A last year; neither of those numbers put his future prospects in a positive light.  There are excuses, to be sure: he was a raw prospect, the Dolphins offensive line was the worst in the NFL, he and Mike Wallace have the chemistry of a pair of tomatoes, etc., but the numbers are bleak enough to cast doubt on Tannehill’s future.  Unless the argument is that Tannehill landed on one of the very worst offenses in the league — which would allow you to lump him in with the Aikmans, Bradshaws, Breeses, and Testaverdes of the world — there is simply no precedent for a quarterback being this below average for this long and then turning into a franchise passer.1 Barnwell is a little (and only a little) more bullish on Tannehill than I am, but 2014 would appear to be Tannehill’s last chance to convince the Dolphins that he was not a wasted pick.  There are a couple of mitigating factors here — the running game has been terrible, and as an immediate starter, Tannehill is at a disadvantage relative to other quarterbacks on this list — but I’m not going to lose sleep over whether this prediction will look bad in a few years.

Andrew Luck (32 career starts): Career RANY/A of -0.06.

Since starting this site, Luck has been one of the quarterbacks I’ve profiled the most.  He wins without much help and is an ESPN QBR star, but he’s below average in ANY/A.  I’m inclined to grade Luck on a curve — after all, the Colts team he inherited didn’t look any better than the ’70 Steelers or ’89 Cowboys or ’87 Bucs.  On the other hand, Reggie Wayne and T.Y. Hilton have given Luck some excellent targets, which has probably been enough to boost his ANY/A to league-average proportions.

Perhaps the best comparison will be to another quarterback drafted first overall by the Colts who had a magical history of producing comebacks: John Elway.  In any event, Luck’s already a franchise quarterback.

Can RG3 get up from a disastrous 2013?

Can RG3 get up from a disastrous 2013?

Robert Griffin III (29 career starts): Career RANY/A of +0.5.

Griffin’s career RANY/A is like measuring the temperature of a person with a foot in the freezer and a foot in a frying pan.  As a rookie, he had a RANY/A of +1.5; last year, it was -0.4, and that number doesn’t begin to explain how ugly things were in D.C.  The simplest explanation is that Griffin is a franchise quarterback who struggled last year as he recovered from ACL surgery and dealt with an ego-maniacal head coach.  But it’s hard to just assume Griffin is a franchise quarterback after 2013.  If Griffin one day turns into a Hall of Famer, we’ll remember that it was obvious from the start, as he had one of the greatest rookie seasons ever.  If he flames out, the first chapter of that book has already been written, too.

Blaine Gabbert (27 career starts): Career RANY/A of -2.15.

Spoiler alert: Gabbert is not a franchise quarterback.  He started at -2.2 RANY/A as a rookie on a team not dissimilar from the ’89 Cowboys; he’s followed that up, however, with a -1.2 RANY/A in 2012 and a -4.7 RANY/A over three starts last year. Suffice it to say if Gabbert turns into a franchise quarterback, it will have taken the greatest reclamation project in NFL history.

Colin Kaepernick (23 career starts): Career RANY/A of +1.06.

Kaepernick was mind-bogglingly efficient in 2012, producing a +1.6 RANY/A over 13 games and seven starts.  That number dropped to +0.8 RANY/A last year, but much of that is due to the loss of Michael Crabtree.  With an all-star crew of receivers set to take the field in 2014, I expect another very strong year out of Kaepernick. He may not be a finished product, but he already has the label (and contract) of a franchise quarterback.

Jake Locker (18 career starts): Career RANY/A of -0.25.

Maybe it’s because I’m a college football guy, too, but doesn’t it feel like Locker has already been around forever? I can’t believe he only has 18 career starts. And his RANY/A is nearly league-average, even if it doesn’t feel like Locker has been even that good.  I was not a fan of him as a prospect, but he has been better than I feared.  While we shouldn’t compare Locker’s first 18 starts to those of a quarterback who started immediately, I think Locker has shown enough that you can’t just write him off just yet.  On the other hand, his numbers last year were a bit inflated by one of the NFL’s easiest schedules. Like Tannehill, this is the crucial season for Locker, who also carries with him the injury prone label. But if Locker can stay healthy and produce strong numbers, Ken Whisenhunt may prove that he really is a quarterback whisperer (to the extent he’s not whispering to someone named Skelton, or Kolb, or Anderson, or Leinart, or Lindley, or Hall….)

Nick Foles (16 career starts): Career RANY/A of +1.45

Foles had a rookie RANY/A of -0.8 before posting an absurd +3.3 RANY/A in 2013. Even the bigger Eagles homer would admit that much of Foles’ success was due to good fortune, the presence of Chip Kelly, or both.  Foles may not have arrived just yet as a franchise quarterback, but if he turns into one, nobody will ever question when we first saw a glimpse of that ability.

