Yesterday, I looked at the day on the calendar when players in the 2018 NFL season were born. To do this, I looked at birth rates in 31-day increments. The days from late July to mid August produced a lot of NFL players, while the Geminis of the world — those born from in the window from late May to late June — produced comparably few NFL players.

We also looked at how this compared to the United States as a whole, which tends to have more kids in the summer and few in the winter. In that respect, the Leos of the world — born from late July to late August — didn’t stand out quite as much. June babies, though, looked really low, while January/February seemed to produce more NFL players than you would expect (those are low birth rates for the United States).

Today, let’s examine all players to enter and play in an NFL game since 1970. Once again, we are looking at 31-day periods. Now, that Tom Brady peak has disappeared, and it is players born on or around February 25th that produce the most players. There is another peak in early September, and September 8th is the birthday of the most NFL players since 1970. [continue reading…]

Tom Brady was born on August 3rd, 1977. In the Super Bowl, Brady’s Patriots defeated the Los Angeles Rams, who had two players — running back Todd Gurley and pass rusher Dante Fowler — who were born on August 3rd, 1994. Rookie defensive back and All-Pro Derwin James was born two years later, on August 3rd, 1996.

Those four were not picked at random: there were 12 players active in the NFL last season who were born on August 3rd, making it an extremely popular birthday in pro football circles. What’s more, there were 203 players who played in the NFL in 2018 who were born “around August 3rd” — i.e., within 15 days of either side of that date. For fans of astrogoloy, these are your Leos, natural born leaders described as “dramatic, creative, self-confident, dominant and extremely difficult to resist, able to achieve anything they want to in any area of life they commit to.”

In the abstract, knowing that 203 players were born in one 31-day window probably means nothing to you. But I did this same analysis for every day in the calendar year. Christian McCaffrey was born on June 7th, the only such player active last season. As it turns out, the 31 days including and surrounding June 7th (i.e., May 23rd through June 22nd) produced only 147 players.  These are your Gemini, who are generally thought to dislike repetition and routine. Oh, and that window just brings in June 22nd, notable for being the only day on the calendar where no 2018 NFL players were born.

The graph below shows each of the 365 days of the year on the X-Axis. ^[1]Eric Kendricks is the only February 29th birthday, and to make life simple, I included him with the March 1 birthdays The Y-Axis shows how many players were active in the NFL last season with birthdays plus or minus 15 days of each date. As you can see, Brady’s August 3rd birthday comes in the middle of an extremely popular window for NFL birthdays, while McCaffrey’s June 7th birthday is at the center of one of the graph’s valleys. [continue reading…]

Jameis Winston and Carson Wentz provide an interesting pair of quarterbacks to evaluate by age. Winston was the first pick in the 2015 Draft, while Wentz was the second pick in the 2016 Draft. And yet Winston is actually one year younger than Wentz, meaning he entered the NFL at an age two years younger than the Eagles star.

It actually becomes more confusing if you look at their ages on Pro-Football-Reference, since that page makes Wentz looks three years older. That’s because Wentz was born at the end of the year (12/30/1992) while Winston was born at the beginning of the year (1/6/1994). So in 2018, PFR lists Wentz at 26 years old, the age he was for just the last two days of the calendar year; meanwhile, Winston is listed at 24, even though he turned 25 just days after the season ended.

Wentz was born 372 days before Winston, making him almost exactly one year older. If we want to compare their seasons by age, we shouldn’t use age as of 12/31, which is an arbitrary cut-off that masks the real age difference between the two quarterbacks. Instead, we should use each passer’s age at the start of each football season. And, you will soon see, Winston has been the better quarterback for 5 of those 6 ages, beginning when both players entered college.

[continue reading…]

Outstanding. Now the real question? Want to be a Footballguy?

That was the e-mail I received from David Dodds on June 6, 2002. The co-owner of Footballguys.com then and now, Dodds was replying to a freelance article I submitted to his site. Two days later, my article was posted, and I had become a paid writer. Today, I will share some of what I have learned over the last 17 years with you.

• The more you write — and read — the easier writing becomes. Writing every day or writing a long piece sounds daunting, but you will be aided by momentum for most of the journey. Reading other work in your field will naturally elicit a thought that you can put to paper. And anytime I write an article, there’s always an idea for a future piece embedded in that work. I have now written a post for 2,548 consecutive days, and I can promise you it gets easier.
• Serious writers need to do three things: Write, Publish, and Promote. Many want to write, or think they would be good writers, but that’s about as far as they ever go. There are no barriers to entry when it comes to writing. Other people will write but delay to publish their work. For me, reader feedback is an incredibly important (hence the promote part) part of the process. Vince Lombardi said it best: “Practice doesn’t make perfect. Perfect practice makes perfect.” Without outside feedback and criticism, you are likely to continue making the same mistakes. You are essentially living in a one person bubble, and that’s a bad environment in which to grow. There are many problems with people on the internet, but one thing people on the internet are really good at is telling you when you did something wrong. For writers, I think that’s a good thing.
• Read good writers. As often as you can. It remains the cheat code to becoming a good writer.
• There is no money in writing. There are exceptions, of course, but readers of this blog don’t get blinded by exceptions. I wish there was money in writing, but that’s just not the case. Do not go into writing to make money. I think of writing the way I think of exercise: it’s just plain good for you in ways that, ironically, are difficult to articulate. But it’s for you the way exercise is for you: do it because you enjoy it, not to make money. Please don’t go into writing to make money.
• Don’t let the perfect be the enemy of the good. This applies in a couple of ways.
  • Occasionally, my posts will have typos or errors. That happens and it doesn’t bother me. 100% accuracy should not be your goal in writing just like your goal in life shouldn’t be to never make a mistake. I post something every day, and being 95% accurate every day of the week is preferable, I think, to being 100% accurate three times a week. Also, nobody is 100% accurate.
  • We all want to write that seminal piece that changes the way people think forever. That’s great, but it takes also takes forever to write that piece. And here’s a neat trick: if you publish Part 1 of your topic early on, you can use reader feedback to get to your destination much quicker than you can by brainstorming. I have also found that you are more likely to write That Great Article if you write 10 articles that you think are decent than one article that you think is The One. That’s because you will be shocked at how bad you are at predicting what reader reaction will be to your article. Unless it’s about Peyton Manning and Tom Brady.

