On Dallas Goedert And The Eagles' Two-Tight End Dominance

Are you curious why the Eagles drafted a second tight end, Dallas Goedert, when they already have Pro Bowler and Super Bowl-winner Zach Ertz on the roster? Look no further than The Athletic, where I penned an article recently that examined the Eagles' use of multiple tight end sets last year. The numbers surprised me, especially what emerged about the Eagles in the red zone, in the playoffs, and specifically the effectiveness of Ertz and Trey Burton in the same formation. Make sure to subscribe to The Athletic Philly (hat tip to the inimitable Sheil Kapadia for asking me to contribute) and check it out:

‘Big bodies on smaller bodies’: Why the Eagles doubled down on the two-tight end offense

Dave Gettleman Vs. The Nerds

The following is a guest post by @sunset_shazz.

New York Giants General Manager Dave Gettleman is a true football man (#TrueFootballMan). He has no time for nerds who sit behind their keyboards. Though some may be concerned about drafting a running back second overall, he is not. From his recent presser after picking Saquon Barkley:

I think a lot of that’s nonsense. I think it’s someone who had this idea and got into the analytics of it and did all these running backs and went through their – whatever. Hey, Jonathan Stewart is in his 10th year and he’s hardly lost anything.

Gettleman appears to believe that the case against using a top 10 pick on a running back rests on perceived longevity. He is misapprehended.

Ben Baldwin, an economist (and Seahawks fan) who makes his living sitting behind his keyboard, summarized the case against using a premium pick on a running back in an excellent post at Field Gulls – do read the whole thing. I’m going to expand on two subsets of his argument: that rookie first round running back contracts are bad values, and bad risks.

The objective of the first round of the NFL draft is to sign an above-average player at a below-average contract for 4 years, with an embedded team option in year 5. Article 7 of the 2011 Collective Bargaining Agreement specifies a rookie wage scale that varies based on the draft pick used to select the player. Importantly, after the 2011 CBA was implemented, the player’s position doesn’t matter: a player picked 2nd overall is paid the same money over 4 years, whether he is a quarterback, running back or long-snapper. Moreover, the market has reached an equilibrium where first round contracts are fully guaranteed. A quick survey of contracts at overthecap.com shows that position value for post-rookie contracts varies significantly in today’s NFL. As a result, the “rookie contract discount” varies dramatically by position. For example, a QB drafted with the 2nd overall pick in 2018 would be the 25th highest paid QB in the league (by average annual compensation) and would have the 15th highest guaranteed money. An RB selected 2nd overall would immediately become the 4th highest paid player at his position, with the highest guaranteed money – all before taking a single professional snap.

The chart above shows selected positions, with their leaguewide positional salary rank plotted against overall draft number (all data courtesy overthecap.com). Running back is the clear outlier – a top 10 pick is automatically among the highest paid RBs in the league.

Here is the same plot for guaranteed money (the rookie contract is compared to the veterans’ amount guaranteed on their current contracts):

Though the 2011 CBA’s wage scale typically serves as a price ceiling for rookies, with running backs drafted in the first round, it serves as a de facto price floor. In terms of guaranteed money, the three highest RB contracts in the league are Barkley (2018, drafted 2nd overall), Leonard Fournette (2017, 4th) and Ezekiel Elliott (2016, 4th). Here are the top 10 picks in this year’s draft, with their annual compensation and total guarantee compared to their league peers by position group (players in top 10 highlighted in red):

The New York Giants have thus expended the #2 draft pick (a considerable use of capital) for the privilege of paying Barkley the #4 annual salary and #1 guarantee at his position. They are paying (through the nose) not just once, but twice!

But perhaps Gettleman is merely acting upon justified conviction. If Barkley is a generational player, surely he’s worth it?

As Ben Baldwin notes in his piece, 1st round running backs have a high bust rate relative to other positions. Data scientist Dr. Sean J. Taylor sent me the following plot, exploring this idea further:

This plot evaluates the set of running backs drafted (or undrafted) between 2009 and 2014. The x-axis represents draft position. The y-axis is the player’s Wins Above Replacement (nflWAR) over the ensuing 4 years. NflWAR is a statistic developed by Yurko, Ventura and Horowitz of Carnegie Mellon University which uses multinomial logistic regression to isolate the contribution of individual players to NFL wins. NflWAR represents a novel effort to advance beyond Approximate Value (AV), and deserves wider recognition.

