Today’s guest post comes from James “Four Touchdowns” Hanson, a relative new reader to the site. As always, we thank our guest posters for contributing.

Elite Quarterbacks: Measuring Overall Team Support

It’s easy for football fans to buy into the mainstream logic that if you have an elite quarterback, your team will have a winning record, enjoy trips to the post-season and even win a few championships. The better the quarterback, the more wins and titles you can expect… right?

But that logic doesn’t always hold up.  Dan Marino, Fran Tarkenton, Warren Moon, Dan Fouts, Jim Kelly, Sonny Jurgensen, Philip Rivers, and so on, provide examples to the contrary. And while his talents have been unfairly portrayed at times, the fact that Terry Bradshaw has four Super Bowl rings while superior passers have none presents a disconnect if you think great quarterback talent is measured by titles.

If we go by an average time of possession of 30 minutes per team, that means that half the time, a team’s quarterback isn’t even on the field. And if 35% to 55% of your team’s offensive plays are running plays… doesn’t that mean the quarterback really only affects 22% to 33% of the total game time? And once you get into other factors that affect a passer’s game, like play design and coaching, offensive line talent, receiving talent, quality of opposition, etc., attributing credit and blame gets pretty murky.

So while we have a general feeling that some quarterbacks receive more support than others, so how do we go about measuring it through metrics? Unfortunately, due to the nature of passing stats, I don’t know of a way to separate a quarterback from his receivers, pass blocking, scheme and play-calling. Whether it’s passer rating, ANY/A, or passing EPA, none of them can tell you which percentage of the credit (or blame) should be shared with those external factors.

That said, we can measure a quarterback’s support by looking at the numbers produced by his running game, defense and special teams. While I’d love to run these numbers for all quarterbacks, my ability to collect them is fairly limited (basically, cutting and pasting from Pro Football Reference – I don’t know enough about Python or R to run a spider to scrape them all in one go), so I will be focusing on four quarterbacks that are perceived to be “elite” by general mainstream consensus – Peyton Manning, Tom Brady, Drew Brees and Aaron Rodgers.

Due to length, I will be looking at these numbers in three separate articles. This one will focus on what support they received in these areas overall and the second will take a look at team support relative to the quarterback’s performance – how often their teams win when they have below-average performances and how often they lose when they perform at a high level (as measured by stats, natch).

Overall ANY/A, Passer Rating, & Expected Points Metrics

So let’s dig into the numbers. Here are the quarterbacks’ average stats per game for their overall careers including playoff games (which is why you may notice a difference between these and career averages that only include the regular season). The cells shaded blue indicate that quarterback is the leader among the four in that metric. While all of these metrics should be familiar to readers of the site, I have also included a metric I’m calling Relative Passer Rating (rPR) and it’s essentially the same concept as Relative ANY/A – it measures passer rating compared to the average for that season. For example, if the average is 80.0 and the passer earns a 90.0, his rPR would be +10.0.

As Peyton Manning is the only one of the four to suffer a physical decline in a career ending season, I have included his pre-2015 averages for the efficiency metrics in parenthesis next to his actual averages, though this will not affect any of the analysis from this point on – it’s more of a “nice to know”.

Great numbers across the board, as to be expected by elite players but it’s amazing how much higher Brady’s win percentage is than the other players despite not producing any clear statistical advantage in the efficiency or traditional metrics – and how much lower Brees’ win percentage is than the others despite his numbers being on par with the other three QBs.

Additionally, as their success in the playoffs makes us the biggest difference in the way people perceive these players, I will break out their playoff numbers in a separate table –

The numbers here probably come as a surprise – I know I didn’t expect Brees to sweep nearly every passing metric for the playoffs. While his smaller sample size comes into play, it doesn’t seem to have affected his win percentage, which is about on par with everyone except Tom Brady.

So there’s our starting point – we can see how each player has performed relative to the others, and while there are some clear leaders in the playoffs, they’ve produced at a similar level overall throughout their careers. Now let’s see how their teams have supported them.

Team Support Measured by Expected Points

As they’re the metrics that seem mostly closely tied to the margin of victory and defeat, I figured we’d start with Expected Points. Mike from Sports Reference defines Expected Points as a way to “break down the contributions each team’s various squads made to the margin of victory.” Those last few words are key; EP are applicable to the margin of victory – or defeat.

With that in mind, I went about measuring the EP of each quarterback’s passing offense against the rest of the team – running game, defense, and special teams. The sum of those three squads make up the Total Support EP number while the point differential is the average margin of wins and losses across their careers.

The EP Gained / Lost metric represents the average EP each quarterback’s running game, defense, and special teams have added or subtracted per game from the passing EP they generated. I also felt that per game average didn’t fully illustrate what effect those added or subtracted points had over the course of each player’s career, so I included the sum or difference in Total EP Gained / Lost.

Finally, since EP is tied to the margin of victory or defeat, I included the Point Differential to give the numbers some context. I also showed what each QB’s passing EP as a percentage of the average point differential in Pass EP % of Outcome so we could see how much credit or blame each quarterback should take for his team’s outcomes on average.

Red indicates negative EP while green indicates positive EP; dark green and dark red indicate who finished first and last in each category respectively.

First off, it’s clear that these players’ passing offenses have been the driving force behind their teams’ success. Compare their passing EP to the average margin of victory and you see that it’s each quarterback’s passing game that has created most or all of that point differential – it looks like most of the time, these guys carry their teams.