Remember the golden rule: it may sound hokey, but treating others the way you would like to be treated is excellent advice for everyone in every field. I’ve found and maintained a lot of great friends by following this advice. Don’t be a bad person.

Be humble and honest with your audience. If you don’t know all the answers (and you probably don’t), say that. If you are unsure of something, say that, too. People are pretty good at detecting these sorts of things, anyway, and you earn reader credibility by treating your audience with respect. I maintain that this very small site has some of the best commenters in the community, and I think that’s because I write with this rule. Whenever I have found myself drawing a lot of criticism for my work, it’s because I have violated this principle. Also, listen to good criticism and ignore bad criticism. Trust your gut on that one.

From time to time people ask me how I got into football writing. The rest of this post (an update from 2013) will explain just that. Seventeen years ago, there was no twitter, and neither blogging nor fantasy football was mainstream. My brother was and is a sports anchor/reporter (and a great one at that), and he was the writer in the family. I was just an 18 year old who hated writing and was good at math. [continue reading…]

What do the following 17 players have in common?

Chris Sanders
Devery Henderson
DeSean Jackson
Malcom Floyd
Kenny Stills
Michael Irvin
Julio Jones
Rob Gronkowski
Brandin Cooks
Jordy Nelson
Doug Baldwin
Adam Thielen
Travis Kelce
Michael Thomas
Terry Kirby
Priest Holmes
Pierre Thomas

They are all, as the title of today’s post implies, Pareto Efficient, at least when it comes to two variables: catch rate and yards per carry.

In general, there is an inverse relationship between catch rate (receptions divided by targets) and yards per catch (yards divided by receptions). This is more clearly true in broad strokes at the position level: running backs have high catch rates and low YPC averages, wide receivers (particularly outside wide receivers) have low catch rates and high YPC averages, and tight ends and slot receivers tend to be in the middle on both categories. The graph below shows the catch rate (on the X-Axis) and yards per reception averages (on the Y-Axis) for all players with at least 400 targets since 1992. As you can see, there is a very clear inverse (i.e., negative) relationship between the two variables. Players with high catch rates tend to have low YPC averages, and players with high YPC averages tend to have low catch rates. [continue reading…]

Yesterday, I looked at how consistent passing offenses — as measured by Relative ANY/A — were from year to year. What happens if we look at rushing consistency from year to year?

To do that, I looked at rushing yards, relative to league average, for all teams from 2002 to 2018. That is shown in the graph below, with rushing yards relative to league average in Year N on the X-Axis, and rushing yards relative to league average in Year N+1 on the Y-Axis. The best rushing offense over this period was the 2006 Falcons, but they were actually below average in 2007 without Michael Vick.  In general, though, we see similar results to what we saw yesterday: a slight (and not as large as yesterday) positive correlation between Year N and Year N+1 productivity.

What about rushing defense? The biggest surprise is how similar the data appear for both rushing defense and rushing offense.  Again, the X-Axis shows rushing yards relative to league average, with a positive number meaning a good rush defense.  The Y-Axis shows the Year N+1 data.  In 2005, the Minnesota Vikings rush defense was slightly below average, but in 2006, Minnesota posted the top rushing defense of this period.

In terms of stickiness from year to year, rushing defense looks more consistent than passing defense, and exactly as consistent as rushing offense.

 

How consistent are passing offenses from year to year? What about passing defenses?

To measure this, I looked at all teams from 2002 to 2017 and measured how effective their passing attacks were by Adjusted Net Yards per Attempt relative to league average. So the best passing offense by this measure was the 2004 Colts, who were +3.97 ANY/A better than average. The next year, Indianapolis was 2.55 ANY/A better than average. In the graph below, I have shown every team during this era, with their Relative ANY/A on the X-Axis and their Relative ANY/A the next season on the Y-Axis.

[continue reading…]

The 2018 Bucs led the NFL in passing first downs and passing first down percentage for the second straight year. For the most part, passing first down percentage tells the story of how successful a team will be.

Passing first down percentage is defined as passing first downs divided by total team pass attempts (which includes sacks). The Bucs led the NFL at 39.8%, and were followed closely by four 12+ wins teams: the Rams (39.3%), Chiefs (39.2%), Chargers (39.0%), and Saints (39.0%). The bottom 8 teams in passing first down percentage all lost at least 9 games, with the Cardinals (25.0%), Bills (25.4%), Jaguars (27.8%), Jets (28.0%), and Redskins (28.2%) in the bottom five.

But Tampa Bay still went 5-11, because despite being outstanding at picking up first downs through the air, the Bucs had three problems:

• Tampa Bay had 35 turnovers, the most in the NFL.

