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Pittsburgh could use this running back.

Things got really ugly this week in Pittsburgh, as Le’Veon Bell remains in a dispute with the Steelers over his franchise tag. Bell is currently refusing to play under the tag, and there is no immediate end in sight.

There is a long-term end in sight, tho: Bell will need to report for the final six games of the season in order to accrue another season of play; otherwise, Pittsburgh could just franchise Bell yet again for the same $14.5M tag after the season.

Bell will come back in November at the latest, which will also make him available to play in the postseason. And that’s where all of this could get interesting. Is it possible that Pittsburgh might wind up better off in the playoffs (assuming they get there, and an opening day tie against the Browns doesn’t engender confidence) if Bell doesn’t have a full workload behind him?

I’m thinking back to Bob Sanders and the 2006 Colts. The hard-hitting safety was one of the best defensive players in the NFL in his prime, but was rarely healthy. In 2005, he was an All-Pro safety; in 2007, he was the AP Defensive Player of the Year. In between? He missed most of the 2006 season due to injury, and the Colts defense suffered for it. Indianapolis ranked 21st in yards allowed, 23rd in points allowed, and 32nd in rushing yards allowed. [continue reading…]

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How Long Do Coaches Last After Adding First Round QBs?

Hue Jackson, Todd Bowles, Sean McDermott, Steve Wilks, and John Harbaugh all saw their teams use first round picks on quarterbacks in the 2018 Draft.  But does drafting Baker Mayfield, Sam Darnold, Josh Allen, Josh Rosen, and Lamar Jackson increase the security of the long-term future of those coaches?  How long of a leash does a coach have after adding a first round QB?

From 2002 to 2016, ignoring the three current situations [1]Carson Wentz was taken in the first round of the 2016 Draft, while Patrick Mahomes and Deshaun Watson were taken in the first round of the 2017 Draft. Their head coaches — Doug Pederson, Andy … Continue reading, there were 43 other quarterbacks drafted in the first rounds of those drafts. How long did those 43 coaches last?  As it turns out, most did not last very long.

In four cases — Ken Whisenhunt in 2015 (7 games), Jack Del Rio in 2011 (11), Josh McDaniels in 2010 (12), and Jeff Fisher in 2016 (13) — the head coach didn’t even finish the season! [continue reading…]

References

References
1 Carson Wentz was taken in the first round of the 2016 Draft, while Patrick Mahomes and Deshaun Watson were taken in the first round of the 2017 Draft. Their head coaches — Doug Pederson, Andy Reid, and Bill O’Brien — are all still around, so it’s too early to determine how long they’ll last with their current teams.
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The Opposite Trends of Field Goals and Turnovers

There are only a few statistics that have clear long-term trends. And two of them happen to intersect in a notable way.

The NFL used to have significantly more turnovers. Both fumbles lost and interceptions thrown have been declining since the merger, and for the decades before then, too.

Another trend: field goal accuracy has gone up, consistently, for generations. Field goal attempts rose for much of history until 1974 — when the goal posts were moved 10 yards from the front of the end zone to the back — and then began rising again. As a result, made field goals have increased significantly.

There have been varying numbers of teams and games on team’s schedules throughout history, so the best way to think of some statistics is on a per team basis. To avoid too many decimals, let’s look at things on a per-100 team game basis for the remainder of this post.

In 1950, teams made 51 field goals per 100 team games, or just over half a field goal per game (they attempted about 1.2). Also in 1950: teams averaged 373 turnovers per 100 team games! In other words, in a given game, if you picked a random play, it was over 7 times as likely to be a turnover than a successful field goal.

In 1960 (NFL data only), teams made 104 field goals per 100 team games, and committed 286 turnovers. So now a turnover was 2.74 times as likely as a field goal.

In 1970, teams made 131 field goals and committed 243 turnovers per team game, making turnovers 1.85 times as likely as successful field goal tries.

By 1980, we were back down to 107 field goals (remember, the goal posts were now 10 yards back) and 232 turnovers per 100 team games, for a ratio of 2.17 turnovers to every field goal.

In 1990, teams made 132 field goals per team game and had 199 turnovers, the first season where teams averaged fewer than two turnovers per game. This was a ratio of 1.51 to 1.

In 2000, teams kicked 147 field goals per team and and had 188 turnovers, meaning there was only 1.28 turnovers for every successful field goal.

In 2008, teams made 165 field goals per team game and committed just 155 turnovers, the first season where there were more field goals made than turnovers forced.

And last year, in 2017, teams averaged 169 field goals per team game and only 138 turnovers, for a rate of 0.82 turnovers per field goal, the single lowest rate in NFL history.

But despite all the words I just wrote, one picture is worth more than all of them. The graph below shows the turnovers committed and field goals made per 100 team games.

Pretty crazy, eh? Entertainment is subjective, of course, but declining turnover rates and increasing field goal rates do not seem like steps in the direction of a more entertaining game.

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2018 Super Bowl Odds: The AFC East Sandwich Strikes Again

With opening day kickoff just a few hours away, let’s look at the final pre-season Super Bowl odds.

The Patriots are the Super Bowl favorite for what feels like the 20th season in a row. New England is an 11/2 favorite to win it all, which means if you bet $100 (or $200) on the Patriots to win Super Bowl LIII, you would win $650 (or $1,100). That means the Patriots would need to have a 15.4% chance of winning the Super Bowl to make that an even bet (the result of 2 divided by (2 + 11)). But if you do that same calculation for every team, you’ll see that the total Super Bowl percentages equal 131%; that’s because of the vig, the amount that Vegas deflates the payout in order to make money.