That said, we can see the EP support (AKA negative plays) has been very different for one player compared to the other three – Brady is the only one to have positive EP support in any category and amazingly has positive EP in ALL support categories. It’s said that the quarterback affects all other players on the field and while that may be true, we’re not seeing it in these results – every other QB has a higher EP average per game than Brady but gets worse support than him.

If we were to round the numbers, it looks like Manning’s and Rodgers’ team support have cost their teams around a field goal in points, on average, while Brees loses roughly 5 points. Meanwhile, Brady’s support has been worth an extra point to his Patriots. We shouldn’t misinterpret these numbers to suggest that somehow Brady is being carried by great support – it’s clear that Brady’s passing game is the engine that drives the Patriots’ success as a team but he’s the only quarterback of the four whose supporting elements haven’t cost him points.

This has led to his teams actually adding positive EP to his games overall, giving Brady nearly 264 expected points to his overall point differential over the course of his career. Manning and Brees, on the other hand, has had their teams cost them over 1,000 expected points and at the rate Rodgers’ offenses have been producing, Rodgers will likely join them by the end of his career.

Of course, there’s only so much we can see from these per game averages – let’s see what that EP looks like spread out over the course of their careers. After all, a team that provides -5 EP in five games and a team that provides +5 EP in two games and -20 EP in three games will both average out to -5 EP per game. But the first team had five straight bad games while the second team had three good games and two catastrophically bad games. Clearly, not the same thing.

So let’s look at how many games each passer and team have provided positive and negative support – and then let’s see how often that support has created a two-possession lead or deficit (9 points), three-possession lead or deficit (17 points), and while I know 24 points is technically three possessions, I think the odds of any team getting three TDs with three two-point conversions is very low – so I set 3 TDs (21 points) as my threshold for three-possessions. Additionally, we should see how often our QBs do the same, so those passing EP numbers are also included.

The leaders in passing EP are marked blue, the leaders in support EP are marked green, and ones with the least EP in each category are highlighted in red –

The numbers reflect what we’ve seen before – Aaron Rodgers leads in almost all categories with passing EP, having the largest number of games with positive EP and the fewest with negative EP. Meanwhile, Brady has the most games with positive support and the fewest games with negative support across all categories by a substantial margin, though he’s tied for the lead with Rodgers for the fewest games with negative EP over 9 points.

That all said, when you look at the actual number of games with negative passing EP, it’s really only a handful of difference between these players – they’re all incredible, posting positive EP in the vast majority of their games and two possessions worth of positive EP in about half of their games. These guys are considered the elites of the sport for a reason and these numbers bear that out.

As before, I included their playoff numbers since that’s the core difference in how fans perceive them. One difference here from the previous chart, though – since none of them enjoyed totally positive playoff support, the QB with the smallest deficit of supporting cast EP is highlighted in yellow–

If you think about it, it’s kind of amazing that despite Manning, Brady and Brees playing over 240 games (Rodgers at 151), a small fraction of them determine how the general public sees these players — the playoff games. Manning has played 292 games and the outcome of 27 will determine how he’s remembered. Brady has played 269 and 34 are what makes him the GOAT to most of the public. Brees has played 243 and the outcome of 11 is what keeps him out of most people’s conversation for GOAT. Kinda nuts – but at least Rodgers still has a lot of his career ahead of him. Hopefully, Green Bay will get the man some help!

With that in mind, I decided to add three rows to this column since we’re dealing with such a small sample size for the playoffs so we can understand the narrative behind the averages a little better – how many playoff appearances they had with positive support EP, their record with that support, and then their record with negative support. While they all had negative support in the playoffs, Brady’s did the least amount of damage – despite both Brees and Rodgers having higher passing EP, their playoff point differential is less than half of Brady’s. Meanwhile, it seems Manning has to assume some responsibility for his lack of playoff success as his passing EP drops dramatically in the playoffs.

That said, in terms of win percentage, they all do better with positive support – Manning seems to do worst but still has an incredible 75% win percentage, way up from his 52% overall.

And while the results so far of this study have shown that Brady has received the most support while not always posting the best efficiency metrics or raw stat totals, we have to give him his due – he is the only QB of the four with a winning playoff record in games with negative EP support.

So how does this all translate to Super Bowls? Two of Manning’s three playoff runs with positive support netted him a Super Bowl title (his other was in 2014 when he was hindered by injuries that would ultimately end his career), while Brees’ and Rodgers’ only playoff run with positive support led to them winning a Super Bowl (Brees’ Special Teams EP in his Super Bowl was nearly 10 points alone!). On the other side, Tom Brady has won three Super Bowls with positive support and two Super Bowls with negative support (though all Super Bowl runs featured at least one game with positive support that contributed more to the margin of victory than passing EP), which certainly adds some credibility to the idea that he’s got a bit more “clutch” to him than the other three quarterbacks.

All that said, we can see how much more the other three quarterbacks lost due to poor playoff support – despite having the lowest passing EP by far, Manning also contributed the most to his team’s point differential, suggesting that overall, they would have done much worse without him.

Below, we see the numbers that tell the story of Manning’s playoff struggles – he’s generated the fewest games with positive EP and the most games with negative EP as a percentage and his poor games have been more catastrophic than the others, while his great games have been more dominant than the others. His passing offenses seem to be “feast or famine” in the playoffs. Beyond that, the story stays the same for the other QBs – Rodgers and Brees generate the best playoff passing EP but suffer the worst support EP of the four QBs, while Brady’s great EP performances seem to be hindered the least by his supporting squads.

So, what is the deal with Manning in the playoffs? We’ve established his support was poor in general, but poor support hasn’t hindered the other QBs as much as it has Peyton (though both Brees and Rodgers playoff sample size is much smaller than Manning’s). It’s a question many have asked and for my money, probably most satisfyingly answered in Scott Kacsmar’s two-part article on Football Outsiders, which you can read here and here.