• The Bucs running backs were very bad: they had 296 carries for just 1,049 yards (3.5 YPC) and picked up only 50 first downs. The 1,049 yards and 50 first downs were the fewest in the NFL by any set of running backs.

• Tampa Bay’s pass defense was also atrocious, which is the point of today’s post.

The Bucs pass defense allowed first downs on 36.5% of all passing plays, which was the worst rate in the NFL. And like most of the teams at the bottom of the list that didn’t have an MVP caliber quarterback, they were unsuccessful last year. [continue reading…]

Yesterday, I wrote a bit about Bills WR Robert Foster, who had a remarkable season for any rookie, let alone an undrafted free agent. While Foster’s traditional stats were solid — 544 receiving yards, with 511 of them coming in the final 7 games — his rate statistics were out of this world. I am always skeptical of using yards per target as a metric of value because targets are an inherently good thing. That said, it can be fun to look at metrics that don’t always measure pure value, and let’s do that today.

In 2018, Foster averaged 12.30 yards per target, thanks to those 541 yards coming on just 44 targets. Last season, all players in the NFL averaged 7.59 yards per target, which means Foster was well above-average. But his performance is even better when you remember that he was on the Bills, playing with below-average passers. Buffalo players averaged just 6.42 yards per target last season, well below the league average.

But wait: that number is juiced because, well, of Foster. Bills players other than Foster averaged just 5.83 yards per target. Therefore, we could say that Foster averaged 6.47 more yards per target than all other Buffalo players.

Of course, some of that value is mitigated by him having just 44 targets, but if we multiply 44 by 6.47, we could say that Foster added 285 yards of value on targets over the average Bills receiver. But we need some context for what that means, so let’s look at T.Y. Hilton.

The Colts star had 1,270 receiving yards on just 120 targets, which means he averaged an impressive 10.58 yards per target. All other Colts players averaged just 6.47 yards per target last year, which of course is pretty awful (sidenote: Indianapolis had a pretty awful set of weapons outside of Hilton). This means Hilton averaged 4.11 more yards/target than his Colts teammates, and produced 494 yards of value over the average Indianapolis receiver.

Hilton wasn’t chosen at random. By this measure — which I have used before — Hilton produced the most value in the NFL last year relative to his teammates. The table below shows the top 50 players by this metric. Let’s use Tyler Lockett, who ranked 2nd by this metric as an example. Last year, Lockett had 965 receiving yards on just 70 targets, a whopping 13.79 yards/target average. His Seahawks teammates averaged 7.52 yards per target, meaning Lockett was 6.26 Y/T ahead of his teammates. Multiply that difference by his 70 targets, and Lockett produced 438 yards of value over his teammates. [continue reading…]

Through 9 weeks of the 2018 season, the Bills looked like one of the worst passing teams of the modern era.  Buffalo ranked last — by a lot — in most major passing categories.

• Buffalo’s passer rating was 51.5; Arizona ranked 31st at 67.5.
• Buffalo ranked 32nd in Net Yards per Attempt at 4.2; Arizona ranked 31st at 4.9, Cleveland was 30th at 5.2, and every other team was at 5.7 or better.
• Buffalo had 3 TD passes and 16 INTs, by far the worst TD/INT ratio in the league. Arizona had the second-worst at 7 TDs and 10 INTs.
• Buffalo ranked 31st in yards per completion, at just 9.9 (the Colts were at 9.7).

But over the last 8 weeks of the season — and admittedly I am using multiple endpoints here to make a point — things changed significantly. From week 10 through the end of the season, the Bills ranked:

• 28th in passer rating, which while not very good, was a much better 78.0.
• 12th in Net Yards per Attempt, at an above-average 6.6.
• Had a TD/INT ratio of 10/7.
• 1st (!) in yards per completion, at 13.5.

In case it wasn’t obvious by those four numbers, much of the improvement in pass efficiency was driven by Buffalo’s huge jump in yards per completion. And much of that improvement was due to the breakout performance by Robert Foster.  The rookie wideout had just two receptions for 30 yards through week 9, but in the team’s final 7 games, he had 25 catches for 511 yards, a 20.4 YPC average. Foster was an undrafted rookie out of Alabama who had just 389 yards in four seasons with the Crimson Tide, but like most players from Tuscaloosa, he profiled as a player with great athleticism.  And, of course, some of Buffalo’s improvement comes from how the men under center changed over the course of the season.

The graph below shows the Adjusted Net Yards per Attempt produced by the Bills in each game in 2018. The black line shows the league average ANY/A of 6.32. The Bills were really bad through week 9, as we showed above, but then rebounded significantly the rest of the way.  The X-Axis shows game number, and the Y-Axis shows ANY/A:

So let’s get to the quarterbacks. Josh Allen, Nathan Peterman, Derek Anderson, and Matt Barkley all started games for the Bills last season.

Peterman started game 1, threw 12 passes in game 6 (relieving an injured Allen), and started game 9.

Allen threw 15 passes in game 1, started games 2 through 6, missed the next four games with an injured elbow, and then started games 11 through 16.

Anderson started games 7 and 8 in relief of an injured Allen, before a concussion re-opened the door for Peterman.

Barkley was the quarterback for the team’s 10th game — a win over the Jets that was arguably Buffalo’s best passing game of the season. Despite the sparkling performance, after that game, Allen returned as the team’s starter for the remainder of the year. And while Allen’s full-season numbers were underwhelming, his statistics improved significantly as the season went on. And how much of that was due to Allen — and how much is due to Foster — is also worth asking.