If you divide each team’s percentage taken from their odds by 1.31, you get the implied odds of that team winning it all. For New England, this means the Patriots really have about a 12% chance of winning the Super Bowl, according to the oddsmakers.

The rest of the AFC East? The Bills have the worst chance in the league at 0.4%, while the Jets, Dolphins, and Cardinals are all at 150-to-1, for an implied percentage of 0.5%. In other words, the Patriots odds of winning the Super Bowl are more than 8 times greater than the odds of any other AFC East team’s of winning it all. As usual, we have an AFC East sandwich, with the Patriots on top of the league, the Jets/Bills/Dolphins at the bottom, and the rest of the NFL in between.

Here are the full odds for each team this year, courtesy of Bovada. [continue reading…]

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I like to run a contest where I ask you 38 questions, you answer them, and we all look silly after the season. This year, Adam Steele did the lion’s share of the work, and we thank him for that.

Below you will find 38 pairs of numbers. In each case, you tell me which number will be bigger. One point for each correct answer. Most points wins.

Ties — and I expect there to be a nontrivial number of them — go to the side that had fewer votes. For example, here is a pair:

Number of wins by the Lions
Number of wins by the Ravens

Let’s say 49 people take the Lions and 44 take the Ravens. If the Lions and Ravens end up with the same number of wins, then each Ravens-backer will get a point and each Lions-backer will not. Last year, JimZornsLemma won with 25 correct guesses out of 38; the average was just 19 correct guesses. Thanks to Jeremy De Shetler for an assist on some of this year’s questions.

GRAND PRIZE: the main prizes will be (1) honor and (2) glory. There may also be some sort of trinket to be named later. By the time this thing is over, more than five months will have passed, so that gives me some time to scrape something together. But you probably shouldn’t enter unless honor and glory are sufficient.

MORE RULES:

1. Everyone is limited to one entry per person. This will be enforced by the honor system. If caught breaking this rule, you, your children, and your children’s children will be banned from all future FP contests.

2. I won’t enter the contest myself, which will allow me to arbitrate any dispute impartially. Any ambiguity in the rules will be clarified by me in whatever way causes me the least amount of hassle.

3. While there are quite a few items that refer in some way to the NFL postseason, unless specifically stated, all the items below refer to regular season totals only.

5. You may enter until 1:00 p.m. Eastern time on Sunday, September 9th, 2018. However, there’s an incentive to entering early because…

6. In the event that the contest ends in a tie, the winner will be the person whose entry was submitted first. [continue reading…]

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Nathan Peterman is the Bills week 1 starter, beating out A.J. McCarron (who signed a 2-year, $8.1M contract with Buffalo in March, and received $4M from the Bills without ever taking a regular season snap before being traded to Oakland on three days ago) and rookie Josh Allen, taken with the 7th pick in the 2018 Draft.

Which is… well, unusual to say the least.  Peterman does not have a strong pedigree nor a track record of success. He was the 5th round pick in the 2017 Draft; as a rule of thumb, 5th round picks don’t start week 1 games for teams unless they have had some success. So, how did he do last season as a rookie?

[continue reading…]

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Tom Brady’s Career By Trailing 16 Game ANY/A

Last week, I looked at the Green Bay Packers passing offense since 2008 in trailing 16 game increments. I thought it would be fun to do the same thing today for Tom Brady.

The blue and red line shows Brady’s trailing 16 game Adjusted Net Yards per Attempt. The black line shows the trailing 16 game league average. As you can see, Brady was around league average for awhile, jumped way up prior to 2007, when he jumped way, way up.

The gaps in the line show his injury in 2008 and suspension in 2016. One interesting note is how Brady dipped just below average on his trailing 16 game average after the MNF game against the Chiefs in 2014.

What stands out to you?

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Khalil Mack had 41 points of AV over the last three years, tied for the 14th-most in the NFL, and 5th-most among defensive players. And after being traded to the Chicago Bears at the age of 27, making him the rare star defensive player to change teams in his prime.

How rare? I looked at players who met the following criteria:

  • Produced at least 35 points of AV over 3 consecutive years while playing for the same team;
  • Switched teams after the season and were 29 years or younger at the start of the new season

The last defensive player to fit those criteria was Ndamukong Suh, who joined the Dolphins as a mega free agent signing after an excellent run in Detroit. Suh signed a 6-year, $114M contract with Miami, and Suh wound up earning half of those contract over 3 years. Suh’s tenure with the Dolphins was disappointing, although mostly compared to the expectations created by his large contract.

Elvis Dumervil wound up in Baltimore after a fax machine mishap wound up in him being released from the Broncos. Dumervil had 17.0, 9.5, and 11.0 sacks in the three prior seasons (well, he missed one full season in between due to injury), and then had 9.5 sacks and 17.0 sacks his first two seasons in Baltimore.

Albert Haynesworth is the opposite end of the spectrum. He was an AP first-team All-Pro in both 2007 and 2008 with the Titans, and then joined Washington for his age 28 season. He is remembered as one of the worst blockbuster free agent signings in history.

Jared Allen was traded from the Chiefs to the Vikings just days before the 2008 Draft. The Chiefs traded Allen and their 2008 6th round pick (which turned out to be John Sullivan, who started 93 games at center for Minnesota) to Minnesota for the Vikings 1st round pick (KC traded up two spots to select Branden Albert), two third round picks (used to draft Jamaal Charles and DB DaJuan Morgan), and 2008 sixth round pick (WR Kevin Robinson). That might have been the rare win-win trade: Albert and Charles were stars, and Allen had a great career in Minnesota. He was a first-team All-Pro his last season in Kansas City and then three of his first four seasons in Minnesota.