His critics seem to feel that the pressure and anxiety caused by the high-stakes “lose and you’re out” playoffs format causes Manning to play worse. Perhaps this is true – but I find it hard to believe that he can, for example, run the same “levels” play over and over in the regular season but once the playoffs start, he suddenly can’t make the reads and throws he’s made literally thousands of times before. I’d need to see film study done to believe this theory – if someone can show me that Manning consistently made more bad reads and missed open receivers in the playoffs, while under the same amount of pressure as usual, I can believe it was psychological.

But there are other possible explanations. The first is that the small number of playoff games can skew numbers due to a high degree of variability found in small sample sizes. Perhaps Manning was just unfortunate that he had a higher percentage of his poor games in the playoffs than the other QBs. After all, his career efficiency averages, even when adjusted for opponent like DVOA, suggest he’s had the same percentage of bad games overall as the other three QBs. I find this plausible but perhaps a bit unsatisfying.

Ultimately, it’s something we won’t be able to figure out here, so let’s move on to looking at support measured by traditional metrics.  That will come in Part II.

After the Jaguars drafted Leonard Fournette with the 4th pick in the 2017 Draft, NFLResearch tweeted the following:

That was, at least for me, a surprise. And it is true: there have been nine running backs drafted in the top 5 since 2000, and those teams have improved by 43 wins. There is some natural regression to the mean built in to any analysis like this, along with two big outliers: the 2016 Cowboys and 2006 Saints used a top five pick on a running back, but also added Dak Prescott and Drew Brees, producing two of the greatest improvements in passing efficiency in NFL history. Those two teams produced 16 of those 43 wins; without those two teams, the average increase drops to a still-impressive 3.9 wins. [continue reading…]

Yesterday, I looked at the players with the worst career winning percentages. Using that same methodology, let’s look at the players with the best winning percentages. And the Otto Graham Browns and Tom Brady Patriots tend to dominate the list.

Below is the list of the top 100 players in adjusted career winning percentage, with a minimum of 100 career games played. [continue reading…]

The only reliable thing in Cleveland

That’s bad, but not as historically bad as I would have thought. From 2000 to 2006, the Detroit Lions had a 0.295 winning percentage, the worst in the NFL. And from 2007 to 2011, the Rams had an anemic 0.188 winning percentage, the worst in the NFL. There was a common denominator for both of those teams: defensive end James Hall.

How did I find Hall? I looked at all players with 100 career games played. Then I calculated the winning percentage for his team in each season of his career, weighted by the number of games he played that season. So when calculating the adjusted career winning percentage for Hall, who played in 165 games and in 16 games for the 2008 Rams, 9.7% of his Adj Car Win% is based off of that team’s 2-14 record. The 2000 Lions went 9-7, but Hall only played in 5 games that year, so the 9-7 mark only counts for 3% of his career record. And the fact that the Lions went 3-2 in the 5 games Hall actually played is irrelevant: for calculating Adj Car Win%, I just used the team’s overall winning percentage multiplied by the number of games he played.

There are over 4,000 players who have played in 100 career games, and Hall has the lowest adjusted career winning percentage. The second-lowest? That honor belongs to John Greco, who was a teammate of Hall’s in St. Louis from 2008 to 2010, and has been a teammate of Thomas in Cleveland ever since.

Below are the 100 players with the worst adjusted career winning percentages: Thomas checks in at #35. [continue reading…]

On Tuesday, I looked at the passing offenses, as measured by ANY/A, of each of the Super Bowl champions. Today, let’s do the same for passing defense. Just over half (26) have ranked in the top 4 in ANY/A, although as we saw with passing offenses, there isn’t a trend towards pass defense mattering more than it used to. If there’s a conclusion to be drawn, it may just be that worse teams are winning it all now than ever before. [continue reading…]

In 1981, the Chargers and Bengals met in the AFC Championship Game. That game isn’t the most memorable game played that day, in part because of the cold weather: nicknamed the Freezer Bowl, San Diego struggled on the road in a game played with a windchill of -32 degrees.

But the bad weather obscured the fact that Dan Fouts and Ken Anderson — by far the top two passers in the NFL that season — were facing off for the right to play in the Super Bowl. Over in the NFC, the 5th and 6th leading passers in ANY/A — Danny White and Joe Montana — were meeting in what would turn out to be one of the most memorable games in NFL history. [continue reading…]

Yesterday, I analyzed whether having a great quarterback is more important than ever when it comes to winning it all. The evidence provides a straightforward answer: no.

Today I want to examine the question from another perspective: what about how the eventual Super Bowl-winning quarterback looked prior to the season? From ’99 to ’02, we had four straight quarterbacks come out of nowhere to win the Super Bowl. In 1998, Kurt Warner threw 11 passes. In ’99, Warner won the Super Bowl, while Trent Dilfer ranked 29th among qualifying passers in ANY/A. But in 2000, Dilfer won the Super Bowl, while Tom Brady threw just three passes. In 2001, Brady won the Super Bowl, while a 33-year-old Brad Johnson looked past his prime, as he ranked 24th in ANY/A; the next year, of course, Johnson won the Super Bowl.

Nobody even thought about Warner or Brady in May of the year they won their first Super Bowls, and Dilfer and Johnson were veterans who did not fit within any definition of the words “great quarterback.” And yet, for four straight years, they shocked the football world.