Geno Smith (16 career starts): Career RANY/A of -1.70.

Smith was bad — really bad — for long stretches as a rookie.  But he finished the season well, and terrible rookie numbers on a talent-deficient offense are not the death knell for a quarterback’s career.  The Jets need to see a lot more from him this year, though, and he’ll need to produce roughly league-average numbers to make the Jets think he’s not just another Mark Sanchez.

Mike Glennon (13 career starts): Career RANY/A of -0.9.

Glennon had a very different rookie campaign than Smith, but the acquisition of Josh McCown sends Glennon to the bench, at least for now.  We don’t know how he’ll fare in (or when he’ll see) his next three starts, but Glennon’s performance through 16 starts likely won’t be enough to write him off.

EJ Manuel (10 career starts): Career RANY/A of -1.0.

Manuel had a rough rookie year, especially when you consider how much worse he looked than Thaddeus Lewis. On the other hand, ten starts of bad (but not horrendous) play certainly isn’t enough to write off Manuel, not when Smith was worse for a longer stretch.  Still, as with Smith, this is a big year for Manuel, especially after the team went out and acquired Clemson’s Sammy Watkins.

  1. I suppose one could point to Phil Simms, but I’d object for a couple of reasons. For one, Simms didn’t crack my initial list, checking in at #86 in my GQBOAT series.  Then again, I’ve made the argument that Simms’ numbers underrate him because of his terrible receivers, so I would morally classify Simms as a franchise quarterback. However, the Giants teams of the late ’70s and early ’80s were so terrible that he really has more in common with the Aikmans of the world than someone like Tannehill. Here is how Simms fared compared to the other Giants quarterbacks during Simms’ first three years and 1978, the year before he came to New York. That’s U-G-L-Y. But if Dolphins fans want to point to Simms as a pro-Tannehill example, so be it. []
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Jerome Bettis is a polarizing Hall of Fame candidate. I’m on the fence with the Bus; I don’t think he’s as deserving as Steelers fans think, but he’s a more deserving candidate than those who mostly remember end-of-career-Bus remember. One thing I’ve heard from time to time about Bus is that he was the greatest “big” back of all time. That’s undoubtedly true, assuming you set the weight1 high enough. Bettis had an official playing weight of 252 pounds, and no running near that weight can match his resume. Cookie Gilchrist, Pete Johnson, Marion Butts, Christian Okoye, Natrone Means, and Mike Alstott had short bursts of success, but they can’t match Bettis’ longevity. Players like Jamal Lewis, Michael Turner, Larry Csonka, Eddie George, Jim Brown, Franco Harris, John Riggins, and Earl Campbell carried the “big back” label, but all were 10-25 pounds lighter than the Bus.

I looked at every running back in history, and calculated his number of rushing yards over 500 in each season (to avoid giving undue weight to compilers). After adjusting for season length, I then calculated career grades in this statistic. In the graph below, the Y-Axis shows this career rushing grade, while the X-axis displays weights. Bettis is represented on the far right with the code “BettJe00.”

[click to continue…]

  1. Try the veal. []
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I’m still short on time, so let’s keep the trivia train rolling.  Yesterday, I looked at the players with the most receiving yards in their last 16 regular season games. Today, the players with the most rushing yards in their last 16 games.

Excluding LeSean McCoy, Adrian Peterson, and Doug Martin, only five players have rushed for over 1,500 yards in their final sixteen games.  The record-holder rushed for 1,702 yards in his final sixteen games.  Do you know who it is?

Trivia hint Show


Click 'Show' for the Answer Show


One other player rushed for at least 1,600 yards in his last 16 games  Can you name him?

Trivia hint Show


Click 'Show' for the Answer Show


What about the other three players who rushed for 1,500 yards in their careers? All three retired early.

Trivia hint Show


Click 'Show' for the Answer Show


Trivia hint Show


Click 'Show' for the Answer Show


Trivia hint Show


Click 'Show' for the Answer Show

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I’m very short on time this week, so here’s a fun trivia question. Last week, I noted that Justin Blackmon gained 1,201 receiving yards in his last 16 games. As it turns out, if Blackmon never plays in another NFL game, that would set the record for most receiving yards in a player’s final sixteen games (this excludes all active players, of course).

Who holds that record now? Two players gained just over 1,100 yards in their final sixteen games. Can you name them?