The Raiders have also gone down this road before with Jon Gruden. In 1998, Gruden joined the Raiders and allowed DT Chester McGlockton to sign with the rival Chiefs.  McGlockton was a Pro Bowl each of the last four seasons, but left Oakland similar to Mack (but 18 months older).  After letting him leave, Oakland received the 31st and 59th overall picks in the draft as compensation.  Gruden responded by saying “We think we can get some players who can impact the team this year and for years to come… It’s going to be the bloodline of our organization.”   Suffice it to say, the 1998 Draft was not a good one for Oakland, other than using the 4th overall on a future Hall of Famer.

The table below shows all players who met the above criteria:

PlayerPosOld Tm3Yr AVN+1 YrNew TeamN+1 AgeN+1 AV
Khalil MackOLBOAK412018CHI27??
DeMarco MurrayRBDAL382015PHI276
Ndamukong SuhDTDET442015MIA287
Elvis DumervilLOLBDEN372013BAL293
Carl NicksLGNOR412012TAM274
Albert HaynesworthRDTTEN362009WAS286
Jared AllenRDEKAN362008MIN2617
Drew BreesQBSDG382006NOR2715
Steve HutchinsonLGSEA432006MIN299
Daunte CulpepperqbMIN412006MIA292
Edgerrin JamesRBIND552006ARI288
Randy MossWRMIN382005OAK288
Patrick SurtainLCBMIA382005KAN297
Jeremiah TrotterMLBPHI432002WAS256
Marshall FaulkRBIND391999STL2625
Dana StubblefieldRDTSFO371998WAS283
Ricky WattersRBPHI351998SEA2912
Curtis MartinRBNWE361998NYJ2513
Chester McGlocktonRDTOAK381998KAN295
Ricky WattersRBSFO491995PHI2611
Deion SandersRCBATL361994SFO2714
Pat SwillingROLBNOR571993DET2910
Charles HaleyLOLBSFO421992DAL288
Tim HarrisROLBGNB391991SFO273
Wilber MarshallRLBCHI401988WAS267
John JeffersonWRSDG361981GNB256
Lydell MitchellRBBAL631978SDG2912
Monte JacksonRCBRAM381978OAK256
Ted HendricksLLBBAL351974GNB2713
Paul WarfieldSECLE371970MIA2811
Miller FarrLCBHOU451970STL277
Homer JonesSENYG421970CLE293
Fran TarkentonQBMIN411967NYG2719
Abner HaynesHBKAN401965DEN284
Buddy DialSEPIT361964DAL272
Lou MichaelsLDEPIT361964BAL292

What do you think?

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It looks like Raiders All-Pro edge rusher Khalil Mack is being traded to the Chicago Bears.

Yesterday, Aaron Donald signed a record-setting contract at $22.5M per year with nearly $87M guaranteed. We can be sure that the Bears are about to give Mack something very similar, and likely slightly more rich, than what the Rams paid to Mack. After trading two first round picks plus something else (we should hear soon), Chicago is not going to fight with Mack over a few million dollars.

What are the Bears getting in Mack?
[continue reading…]

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I looked at all players (excluding quarterbacks) since 1990 with at least 50 carries in a season. I then grouped those players into 50-carry increments based on their number of carries that season: i.e., 50-99 carries, 100-149 carries, and so on. The chart below shows that data, along with the average number of carries for each group, the average number of rushing yards, and the average yards gained per carry:

Carries# of PlayersAvg RushAvg Rush YardAvg YPC
50-99619722884.03
100-1493381224914.01
150-1992771726894.00
200-2492222239274.15
250-29918127211474.21
300-34912732213614.22
350+3937116424.42

Those results are probably not very surprising. The players with the most carries have the most rushing yards, and the yards per carry average tends to increase, too. This is in some ways an example of survivorship bias: the players who are performing the best will continue to keep getting carries, moving them into the higher-carry buckets.

Now, what happens the next season? Take a second and think about what you expect…. [continue reading…]

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The Saints Had The Worst Fumble Luck In 2017

There were 644 fumbles during the 2017 season, with 276 being recovered by the opposing team. That’s a 43% fumble recovery rate for the defense, and a 57% fumble recovery rate for the offense; in other words, exactly 3 out of every 7 fumbles were recovered by the opposing team, and 4 out of every 7 fumbles were recovered by the fumbling team.

The Baltimore Ravens fumbled 19 teams, but only lost 4 of those fumbles. So the Ravens offense recovered 79% of those fumbles, when we would have “expected” them to recover 10.9 of those fumbles. Therefore, Baltimore recovered 4.1 more fumbles than we would have expected. On defense, Baltimore’s opponents had 22 fumbles, and Baltimore recovered 12 of those 22 fumbles. So the Ravens defense recovered 55% of all fumbles by opponents, when we would have “expected” them to recover 9.4 of those fumbles; therefore, Baltimore’s defense recovered 2.6 more fumbles than expected. Add it up, and there were 41 times that the football hit the ground during Ravens games, and Baltimore recovered 27 of them, which was 6.7 more than expected.

On the other side of things we have the New Orleans Saints. Despite being one of the best teams in the NFL last season, New Orleans had really bad fumble luck (perhaps that’s why they “only” went 11-5 despite being the most efficient team in the league). On offense, the Saints had 19 fumbles but only recovered 9 of them; that’s 1.9 fewer fumbles recovered than expected. And on defense, New Orleans forced 20 fumbles but only recovered 5 of them, which was 3.6 fumbles below average! Together, the Saints recovered just 14 of 39 fumbles in all games, which was 5.4 fewer fumble recoveries than expected. [continue reading…]

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NFC East Passing Since 2002

A question for the crowd today: which is a better way to present data on the NFC East passing attacks since 2002? The measure today will be Adjusted Net Yards per Attempt.