So I wondered: how did each passer rank the year before they won the Super Bowl? A brief departure:

• Jeff Hostetler and Doug Williams each started just two games in 1990 and 1987, respectively, but both won the Super Bowl. Neither played much in the year before they won the Super Bowl, either. For purposes of this study, I am going to use Phil Simms and Jay Schroeder as the starting quarterbacks of the ’90 Giants and ’87 Redskins, although using Hostetler and Williams obviously makes the case even stronger for the “come out of nowhere” theory.

• Earl Morrall led the first 4th quarter comeback in Super Bowl history, but because Johnny Unitas was both the Colts starter for nearly all of 1970, and the starter in Super Bowl V, I am listing Unitas as the quarterback for that Colts team. Morrall does, however, get credit as the 1972 Dolphins quarterback, which is consistent with giving Simms and Schroeder credit.

Failed to Qualify

In addition to Warner and Brady (and ignoring Hostetler and Williams), there were 7 other quarterbacks who failed to register enough attempts to qualify for the passing crown the year before they won the Super Bowl. That includes 5 straight quarterbacks in the early ’70s.

In 1970, Roger Staubach was the backup to Craig Morton, but Staubach won the championship with an all-time great season the next year. In ’71, Morrall was the backup to Bob Griese, but Morrall was the main starter for the ’72 Dolphins due to Griese’s injury. And that means Griese ’72 makes our list, too, since Griese won it all in ’73. Terry Bradshaw in 1973 had just 180 pass attempts, so he didn’t have the 196 passes necessary to qualify for the passing crown (but he was so bad that if he did qualify, he would have ranked just 21st out of 24 passers in ANY/A). The next year he started 7 of 14 games for the ’74 Steelers but ranked 2nd on the team in pass attempts with just 148; as a result, he doesn’t have enough attempts to qualify when looking at Year N-1 performances, which is relevant since the ’75 Steelers won it all. So the ’71 Cowboys, ’72 Dolphins, ’73 Dolphins, ’74 Steelers, and ’75 Steelers all get labeled as having starting quarterbacks who the year before, did not have enough pass attempts to qualify for the passing crown.

The other two quarterbacks are Jim Plunkett and Jim McMahon. In 1979, Plunkett threw 15 passes for the Raiders and was viewed as a draft bust; a year later, he won the Super Bowl. In 1984, McMahon suffered a season-ending injury after just 143 attempts. [continue reading…]

If you’re short on time, let me save you a read: no.

And now for the long answer.

The graph below shows where each Super Bowl champion since the AFL/NFL merger ranked in Adjusted Net Yards per Attempt:

[continue reading…]

The most efficient runner in NFL history? That depends.

Jamaal Charles is now a Denver Bronco, making him the second superstar running back in two weeks to join a new team at the tail end of his career. In his prime, Charles was a very good receiver and a player that could be the centerpiece of an offense. However, he will likely be remembered for a singular skill: rushing efficiency.

Charles has a career YPC average of 5.45, easily the best in history among running backs in the NFL. That number is at least a little misleading. While rushing efficiency has not soared the way passing efficiency has, we are currently in a high-YPC environment. Two years ago, I calculated era-adjusted yards per carry: at the time, Charles was at 5.49, while the league average was 4.21. For reference, the league average during the careers of Jim Brown, Gale Sayers, and Barry Sanders was 4.08, 3.95, and 3.93, respectively.

I am not a big fan of yards per carry as a statistic, but hey, it’s still interesting trivia. It’s a little silly and mostly an academic exercise, but let’s pretend that we replaced every Charles rush attempt with a league average rush attempt. How much worse off would Kansas City have been? Well, a whole lot. Let’s use his 2010 season as an example. He had 230 carries for 1,467 yards, producing an incredible 6.38 YPC average. The league average that season was 4.21, meaning he was 2.17 YPC above-average. Given his 230 carries, we would have expected him to rush for just 968 yards, meaning he produced 499 rushing yards above average. And for his career? Charles is at +1657. [continue reading…]

Just 9 of 32 teams had a different player lead the team in pass attempts in 2015 and 2016. Take a look:

As you would suspect, some of the changes were intentional, and some were not. Denver saw their 2015 QB retire, while Minnesota lost their starting quarterback in the preseason…. which led Philadelphia to trade their 2015 starter and anticipated 2016 starter to the Vikings.

Three others were injury related. The Cowboys switched quarterbacks intentionally, of course, but still had their expected 2016 quarterback change due to injury. And Chicago lost Cutler early in the year, and he was limited to five starts.

The other four? The 49ers started 2016 with Gabbert, but benched him for Kaepernick. And the Texans, Rams, and Browns intentionally moved on from their quarterbacks, although McCown stayed with Cleveland and did wind up starting three games. [continue reading…]

Before the 2017 NFL Draft, I recorded a podcast with Ed Feng of The Power Rank. We talked about the background behind the creation of my draft value chart, some explanations for why the traditional chart still is used, and some general Football Perspective writing.

You can listen to it here.

Using data from Pro-Football-Reference for the 2016 NFL season, I calculated the average number of snaps per play taken by players at each position across the league. Here are the numbers for offense:

QB: 1.00
RB: 1.10
WR: 2.60
TE: 1.25
OT: 2.05
OG: 2.00
OC: 1.00

Some of these are pretty obvious: you have exactly one quarterback, one center, one left guard, and one right guard on each play. For the most part, you have two tackles, although occasionally teams will have three tackles on the field (apparently, about once every twenty plays).