Allen averaged 6.5 yards per attempt last season, which is really bad (it landed him in the bottom three with Joe Flacco and fellow rookie Josh Rosen).  On targeted passes (i.e., excluding throwaways and spikes), he averaged 6.7 yards per attempt.  But Allen averaged 10.9 yards per attempt on passes targeted to Foster and 6.1 yards per attempt on passes targeted to all other Bills.  For Buffalo fans, it may not really matter, and the optimistic spin is that the future is very bright: these two rookies from 2018 may well lead the next great passing attack in western New York.

The graph below shows the passer rating in every week of every NFL season since 1950. The red dot at the top left? That’s the passer rating from week 7, 1958, when six games all took place on Sunday, November 9th.

November 9th, 1958 should be remembered as the greatest passing day of the NFL’s first 50 years. It was a star-studded day with names even modern fans will recognize. Two of the best quarterbacks of the 1960s, Bart Starr and Norm Van Brocklin, were starters that day, while the 49ers saw both future MVP John Brodie and Hall of Famer Y.A. Tittle take the field. And all four lost. [continue reading…]

Prior to the 2018 season, there had never been a week in the NFL where at least 5 games were played and the league as a whole had a passer rating of 97.0. In week 2 of the 2018 season, the league-wide passer rating was 102.6. In week 4, it was 98.2. In week 8, it was 98.0. In week 10, it was 100.9. In week 12 it was 101.2.

The graph below shows the passer rating of every week in the NFL since 1950, shown chronologically along the X-Axis, where at least five games were played:

[continue reading…]

In 2017, the Carolina Panthers went 11-5, thanks largely to a 7-1 record in games decided by 7 or fewer points. This means that in non-close games, Carolina went 4-4.

In 2018, Carolina was even better in non-close games, going 5-2. But the Panthers didn’t replicate their close-game success; in fact, the Panthers luck swung wildly in a different direction, going just 2-7 in close games.  This has actually been a trend of the Ron Rivera / Cam Newton Panthers.  The duo got off to a very rocky start, going 1-5 (0.167) in close games in their first year together in 2011, and then 1-7 (0.125) in close games in 2012.  After that season, I wrote that Carolina looked like a team on the rise that would probably start experiencing good luck soon.  That turned out to be one of my most accurate predictions, as in close games, Carolina went 5-2 (0.714) in 2013, 4-2-1 (0.643) in 2014, and then 6-1 (0.857) in 2015.  The Panthers then swung the other way, going 2-6 (0.250) in 2016, then up to 7-1 (0.875) in 2017, and back down to 2-7 (0.222) in 2018. [continue reading…]

It is the soldier, not the reporter, who has given us freedom of the press. It is the soldier, not the poet, who has given us freedom of speech. It is the soldier, not the campus organizer, who has given us the freedom to demonstrate. It is the soldier, who salutes the flag, who serves beneath the flag, and whose coffin is draped by the flag, who allows the protester to burn the flag.
— Father Dennis Edward O’Brien, USMC

Today is a day that we as Americans honor and remember those who lost their lives protecting our country. As my friend Joe Bryant says, it’s easy for the true meaning of this day to get lost in the excitement of summer and barbecues and picnics. But that quote helps me remember that the things I enjoy today are only possible because those before me made incredibly selfless sacrifices. That includes a number of football players who have lost their lives defending our country.

The most famous, of course, is Pat Tillman, the former Arizona Cardinals safety who chose to quit football to enlist in the United States army. On April 22, fifteen years ago, Tillman died in Afghanistan. Over thirty years earlier, we lost both Bob Kalsu and Don Steinbrunner in Vietnam. You can read their stories here. For some perspective, consider that Hall of Famers Roger Staubach, Ray Nitschke, and Charlie Joiner were three of the 29 NFL men who served in the military during that war.

An incredible 226 men with NFL ties served in the Korean War, including Night Train Lane and Don Shula. Most tragically, World War II claimed the lives of 21 former NFL players.

• Nick “Mike” Basca
• Charlie Behan
• Keith Birlem
• Al Blozis
• Chuck Braidwood
• Young Bussey
• Ed Doyle
• Grassy Hinton
• Smiley Johnson
• Eddie Kahn
• Alex Ketzko
• Lee Kizzire
• Jack Lummus
• Roy “Bob” Mackert
• Frank Maher
• Jim Mooney
• Gus Sonneberg
• Len Supulski
• Don Wemple
• Chet Wetterlund; and
• Waddy Young

Another player, Dave Schreiner an All-American at Wisconsin and the 11th pick in the 1943 Draft never played in the NFL: he served in the War and was killed in the Battle of Okinawa. Jack Chevigny, former coach of the Cardinals, and John O’Keefe, an executive with the Eagles, were also World War II casualties. The Pro Football Hall of Fame has chronicled the stories of these 23 men, too. Lummus received the Medal of Honor for his bravery at Iwo Jima, and you can read more about his sacrifice here. In 2015, the Giants inducted him into the team’s Ring of Honor. [continue reading…]

I have written before about the remarkable close-game success that Adam Gase and the Miami Dolphins had from 2016 to 2018. The disparity is remarkable: Miami went 3-19 in games decided by more than 8 points under Gase, the second-worst record in the NFL in non-close games (only the Browns were worse). But in games decided by 8 or fewer points, Miami went 20-6, the best record in the NFL.

So you have a huge split here: under Gase, the Dolphins were outstanding in close games but awful in non-close games. What does that mean? Was Gase an outstanding coach who could win any game as long as the talent level of the two teams were close? Or was Gase an awful coach who just happened to get really lucky? With a 23-25 record, Gase looks like an average coach — so perhaps he’s somewhere in the middle of these two extremes?