We can do it in graph form, like this, with each team having color-coded lines and ANY/A on the Y-Axis:

Or we can do a heat graph that shows the actual data, with blue shading for the best years and red shading for the worst years:

If you wanted to tell a story about the NFC East passing since 2002, what story would you tell, and which graph is more useful?

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Who are the worst passing teams of the last three years? The Browns are the obvious and correct choice as the worst passing attack, and you won’t hear arguments from many if you include the Texans, Ravens, and 49ers in that group. Those four teams have all averaged 5.7 or fewer net yards per passing attempt since 2015, the simplest measure of passing efficiency.

But the fifth-worst team over the last three years is a shocker: it’s the Green Bay Packers. Yes, Aaron Rodgers missed most of last season (and Brett Hundley was terrible in his stead), but you may not remember that the Packers offense had a lot of struggles in 2015 playing without Jordy Nelson, who has been very instrumental to Rodgers’s success. Yes, Rodgers had his always glowing TD/INT ratio in 2015, but he ranked 32nd out of 36 qualifying passers in NY/A that season.  And Rodgers’s struggles creeped into September of 2016, too, before he finally turned things around.

Still, we think of him as Aaron freakin’ Rodgers, so it’s jarring to see that — even with half a season of Hundley — Green Bay ranks in the bottom five of any passing stat.  To be sure, NY/A has always been Rodgers’s weakest stat, since his TD rate and INT rate are what have buoyed his success. So let’s instead look at Adjusted Net Yards per Attempt, a stat that Rodgers remains the career leader in since 1970.

The graph below shows the trailing 16-game ANY/A average of the Packers passing attack over each 16 game period beginning with the 16th game of the 2008 season (Rodgers’s first as a starter).  The Packers line is in green and yellow; the league average is in black.  As you can see, things are not trending in the right direction, and even as of the middle of 2016, the T16G average was pretty ugly: [continue reading…]

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Yesterday, I looked at player weight in the NFL. Today I want to take a more granular approach and look at weight by position. Of course, in modern times, positions have blended quite a bit. In a 3-4 defense, the edge rusher would be a linebacker, while in a 4-3 defense, that same player would be a defensive end; defensive front sevens are so versatile that Khalil Mack once received All-Pro honors at both OLB and DE… in the same season! And it’s not just the front seven players that are hybrids: Deone Bucannon and Mark Barron converted from safety to linebacker after entering the NFL, while 2017 Eagles rookie Nathan Gerry converted from safety to linebacker when he entered the draft.

So while there’s an element of trying to fit square pegs into round holes, I nevertheless labeled every defensive player from 2017 as either a DL, LB, or DB. In my database for each player, I have their weight and 2017 AV score. I have graphed that data below, with player weight on the X-Axis and AV score on the Y-Axis.  All players in red are defensive backs, while players in black are linebackers, and defensive linemen are showed in blue.

There is some fluidity in player positions, but some broad trends clearly emerge.  Yes, there is overlap between defensive backs and linebackers, and between linebackers and defensive ends, but that overlaps is mostly at the edges. Below is the same data (with player weight on the X-Axis), but showing the range of weights for defensive backs (the top line), linebackers (middle), and defensive linemen (bottom).  This chart is particularly neat, because in one picture it highlights both the rule and the exception: it’s clear what the ranges are for players at DL, LB, and DB, but it is also clear that there’s overlap at the margins: [continue reading…]

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Let’s travel back to 1990. On average, most defensive backs weighed between 172 and 210 pounds, most linebackers between 225 and 250 pounds, and most defensive linemen between 260 and 290 pounds. The graph below shows the amount of AV produced by defensive players at each weight in 1990:

If you look carefully, you’ll notice a few low spots on the graph. Very little AV is coming from players who weighed between 211 and 220 pounds, and also at 256 to 259 pounds. Let’s graph this another way: below, I show the percentage of all defensive AV produced by players who are X pounds or lighter. For example, about 11% of AV is produced by players 187 pounds or lighter, about one-third of AV is produced by players 210 pounds or lighter, and 34% is from players 220 pounds or lighter. The graph gets very flat between 211 and 220 pounds, indicating the lack of players in that range: [continue reading…]

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Vegas Records Vs. Actual Records

Last year, the Steelers went 13-3, but you would have lost money if you bet on Pittsburgh every week. Despite that record, the Steelers went just 7-9 against the spread. Since 1978, teams that have won games are 7706-1514-256 in 9,476 games against the spread, which means teams that win cover the spread about 83% of the time.

The biggest outlier was the 1986 Bears. You probably have heard of a team called the ’85 Bears, who stomped through the league en route to a Super Bowl title. The next season, Chicago went 14-2 but went just 6-10 against the spread! There were 7 games where the Bears were favored by at least 9.5 points and won the game but didn’t cover; that is, of course, a record. The only other teams with even four such games where they failed to cover were the ’98 49ers and ’07 Patriots.

The graph below shows, on the X-Axis, the winning percentage of all teams in all seasons since 1978. The Y-Axis shows their winning percentage against the spread. You can see the Bears as the low dot on the bottom right, at 0.875 winning percentage and 0.375 winning percentage against the spread. [continue reading…]

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Today’s guest post/contest comes from Thomas McDermott, a frequent guest contributor. As always, we thank him for his hard work.