Trivia hint 1 Show


Trivia hint 2 Show


Trivia hint 3 Show

Click 'Show' for the Answer Show

Rounding out the top five: Hart Lee Dykes caught 71 passes for 1,098 yards in his final sixteen games, as an off-the-field incident (which has nothing on this off-the-field incident) and repeated knee injuries ended his career. Finally, Terrell Owens gained 80 receptions, 1,087 yards, and 10 touchdowns in his last sixteen games.

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Rivers was outstanding in 2013, despite this throwing motion

Rivers was outstanding in 2013, despite this throwing motion.

The Denver Broncos set numerous offensive records last year. The Chip Kelly Eagles had a fascinating offense that was lethal for stretches. The Saints offense was its usual efficient self, and the Chicago Bears under Marc Trestman had one of the best offensive years in franchise history.

Yet all of those teams had at least 61 drives last year that ended in a punt. San Diego , meanwhile, punted just 56 times. The Chargers only had 21 turnovers, which means only 77 San Diego drives could be clearly labeled as failures, or “bad drives.”1

That’s pretty impressive; the 2013 Chargers were just the 36th team during the 16-game era to have fewer than 80 “bad drives” in a season. On the other hand, the Chargers were one of just five of those teams to score fewer than 400 points. San Diego’s offense was very efficient last year, but the 77 “bad drives” statistic is a bit misleading. That’s because the team had just 158 total drives last year according to Football Outsiders, while the average team had 186 drives.

Why did the Chargers have the fewest drives in the NFL? A bad defense certainly helped limit the team’s number of offensive drives: San Diego forced only 82 “bad drives” all year, too. But the main reason was that the offense was not just efficient, but uniquely efficient. According to Football Outsiders, San Diego averaged 3:22 per drive, a full 15 seconds more than the #2 team in that metric, Carolina. And the Panthers were the only other team to average at least three minutes per drive. One reason for the long time of possession is that the Chargers moved at a glacial pace between plays, rating as the 2nd slowest team according to Football Outsiders. The other teams in the bottom four in pace were all run-heavy — Carolina, Seattle, and San Francisco — which marks yet another way in which the Chargers were outliers. In several metrics — first downs per drive, yards per drive, and points per drive — San Diego and Denver were the top two teams in the NFL.  But in pace, Denver ranked 4th, making the Broncos offense look and feel much different than San Diego’s attack.

Another reason the team’s average drive took so long to complete: San Diego averaged 6.85 plays per drive, with New Orleans second in that statistic with 6.35 plays. That’s because the Chargers had a very horizontal passing attack. According to NFLGSIS, Philip Rivers ranked 6th from the bottom in average length of pass at 7.75; only Jason Campbell, Sam Bradford, Matt Ryan, Alex Smith, and Chad Henne threw shorter passes. With the exception of Ryan, none of those quarterbacks came close, however, to matching Rivers’ league-leading completion percentage. What we have here is your classic hyper-efficient, short-area passing game, and the Chargers executed it beautifully.

In fact, here’s another unique part of the San Diego offense: it rarely targeted wide receivers. San Diego was one of just three teams to throw more passes to non-wide receivers than to wide receivers. Here’s how to read the table below: the Chargers threw 25% of all pass attempts to running back, 47.1% to wide receivers, and 27.7% to tight ends. Based on those percentages, San Diego ranked 4th in percentage of pass attempts to running backs, 30th in percentage of pass attempts to wide receivers, and 2nd in percentage to tight ends. [click to continue…]

  1. The Chargers were 5/6 on fourth down attempts, so it’s not as though these numbers are skewed by failed fourth down attempts. []
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Thoughts on Running Back Yards per Carry

The man in odd-numbered games

The man in odd-numbered games.

Regular readers know that I’m skeptical of using “yards per carry” to evaluate running backs. That’s because YPC is not very consistent from year to year. But it’s also not consistent even within the same year. For example, In 2013, Giovani Bernard rushed 92 times for 291 yards in even-numbered games last year, producing a weak 3.16 YPC average. But in odd-numbered games, Bernard averaged 5.18 YPC, rushing 78 times for 404 yards!

Jamaal Charles also showed a preference for odd-numbered games, averaging 5.80 YPC in games 1, 3, 5, etc., and only 3.96 YPC in even-numbered games. Buffalo’s C.J. Spiller had a reverse split, producing 5.57 YPC in even games and 3.61 YPC in odd games.