First, a quick visual to show how extreme this performance really was. The graph below shows each team over the last three years, and their winning percentage in close games (X-Axis) and non-close games (Y-Axis). A team that was awful in non-close games but great in close games would be at the bottom right of the chart: as you can see, Miami is all alone there. [continue reading…]

It’s time for a topical post here at Football Perspective, so let’s look at the careers of Roman Gabriel and Ken Stabler. Both were very good quarterbacks for a long time, and occasionally great quarterbacks for stretches. When they retired, each was a borderline HOF candidate. Statistically, Gabriel had the better career, including posting an ever-so-slightly better era-adjusted passer rating. Both are safely in the top-50 quarterbacks of all time, and while Stabler (who won a Super Bowl) is in the Hall of Fame, Gabriel probably was the better quarterback on a consistent basis.

But that’s not what I want to discuss today, because it’s not that interesting whether you have Gabriel at 28 and Stabler at 35 on your all time list, or vice versa. What *is* interesting is how they are two of the biggest outliers in two metrics that often go together. [continue reading…]

I originally wanted to write today about the Dallas Cowboys, and whether them being labeled America’s Team was reflected in their appearances on Monday Night Football. The Cowboys have been on MNF a whopping 83 times since the program began in 1970, just one behind the Miami Dolphins for the most in the NFL. The 49ers, Broncos, Redskins, Steelers, and Bears all have between 70 and 75 appearances, too.

Then I wondered: do the Cowboys usually play at home or on the road on MNF? As it turns out, it’s usually on the road. Dallas has played 43 road games on Monday Night Football, as have the Raiders, two teams that NFL fans love to hate. But it’s the New York Giants that have the most road games on Mondays with a whopping 47. That stands in stark contrast to just the 19 home games the Giants have had on Monday nights. The table below shows the MNF data for all 32 teams. The Giants have played, on average, 0.57 more road games than home games per season on MNF, easily the most in the league. Meanwhile, the Dolphins — perhaps because Miami is a good location for a night game — have played 0.57 more home MNF games per season than road MNF games.

[continue reading…]

On Monday, I posted the implied SRS ratings from the Vegas lines released last week covering the first 240 games of the season. And, as discussed, the Oakland Raiders have the toughest schedule in the league, by virtue of competing in the AFC West with two elite teams, and facing the tough AFC South and NFC North Divisions. The table below shows each team’s SOS for all 16 games in 2018:

[continue reading…]

Yesterday, I published the implied SRS ratings from the Vegas point spreads produced last week. Today, I want to publish the weekly point spreads, along with some notes.

With 240 games involved, each team’s rating gets generated by looking at the point spread in their game and the point spreads in all other games by them, and the point spreads in all other games by their opponent, and the games for their opponent’s opponents, and so on. For the most part, the results match up pretty well. For example, in week 7, Houston is a 4-point road underdog in Indianapolis, and in week 11, the Texans are a 2-point home favorite against the Colts. The Steelers are 9-point home favorites against Cincinnati in week 4, and Pittsburgh is a 3-point road favorite against the Bengals in week 12.

For the most part, after we have derived each team’s rating, the point spreads then make a lot of sense. Pittsburgh has an SRS of +1.5 and Cincinnati has an SRS of -4.3, so it makes perfect sense that the Steelers would be 9-point home favorites and 3-point road favorites against the Bengals. But there are two games that don’t make much sense.

In week 8, the Bengals are facing the Rams in London. Los Angeles has an SRS rating of +5.4, making them 9.8 points better than Cincinnati. And if anything, home field should favor Cincinnati, as this is a really far trip for LA. And yet the Rams are 13-point favorites! In addition, the Seahawks have an SRS rating of +1.9, while the Falcons have an SRS rating of +0.4. All else being equal, the Seahawks should be 1.5 point favorites on a neutral field and 1.5-point dogs on the road. And yet when Seattle flies cross country to Atlanta, it is the Seahawks that are 1.5-point favorites. Weird.

Other than those games, all other point spreads are within 2 points of what we would project based on the SRS ratings of the two teams and the location of the game. This is why you don’t really need many games to come up with implied SRS ratings: for the most part, Vegas uses their team ratings to set lines, and the transitive property is applicable here (and matchup strengths are not).

Below are the point spreads for every game this season. Here’s how to read the table, which is fully sortable and searchable. Arizona has an SRS rating of -5.8, and in week 1, hosts Detroit who has an SOS of -3.3. The point spread in that game is a pick’em, and based on that point spread, the expected MOV is the Lions by 3 points. [continue reading…]

After the release of the 2019 Schedule, the next big item on the agenda is figuring out who are the best and worst teams in the NFL. Every year, CG Technology releases point spreads for each of the first 240 games of the NFL season (i.e., spreads for every game during each of the first 16 weeks). And, every year, I then use those weekly ratings to derive the Vegas ratings are for each team. Hence the title of today’s post: we can use the Vegas point spreads in each game to derive the implied ratings by CG Technology for each team.

The way to do this is to take the point spread in each game, adjust for home field (except for the five international games), and then determine by how many points Vegas thinks Team A is better than Team B. For example, when the Jets are favored by 6 points in a home game against the Dolphins, we can take this to mean that Vegas thinks New York is about three points better than Miami. When we see that the Jets and Dolphins game is a pick’em for the matchup in Miami, this helps reinforce that view. And when Vegas says the Jets are a pick’em against the Browns at home, that tells us that Vegas thinks the Jets are about 3 points worse than the Browns *and* that the Dolphins are about 6 points worse than Cleveland. Using the iterative SRS process, and because the transitive property of point spreads applies, we can generate team ratings based on the 240 point spreads involved.