The more interesting numbers come in the split among the skill position players: running backs (including fullbacks) only get about 1.10 snaps per play; in the ’70s, that number would be much closer to 2.0, although there’s no way of getting more precise than that. Tight ends are at 1.25, while the average play in 2016 featured more than 2.5 wide receivers on the field. Three wide receivers has become the base formation in the NFL. [continue reading…]

Teams needed stability at the QB position so they traded up for these guys

That 2004 first round may have marked a turning point in the league’s perception when it comes to “getting your guy.” The fact that Manning, Rivers, and Roethlisberger have become long-time starters with national recognition has likely had an impact when decisionmakers think about trading for a first round quarterback.

Since then, there have been 16 times over the last 13 drafts that a team “traded up” in the 1st round to get a quarterback. ^[1]Note that this does not include Jason Campbell, whom the Redskins selected in 2005. Washington did trade up for the pick, but they did so before the draft, not knowing that Campbell would be … Continue reading On average, those teams have paid about 149 cents on the dollar (according to the Football Perspective Pick Value Calculator) to move up in the draft to get their quarterback. But let’s put aside the cost for now, because there’s a theory that “if you think you have found your franchise quarterback, it doesn’t matter what it cost to get him.” So we have 16 cases where teams were really sure they found a quarterback that they had to get, and traded up to grab him. How did those quarterbacks fare?

While it’s too early to trade the last six quarterbacks selected in the previous two drafts, it’s not too early to grade the first 10. Seven of the 10 were busts, even if I am unfortunately labeling Teddy Bridgewater as such. An 8th was Mark Sanchez, and while he’s a pretty clear bust (he has the 9th worst era-adjusted passer rating in history among players with 1500 pass attempts), he won 33 regular season games and 4 playoff games with the Jets. It was a trade the Jets would have been better off not making, but he at least reached some level of success. The two “successes” were Jay Cutler and Joe Flacco, and neither could be called an unmitigated success. ^[2]Flacco, for his career, has a below-average passer rating. Cutler lasted only three years with the Broncos, before the team traded him … albeit for two first round picks.

Now, let’s get back to the cost.  As I did yesterday, I am valuing all drafts picks in the current draft using the Draft Pick Value Calculator I created.  For future picks, I used the middle of each round and applied a 10% discount rate; so a Year N+1 2nd round pick was worth 90% of whatever the draft value calculator says the 48th pick is worth.

Look at the first trade on the list.  This shows the Texans, who in 2017, traded up for Deshaun Watson by dealing with the Browns.  Houston sent the 25th pick and a 2018 1st, while the Browns sent back the 12th pick.  The pick equivalent on the Houston side is therefore 25 and 16 (projected 2018 1st round pick), and the pick equivalent on the Cleveland side is just 12.  The value given up shows the value my pick value calculator assigns: 14.1 for the 25th pick, and 15.2 for the 16th pick in next year’s draft (i.e., this represents 90% of the value of the 16th pick in this year’s draft).  The value received is just the value of the 12th pick, or 18.8.  The return is 156% — the Browns gained 156 cents on the dollar, with a raw difference of 10.5 points of draft value, by consummating this trade. [continue reading…]

As a junior in college, Mitchell Trubisky produced impressive, if not dominant, passing stats. Here were the 2016 leading passers in the ACC:

While Trubisky had a very good year, both the Atlantic Coast Sports Media Association and the ACC coaches only named him to the third team in the conference, behind Lamar Jackson and Deshaun Watson. [continue reading…]

Over the last 15 years, NFL teams have put less emphasis on drafting running backs. The amount of draft capital used on the position had been on a steady decline, although there was a notable reversal this year. In fact, 2017 falls behind only 2005 (where Ronnie Brown, Cedric Benson, and Cadillac Williams went in the top 5, and Frank Gore, Brandon Jacobs, Marion Barber and Darren Sproles went in the later rounds) and 2008 (Darren McFadden, Jonathan Stewart, Felix Jones, Rashard Mendenhall, and Chris Johnson all went in the first round, and Matt Forte, Jamaal Charles, Ray Rice, and Justin Forsett went in the later rounds) in terms of draft capital used on running backs and fullbacks.

Take a look: while there is obviously a general decline over the last four decades, the 2017 Draft was a great one for running backs: [continue reading…]

Which positions were the most valued in the 2017 Draft? Well, using my draft value chart, it’s very easy to answer such a question:

[continue reading…]

The Crimson Tide edged out some stiff competition

One of the stories from the first round of the 2017 Draft: what happened to all the Alabama players? Through the first 15 picks, there were zero Crimson Tide selections, surprising mock drafters across the country. TE O.J. Howard was heavily linked to the Jets at 6 to the Jets, DE Jonathan Allen was viewed as a top 10 pick rumored to go as high as 3rd overall, and LB Reuben Foster and Marlon Humphrey were regularly mocked as top 15 picks.

Well, after the top 15, things went much better for Alabama.  Humphrey went 16th to the Ravens, Allen went 17 to the Redskins, and Howard went 19th to the Bucs. By the end of the night, Foster had gone, too, being selected 31st by the 49ers.  Three more players from Alabama went in round 2, two more went in round 3, and a 10th Crimson Tide star went in the fifth round.

Only Michigan had more players drafted (11), and no other school had more than eight draftees.  But while the Wolverines had more drafted players, only three players from Michigan were drafted in the first 80 picks — compared to nine for Alabama. I calculated the draft value — using my draft value chart — for each college in the 2017 Draft. [continue reading…]

There were 19 defensive players selected in the first round of the 2017 Draft. That’s a lot: from ’02 to ’17, there were an average of 16.6 defensive players taken in the first 32 picks, with a minimum of 13 (2004) and a maximum of 19 (’06, and ’17). And it wasn’t just low picks: the first selection in the draft was DE Myles Garrett, and the 49ers drafted DE Solomon Thomas with the third pick.