Okay, this stuff is meaningless, you say. Who cares about these random splits? Well, there are a couple of reasons to care. For starters, these splits serve as a great reminder that splits happen. If Spiller averaged 3.61 YPC in the first half of the year and 5.57 in the second half, the narrative would be that Spiller was finally healthy by the end of the year, and was set up for a monster 2014 campaign. Meanwhile, if Charles had seen his YPC fall from 5.8 YPC in the first eight games to 3.96 in the back eight, the narrative would be that he couldn’t handle a heavy workload, was breaking down, and could be a huge bust this year. Narratives are easy to invent, and remembering that “splits happen” is an important part of any analysis. [click to continue…]

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Johnson's target ratio is no joking matter

Johnson's target ratio is no joking matter.

Yards per Route Run, a metric tabulated by Pro Football Focus, is one of my favorite statistics to use to examine wide receiver performance.  To me, it’s the wide receiver version of yards per pass, as it takes production and divides that by opportunity.  However, there are some folks who prefer Yards per Target to YPRR, under the idea that a target is a better way to define an opportunity than a route.

Which view is correct?  Fortunately for our analysis, Yards per Route Run can be broken down into two metrics: Yards per Target and Targets per Route Run.  In other words, YPRR already incorporates Yards per Target, but it adjusts that statistic for Targets Per Route Run.  This makes it very easy for us to compare the two statistics: essentially, the question boils down to how valuable it is to know a receiver’s number of Targets per Route Run.

For example, Kenny Stills had the most extreme breakdown of any player in the NFL in 2013. He was off-the-charts good in yards per target (13.9), but saw targets on just 9% of his routes run last year. As a result, Stills averaged just 1.29 yards per route run, a pretty unimpressive figure.

Steve Johnson was the anti-Stills. While Johnson had the worst year of his career since becoming a Bills starter, he still managed to pull down targets on 25% of his snaps. However, he averaged only 6.3 yards target, leaving Johnson with a poor 1.56 yards per route run average. Of course, when comparing Stills’ numbers to Johnson’s, one might note that Johnson was playing with EJ Manuel and Thaddeus Lewis while Stills was playing with Drew Brees, which provides some explanation for the drastic differences between the two receivers in yards per target.1 But putting the quarterbacks issue aside, the question today is a more global one.

Since the only difference between YPRR and Y/T is the metric “targets per route run,” it’s worth asking: is Targets Per Route Run a metric worth looking at? Is it more useful than Yards per Target? Well, the word “useful” will mean different things to different people. What I’m curious about is the stickiness of each metric. And there is a pretty clear answer to that question.

Among the three metrics — YPRR, Y/T, and TPRR — it’s Targets Per Route Run that’s the most consistent from year to year. From 2007 to 2012, there were 344 wide receivers who saw at least 40 targets in Year N, and then played for the same team and saw at least 40 targets in Year N+1.2 [click to continue…]

  1. I suppose one counter to that would be that Stills was competing with Jimmy Graham, Marques Colston, and the Saints obsession with throwing passes to running backs, while Johnson was competing with Scott Chandler, Robert Woods, and Fred Jackson for targets. []
  2. While there are some issues with survivorship bias here, I’m not sure (1) how to get around them, and (2) that those concerns bias the results in a way that’s more biased towards one of the variables we’re examining than the others. []
{ 32 comments }

A couple of weeks ago, I posted a list of the best rushing teams in 2013 using Adjusted Yards per Carry. That metric, you may recall, is calculated as follows:

Rushing Yards + 20*RushingTDs + 9*RushingFirstDowns

We can use the same formula to grade every team across history. To account for era and quantity (having more above-average rush attempts is better), I calculated each team’s AdjYPC average, subtracted the league-average AdjYPC average, and multiplied that difference by the team’s number of rush attempts.

The top team by this method isn’t even an NFL team: it’s the 1948 San Francisco 49ers. You may recall that the 49ers and Browns staged two epic battles that season, and may have been the best two teams in pro football. That season, San Francisco averaged 6.1 yards per carry and rushed for 35 touchdowns on 600 carries; along with 152 first downs, and the 49ers averaged 9.5 Adjusted yards per Carry. That’s the highest average ever, just narrowly topping the production of the franchise’s Million Dollar Backfield six years later. Joe Perry was on both teams because Joe Perry was the man.

The table below shows the traditional rushing data for the top 200 rushing teams of all time; the VALUE column represents the number of Adjusted Rushing Yards produced above average (i.e., relative to the league average AdjYPC). I’ve also listed the three most prominent rushers on each team. [click to continue…]

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Rushing EPA and Yards per Carry

Today I want to look at how traditional rushing statistics compare to rushing Expected Points Added, one of the main stats used over at Advanced Football Analytics. In my analysis, I used the EPA numbers for each team in each season from 2002 to 2013.

