The following is a post by @sunset_shazz.

The National Football League has, over time, become a passing league. The best, most analytically sophisticated teams build around the passing game. In 2018, the final four teams in the NFC and AFC Championship games enjoyed the top 4 passing offenses, in terms of efficiency. In 2019, run-first teams are typically built by dinosaurs who can barely dress themselves.

The Philadelphia Eagles have made forward-thinking analytics a centerpiece of their strategy, and their fearless head coach is a protégé of one of the game’s great passing innovators; surely they are a pass-friendly offense?

How often do teams pass when it's up to them?

--Chiefs, Bills, Packers, Patriots all pass-heavy
--49ers and Colts very run-heavy
--To no one's surprise, Seahawks still conservative pic.twitter.com/XdnMyD00YL

— new-age analytical (@benbbaldwin) October 8, 2019

As I’ve noted before, any discussion of pass/run ratios must acknowledge the importance of game script, which is the time-weighted score differential transpiring over the course of a game. The higher a team’s game script, the greater its propensity to run in order to bleed the clock and secure the win. By the same token, a team with a negative game script is more likely to pass in order to attempt a come-from-behind victory.

Which brings us to the 2019 Philadelphia Eagles. In weeks 1-4, as we’ve been repeatedly told, this team faced a 10 point deficit in every single game. Yet, despite having fallen behind early in four of their games, the Eagles, through week 5, have been one the run-heaviest teams in the league.

Recall the Pass Heavy Index, which computes the pass/run ratio relative to expectation given a team’s average game script:

The Eagles rank 23rd in the league, passing the ball 5.5% less frequently than would be expected, given their average game script (0.5). This is their lowest rank since Doug Pederson’s first year as a head coach, with a rookie quarterback. In the last two years, the Eagles have ranked 8th and 10th in terms of situation-adjusted pass heaviness.

Moreover, the first five games of 2019 have been the second most run-heavy five game stretch of Pederson’s tenure. The last time the Eagles were this run-heavy occurred during weeks 3 – 7 of the 2017 season, after Pederson endured considerable criticism from the likes of Frank and Joe from Manayunk following a week 2 loss vs Andy Reid’s Chiefs, when the Eagles passed on 75.4% of offensive plays, Pederson’s most pass-heavy game ever, given the game script (27.8% above expectation).

Does the recent run-heavy approach portend a shift in tactical emphasis, or could it be merely statistical noise? The plot below shows every Eagles regular season game under Pederson’s tenure, with 2019 games in red, labeled with opponents. The regression line shows the league-wide average pass-run ratio; the degree to which a data point is above or below the line reflects the “Pass Heavy Index” for that particular game (positive or negative).

Week 2 (at ATL) and 5 (NYJ) were pass-heavy, when adjusted for game script. Week 1 (TB) and 3 (DET) were more run-heavy than is typical for a Pederson offense. A significant proportion of 2019’s run-heavy tilt is due to the sublime Green Bay game at Lambeau when Doug passed the ball 24.2% less than what would have been expected, given the game script. This game was, by far, the most run-heavy of his head coaching career. There was reasoning behind this anomaly: Mike Pettine chose to keep his defensive personnel in dime and nickel versus the Eagles two tight end sets, allowing Zach Ertz and Dallas Goedert to run block against relatively light boxes. As Jason Kelce pithily explained to Sheil Kapadia, the objective in the game was to put the defense in a bind: “Everything’s just trying to get honest numbers out of them.”

Had Pettine loaded the box with base personnel, you can be sure that the Eagles would have been pass-heavy, particularly with Goedert and Ertz on the field. Removing the unusual circumstances of the Green Bay game, the Eagles -0.5% Pass Heavy Index would rank 17th in the league. Their current league rank, in terms of pass-run ratio, is at least partly an artifact of the game-theoretical nature of Doug Pederson’s offense, which is predicated on running versus light boxes and passing versus heavy boxes.

Per the NFL ScrapR box score app created by The Athletic’s Ben Baldwin, this tactic paid huge dividends. The Eagles at Green Bay had both a higher success rate and expected points added (EPA) per play in the running game, compared with the passing game.

Against GB, he called the most run-heavy game of his career, adjusted for situation. This tactic was successful against Mike Pettine's defense, which dared the Eagles to run. Per #nflscrapr box score app by @benbbaldwin, the Eagles had higher EPA & Success Rate running the ball. pic.twitter.com/bSxRuaqHSW

— sunset shazz (@sunset_shazz) October 12, 2019

Note that a more efficient run vs. pass game is unusual. In weeks 1 through 5 of 2019, as well as during Pederson’s prior tenure, the Eagles had a higher average success rate and EPA passing the ball versus running.