Author’s note regarding the numbers: The Win Probability (WP) numbers shown below were, for the most part, generated using a formula presented by Wayne Winston in his book Matheletics, and subsequently improved upon by Pro Football Reference for their WP model. [1]While in the process of messing around with PFR’s WP Calculator, I have noticed differences between the results from the Calculator and the results from using the formula as shown on their web … Continue reading

The heart of the formula is the Excel NORMDIST function, which returns the normal distribution (the probability) for a given mean and standard deviation. I have made some minor adjustments to this formula, but it is basically the same. The formula requires the use of Expected Points data; the EP dataset I use comes from Brian Burke’s Advanced Football Analytics site (when it was active), and I have adjusted those numbers for era. Since the formula falls apart in certain areas – most importantly, the 4th quarter when the game is close – I abandon it and use other data to generate a WP number. Field goal success rates, 4th down success rates, drive results, PFR’s Play Index and the recently provided play-by-play data from Ron Yurko on GitHub, are some of my alternate sources. I realize there’s a “black box” aspect to Win Probability analysis (if you can find two models that completely agree, let me know), since it’s not something that can be easily checked, and perhaps especially suspect when the author openly states that he adjusts the numbers “manually”. To that I can only say that my intent is to provide as accurate a picture as possible of the games that I analyze, and I’m open to any suggestions, comments or questions. Thanks. [continue reading…]

References

References
1 While in the process of messing around with PFR’s WP Calculator, I have noticed differences between the results from the Calculator and the results from using the formula as shown on their web page. I have a hunch that PFR has probably moved on from the formula and is using a more sophisticated model (which perhaps incorporates the formula?), but I’m not sure.
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Who Will Win 2018 Coach of the Year?

There is no tougher award to predict than AP Coach of the Year. On the other hand, that makes it one of the most fun awards to speculate and discuss. Regular readers know that I have been at this game for awhile, with no success at all.

  • In 2012, I selected Mike Mularkey as my pick. That turned out be very, very wrong — the Jaguars went 2-14! — but in COTY predicting, it’s win or go home, so swinging for the fences makes sense. Bruce Arians, who went 9-3 as interim head coach of the Colts, won the award for his magical work in transforming a bad Indianapolis team.
  • In 2013, I selected Sean Payton; unfortunately for him, an 11-5 record was not enough. That honor instead went to Ron Rivera, whose Panthers went from 7-9 to 12-4, on the back of a dominant defense.
  • In 2014, I chose … Jay Gruden. Washington went 4-12. Arians, now in Arizona, won the award for taking a Cardinals that ranked 24th in both yards and yards allowed and barely outscored its opposition to an 11-5 record: Arizona went 6-0 under Carson Palmer, but going 5-3 with Drew Stanton secured the honors for Arians.
  • In 2015, I chose Dan Quinn. That was a year too early: Atlanta finished just 8-8 this year before going to the Super Bowl in ’16.  Instead, Rivera won again, as Carolina went from 7-8-1 to 15-1.
  • In 2016, I went with Bill O’Brien as my pick; he responded with his third straight 9-7 season. I was in the wrong state of mind but the right state: Cowboys coach Jason Garrett won the award, after Dallas vaulted from 4-12 to 13-3.
  • Last year, I didn’t write an article about my picks, but I leaned towards Chargers coach Anthony Lynn.  Well, 2017 turned out to be a perfect example of how hard this award is to pick.  Entering the season, Sean McVay was one of five head coaches who had 50-1 odds to win, tied for the longest odds of any coach.  And McVay turned around the Rams from 4-12 to 11-4 before securing a first round bye.  He picked up 35 of 50 votes to win the award.

The reason this award is so hard to pick is because in some ways, every coach is on an even playing field in week 1. The winner of this award is the one who usually exceeds expectations the most, so there is a natural equalizer in place.

That’s not entirely true, of course.  A team needs to have a good season to get the award, and some teams are so low on talent that a good season is a longshot.  Here are the only odds I can currently find on the award for 2018: [continue reading…]

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Pass Efficiency By Pass Direction

Thanks to the always wonderful PFR Play Index, it’s easy to analyze NFL pass plays by direction. The official play-by-play logs released on behalf of the league label all passes as either short (less than 15 yards from the line of scrimmage) or deep, and either left, middle, or right.

The table below shows the results of passes last year in those six directions, along with the totals:

A few things stand out. NFL teams have a slight preference for throwing right rather than throwing left, which likely reflects (or is a cause of!) the fact that every quarterback in the NFL is now exclusively righthanded. The effect is not large, but there are slightly more short passes to the right than the left.

Another is that passes in the middle of the field seem very good, but with two big caveats. On short passes, teams averaged 7.41 Y/A and pick up a first down on 40% of all throws; meanwhile, teams averaged 5.93 Y/A and a 32% first down rate on short throws to the left or right. That makes passes to the short middle appear about 20% more efficient than passes that are thrown short and outside. And on deep throws, the completion percentage is significantly higher on throws in the middle of the field (46% vs. 37%), with higher first down rates (duh) and AY/A averages, too.

Ah, but the caveats. One is that throws in the middle of the field are less safe, almost certainly due to the heavy congestion. The interception rate was twice as high on short throws last season as it was on short throws to the outside, and it was also twice as high on deep middle throws relative to passes deep and to the outside. Throwing in the middle of the field is riskier, but it appears to have a higher reward.

We’ll get to the other caveat in a minute, but first, let’s look at passes from 2013 to 2016, again using the PFR Game Play Finder.

We see similar results here. The NFL still looks like a right-handed league, with just a few more passes coming to the right than the left. On short passes, the interception rate is much higher on passes to the middle of the field, but the Y/A and first down rates are also way higher. In fact, both AY/A and first down rates were 22% higher on passes short and to the middle relative to short and outside. On deep throws, completion percentage was significantly higher on throws in the middle of the field, as was the touchdown rate, first down rate, and well, the interception rate.