I draw 3 conclusions from the scatterplot above:

1.     Taking a running back early is risky (bottom left quadrant);

2.     It is possible to find success at running back in later rounds;

3.     Running backs don’t really matter very much (note the Y-axis scale – over 4 years, you get at best 1.5 extra wins from a running back, and typically 0.25 extra win; quarterbacks are approximately 4x more important).

The risk of a bust is even more acute with highly drafted running backs, because the financial commitment to the player is so much higher, relative to other players at the same position. A bust at QB taken at 2nd overall saddles you with the salary of a bottom quartile starter. A bust at RB at the same pick saddles you with a top 4 salary.

But there is no good alternative to taking such a risk, right? Don’t you need to take risks at RB in order to win championships? Ezekiel Elliott, Leonard Fournette and Todd Gurley (taken 4th, 7th and 10th overall, respectively) are commonly cited as evidence of risks that have paid off.

One recurrent theme here at MoK is the application of insights from behavioral science to football. Our past posts have relied heavily on the work of Gary Becker, Daniel Kahneman & Amos Tversky, William F. Sharpe, Kahneman and Tversky again and Joseph Henrich. Today’s post is dedicated to 1990 Nobel Laureate Harry Markowitz who demonstrated that a portfolio of individually risky assets can collectively carry less risk than any of its underlying constituents, even when adjusted for its prospective return.

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The above chart shows the three commonly cited high pick successes, and the RB-by-committee groups of the two Super Bowl teams. The “Draft Capital” column dispenses with the archaic Jimmy Johnson scale, instead using Dr. Michael Lopez’s blended draft curve which improves on prior efforts by not only paying attention to expected/modal outcomes, but also giving weight to the probability of drafting a superstar (i.e. the right tail of the distribution). PHI and NE expended between 1/4 and 1/7 the draft resources for their running backs as JAX, DAL and LAR. Though PHI and NE paid relatively high 2017 cap numbers, they locked up minimal resources over the long term (i.e. they could cut bait in 2018). The “gty” column shows guarantees over the entire contracts of those players (Sproles’ and Blount’s initial guarantees for PHI, Gillislee’s, Burkhead’s and White’s for NE).

The advantage of the portfolio approach is: you can be wrong, and still have success. Donnel Pumphrey is not good at football and Darren Sproles was lost for the season. Gillislee, Burkhead and White did not cover themselves in glory in 2017. The portfolio approach diversifies you against injury, suspension or disappointing play. Yet, each portfolio achieved similar yards/attempt and total yards as the 3 high draft picks, for less overall guarantee / draft capital. As a team, NE and PHI ranked 1st and 8th in offensive DVOA, respectively (the Eagles won the Super Bowl). Also, note that NE’s total 2017 expenditure, while high, was less than Le’Veon Bell’s cap number. JAX additionally paid $6MM in 2017 for Chris Ivory, who offered minimal return for this expenditure. As Harry Markowitz showed, the portfolio approach offers something vanishingly rare in economics: a free lunch. A properly constructed portfolio lowers risk, without sacrificing expected return. (Though running backs are risky, they are independently risky. Idiosyncratic risk is diversifiable.)

In summary, Dave Gettleman in his press conference constructed a straw man. The case for positional value does not rest on running back longevity. Instead the TL;DR argument is as follows:

• Using a high draft pick on a running back is a bad bet. At best, you expend draft capital in order to pay a guaranteed contract at a market equivalent price for a good player. At worst, you overpay twice: in draft capital and guaranteed salary for a bad player.
• Drafting a running back is risky.
• By assembling a portfolio of RBs, one can achieve similar performance to drafting a star, while diversifying risk, and saving draft / guarantee capital to deploy elsewhere.
• Your mother was right about eggs and baskets.

The above argument relies upon the prior work of a number of individuals who sit behind keyboards, all of whom have advanced degrees in a quantitative field such as economics, and none of whom have played a snap of professional football. Gettleman, a #TrueFootballMan, will confidently dismiss this argument, regardless of its merit, due to its provenance. Eagles fans should pray he never gets fired, and lives forever.

Nick Foles Is The Playoff GOAT

The following is a guest post by @sunset_shazz.

Nick Foles is a high-variance quarterback. His performance ricochets from abysmal to sublime with such frequency that he made me re-adjust my chart axis, twice. And yet: including the 2013 loss to the Saints (in which he engineered a comeback from a 13-point deficit and left the field with the lead) his postseason play has been consistently excellent. There have been 93 quarterbacks since the 1970 merger who have played at least 4 playoff games. Of these, Foles ranks 1st in completion percentage and 2nd in Adjusted Net Yards / Attempt (ANY/A).