Stickiness from year to year

Yards per carry is not a sticky metric: by that, I mean, it is not very consistent from year to year. The correlation coefficient between a team’s yards per carry in Year N and yards per carry in Year N+1 was just 0.31. Sometimes the square of the correlation coefficient is described in terms of “explanatory power”: loosely speaking, this means roughly 10% of a team’s YPC average in Year N+1 can be explained by its YPC average in Year N.

Now, a lot of metrics aren’t sticky from year to year, because the NFL is a highly competitive league. In fact, Rushing EPA per play has a lower correlation coefficient from year to year at just 0.30. That’s a strike against EPA. On the other hand, Burke’s success rate metric has a CC of 0.39, which is more impressive. The CC for Net Passing Yards per Attempt year over year is 0.43. [click to continue…]

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One of my first posts at Football Perspective was one of my favorites: the top receivers and the men who threw it to them. I like referencing that post from time to time, so I decided to update the numbers through the 2013 season.

I looked at all regular season games since 19601, and calculated the percentage of passing yards produced from each quarterback. Then, I assigned that percentage to the number of receiving yards for each receiver. For example, in this Raiders game from 1995, Vince Evans threw for 75% of the Raiders passing yards, and Jeff Hostetler was responsible for the other 25%. Therefore, since Tim Brown gained 161 yards, 121 of those yards are assigned to the “Brown-Evans” pairing and 40 to the “Brown-Hostetler” pairing. Do this for every game since 1960, and you can then assign the percentage of career receiving yards each receiver gained from each quarterback.

For example, 32% of Brown’s yards came from Rich Gannon, 26% from Hostetler, 12% from Jeff George, and 9% from Jay Schroeder. That breakdown isn’t too unique: in fact, of the six receivers with the most receiving yards since 1960, all six (including Brown) gained between 29% and 37% of their career receiving yards from their top quarterback.

The table below lists the top 7 quarterbacks for each receiver, although I only included quarterbacks who were responsible for at least five percentage of the receiver’s yards. It includes the 200 players with the most receiving yards since 1960. [click to continue…]

  1. Sorry, Don Hutson. []
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A few days ago, I was in Vegas with friends and without a car. So I took the chance to shop NFL futures odds to the extent that I felt it was worth it to walk to a given sportsbook. I decided the 3+ mile walk to the Superbook was not worth the opportunity cost in the 105 degree heat, so I didn’t get their numbers. But I did get numbers from three of the major oddsmakers: William Hill, Cantor Gaming, and MGM. Tomorrow, I’ll talk about some bets that seem potentially attractive. As I described recently, the numbers are pretty good now and don’t leave obvious opportunities for the most part, I think.

Yes, I still did like some bets. I only found one season win total I really wanted to bet on, and it’s not one of the ones I would have bet back in March. I made a few bets at the William Hill sportsbook, just a little hole in the wall at the Hooters’ Casino a little ways off the Strip, which could just as easily have been in Nevada towns forgotten by time like Laughlin or Mesquite as in Las Vegas. Then I made a few bets not too far from the beautiful people at the Cantor book in the Cosmopolitan. I spent way too much time thinking about all this stuff, which might not have been necessary if I only had that time machine and could have bet on the initial lines. But there’s also some cool stuff by looking at the teams’ odds that have changed the most in both directions.

Season Win Totals

Some interesting movements have happened in the numbers that Cantor Gaming released in March. Those changes reflect everything that happened in free agency and the draft, but also maybe some numbers that people would have bet on anyway even if nothing had changed personnel-wise.  Below are the opening numbers along with the numbers I gathered during the last week. The Cantor numbers are mostly from 6/18 because their books that I went to would only give me the numbers one at a time. I gathered about eight of those numbers because I was at least considering them as wagers. For those teams, the most recent line is the one that I posted. The other companies’ books gave me complete printouts of all their season win-total lines.

A note on the odds: Lines like -140 mean that you would wager $140 to win $100. Lines like +130 mean you wager $100 to win $130. The numbers are usually split by 20 on either side, which represents the vig, or Vegas’s commission. For example, Denver being at -115 for the over would usually go with -105 on the under. For bigger odds, the over and under can be split by more. Also, the MGM has a slightly larger vig, with a 30 split between the over and under. [click to continue…]

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There is no doubt that in modern times, passing is king. But until pretty recently, we were at the peak level in NFL history with respect to individual rushing performance. On the team level, rushing production ebbed and flows, with high points in the late ’40s, mid-’50s, and mid-’70s, but on the individual level, the 2006 season may have been the high point.