I will close with a prediction: in the following weeks, Doug will unleash Carson. As defensive coordinators begin to recognize that Jordan Howard is quietly efficient running behind Jeff Stoutland’s offensive line, they will increase the number of defenders in the box.

And Pederson and Wentz will take what the defense gives them.

The following is a post by @sunset_shazz.

We have previously discussed in these pages the principal-agent problem, and how idiot principals (aka NFL owners) are to blame for suboptimal decision-making by agents (aka GMs and coaches). In an entertaining Twitter thread, some analytics nerds were discussing the use of RPOs and the implications for run/pass decision making. A turning point in the thread:

Brian Burke believes that coaches are optimizing for success rate. They want to move the chains and string together first downs to form drives. If you get nearly 5 yards per rush on average there’s no way you’ll convince a coach to pass.

— Josh Hermsmeyer (@friscojosh) May 18, 2019

Josh is citing this piece of descriptive analysis from Brian Burke back in 2014, where Burke showed (from a positive, as opposed to normative standpoint) how coaches tend to maximize the success rate of each individual play, which doesn’t necessarily map to game-level success (“just win, baby”).

I replied with my time-worn take that this suboptimal decision-making is the fault of the principals, who have set up bad incentive structures. Sean Domnick asks an excellent question: why did the coaches choose this particular strategy? It’s not as if the owners incentives are particularly clear in favoring per-play success rate:

Yeah, right now I'm just curious about why incentives lead to success rate optimization compared to other metrics.

— Sean Domnick (@sean_domnick) May 18, 2019

The hypothesis below is drawn from social science, and is speculative.

Decision theory is a very young field. My Decision Sciences professor in the nineties came from Xerox PARC and leaned heavily on Kahneman, Slovic and Tversky. But prior to formalized decision theory, Friedrich Hayek and Edmund Burke posited mechanisms by which traditional decision-making can evolve to be “good enough” over time. Conventional wisdom improves through an iterative, blind process. In the decision space, though traditional decision-making may be suboptimal, it can still be good enough to have adaptive success. Joseph Henrich has, using modern data collection methods, formalized how norms and traditions may evolve to be broadly efficient. Bill Walsh did not have access to modern data, software or computing power. But his analysis was good enough, and was dominant enough, to change how coaches stretch the field and emphasize short passes.

Yes, per-play success rate is suboptimal. But it is an evolutionarily stable strategy in that it is dominant, provided that nobody has implemented a better strategy. Moreover, to the extent that norm violators are punished (cf. Henrich) herding behavior is a rational response to the threat of being fired. Thus, a “good enough” strategy such as per-play-success-rate maximization will perpetuate because everybody is doing it.

1)     Maximizing success rate, though suboptimal for maximizing wins, has the advantage of being an easy, tractable heuristic that dominates over other, worse, decision rules.

2)     Over time, principals and agents will coalesce around this (arbitrary, though minimally successful) decision rule, which becomes self-perpetuating.

Is this equilibrium stable? It is until it isn’t. Just as Roger Bannister showed the world what is possible, it takes only one coach to show that sound evidence-based decision-making will dominate the conventional wisdom. We are at a moment in time when internet nerds are mining data to show the traditionalists there is a better way. My tentative prediction is that NFL coaching heuristics will change for the better, just as they have in the NBA and MLB.

The following is a guest post by @sunset_shazz.

Now is the winter of our NFL offseason, during which we contemplate Nick Foles’ trade value, as well as Joe Flacco’s. On twitter, @agoldman79 makes a key observation:

This assumes rational actors. When trades like the Amari Cooper trade happen, it disproves that.