But the elephant in the room in this analysis isn’t that passes thrown in the middle of the field are more effective than passes thrown to the outside. Perhaps the most important data point in these tables isn’t even given its own column, but savvy readers likely picked up on it. On short passes, just under 25% of them (in both 2017 and 2013-2016) were thrown to the middle of the field; on deep passes, 22% of throws (2017 and 2013-2016) were to the middle of the field. Meanwhile, passes to the right (short/deep/2017/2013-2016) approach 40% in all cases.

This means we are not necessarily comparing apples to oranges. There is a likely survivorship bias going on here. I don’t have data on time from snap to throw, which would supplement (and make more interesting!) today’s analysis, but it seems likely that passes to the middle of the field happen quicker. In other words, a quarterback is much more likely to do this:

Read 1 is to middle of the field —–> player covered —-> Read 2 –> throw to outside

Than to do to this

Read 1 is to outside of the field —–> player covered —-> Read 2 –> throw to middle of field

If your first option is covered, that play’s overall success rate is likely going down. A “backup” plan is to throw to the outside, which would explain why passes to the outside are both greater in number and lower in efficiency.

And let’s not forget that targets themselves — or pass attempts, in this case — are also measures of quality.  The fact that teams throw to the outside more is evidence (not conclusive, of course, but evidence nonetheless) that throwing to the outside more is better.  Let’s consider the 2017 Eagles, with Alshon Jeffery and Torrey Smith (who spent most of their time on the outside) and the team’s slot receiver, Nelson Agholor, who had been heavily criticized much of his career.   Agholor averaged 8.08 yards per target last year on 95 targets, while Jeffrey (6.58) and Smith (6.42) were much worse on 120 and 67 targets, respectively.

Let’s look just at short passes for the Eagles last year.  Philadelphia quarterbacks had a 96.5 passer rating and picked up a first down on 31.5% of passes that were short and to the outside; meanwhile, those same players had a 119.6 passer rating and picked up a first down on 47.8% of passes that were short and in the middle of the field.

But for Philadelphia, short throws to the middle of the field comprised only 21% of all short throws.  Perhaps, just like with Agholor, Philadelphia only threw to the middle of the field when the middle was open, and threw short no matter what (and if everyone was covered, a check down to the running back in the flat may be more likely to be charted as an outside pass).

It’s easy to look at efficiency numbers and conclude that teams should be doing more of what’s most efficient. But that only works when we’re comparing the same sort of data, and it’s not clear that we can do that just yet.

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Adam Steele on QB Defensive Support: Part 2

Adam Steele is back for another guest post, with today’s work being initially published at The GridFe. You can view all of Adam’s posts here. As always, we thank him for contributing.


Yesterday, I outlined my methodology for measuring QB defensive support, and looked at the best and worst seasons using that metric. Today’s post will explore the career numbers for the 107 quarterbacks in my study.

I originally planned to simply tally the seasonal numbers without adjustment, but that presented a problem: The top of the support leaderboard was disproportionately filled with QB’s who were exceptional at avoiding interceptions. This makes sense because throwing picks generally makes it harder to prevent the opponent from scoring, although I expected the effect to be minimal enough that it wouldn’t really make a difference. Well, it does make a difference.

To counter the interception issue, I added three point of defensive support for every marginal INT thrown in the regular season. Marginal INT’s are calculated by taking the difference between a given QB’s INT% and the league average rate [1]Technically a rolling three year average calculated by Bryan Frye, then multiplying that difference by the number of pass attempts. I know from expected points models that the typical INT is worth roughly -3 EPA, so each marginal INT makes the defense look three points worse than it actually is. As an example, Len Dawson threw -31 marginal INT’s during his career, which “saved” 93 points for his defense. Dawson’s career support is then adjusted by -93 points.

A more rigorous study would also adjust for playoff interceptions and lost fumbles, but I don’t currently have the means for easily compiling the marginal version of those statistics. Again you’ll just have to use common sense and make mental adjustments when necessary. [continue reading…]

References

References
1 Technically a rolling three year average calculated by Bryan Frye
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Adam Steele on QB Defensive Support: Part 1

Adam Steele is back for another guest post, with today’s work being initially published at The GridFe. You can view all of Adam’s posts here. As always, we thank him for contributing.


I’ll start by stating the obvious: QB wins is a terrible metric for evaluating the performance of quarterbacks. But since this criteria remains at the forefront of the public discourse about NFL signal callers, I figured it would be worthwhile to provide some important context.

The game of football is won and lost in three phases, two of which have very little to do with the QB. In this post I’m going to present a cursory analysis of which quarterbacks benefited the most and least from the play of their teams’ defenses and special teams.

When I reference defensive support, I’m actually referring to the points allowed by a given quarterback’s team compared to the average team during that season. This is not an exhaustive study; my numbers do not adjust for field position, number of drives, turnovers, weather, or any other hidden variable that affects points allowed. But including all the noise also includes all the signal.

From the quarterback’s perspective, it doesn’t matter why his team gave up x number of points – his chances of winning the game are the same (or nearly the same) regardless. If his team allows 35 points, it doesn’t matter to the QB whether his own defense was bad or the opposing offense happened to be a juggernaut. If his team allows only 10 points, it doesn’t matter to the QB whether his defense was stout or the opposing kicker missed three field goals. This is why I’m content to use straight points allowed and ask the reader to make common sense adjustments for QB’s in extreme circumstances.

I collected data from the top 100 quarterbacks in career pass attempts (modern era) plus Hall of Famers and Super Bowl winners outside of the top 100. I only wanted to count games in which the QB played a significant amount, so I set a threshold of 14 pass attempts for a game to count (14 attempts per game is the NFL’s official minimum for rate stat leaderboards). It doesn’t matter whether the QB started the game or not, as such data gets murky the further in time we look back (for another look, here is an old Chase article on points allowed per game in games started by each quarterback). Playoff games are included in the points allowed totals as well as league baselines; said baselines are calculated by removing the points allowed in qualifying games of the QB is question, then finding the league average points per game in all other games during a given season.