That year, Pittsburgh’s Willie Parker rushed 337 times for 1,494 yards and scored 13 touchdowns, but Parker ranked just sixth in rushing yards. He also caught 31 passes for 222 yards, but Parker ranked only 7th in yards from scrimmage. That season, the average leading rusher on the 32 teams gained 1,124 rushing yards. Again, that was average. Last year, the average leading rusher gained 912 yards. Consider that in 1991, after Emmitt Smith, Barry Sanders, and Thurman Thomas, the fourth-leading rusher was New York’s Rodney Hampton, and he gained 1,059 yards. In other words, 2006 and its surrounding seasons — even if it might not feel like it — really was a different era of football for running back statistics.

The graph below shows the average rushing yards gained by the leading rushing for each team in every season since 1932. All team seasons of fewer than 16 games were pro-rated to 16 games1; the NFL line is in blue, while the AFL/AAFC line is in red. [click to continue…]

  1. But this does not pro-rate for injury. []
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A couple of weeks ago, Brian Burke of Advanced Football Analytics (formerly Advanced NFL Stats) wrote a great post on the value of a first down. From that post, we concluded that the marginal value of a first down is 9 yards, and we’ve previously determined that the marginal value of a touchdown is 20 yards. Therefore, we can create an Adjusted Yards per Carry statistic, which can be calculated as follows:

Adjusted Yards per Carry = (Rushing Yards + 20 * Rushing TDs + 9 * Rushing First Downs) / Rushes

If we use this metric to analyze the 2013 season, how would it look? Last year, the Eagles averaged 5.13 yards per carry and 8.29 Adjusted YPC, courtesy of the fact that the team led the NFL in rushing first downs. Philadelphia also ranked 1st in the NFL in both of those metrics and in overall rushing yards. [click to continue…]

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Average margins in Wins and Losses

Okay, some fun trivia to kick off the week. Do you know which team last year had the worst points differential in games they lost? I’ll put the answer in spoiler tags.

Click 'Show' for the Answer Show


Where does that rank historically? I thought it would be fun to look at the teams since 1950 with the worst average margin of defeat looking exclusively at performance in losses. This was a bit of a tricky one, but Scott Kacsmar was able to guess it on twitter. The answer?

Show' for the Answer Show


The table below shows the 100 teams with the worst average points differential in losses since 1950. As always, the tables in this post are fully sortable and searchable. For viewing purposes, I’m displaying only the top 20, but you can change that in the dropdown box on the left. [click to continue…]

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Last off-season, I looked at passing performance on “third downs”, and I thought it would be fun to revisit that idea this summer. As before, I am putting that term in quotes because I’m including fourth down data in the analysis, but don’t want to write third and fourth down throughout this post.

To grade third down performance, I included sacks but discarded rushing data (again, 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. Here were the conversion rates I calculated last year.

To Go
Passes
First Downs
Rate
Smoothed Rate
130915851.1%50.8%
241520850.1%48.5%
348720742.5%46.2%
451222744.3%43.9%
555922640.4%41.6%
654122842.1%39.2%
752118134.7%36.8%
842614333.6%34.5%
936511631.8%32%
1072822030.2%29.6%
112137133.3%27.2%
121533925.5%24.7%
131352417.8%22.2%
141072220.6%19.7%
151432215.4%17.2%
166258.1%14.6%
17681217.6%12%
185036%9.5%
195335.7%6.8%
204836.3%5%

[click to continue…]

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The Value of a First Down

What is the value of a first down? By that I mean, how many marginal yards is a first down actually worth? Here’s another way to word the question: If 3 first downs and 80 yards are worth X, then 2 first downs and [???] many yards are equal to X?

Calculating the marginal value of a yard isn’t easy. In fact, it’s been bugging me for years, because I’ve never quite been sure how to derive them. Then, a light bulb went off in my head: I needed to reach out to Brian Burke. I had an idea, but not the data or the means to execute.

Burke, of course, runs the fantastic website Advanced Football Analytics (formerly Advanced NFL Stats). I asked him if he would run some queries, and Brian was kind enough to do so. Fortunately, Brian’s not just a guy with access to lots of data, but one of the smartest minds in the industry. I wholeheartedly endorse his methods below, and I’m very thankful for his help. On top of running the numbers, he also provided an excellent writeup on his work. What follows are Brian’s words and analysis.


To estimate the value of achieving a 1st down without counting any of the value of the yardage gained, we can use the Expected Points model. The value of the 1st down itself minus yardage value will be the discontinuity in EPA when a play’s gain crosses the threshold for a 1st down. That discontinuity represents the value of the conversion apart from any yardage gained.