Defensive backs Jamal Adams and Marshon Lattimore went 5th and 11th, respectively, before six straight defensive players went, beginning with the 13th pick: Haason Reddick, Derek Barnett, Malik Hooker, Marlon Humphrey, Jonathan Allen, and Adoree’ Jackson. And from 21 to 31, 9 of the 11 players taken were defenders: Jarrad Davis, Charles Harris, Gareon Conley, Jabrill Peppers, Takkarist McKinley, Tre’Davious White, Taco Charlton, T.J. Watt, and Reuben Foster.

All told, the 19 defensive players drafted in round 1 were selected with picks holding 333 points of value on my chart. Since 1992, only one year — the ’06 draft — saw more draft value spent on defensive players in the first 32 picks.
[continue reading…]

Two of the top three picks in the 2017 Draft.

There were six trades in the first round of the 2017 Draft, so let’s do some quick analysis of those moves. To do so, I will be using two draft calculators used: the one I created and the traditional one referenced as the Jimmy Johnson chart.

Football Perspective calculator
Traditional calculator

1)
49ers trade: 2nd overall
Bears trade: 3rd overall, 67th, 111th, and 2018 3rd round pick

Chicago traded up for UNC quarterback Mitch Trubisky. The 49ers moved down 1 spot and still grabbed Solomon Thomas, the player most expected San Francisco to take at #2.

If you value a 2018 3rd round pick as equivalent to the 100th overall pick, The 49ers traded 30.2 points of value, and received 45.3 points of value, meaning San Francisco received 150 cents on the dollar. On the traditional chart, SF traded 2600 points for 2627 points, making it a nearly perfectly even trade. If you valued a 2018 3rd as equivalent to the 110th pick, it’s a perfectly even trade.

In other words, this is a sign that teams used the traditional chart.  But we know that on average, the FP chart will provide a more accurate representation of player value. As a result, this was an outstanding trade for the 49ers, and a very, very risky one for the Bears.  If Trubisky doesn’t turn into a star, this is going to be a bad trade for Chicago.

Good analysis available here from Bill Barnwell. [continue reading…]

As it turns out, drafting Mark Sanchez brings with it a form of Draft PTSD. Since selecting the USC quarterback with the 5th overall pick in the 2009 NFL Draft, the Jets have used 8 consecutive picks on defensive players.  That includes four busts (Wilson, Coples, Milliner), two hits (Wilkerson, Richardson), and three players where it’s probably still too early to evaluate.  Richardson has been great at times, but has also been frustrating on and off the field; in any event, his tenure with the team is likely coming to an end soon.  Pryor and Lee are still works in process, so it’s been mostly a mixed bag for the Jets over the last seven years:

Will that streak end tonight? If not, the Jets will set a record by becoming the first team to use 9 consecutive draft picks on players on one side of the ball. Right now, New York shares the distinction with three other franchises who have used eight consecutive first round picks in the NFL draft on efforts to rebuild one side of the ball. [continue reading…]

Peterson with a rare cameo by a good quarterback.

In ’09, the Vikings had a Relative ANY/A of +2.05, easily the best passing game the franchise has produced in the last decade.  In fact, the only other time in the last ten years that Minnesota had an above-average ANY/A was last year, when Peterson rushed for just 72 yards in three games.

Most of his time in Minnesota, though, the team’s passing attack has been below-average — or outright bad.  For example, in 2012, Peterson rushed for 2,097 yards.  That represented 17.9% of his career total, and it came when the Vikings had a Relative ANY/A of -0.94.  Overall Peterson has a weighted average RANY/A — i.e., the Vikings RANY/A in each season of Peterson’s career, weighted by the number of rushing yards Peterson had — for his career of -0.52.  Take a look. [continue reading…]

Every April 1st, friend-of-the-program Matt Waldman (@MattWaldman) releases his Rookie Scouting Portfolio. The RSP is, well, insane. It’s a 395-page draft guide that not only provides rankings and analysis of 158 players, but also provides 1,684 pages of scouting checklists and play-by-play notes.

Matt does top-notch work year round, and I can confidently state that the Rookie Scouting Portfolio is the most comprehensive analysis of rookie draft prospects at the offensive skill positions I’ve ever seen. But it’s not just about rankings and his analysis; he makes the evaluation process as transparent as possible to the reader, by identifying:

Matt documents what he sees with play-by-play detail. Yes, that’s a lot of work. No, you don’t have to read that part of the book to get tremendous value from the RSP. And here’s something pretty neat: Matt ranks every player graded by position and then writes a post-draft analysis with rankings assembled in a tiered cheat sheet. This is free with the RSP purchase and available a week after the NFL Draft.

The RSP is $21.95 and available at www.mattwaldman.com. Matt donates 10 percent of every sale to Darkness to Light, a non-profit that combats sexual abuse through individual community and training to recognize how to prevent and address the issue. You can read more about why to buy the RSP here.

It’s one of football’s oldest maxims: the key to winning the game is to win the turnover battle. This works better as an explanation for why a team won rather than as a cause of success — correlation doesn’t equal causation, of course — but that doesn’t mean the stat is useless.

We know that turnover rates have declined significantly over the last several decades. So here’s the question of the day: does winning the turnover battle matter more or less now than it used to?