In the two tables below, Win % is from qualifying games only, O/U is the number of games over or under .500 in qualifying games, and Playoffs indicates which round of the postseason was reached the by the QB’s team (even if he didn’t play in the postseason).

Here are the top 100 seasons of defensive support among the quarterbacks in my study: [continue reading…]

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Paxton Lynch’s Broncos Career Is Nearly Over

Paxton Lynch was the 26th pick in the 2016 Draft, but it looks like his Broncos (and perhaps NFL) career is coming to a close. As a rookie, he sat on the bench behind Trevor Siemian, who was taken with the 25t0h pick in the 2015 Draft and vastly outplayed Lynch during the summer of ’16 to win the job. And in 2017, Lynch missed time with shoulder and ankle injuries, but he also was the third most productive QB on a bottom-5 passing offense, looking worse than both Siemian and Brock Osweiler. Lynch has now fallen to third on the team’s depth chart even with Siemian and Osweiler gone: Case Keenum was brought in to be the team’s starter, and Chad Kelly — the 253rd pick in the ’17 Draft — has moved ahead of Lynch on the depth chart.

That’s right: the Broncos 1st round pick in 2016 has now been beaten out by 7th round picks from both the 2015 and 2017 drafts.

Let’s assume Lynch finishes 2018 with zero touchdown passes.  That would give him 4 career touchdown passes through three seasons.  The graph below shows quarterbacks drafted from 1992 to 2015 plus Lynch, with the X-Axis representing draft slot and the Y-Axis showing touchdown passes through three years.  Lynch is the red dot. [continue reading…]

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2017 Contest: 38 Questions in Review, Part III

Back in August, I asked you 38 questions that served as prop bets for the 2017 NFL season. Thanks to the tireless work of Jeremy De Shelter, who helped compile all the results. Earlier this offseason, I looked at Part I and Part II. Let’s move on to Part III…

Number of playoff (non-Super Bowl) games won by the visiting team, +0.5
Maximum number of TDs thrown by Kirk Cousins in a single game

Cousins topped out at 3 passing TDs in a single game, done four times in 2017.  Meanwhile, road teams won… 3 games in the 2017 playoffs, with Atlanta and Tennessee winning on the road in the Wild Card round, and Jacksonville winning in Pittsburgh in the second round.

That Steelers loss was critical: only 39% of you picked the road playoff wins side, which means the Jaguars upset gave the minority group the win.

Punts by Jets opponents, -5
Jets team offensive passer rating.

The Jets were supposed to be terrible on offense and not too bad on defense, making this a tricky one to analyze.  The votes here were pretty split, with 53% of contestants voting for the Jets offensive passer rating side to be higher.  Jets opponents had 87 punts in the league last year the 4th most behind Jacksonville, Arizona, and Denver.

Meanwhile the Jets passing attack was surprisingly…. decent? New York finished with an 86.1 passer rating, slightly above the league average of 85.1, and 15th-best in the NFL.

In other words, this was a good line where the hook made the difference! The line finishes 82 vs. 86.1, swinging the side towards the brave 47% who backed the Jets passing attack. [continue reading…]

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Yesterday, I looked at Norm Van Brocklin and how he set the single-game passing record with 554 passing yards.  This was done way back in 1951, in a game against the New York Yanks.  In that post, I noted that big passing games in Van Brocklin’s era tended to come against bad teams in blowouts, while big passing games now come in more competitive games. Let’s investigate that a bit more today.

In the 1950s, there were 10 games where a team threw for at least 400 gross passing yards (that is, without deducting sack yards). In those games, the average team threw for 444.5 yards, while the opponent had just 159.3 passing yards. And the 400-yard passing team led by, on average, 5.4 points, 13.3 points, 24.6 points, and 26.6 points after each quarter.

In other words, those were one-sided affairs where the winning team was able to name its score (and number of passing yards).  Van Brocklin’s game against the Yanks is a good example; in modern times, this is much less common, with the Patriots/Titans snow game from 2009 being an outlier (New England passed for 442 yards, while Tennessee had negative passing yards even without including sacks!).

Let’s compare that to the 2010s. There have been 126 passing games of 400+ yards, with an average of 436.6 passing yards. On average, the opponents in those games had a 300-yard game — 306.9 passing yards, to be exact. And the games were almost always close, with the margin being within 2 points at the end of each quarter (in fact, it was negative for the big passing team). [continue reading…]

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Super Bowl LII. Nine seconds left, New England down by eight. Tom Brady had already thrown for 505 yards, but the Patriots still needed another 51 yards from him to have a chance to extend the game. Brady launches a prayer to Rob Gronkowski, who …

… nearly comes down with it in the end zone. Had the Hail Mary been completed, Brady would have thrown for 556 yards, setting a new single-game passing yardage record. The current record, as trivia experts know, is 554 passing yards, set by Norm Van Brocklin way back in 1951.

Eight years ago, I first wrote about how Van Brocklin held the record for most passing yards in a single game. I’ll be reprinting and updating that post today.

Let’s begin with the obvious: Van Brocklin is a Hall of Famer and all-time great quarterback who, at his very best, produced some of the most efficient and valuable seasons in NFL history. He should be on most top-20 quarterback lists, and his net yards per pass attempt — one of the most basic but important measures of quarterback play — is the best of all time.