Obviously, this is not statistically dispositive. Nothing about playoff analysis is. Mark Messier and Reggie Jackson’s playoff performances comprised a mere fraction of their total careers, yet their knack for elevating their game on the biggest stage is what made them memorable. One way to think about the playoffs: there is a tide in the affairs of men, which, taken at the flood, leads on to fortune. As I will show, Foles has taken the tide at the flood in historic fashion.

Note, from the chart above, that the fewer games played, the greater variance in ANY/A between individual players. But what about each player’s game-by-game variance? I measured the standard deviation of each player’s game ANY/A, and scaled this by his mean ANY/A, thus constructing a coefficient of variation.

Of all 93 QBs in the sample, Foles has been the 4th most consistent (i.e. has the 4th lowest variation). Moreover, he has the lowest variation of the 16 QBs who have only played 4 games.

Perhaps Foles has benefitted from playing in a QB-friendly era? I compared each QB’s game ANY/A to the league average for the year in which that game was played. One can then plot mean Relative ANY/A against the coefficient of variation:

Foles has the 5th highest Relative ANY/A in addition to having the 4th lowest variation. One way to think about the above graph is to imagine an “efficient frontier” on the upper left quadrant. When considering similar efficient frontiers in the context of financial economics, Nobel Laureate William F. Sharpe constructed a “Sharpe ratio” which compares a fund manager’s relative return (e.g. versus an index) to the standard deviation of the fund’s return.

I similarly devised a playoff QB Sharpe Ratio, which is each QB’s mean Relative ANY/A divided by the standard deviation of his game ANY/A. Think of it as one number which captures both efficiency and consistency of play. The following table shows the top 10 playoff QB Sharpe Ratios since the merger:

All 10 of these quarterbacks played in a Super Bowl, and all but two of them were champions. Only Bengals starter Ken Anderson and Bills backup Frank Reich did not win the season’s final game. (Reich, of course, will receive a ring as Offensive Coordinator of the 2017 Super Bowl champions.)

By this metric, Foles will have to settle for second place out of 93 playoff QBs. The Raiders’ Ken Stabler, who played in 13 playoff games between the 1971 and 1979 seasons, passed for 3.08 ANY/A above average (3rd) and had the 8th lowest coefficient of variation in the sample. Combining efficiency and consistency, he is the greatest playoff quarterback of all time. Here are the rankings of some other notable QBs, and Eli Manning:

Obviously, I’m not suggesting Foles is better than any of those quarterbacks (except Eli; he’s indisputably better than Eli, it’s not even close). However, in the inherently limited sample that consists of the playoffs, Foles has performed at a historically great level, in terms of both efficiency and consistency. Also, he can catch.

Clearing Up The Coaching Confusion

The following is a guest post by @sunset_shazz.

Should the Carolina Panthers have fired Head Coach Ron Rivera or traded QB Cam Newton the day after they lost the Super Bowl? Scott Kacsmar at FiveThirtyEight argues they should have done either or both. Do read the whole piece; the argument is presented as follows:

• In NFL history, only 4 coaches have won their first Super Bowls after 5 seasons on the job with the same team;
• No team has ever started the same quarterback under the same head coach for more than 5 years and seen that duo win its first championship.

Having examined the history of prior first time Super Bowl winners, FiveThirtyEight infers that these characteristics are conducive to winning championships. The study’s conclusion: “If championship success doesn’t come within five years, things tend to get stale, and someone eventually has to move on from their position of power.”

Can you spot the flaw in this reasoning?

How about if I used the same exact logic, using a more emotionally salient characteristic:

• In NFL history, only 4 minority head coaches have won Super Bowls. Therefore you shouldn’t hire minority head coaches. [1]

Does that framing device make the flaw in reasoning clearer?

FiveThirtyEight’s study suffers from the confusion of the inverse, a statistical fallacy that undergraduates are commonly taught to avoid. One of the best recent treatments of this problem was a brilliant piece by Katherine Hobson on the lab-testing startup Theranos  (also, funnily enough, at FiveThirtyEight). Chapter 8 of Nate Silver’s excellent The Signal and the Noise provides a lucid discussion on this topic, in the context of Bayes’s theorem.

Here is the issue: the fraction of Super Bowl winners that possess a certain characteristic, by itself, tells you nothing about the probability that those who possess that characteristic will win a Super Bowl. A better way to estimate the latter would be to go back and examine the historical success rate of coaches who possess the characteristic you’d like to study.