For example, on 2nd and 10, the EPA would increase smoothly for each yard gained up to 9 yards gained, then jump to a much higher EPA crossing the 10-yard mark where the conversion occurs. After that point, the EPA should increase smoothly again with each marginal yard gained above what was needed for the conversion.

Here is an illustration. The Y-axis represents Expected Points Added, the X-axis the amount of yards gained on the play.

EPA 2nd 10

The EPA for a 9-yd gain is 0.57, and the EPA for a 10-yd gain is 1.04. That’s a discontinuity of 0.47 EP, meaning that the 1st down itself is nearly equivalent to the 9-yards gained up to the point of conversion.

But we also need to correct for the yardage value of that 10th yard. One yard of field position is generally worth 0.064 EP. So in this case the discontinuity itself is worth 0.47 – 0.064 = 0.41 EP.

If we wanted to assign a “bonus” of yards to a player who is credited with achieving the conversion over and above the yardage itself, we could use this value’s yardage equivalent. 0.41 EP / 0.064 EP/yd = 6.4 yds. That’s the bonus for 2nd down and 10, but there are many other down and distance situations to consider.

For example, on 3rd and 10, the discontinuity is 1.57 EP, equivalent to nearly 25 yds. First and 10 is very strange because the discontinuity is negative. This makes sense, however, because an offense should prefer a 2nd & 1 to a 1st & 10 anywhere on the field. It would be silly to penalize a player for gaining the extra yard to convert, so my opinion would be to say the EP bonus for a conversion on 1st down is zero.

3rd 10

After examining a smattering of 2nd and 3rd down situations, the 2nd-down bonus EP is about 0.35 and 3rd-down bonus EP is roughly 1.4.

4th down conversions would obviously mean a very large bonus EP. They essentially have the value of a turnover–close to 4 EP or so. Since 4th downs are qualitatively different (and relatively rare) I’m going to set them aside.

In general, 32% of conversions come on 1st down, 38% come on 2nd down, and 30% come on 3rd down. So the weighted value of a conversion alone would roughly be:

[0.32 * 0] + [0.38 * 0.35] + [0.30 * 1.4] = 0.55 EP

The conversion bonus of 0.55 EP can be translated into yards by dividing by 0.064 EP/yd, which ultimately makes the equivalent yardage bonus for a conversion: 8.7 yards.



Figuring out the value of a first down will have many applications for Football Perspective going forward. Please leave your thoughts in the comments, as I’d love to hear what you guys have to say. And thanks again to Brian for his great work.

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Adjusting for strength of schedule is important

Adjusting for strength of schedule is important.

Every year at Footballguys.com, I publish an article called Rearview QB, which adjusts the fantasy football statistics for quarterbacks (and defenses) for strength of schedule. I’ve also done the same thing for years (including last season) using ANY/A instead of fantasy points, which helps us fully understand the best and worst real life performances each year. Today I deliver the results from 2013.

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 2013 was 5.86, a slight downgrade from 2012 (5.93). Nick Foles led the way with a 9.18 ANY/A average last year, the highest rate in the league among the 45 passers with at least 100 dropbacks. Since the Eagles quarterback had 317 pass attempts and 28 sacks in 2013, that means he was producing 3.32 ANY/A (i.e., his Relative ANY/A) over league average on 345 dropbacks. That means Foles is credited with 1,145 Adjusted Net Yards above average, a metric labeled “VALUE” in the table below. Of course, Peyton Manning led the league in that category last year, with a whopping 2,037 Adjusted Net Yards over Average.