On the other hand, he set the record in 1951.  How the heck did that happen?  Below, I have plotted all games where a team has passed for at least 450 gross yards (that is, without deduction for sacks).  As you can see, the dot at (1951, 554) is a pretty large outlier: [continue reading…]

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Red Zone Performance Since 2002

As noted yesterday, red zone performance is mildly correlated with team success. One team that was a big outlier last year was the Steelers. Pittsburgh’s offense made it into the red zone 63 times, but converted those trips into touchdowns just 32 times (50.8%), slightly below-average. On defense, the Steelers allowed touchdowns on 24 of 39 red zone trips, a 61.5% rate that was the 5th-worst in the league. Of course, the Steelers were actually one of the best teams in the league.

On one hand, this seems kind of silly: of course red zone performance alone won’t tell us much about a team’s record! It says nothing about a team’s turnover rate, how effective the team is at producing or preventing big plays, overall team efficiency, or special teams. Perhaps the most remarkable part is that it does tell us quite a bit.

The table below has a lot of information, so to analyze it, let’s look at the best red zone team of the modern era, the 2005 Seattle Seahawks. That year, the Seahawks offense had 60 red zone trips and scored a touchdown on 43 of them, a conversion rate of 71.7%.  Given that 53% of red zone trips yield a touchdown, this means Seattle’s offense scored 11.1 more red zone touchdowns than expected. On defense, Seattle faced 47 red zone trips, and allowed just 19 touchdowns, a 40.4% conversion rate that was 6.0 touchdowns better than average. Therefore, overall, the team’s red zone performance yielded 17.1 touchdowns better than average, the most of any team since 2002.  Based on the best-fit formula derived yesterday, a team’s expected winning percentage based on its red zone team value is 0.0154*(Team Value) +0.500.  Since Seattle’s red zone value was +17.1, we would have expected the Seahawks to win 76.3% of their games; they actually won 81.3% of their games, a difference of 0.049.

[continue reading…]

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On Sunday, I noted that roughly 2/3s of all touchdown passes now come from inside of the red zone. That number is, of course, even higher when we look at all offensive touchdowns. The graph below shows the percentage of passing and rushing touchdowns that came from within the red zone in every year since 1950. Prior to 1970, less than 70% of all touchdowns came from within the red zone; since then, it’s been at about 75%. What’s interesting is that while there is an obvious increase over the course of pro football history, the rate has been relatively steady over the last five decades:

[continue reading…]

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Yesterday, I looked at a larger percentage of touchdown passes are coming from shorter distances. While longer touchdown throws used to rule the day, about two-thirds of all touchdown passes now come from inside the red zone.

The average touchdown pass was, at one point, north of 30 yards. Now? It’s south of 20 yards:

Three years ago, I wrote about the average length of touchdown pass. Let’s update that post today, using all quarterbacks who either threw 100+ career touchdown passes or threw a touchdown pass in 2017 and have at least 20 career touchdown passes.  Ed Brown is your career leader, although if we raise the limit to 125 touchdowns, Norm Van Brocklin would still be your career leader: [continue reading…]

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Back in June, I wrote about how the average length of touchdown passes was declining significantly. I wanted to revisit that post with a graph that really highlights the change in the type of touchdown passes thrown over time. The graph below shows, for each season from 1950 to 2017, the percentage of touchdown passes each year that were:

  • At least 50+ yards, plotted in dark red;
  • 30-49 yards, plotted in in red;
  • 20-29 yards, plotted in gray;
  • 10-19 yards, plotted in light blue;
  • Inside of 9 yards, plotted in dark blue.

The overall trend is obvious: while in the ’60s, fewer than 25% of touchdown passes came from inside of 9 yards, in modern times, well over 40% of touchdown passes come from that range. On a raw basis, as recently as 1975, there were more touchdown passes from 30+ yards (85 from 30-49, 44 from 50+) than from 9 yards and in (128).  Last season, there were 314 touchdown passes from the 9 yard line or closer, and 157 from 30+ (76 from 31-49, 71 from 50+).

What’s really notable is that while passing touchdowns are on the rise, that is entirely a function of short touchdowns. There are 32 teams and 16 games per season, providing for 512 team games in modern times. In the graph below, the dark red line shows the number of passing touchdowns in each season per 512 team games. The high-water mark was 2015, when there were 842 passing touchdowns, and therefore 842 passing touchdowns per 512 team games. But the dark blue line shows the number of passing touchdowns per 512 team games but excluding all touchdown passes inside of 9 yards.  That number has been relatively constant across NFL history (well, at least since the merger), but if anything, the trend (both shot-term since 2015, and long-term) is negative for gross passing touchdowns once you eliminate the shortest throws.

Pretty interesting, eh?

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During the season, I looked at the Carolina Panthers and how their team rushing stats were a bit misleading.

The Carolina Panthers have rushed for 982 yards this year, an average of 109.1 per game.  That ranks 15th in the NFL, and just a hair above the league average rate of 108.1 rushing yards/game.  But the Panthers don’t have anything resembling a traditional ground game: of those 982 yards, starting running back Jonathan Stewart has just 350 of them, while quarterback Cam Newton has 341 rushing yards, the most of any quarterback in the NFL in 2017.

In addition, wide receivers Curtis Samuel, Damiere Byrd, and Russell Shepard have combined for 87 yards; that’s the third-most rushing yards in the league for any team behind the Rams (Tavon Austin) and Raiders (Cordarrelle Patterson) among non-QB/non-RBs. In fact, Panthers running backs are averaging just 61.6 rushing yards per game, the fewest in the NFL.

Carolina’s running game improved the rest of the way, but that was thanks to both Newton and the traditional ground game improving. The Panthers finished the season 4th in rushing yards, which sounds really good! But Carolina also ranked just 27th in rushing yards by running backs, which, well, doesn’t sound very good. The table below shows where each team ranked in rushing yards by running backs, along with in just raw rushing yards: [continue reading…]

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