— Adam (@agoldman79) February 9, 2019

To paraphrase Larry Summers: There are bad GMs. Look around. Our friend Brent at Eagles Rewind wrote about #BadGMTheory way back in 2013, and the evidence continues to accumulate. The 32 NFL teams, as a whole, seem to make decisions which are suboptimal. For example, Bucs GM Jason Licht, who is 27-53 (0.338), famously traded up to draft a kicker in the 2nd round, yet still enjoys a W-2 income. Giants GM Dave Gettleman ignored the data, mocked the nerds at their keyboards, and drafted a running back with the 2nd overall pick. Though an excellent player, the offensive rookie of the year has not proven to be worth either the high pick or the highest guaranteed salary for a running back in the NFL. Other GMs are adept at public relations while remaining cartoonishly inept at actual decision-making. Sam Bradford’s candidacy as a First Ballot Hall Of Fame Negotiator rests entirely on the exploitation of incompetent counterparties. I have found, previously, evidence that NFL GMs inefficiently prefer white candidates, to the detriment of playoff berths, when hiring coaches. All these decisions are examples of terrible ex ante process, regardless of ex post outcome.

Why is NFL decision-making so bad?

In an intensely competitive, testosterone-and-bravado-fueled meritocracy, why is NFL organizational stupidity so pervasive, persistent and pronounced?

I am always suspicious of monocausal reasoning, and my guess is that the answer is likely multivariate: NFL culture is dominated by “football guys” who are resistant to change, there is an old boy network that inhibits ideas from other domains from propagating, and of course there are idiots.

Ultimately, the crux of the principal-agent problem lies with the principals, rather than the agents. NFL owners are bad at setting the incentives for their employees. Dirk Koetter (as a coach, not a GM) admitted on the record that he made decisions in order to keep his job, even when he knew it would lose more games in the long run. But this answer merely begs the question.

Why are NFL owners so bad?

A key theoretical underpinning of the Efficient Market Hypothesis (EMH) is that “noise traders” (idiots) will have less influence over time than their more successful counterparties. Market forces impose a Hobbesian, Darwinian discipline, through losses (rather than profits): those that are unfit perish.

For NFL teams, relative profits (there are never losses) are not determined by a market. Each team shares a common pool of more than $8 billion in league-wide revenue which is derived from gate sales, merchandising, broadcast rights, etc. The NFL is a Marxian socialist’s paradise. You can be an incompetent charlatan who has committed fraud and racketeering and your NFL team will still make money because (a) the team has a franchise which effectively secures monopoly rents and (b) revenue sharing by the NFL creates the most generous social safety net the world has ever seen.

Simply put: the NFL lacks a mechanism for creative destruction.

Very few other domains of American life enjoy such coddled insulation from market forces. A hedge fund that defaults on a trade typically blows up. A dishwasher, housekeeper or middle class entrepreneur who behaves as incompetently as the Browns would face inevitable penury. Only Jed York is afforded the latitude to be as incompetent as Jed York.

The NFL’s socialist utopia is sanctioned by a Federally-mandated antitrust exemption. Never mind Alexandria Ocasio-Cortez – the most fervent big-government socialists in America are represented at 345 Park Ave. As a consequence, the relative absence of market discipline allows them to make decisions with the acuity of Soviet central planners. Incompetents of the world, unite!

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

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.

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.

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.

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.

The following is a guest post by @sunset_shazz.

The news of Cleveland head of personnel Sashi Brown’s dismissal was met with uncharacteristic emotion by the customarily sober Aaron Schatz. Evidently, some quarters of the analytics community regard Brown, his colleague Paul DePodesta and the rest of the Browns front office team as one of their own. Brown’s strategy consisted of trading high draft picks for more (albeit lower ranked) picks. The opportunity cost of this strategy was to pass on both Deshaun Watson and Carson Wentz.

Talent evaluation is hard. You will be wrong more often than you will be right. I won’t fault Brown for misevaluating quarterbacks, just as I don’t fault 32 teams for repeatedly passing on Tom Brady (even the Patriots passed six times before using their 7th highest pick on him). There is data that suggests the market for talent evaluation is efficient and that no team has a sustainable edge.

But it doesn’t follow that just because you aren’t able to out-evaluate your peers, you should always trade down. Higher draft picks have higher success rates. The idea that lower picks might be undervalued dates from a classic 2005 paper by Cade Massey and newly-minted Nobel Laureate Richard Thaler. Massey-Thaler observed that NFL teams were overvaluing high picks relative to “the surplus value of drafted players, that is the value they provide to the teams less the compensation they are paid.” (Emphasis in the original.)