As it turns out, the importance of winning the turnover battle has been remarkably static throughout NFL history. Last year, teams that won the turnover battle won 78% of their games. And from 2007 to 2016, teams that won the turnover battle won 78% of their games. In the decade of the ’70s, when turnover rates were much higher, teams that won the turnover battle won 78% of their games. From 1950 to 2016, the average winning percentage of teams that won the turnover battle was 78%, too. Take a look: [continue reading…]

The Atlanta Falcons play zero division games in September and October. Atlanta travels to Carolina in week 9, and doesn’t play another division game until hosting the Bucs in week 12. Atlanta’s schedule finishes with the Saints, Bucs, Saints, and Panthers in the final four weeks of the season. The six NFC South games for Atlanta occur in weeks 9, 12, 14, 15, 16, and 17, which produces an average of 13.8.

The Patriots have a similar story, with an average of 13.0. New England travels to MetLife Stadium in week 6, before matching Atlanta with five of the six division games coming in the final six weeks: the Patriots final six games are, in order, Miami, @Buffalo, @Miami, @Pittsburgh, Buffalo, New York. Tampa Bay isn’t far behind, with its six division games coming in an average of week 12.8.

On the other hand, you have the Jets. New York’s average division game last year was week 11.8, which was the latest in the NFL. This year, the Jets have the earliest slate, at 7.2. New York travels to Buffalo in week 1 and the team’s home opener comes in week 3 against the Dolphins. The Jets play New England in weeks 6 and 17, but gets Miami in week 7 and Buffalo in week 9 on Thursday night. That means from November 3rd through December 30th, the Jets don’t play a single division game.

Here’s that data for every week, showing which week their division games are played. The table is fully sortable, so you can see, for example, that three teams have their 4th division game in week 15, and therefore their final three games are against division opponents: [continue reading…]

Eli thinks the Giants schedule is fraudulent.

On the other hand, you have the Giants. New York has four games against opponents with extra rest, including three where the opponent is coming off of a bye. Denver has a bye in week 5, and plays the Giants at home in week 6; Seattle has a bye in week 6, then travels to New York in week 7; Kansas City has a bye in week 10, then travels to MetLife to face the Giants in week 11. The Giants have a fourth game against an opponent coming off of a bye — against the Rams in week 9 — but both New York and Los Angeles have a bye in week 8. Finally, the Cowboys play on Thanksgiving in week 12 and on TNF in week 13 before facing the Giants on the road in week 14; that gives Dallas 10 days of rest before that December matchup, compared to 7 for the Giants.

So the Giants got a raw deal there: the 49ers (Arizona week 9, Dallas week 7, Washington week 6) and Lions (NO week 6, Cle week 9, GB week 8) are the only other teams to face three opponents coming off an extra week’s rest. Washington has a week 5 bye and hosts San Francisco in week 6, Dallas has a week 6 bye and travels to San Francisco in week 7, and Arizona has a week 8 bye before going to San Francisco in week 9. Detroit travels to New Orleans in week 6 after the Saints week 5 bye, heads on the road to face the Packers in week 9 after Green Bay’s week 8 bye, and hosts the Browns in week 10 after Cleveland’s week 9 bye.

Okay, so the Chiefs and Rams get a break when it comes to their opponents’ rest days, and the Giants, Lions, and 49ers are victims of poor scheduling. What about the other side — i.e., each team’s own rest? Well, Kansas City is the big winner here, too.

Because the Chiefs have two Thursday night games — the kickoff game and week 7 in Oakland — Kansas City has 10 days of rest before its game in weeks 2 and 11 days of rest before its Monday Night game in week 8, giving the Chiefs three more days of rest than the Eagles and Broncos, respectively. And since the Chiefs face the Giants after Kansas City’s bye, that’s a third game with extra rest.

Meanwhile, a few teams — including the Giants — are in worse shape. Because New York plays the previously idle Rams after the Giants own bye, New York doesn’t have any games with a long rest advantage. The Giants only rest advantage comes when — after playing on Thanksgiving in week 12 — New York has three extra days in advance of a trip to Oakland. [continue reading…]

The color-coded schedule is back!

Download the Excel file here

Best version for an iPhone 6s

Who Plays On What Days?

There are 17 games on Thursdays this year, which matches the number of weeks of the season. There is one game each week of the season most weeks, but there are no Thursday games in weeks 16 and 17, and three on Thanksgiving (week 12). The three Thanksgiving games: Vikings at Lions, Chargers at Cowboys, and Giants at Redskins. The Browns and Jaguars do not get one, but the other 30 teams get at least one Thursday game. That means four teams play twice on Thursday: the Chiefs (week 1 at New England, week 7 at Oakland) and Patriots (week 5 at Tampa Bay) play in both the NFL Kickoff Game and during the regular Thursday night schedule. And Washington and Dallas — who play different opponents on Thanksgiving — play against each other on Thursday the following week.

Once the college regular season ends, the NFL does take over two Saturdays: December 15th and December 23rd. Those games are all rivalry games: Bears/Lions and Chiefs/Chargers in week 15, and the Colts going back to Baltimore and the Vikings and Packers the following week.

There are 17 games on Monday night football, and 18 games on Monday: one each week for 16 weeks, with no week 17 game, but two in weeks 1 and 16. The Saints travel to Minnesota in the early MNF game in week 1, while the Chargers visit Denver in the last game. At the end of the year, the Steelers head to Houston on Christmas Day, which falls on a Monday, to play in the 4:30 time slot. That night, the traditional MNF game is Raiders/Eagles, which is sure to feature a pair of merry fanbases.