I compiled every season coached since the 1970 merger, then excluded the seasons after a coach has won his first Super Bowl.  Coaches who were tenured 5 years or fewer with their teams won 24 first Super Bowls in 1009 opportunities, for a success rate of 2.38%. Coaches tenured 6 or more years won 4 first Super Bowls in 176 opportunities, a 2.27% success rate. Using a technique previously used in the Duck Bias study, I applied the cumulative distribution function of the binomial distribution to test whether the success rates were different, to a statistically significant degree. The P value of 0.592 indicates no statistically significant difference.[2]

However, Super Bowl success is a noisy, sparse data set, due to the very small sample size. An alternative measure of coaching success which enjoys the advantage of more data is the frequency with which a coach makes the playoffs. I compiled the playoff rate for every coach in the dataset, and compared this with the base rate of success for that year.[3] The data shows that coaches with longer tenure are actually ­­more likely (47.7%) to make the playoffs than shorter tenured coaches (31.1%) and the base rate (38.0%); both of these differences are statistically significant.

Obviously, this data doesn’t tell you anything about causation. There is likely a survivorship bias / selection effect: those coaches who are kept by their team after 5 years without a championship are likely of higher quality than average, which is probably why their subsequent success rate is higher.

For Coach-QB pairings, 28 Super Bowls were won in 1137 opportunities for the short tenured pairs, a 2.46% success rate. There were only 48 seasons where a Coach-QB pairing lasted more than 5 years without having won a Super Bowl. The zero success rate is, statistically speaking, the effect of randomness, rather than a measured effect. In terms of playoff success rate, once again the longer tenured coaches had a higher success rate, though this effect was not found to be statistically significant.

The data is pretty clear: you shouldn’t fire your coach, or your QB, just because he has not won a Super Bowl after an arbitrary number of years. The only reason short tenured coaches seem to have been historically more successful is they vastly outnumber the long tenured ones. FiveThirtyEight’s model was fooled by the fallacy of the inverse.

But given that we’re in the midst of a coaching carousel accompanied by a Rooney Rule kerfuffle: what about the reductio ad absurdum argument I cheekily proffered above? What does the data say about minority head coaches?

I used Wikipedia’s Rooney Rule page to code every minority head coach in the dataset, presented below. This data comprises every coaching season between 1970 and 2016, including all seasons for coaches who won multiple Super Bowls.

Minority coaches won Super Bowls in 3.31% of their opportunities, which is statistically indistinguishable from the base rate of success of 3.22% (note that minorities have disproportionately coached in more recent years, after the league has expanded, which lowers the base rate of championship success). Interestingly, minority coaches made the playoffs 58 times in 121 opportunities (47.9%) which is 11.6 more times than one would expect given the base rate, a difference that is statistically significant (p=0.02). This is a noteworthy result: historically, the presence of a minority head coach is associated with a 25% greater rate of making the playoffs.

Again, one shouldn’t make causal inference claims from historic data. I’m not arguing that minorities are inherently better coaches. In this situation, there is no survivorship bias. Might there be a selection effect? The late Nobel Laureate Gary Becker argued in 1957 that employment discrimination (racial or otherwise) is inefficient. Not only does the victim of discrimination bear a cost, but so does the discriminating employer (through lower productivity per unit labor cost). Axiomatically, to the extent that some employers exhibit an unfounded bias, an employer who doesn’t discriminate can capture a portion (but not all) of the foregone surplus. Moreover, selecting from a pool of employee candidates who are the victims of racial discrimination will yield supernormal productivity. Becker’s theory of discrimination is one plausible explanation for the effect shown by the data.

What does this mean for NFL teams today? The data is unequivocal that Ron Rivera shouldn’t be fired solely because he hasn’t yet won a Super Bowl. The data also shows that Al Davis, who got many things wrong, got a few things very right. An NFL team should examine the pool of minority head coach candidates very carefully, and should strongly consider hiring from this pool.

Not merely because it’s the right thing to do, but because the data suggests it helps you Just Win, Baby.

[1] To be clear, this is not argued by FiveThirtyEight. I employed this reductio ad absurdum to permit the reader to more easily intuit the confusion of the inverse.

[2] The P-value is the probability that, conditional on the null hypothesis being correct (i.e. no effect), one would observe the data in question by chance. Though subject to recent debate, the conventional standard for social science is to reject P-values greater than 0.05.

[3] The base rate of success is # of playoff teams / # of total teams in the league, both of which have changed over time. I accounted for the league’s expansion of teams in 1976, 1995, 1999 and 2002, as well as the evolution of the playoff format from 8 to 12 teams in 1978 and 1990, and the 16 team playoff that occurred during the strike-shortened 1982 season.