Here’s how to read the table below, in each case excluding week 17 action. After adjusting for home field advantage, the Patriots are expected to beat their average opponent by 6.6 points. On average, New England’s opponents (after adjusting for *their* strength of schedule) are 1.0 points below average, which means the Patriots are expected to be 5.5 points better than average (difference due to rounding). That’s the best in the league; the worst team in the league is the Cardinals. [continue reading…]

It is a football truism that the team that wins the rushing battle wins the game. The causation arrow also runs the other way, of course: the team that wins the game usually wins the rushing battle.

Since 1950, how successful were teams that won the rushing battle? Those teams won 72.9% of the time. That number is 72.7% since 1970, 71.9% since 1990, 71.3% since 2002, and 70.5% over the last 10 years. And the numbers are nearly identical, of course, if we ask the question the other way (among teams that won, how often did they win the rushing battle?).

Now, there are obviously key factors that drive both of these results: number of carries and leading late in games. The number of carries a team has is highly correlated with how many rushing yards a team gains. And the number of carries a team has is highly correlated with what the score margin is late in the game. And, finally, the score margin late in the game is highly correlated with how often the team wins the game.

That’s a few steps, but given the strong correlations, it makes it difficult to really evaluate a team’s rushing game and the causal relationship between rushing and winning. In a lot of games, you can see something like this:

Team that is winning late –> is team that runs more late in games –> is team that finishes with more runs –> is team that finishes with more rushing yards –> is team that wins.

But what if we flip the script and look at games where that’s not true. Specifically, I’m thinking of all games where the team that wins the game was losing at the end of the third quarter. In that universe, we have a much more even environment to examine the rushing game. For example, since 1950, there have been 2,085 games where a team was losing after three quarters and won the game. On average, those teams trailed by 5.4 points after three quarters, but of course won every game.

Now, what percentage of the time do you think the winning team — that is, the team that trailed after three quarters — won the rushing battle?

This isn’t just a rhetorical question. I want you to think about it. On average, over these two thousand plus games, the winning team trailed by 1.5 points after the first quarter, (4.6 to 3.1), by 3.8 points at halftime, (12.0 to 8.2), 5.4 points after three quarters (17.1 to 11.7), and then was ahead by 4.2 points (23.7 to 19.5) after the fourth quarter.

Knowing this, how often do you think the team that won also won the rushing battle?

I’ll give you another minute to think about it.

.
.
.
.
.
.
. [continue reading…]

In week 1 last year, Tampa Bay quarterback Ryan Fitzpatrick had a game for the ages. He averaged 17.75 ANY/A on 28 dropbacks, and was the catalyst in the Bucs 48-40 upset over the Saints.  Tampa Bay’s running game underperformed, and its defense was dreadful, but Fitzpatrick and the passing offense were so effective that the Bucs won anyway.

Here is the Expected Points Summary from that game, courtesy of PFR.

Passing is king in football, and you won’t be surprised to learn that in lots of games, the passing game is the reason a team wins. Even in games where a player throws a pick six — say, this Chargers/49ers game from September — strong passing the rest of the way can make up the difference. In this game, Los Angeles won by 1 point and finished with +2.2 points of passing EPA, as Philip Rivers still finished with 6.43 ANY/A.

In total, there were 113 games last year where a team won and their passing EPA was larger than the margin of victory.  This is in stark contrast to yesterday, where there 23 games where the team that won had a rushing EPA larger than the margin of victory.  This means that in 44% of games, the team that won would “have lost” if they had an average passing attack. [continue reading…]

No study is perfect, but some are useful anyway. Pro-Football-Reference.com publishes Expected Points Data for every game broken down by unit. For example, let’s take a look at the week 6 game from the 2018 season between the Broncos and Rams. In this game, Jared Goff and the passing attack produced just 174 net yards on 33 dropbacks, while throwing one interception and zero touchdowns. In other words, it was a really bad game (3.91 ANY/A), statistically his second-worst of the season (behind his disaster performance in Chicago). Meanwhile, Todd Gurley rushed 28 times for 208 yards and 2 TDs, a monster performance that included a 4th down conversion.

The Rams won the game by 3 points, while the team’s running game produced 15 points above expectation. So — if we are willing to make a few assumptions about how a game works — we can say that the Rams won against the Broncos in a game where they would not have won had their rushing game been average.

There were 23 games in 2019 where a team won and where the team’s rushing EPA was greater than the margin of victory. That represents 9% of all winning teams in 2018. [continue reading…]

We know that there is a strong correlation between winning percentage and rushing plays: the teams with the best records tend to run most often, as leading in the second half of games is strongly correlated with both winning games and running in the second half of games. Last year, the top 5 teams in rushing attempts all made the playoffs, and 9 of the top 10 teams (sorry, Buffalo) in rushing attempts won at least 9 games.

The flip side of this is that there is a negative correlation between winning percentage and pass plays. But what I wanted to look at today is how this has changed over time.

I grouped all games into ones where a team passed between 20 and 29 times, 30 and 39 times, or 40 to 49 times. I then checked, for every season since 1960, how often teams won when meeting those criteria. The results are in the graph below: [continue reading…]

According to Ian Rapaport, Jets head coach — and now interim GM — Adam Gase was apparently unhappy with the amount of money ex-New York GM Mike Maccagnan spent to lure Le’Veon Bell to the team in March. Gase “just did not love spending that much money for a player at that position” according to Rapaport. Which, frankly, is a little silly.

