Minnesota Vikings kicker Blair Walsh was released by the Vikings this week, and given his struggles this year, it’s hard to argue with Minnesota’s decision. Walsh will be infamously remembered for missing a chip shot in the playoffs against the Seahawks last year, and those demons have carried over to his 2016 performance.

How much so? I looked at every kick of Walsh’s career, beginning in his rookie season of 2012. For every made extra point in 2012, 2013, or 2014, I gave him +0.01 points, and +0.06 points for every made extra point in 2015 or 2016. Then, for every miss, he received -0.99 or -0.94 points, as applicable.

Extra points were easy; field goals were slightly harder. The graph below shows the average success rate on field goals in 3-year increments, from 2012 to week 10 of 2016:

I used those numbers to give Walsh points for each field goal attempt, too. For example, 48-50 yard kicks have been made 70% of the time over the last five years, so if Walsh attempted a 49-yard field goal, I gave him +0.9 points if he made it, and -2.1 points if he missed it.

Using that methodology, here is how Walsh has fared in every regular season game of his career:

As a rookie, Walsh was at +11.4 in this system, and would be even higher if I era-adjusted in sample (for convenience, I treated 2012 games the same as 2015 games, which probably is not appropriate). In 2016, he had -7.1 points by this system, including two miserable games in weeks 1 (missed extra point, missed 37-yarder, missed 56-yarder) and 9 (missed extra point, missed 46-yarder in a game the Vikings lost in overtime).

The Titans were the big Game Scripts story of week ten, as Tennessee rolled out to a 21-0 lead against the Packers. The Titans have been remarkable over the last few weeks: since the start of week five, the team is averaging 33.7 points per game, the most in the NFL.

Tennessee has scored at least 25 points in six straight games for only the third time in franchise history, and only the second time since the merger. The Titans have crossed the 35-point mark in three straight games, a franchise first.  The team is 2nd in yards per carry and 6th in yards per attempt; this is an offense flying high right now. [continue reading…]

Today at 538: the Steelers and Seahawks had some interesting two-point conversion decisions in week ten.

According to ESPN Stats & Information Group, there have been 1,045 two-point conversion attempts since 2001, with teams converting 501 of those tries. That’s a 47.9 percent conversion rate; given that a successful attempt yields 2 points, that means the expected value from an average 2-point try is 0.96 points.

Interestingly, that’s almost exactly what the expected value is from an extra point these days. Since the NFL moved extra-point kicks back to the 15-yard line last season, teams have a 94.4 percent success rate, which means that an extra point has an expected value of between 0.94 and 0.95 points.

This means that, all else being equal, the average team should be indifferent between going for two or kicking an extra point. Unless the game situation (i.e., late in the second half) or team composition (e.g., a bad kicker, or an offense or an opposing defense that is very good or very bad) changes the odds considerably, the decision to go for two or kick an extra point shouldn’t be controversial. In the long run, things will even out, because the expected value to the offense is essentially the same in both cases.

That’s the long run. In the short run, there will be ugly outcomes. And we saw two of those play out this weekend.

You can read the full article here.

There was just one 4th quarter comeback in week nine, and it came in the Lions/Vikings game.  Trailing 13-9 with 4:14 left, Minnesota embarked on a 19-play, 79-yard drive for a touchdown to take a 16-13 lead.  That would have been the only 4th quarter comeback of the week, but Matthew Stafford completed two passes for 35 yards to put Detroit in position for a field goal to tie the game.  Matt Prater connected from 58 yards, and the Lions won in overtime.

But the Lions led for most of that game, and finished with a Game Script of +2.3.  Only one other winning team in week nine had a fourth quarter score to take the lead: Miami, who returned a kickoff for a touchdown against the Jets. But Miami led 14-13 at halftime, and 20-13 at the end of the third quarter; the Dolphins finished with a Game Script of +2.3.

So there were no teams that won games in week nine with a negative Game Script.  Below are the full results: [continue reading…]

Today at 538: the Eagles appear to be much better than your average .500 team.