Rk
Name
Tm
Cmp
Att
Pyd
TD
INT
Sk
SkYd
DB
ANY/A
VALUE
1Peyton ManningDEN45065954775510181206778.872037
2Nick FolesPHI2033172891272281733459.181145
3Drew BreesNOR44665051623912372446877.511130
4Philip RiversSDG37854444783211301505747.791107
5Aaron RodgersGNB1932902536176211173118665
6Josh McCownCHI149224182913111372358.54629
7Russell WilsonSEA2574073357269442724517.1555
8Tony RomoDAL34253538283110352725706.54384
9Colin KaepernickSFO2434163197218392314556.65358
10Matthew StaffordDET37163446502919231686576.4355
11Andy DaltonCIN36358642933320291826156.29265
12Ben RoethlisbergerPIT37558442612814422826266.24238
13Tom BradyNWE38062843432511402566686.13175
14Michael VickPHI7714112155315991566.93166
15Jay CutlerCHI22435526211912191323746.23136
16Andrew LuckIND3435703822239322276026.06120
17Sam BradfordSTL159262168714415972776.166
18Alex SmithKAN3085083313237392105475.9441
19Matt McGloinOAK1182111547886532175.9622
20Jake LockerTEN111183125684161051995.68-36
21Matt CasselMIN153254180711916852705.69-46
22Brian HoyerCLE5796615536481025.22-66
23Cam NewtonCAR29247333792413433365165.69-88
24Thaddeus LewisBUF93157109243181001755.35-89
25Ryan FitzpatrickTEN21735024541412211093715.62-90
26Matt RyanATL43965145152617442986955.72-103
27Carson PalmerARI36257242742422412896135.67-119
28Matt FlynnGNB124200139285241352245.32-121
29Case KeenumHOU137253176096192012725.4-126
30Kellen ClemensSTL142242167387211382635.25-162
31Jason CampbellCLE1803172015118161043335.32-182
32Robert GriffinWAS27445632031612382744945.48-188
33Christian PonderMIN152239164879271192664.75-296
34EJ ManuelBUF1803061972119281593344.87-330
35Josh FreemanTAM63147761248611553.61-349
36Kirk CousinsWAS81155854475321603.67-351
37Brandon WeedenCLE141267173199271802944.51-398
38Mike GlennonTAM2474162608199403144564.98-405
39Matt SchaubHOU21935823101014211623794.53-504
40Terrelle PryorOAK1562721798711312033034.09-537
41Chad HenneJAX30550332411314382435414.86-544
42Ryan TannehillMIA35558839132417583996465-559
43Eli ManningNYG31755138181827392815904.53-788
44Geno SmithNYJ24744330461221433154864.17-824
45Joe FlaccoBAL36261439121922483246624.5-904

Manning paces in the field in Value over average, of course: that’s not surprising when the future Hall of Famer set the single-season record for passing yards and passing touchdowns. Foles, Drew Brees, and Philip Rivers formed the next tier of quarterbacks, far behind Manning but well ahead of the rest of the league.

And at the bottom of the list was the defending Super Bowl MVP, Joe Flacco. With a 4.50 ANY/A average, Flacco only edged out four other quarterbacks in that statistic, and none of the other passers came close to accumulating as many dropbacks as Flacco. After him comes the two New York quraterbacks, Geno Smith and Eli Manning.

But the point of today’s post is to adjust those numbers for strength of schedule. The solution is this post — a methodology I’ve labeled Rearview adjusted net yards per attempt, which adjusts those numbers for strength of schedule. The system is essentially the same as the one used in the Simple Rating System. Let’s look at Matt Ryan, who averaged 5.72 ANY/A last season, on 695 dropbacks. If we want to find Ryan’s SOS-adjusted rating, we need an equation that looks something like this: [click to continue…]

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These two men look important

The two best regular season quarterbacks of all time?

Yesterday, I explained the methodology behind the formula involved in ranking every quarterback season since 1960. Today, I’m going to present the career results. Converting season value to career value isn’t as simple as it might seem. Generally, we don’t want a player who was very good for 12 years to rank ahead of a quarterback who was elite for ten. Additionally, we don’t want to give significant penalties to players who struggled as rookies or hung around too long; we’re mostly concerned with the peak value of the player.

What I’ve historically done — and done here — is to give each quarterback 100% of his value or score from his best season, 95% of his score in his second best season, 90% of his score in his third best season, and so on. This rewards quarterbacks who played really well for a long time and doesn’t kill players with really poor rookie years or seasons late in their career. It also helps to prevent the quarterbacks who were compilers from dominating the top of the list. For visibility reasons, the table below displays only the top 25 quarterbacks initially, but you can change that number in the filter or click on the right arrow to see the remaining quarterbacks.1

Here’s how to read the table. Manning’s first year was in 1998, and his last in 2013. He’s had 8,740 “dropbacks” in his career, which include pass attempts, sacks, and rushing touchdowns. His career value — using the 100/95/90 formula2 is 12,769, putting him at number one. His strength of schedule has been perfectly average over his career; as a reminder, the SOS column is shown just for reference, as SOS is already incorporated into these numbers (so while Tom Brady has had a schedule that’s 0.25 ANY/A tougher than average, that’s already incorporated into his 10,063 grade). Manning is not yet eligible for the Hall of Fame, of course, but I’ve listed the HOF status of each quarterback in the table. Note that I only have quarterback records going back to 1960; therefore, for quarterbacks who played before and during (or after) 1960, only their post-1960 record is displayed. In addition, SOS adjustments are only for the years beginning in 1960. [click to continue…]

  1. Note that while yesterday’s list was just from 1960 to 2013, the career list reflects every season in history, using the same methodology as used in GQBOAT IV. []
  2. And including negative seasons. []
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