For starters, other than Bell, all other Jets running backs are making less than $1M this season (the top three are Elijah McGuire, Trent Cannon, and Ty Montgomery).  And the Bell contract is hardly debilitating: he will cost the Jets $9M in salary cap space.  As a result, New York has allocated $12.4M in 2019 salary cap dollars to the running back position, which …. simply isn’t that much.

The two teams that have allocated the most 2019 salary cap dollars to the running back position are the Bills (LeSean McCoy, Frank Gore, and T.J. Yeldon) and 49ers (Jerick McKinnon, Tevin Coleman,Raheem Mostert), and perhaps you want to argue that those are not the teams one should emulate.  But last year’s two Super Bowl participants are also in the top five in salary cap dollars spent at running backs.  The Rams have the highest paid running back in the NFL in Todd Gurley and also are paying over $2M this season to Malcolm Brown.  New England is spending $4.6M on James White, $3M on Rex Burkhead, $2.2M on Sony Michel, and $1.7M on Brandon Bolden.  And in terms of capital spent, New England used a 1st round pick on Michel in 2018, have two higher paid backs on the roster, and then used a 3rd round pick this year on RB Damien Harris from Alabama.

Arizona (David Johnson) rounds out the top 5 in RB salary cap dollars spent, and the Jets are sixth.  So while Gase may think the Jets overpaid for Bell, it’s hard to make the argument that this was a big mistake.  The Jets are one of 10 teams that still have $25M of salary cap space available for 2019, and there are not many ways left to use that space.  Maybe Gase thinks Bell was overpaid by a couple of million dollars, but that will have little practical impact on the team in 2019.  In 2020, Bell’s cap hit will be $15.5M, but that is not going to hamstring the team.

One reason for that, of course, is the presence of Sam Darnold.  In general, we do see an inverse relationship between how many 2019 salary cap dollars a team spends at RB with how many salary cap dollars a team spends at QB. Take a look at the graph below, with all data courtesy of Over The Cap. [continue reading…]

In 2001, the Jets did something pretty conventional.  On January 12, 2001, they hired a new GM in Terry Bradway, and six days later, Bradway hired a new head coach in Herm Edwards.  That’s pretty much the normal way a team operates: ownership (in this case, it was brand new owner Woody Johnson) picks a GM to build the organization, and then the GM picks a head coach to build the team.

Apparently, Johnson has since found the old fashioned way to be pretty boring.

In January 2006, the Jets traded Edwards to the Chiefs with Bradway’s tenure on the rocks. On January 18, 2006, the Jets hired a new head coach in Eric Mangini.  But then, a few weeks later, on February 7, 2006 Bradway was fired and assistant GM Mike Tannenbaum was promoted.  Tannenbaum was a friend of Mangini and “part of the interviewing process” — a common Jets theme — but acknowledged that it was Bradway who made the final call to hire Mangini.

Regardless, in late December 2008, Mangini was fired, and on January 20, 2009, Tannebaum hired Rex Ryan to replace him.

That worked out because the Jets had great seasons in 2009 and 2010, but after bad seasons in 2011 and 2012, Tannenbaum was fired on the last day of the 2012 calendar year. You might think that Ryan would be shown the door with him, but if you think so, you don’t know the Jets.

With Johnson determined to hire a GM while keeping Ryan in place, the best Johnson could do was hire John Idzik — another salary cap guru — to replace Tannenbaum.  That happened in January 2013.  But Idzik couldn’t help turn around a depleted Jets roster, and he never got a chance to hire his own head coach, either: at the end of the 2014 season, Johnson fired both Idzik and Ryan.

This gave the Jets a chance to bring on a new GM and have that GM hire his own head coach. It also gave the Jets a chance to reinvent the circus wheel, and that turned out to be the more appealing option.  On January 12, 2015, Johnson hired both HC Todd Bowles and GM Mike Maccagnan.  Maccagnan didn’t hire Bowles, but he was “part of the interviewing process” as the Jets ran a dual-track hiring approach. As Rich Cimini noted that day:

In hiring Maccagnan, the Jets have changed their power structure. He and Bowles will report directly to Johnson, who envisions the GM and coach as equal partners. Previously, the coach reported to the GM. Maccagnan will have control over the 53-man roster and final say on the draft; the coach will decide the weekly lineups. The lines were blurred with Ryan and Idzik, especially with quarterback decisions.

You may be surprised to learn that such a strategy did not work out.  Under Bowles and Maccagnan, from 2015 to 2018, the Jets were a bad football team.  At the end of the 2018 season, it made a lot of sense to fire both and start over.

But making a lot of sense is viewed as boring by the Jets.  Instead, New York fired Bowles and hired the underwhelming Adam Gase, whose Dolphins ranked 29th in points differential over his three years in Miami.  If you think limiting prospective coaching hires to only those who are willing to work under a bad GM who is on the hot seat and has a 24-40 record would limit the coaching pool, you are correct.  The Jets — after mismanaging the hiring process with another coach — were left with Gase, a candidate who would not have otherwise been a head coach in the NFL in 2019.