Neutral Site Games

There are five special site games: the Patriots play “at” Oakland at 4:25 on the east coast in week 11 in a game in Mexico City, to go along with the four London games. The Jaguars, Dolphins, Rams, and Browns all lose home games, too, to face the Ravens, Saints, Cardinals, and Vikings respectively. Those London games take place in weeks 3, 4, 7 and 8: all but the Rams-Cardinals game in week 7 kick off at 9:30 on the east coast, while the NFL thankfully isn’t making west coast fans wake up at 6:30 to see the Rams/Cardinals, which kicks off at 1:00 on the east coast.

Part I

Part Ii

Last season, Washington and Detroit each played in 9 games where there was a 4th quarter score to take the lead (i.e., the game was either tied, or the team that scored was trailing before the score and leading after the score). On the other side, the 49ers played in just two such games.

The record for games with a 4th quarter score to take the lead is 11, set by the 1989 Chargers, and matched by the 1997 Cardinals and 2013 Lions.

Yesterday, I looked at 4th quarter comebacks using a narrow definition: I only included games where the winning team trailed after three quarters, which was the case in about 16% of all games. That number doubles if you use today’s broader definition: the graph below shows the number of games where a team scored in the 4th quarter to take the lead:
[continue reading…]

Part I

The Detroit Lions went 9-7 in 2016, but it was a remarkable 9-7. That’s because quarterback Matthew Stafford recorded 8 fourth quarter comebacks and 8 game-winning drives! That’s right: in all but one win for the Lions (and therefore, all but one game), Detroit trailed at some point in the 4th quarter.

That makes those 4th quarter comebacks sound impressive: if not for those 4th quarter comebacks, the Lions would have gone 1-15. And they were impressive! But here’s a way to make them appear less impressive: Detroit won just a single game last season where the team trailed entering the 4th quarter.

No, really. The Lions trailed by 3 points entering the 4th quarter in a home game against Jacksonville, and won 26-19. The Lions were 1-6 when trailing after three quarters in 2016. Detroit did win two games when tied after three quarters, and went 6-1 when leading after three quarters.

This isn’t intended to diminish Stafford’s performance last year, but rather to put some perspective around the idea of 4th quarter comebacks/game-winning drives. In a lot of competitive games, there are a number of lead changes in the 4th quarter, and it makes sense to call all lead-changing drives a comeback.

That said, let’s look at a different definition of a 4th quarter comeback: one where a team won after trailing while entering the 4th quarter. By that measure, Oakland led the NFL with 5 such comebacks, and the Raiders went 5-4 when trailing after three quarters. Although maybe pump the brakes a little bit if this fact alone causes you to elevate Derek Carr in your brain: the Raiders trailed entering the final frame by 1, 1, 3, 4, and 11 (opening day against New Orleans) points in those games.

In 2016, just 39 games saw a team trail entering the 4th quarter and go on to win; another two ended in ties. For context, there were 245 games overall in 2016 where a team trailed entering the 4th quarter overall. ^[1]Said another way, there 11 games that were tied entering the 4th quarter. That means teams won ^[2]Counting ties as half-wins. 16.3% of games when trailing entering the 4th quarter. That’s not remarkable at all, and matches the long-term average throughout football history. The graph below shows the winning percentage, by season, among teams that trailed entering the 4th quarter: [continue reading…]

Drafting quarterbacks is more art than science. And by art I mean film noir.

Let’s be clear: this sort of analysis is mostly trivia in nature.  That’s because past draft picks are simply sunk costs, although that’s generally only clear after a team has reached an evaluation on a player.  The Jets drafted Mark Sanchez in 2009, and that didn’t work out.  Four years later, the team selected Geno Smith in the second round, and that didn’t work out, either. In between, the Jets spent a 7th round pick on Greg McElroy, but spending much time lamenting the use of a 7th round pick is not productive.  Similarly, a year after drafting Smith, the Jets selected Clemson’s Tajh Boyd in the 6th round. New York then upped the ante by grabbing Bryce Petty in the fourth round in 2015, a move which seems unlikely to pay off.

And while those picks may not have been good, they were old made under an old regime. General manager Mike Maccagnan came on board in 2015, and while he didn’t draft a quarterback that year, he did trade a 7th round pick for Ryan Fitzpatrick, a moved that was heralded as a steal last December.  So far, the only quarterbacks drafted by Maccagnan were Petty in ’15 and the second round pick used on Christian Hackenberg last year.  Petty has underwhelmed in limited action, while there has been no ability to grade the Hackenberg pick so far, as he (intentionally) did not see the field last year.

So yeah, the Jets have drafted a lot of quarterbacks.  And for the most part, those picks have been bad.  But that doesn’t mean the Jets should stop drafting quarterbacks or that drafting quarterbacks is a bad idea. It just means the team hasn’t found its quarterback yet — unless, again, they already have in Hackenberg (or perhaps Petty).

Two years ago, I looked at the draft capital spent on quarterbacks from 2000 to 2014.   Today I want to do the same thing but from 2009 (when the Jets drafted Sanchez) to 2016.  Again, I’ll be assigning draft picks value based on the Draft Pick Value Calculator, which comes from the values derived here and shown here. If we assign each draft pick its proper value, and then sum the values used to select quarterbacks by each team over the last eight years, we can see which teams have devoted the most draft capital on quarterbacks.

And while the Jets have used six picks on quarterbacks over that time period, New York isn’t alone. The Broncos have, too, and Denver may not be much closer than the Jets are when it comes to finding their franchise quarterback of the future. The table below is sorted by total value, and the Jets rank “only” 4th in that regard, behind the Rams (who have spent two number one picks on passers during this time frame), the Bucs (a #1 and another first) and the Titans (a #2 and a #8). I hvae also listed each quarterback selected by each team during this time frame, from most valuable pick used to least. Take a look: [continue reading…]