The Philadelphia Eagles are one of the more confusing teams in the NFL. At 4-4, it’s easy to assume that the Eagles are an average team, yet Philly has outscored opponents by 57 points this season, the third-best differential behind the 7-1 Cowboys and 7-1 Patriots. Furthermore, Football Outsiders has the Eagles first in the NFL in defense-adjusted value over average, a metric that measures team performance on a play-by-play basis. So what’s the deal — are the Eagles secretly one of the best teams in the league, or have they somehow gamed the system?

The obvious reason the Eagles are 4-4 despite putting up impressive numbers in the two stats mentioned above is that they clustered a lot of very strong play into just four games. In the team’s four wins, the Eagles have outscored opponents by a total of 76 points, an average of 19 points per victory. That makes Philadelphia one of four teams with an average margin of victory of at least 19 points in wins, joined by the Steelers (19.3 in four wins), Cardinals (23.3 in three wins) and 49ers (28.0 in one win).

You can read the full article here.

Three victorious teams stood out in week eight as pass-heavy:

• New England blew out the Bills, and led 41-17 until the final minute of the game. But despite a Game Script of +13.0, that didn’t stop the Patriots from throwing on over 60% of all plays. Tom Brady has deservedly received a lot of press this week, but the ratio against Buffalo was also a sign of an emerging problem: the running game hasn’t been very good. LeGarrette Blount had 18 carries for 43 yards, and New England’s running game has been inconsistent all year. Of course, that could just lead to more Brady throws, which may not be such a bad thing.

• For Oakland, Derek Carr had 59 pass attempts (and just two sacks) in a monster game against the Bucs. The Raiders running backs had some success, but this was a competitive game throughout. That’s a sign, tho, that the Raiders want to put the ball in the hands of Carr and Michael Crabtree (16 targets) and Amari Cooper (15 targets).

• The Chiefs rolled against the Colts, but even though Alex Smith went down, Kansas City stayed pass happy under Nick Foles. Kansas City passed more than you would expect from a team with a +8.0 Game Script, but that also may be a sign that the Colts pass defense is so bad that teams will pass on it regardless of situation.

Below are the Week 8 Game Scripts numbers. [continue reading…]

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

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

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

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Adjusted Points Per Drive

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

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

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

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

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

Previously:

• Week 1 Game Scripts
• Week 2 Game Scripts
• Week 3 Game Scripts
• Week 4 Game Scripts

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

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

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

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

Previously:

• Week 1 Game Scripts
• Week 2 Game Scripts
• Week 3 Game Scripts

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

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

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

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

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

Previously:

• Week 1 Game Scripts
• Week 2 Game Scripts

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

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

Previously: Week 1 Game Scripts

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

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

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

Today at 538: What is wrong with Aaron Rodgers?

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

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

You can read the full article here.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bush was the second overall pick in ’06, of course, and he entered the NFL at just 21.  He got off to a blazing start, tying the NFL record for receptions through two seasons set by Larry Fitzgerald. ^[1]By the end of that season, his teammate Marques Colston broke that record, and A.J. Green, Odell Beckham, and Jarvis Landry have all since broken that Bush’s mark. But Bush has not maintain that level of play, and the future isn’t all that bright. He turned 31 in March, and just signed with the Bills, his 5th NFL team.  Bush had just 4 catches  in five games last year, before an ACL injury in St. Louis ended his season.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Last year, Denver had a pretty tough schedule. In only five games did they face an opponent that an average team would have been favored to win: ^[1]I.e., home games against teams with SRS ratings of 3.0 or worse, or road games against teams with SRS ratings of -3.0 or worse. home games against San Diego, Baltimore, and Oakland, and road games against the Colts and Browns. In those games, Denver went just 3-2, with all five games being decided by one score.

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

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

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

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

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

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

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

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

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

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

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

You can read the article here.

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

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

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

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

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

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

However, I wanted to attach generic values to every way a drive can end. ^[1]With the exception of kneel down drives to end halves or games, as those don’t demonstrate an offense’s (or defense’s) ability to actually play the game. This is not a rigorous study, and it is meant to be a starting point for future research rather than a conclusive formula to govern the way anyone interprets on-field action.

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