But today, on May 15, 2019, in a stunning move for those who don’t follow the Jets, the team has fired Maccagnan, a move that is long overdue but comes at a very suspect time.  It means the new Jets GM will have to inherit Gase as his coach, in typical Jets fashion. And the early reports are not very promising, as Gase is going to be acting as interim GM and assisting in the process of finding his own boss:

Six months ago, the Jets entered the offseason with a potential franchise quarterback in Sam Darnold, the #3 pick in the Draft, and a ton of cap space.  It was the perfect opportunity for the Jets to attract a strong GM candidate, and in turn, have that GM be able to find a strong HC. Instead, the Jets let Maccagnan run the offseason, hire Gates, and spend nearly $200M on free agents, and oversee the 2019 Draft…. and then fire him.  And now, the Jets are looking to to attract a GM who will have no power to make material changes in 2019 and be tied at the hip to Gase.

That strategy makes a lot of sense, but only if you are talking about the Jets.

Yesterday, I noted that the average height of all wide receivers — weighted by receiving yards — declined steadily during the ’80s, and then reverted by steadily rising during the 1990s. There is no natural way to measure something like “wide receiver height” because it involves taking an average. And taking an average means you need a numerator and a denominator, and there’s no clear answer as to what the denominator should be.

Should “average wide receiver height” be an average of all wide receivers in the NFL? Maybe, but what does “all” mean? Does it include only players who made it to the final 53 man roster? Only players who played in a single NFL game that year? Only starters?

Should a wide receiver who played in zero games but be on the roster be counted exactly the same as Jerry Rice? I don’t think so, and one easy and neat way to deal with all of these questions is to take a weighted average, and to use receiving yards as the weighing mechanism. So if Rice was responsible for 2% of all NFL receiving yards, and a 6’7 backup who never made it into a game was responsible for 0% of all receiving yards, then the “average height of all wide receivers” is comprised of 2% Rice and 0% of the backup.

This works well, in my opinion, but there is a potential drawback to this approach. If the structure of the league changes — say, the introduction of three-WR sets with smaller slot receivers — that would change the average by a noticeable amount. Teams may have always had a 5’10 player as their third receiver, but they could jump from 200 yards to 700 yards just by moving to an offense that gets them on the field. [continue reading…]

In 1973, the top five fantasy wide receivers were 6’8 Harold Carmichael, 6’3 Charley Taylor, a pair of 6’1 guys in John Gilliamand Isaac Curtis, and 5’10 Harold Jackson.

In 1988, the top five fantasy wide receivers were 6’2 Jerry Rice, a pair of 5’11 players in Henry Ellard and Ricky Sanders, and a pair of 5’9 targets in Mark Clayton and Drew Hill.

Ten years later, in 1998, the top five fantasy wide receivers were a pair of 6’4 wideouts in Randy Moss and Keyshawn Johnson, a 6’3 Terrell Owens, a 6’2 Eric Moulds, and a 6’1 Antonio Freeman. 6’3 Cris Carter was the sixth-ranked fantasy wideout.

Those years are representative of the broader trend in the NFL: the “average” wide receiver was a bit over six feet tall throughout the 1970s and through 1985; during the ’80s, receivers kept getting shorter, and the “average” receiver was just 71.6 inches tall — also known as a hair under six feet — in 1990. During the ’90s, however, the trend reversed, and by 1998, the “average” receiver once again looked like he did (from a height perspective, at least) prior to the mid-’80s. The “average” wide receiver now is consistently just shy of 6’1 tall, league-wide.

What do I mean by average? I took the height of every wide receiver in the NFL each year, and then took a weighted average of those players based on the number of receiving yards they had. This means a 6’3 receiver with 1200 yards counts three times as much as a 5’10 receiver with 400 yards when determining the weight of the “average” wide receiver in the NFL in a given season. Take a look:

[continue reading…]

As noted yesterday, Steve Smith played on teams that didn’t pass very often (just 94% as often as the average team) or pass well (his average team’s passing offense produced a -0.12 Relative ANY/A). He’s the only player in the top 20 in career receiving yards who played on teams that were below average in both categories; Muhsin Muhammad and Joey Galloway are the only other such players in the top 40, and Eric Moulds (who ranks 49th) is the only other such player in the top 75 in career receiving yards. There’s a reason that Smith has long been a Football Perspective favorite.

Using the results from Friday’s post and Saturday’s post, we can sum the results to see which receivers really played in the most disadvantageous environments. To do that we need to convert each player’s results in both Pass Ratio and Relative ANY/A into Z-scores.

For Pass Ratio, the average for this group of 200 players was 102.5%, and the standard deviation was 6.7%. So for Smith, whose teams passed on 93.6% of plays, he has a Z-Score in the Pass Ratio variable of -1.33, since his teams were 1.33 standard deviations below average (102.5% minus 93.6% is 8.9%, and 8.9% divided by 6.7% is 1.33). For Relative ANY/A, the average was +0.35 and the standard deviation was 0.63. For Smith, he has a Z-Score in pass efficiency of -0.74, since his teams were 0.12 ANY/A below average (+0.35 minus -0.12 is 0.47, and 0.47 divided by 0.63 gives us how many standard deviations his teams were below average).

Note that the averages here for both pass quantity and quality are above average. That’s not surprising, but it is noteworthy. One, the players with the most receiving yards tended to play on better passing offenses, but we also give more weight to a player’s best seasons, which tend to come when they play with good quarterbacks who frequently pass.

The table below shows the full results for all 200 players. Here’s how to read the Jerry Rice line. He is the career leader in receiving yards with 22,895, and he played from 1985 to 2004. His average team had a RANY/A of +1.57, which was 1.91 standard deviations above average. His average team also had a Pass Ratio of 105.8% of league average, which was 0.51 standard deviations above average. Add those numbers together, and he has a Z-Score total of 2.42. [continue reading…]