Two things have changed since 2005. First, the 2011 Collective Bargaining Agreement repriced the rookie wage scale, increasing the “surplus value” of higher picks relative to lower picks. [1] Secondly, the market learned and absorbed the Massey-Thaler result. We know from other domains that market inefficiencies almost always disappear as soon as they are found. Sashi Brown’s claim that talent markets are efficient – yet the market for picks is systematically inefficient – is an extraordinary claim, which demands extraordinary evidence. More likely, the market has moved from partial to general equilibrium. Football has no farm system; the strategy of hoarding picks is constrained by the 63-man roster and practice squad. Successful teams with quietly robust analytics departments (e.g. the Eagles and Patriots) trade both up and down, depending on the situation; constrained optimization is complicated. Moreover, as Brian argued in 2011, in order to fill the most critical position on an NFL team, the data shows you need to pick early.

But the real reason that Jimmy Haslam absolutely had to fire Sashi Brown is simple: his staff failed to execute a trade which was negotiated in good faith. Whether it exists under a buttonwood tree in lower Manhattan or in a coffee house in eighteenth century London, all markets are governed by rules, traditions and norms. As an example, the market for trading picks, which often occurs while teams are on the draft clock, is governed by trust and verbal agreements (not legal documents).

As Joseph Henrich notes in his brilliant anthropological survey The Secret of Our Success, pro-social norms are enforced in small communities through punishment, such as ostracism. In any community of traders, walking away from a duly negotiated agreement is a flagrant violation, a taboo. I will never forget how at the beginning of my career the head of our firm reacted to a similar situation: “We will never do business with those scumbags again, and we will make sure everybody knows how they have behaved.”

Punishing norm violators is not irrational. A trust-based community of traders is a fragile equilibrium. Erosion of trust can cause permanent, deadweight losses; thus pro-social norm enforcement evolves over time, and is Pareto efficient.

If Haslam had not fired Brown, some or all of the other 31 teams would have punished the norm violator by refusing to trade with Cleveland. This is both individually rational (trading with a deadbeat counterparty is risky) and collectively rational (pro-social norms promote gains from trade). Brown’s reputation as a counterparty was permanently impaired, thus compromising his ability to discharge his duties, and leaving Haslam with no choice but to clean house.

[1] Brian Burke found continued persistence of a second day surplus value anomaly, though it’s still early in terms of data, and as he notes, the replication model is sensitive to key methodological assumptions. His results cover the player market, but not the draft pick market.

The following is a guest post by @sunset_shazz.

As the inimitable Jimmy Kempski recently explained, the Eagles’ defensive game plan is rather simple:

1. Stop the run.
2. Make the opposing offense one-dimensional.
3. Get after the quarterback.

Indeed, Eagles opponents do appear to give up on the run – this year the defense has faced the fewest rushing attempts per game. One caveat: they also have the second-highest point-differential. As everyone knows, you face fewer run attempts when holding a lead.

Are the Eagles’ opponents giving up on the run because they’ve fallen behind? Or are the Eagles facing fewer rush attempts due to their stout run defense, irrespective of the scoreboard?

I examined Game Script data compiled by Chase Stuart. Game Script is basically the average score margin over a total game. As a stylized example, let’s say Team A returns the opening kickoff for a touchdown, kicks the extra point, then neither team scores for the rest of the game. Team A’s Game Script in this simplified example would be +7 (the average lead held the entire game); Team B’s Game Script would be -7. A higher game script is often associated with more rushing by the team who’s leading (because you run when you win, not win when you run) and more passing by the opposing team (which has a negative game script).

The plot below shows each team’s average Game Script on the X-axis and average Pass/Run Ratio on the Y axis (all data through weeks 1-10). The regression line represents the expected Pass/Run Ratio, given Game Script, computed from the 146 games that were played through week nine. [1] A team that is toward the right (LAR, PHI) has enjoyed a higher average lead, and a team toward the top (SFO) has a higher pass/run ratio.

By comparing each team’s pass-run ratio to what one would theoretically expect given game situation (denoted by the regression line above), one may construct a “Pass Heavy Index”:

This year, Bill Belichick has been 10.9% more likely to call a pass, given game situation, than average. With Mitchell Trubisky behind center, John Fox is 15.5% less likely to call a pass, given game situation, than average. Despite almost being run out of town after a week 2 game in which his Pass Heavy Index was +27%, Pederson is basically in the middle of the pack.

What about the defense? One may similarly plot each team’s opponent’s average pass/run ratio against the opponent’s average game script:

Did you notice the outlier on the upper left? One may also compute each team’s opponents’ Pass Heavy Index:

The table above shows that Eagles opponents are not only passing more than any other team’s opponents (70.1% of the time), but that they are the most pass heavy adjusted for game situation. The evidence supports Kempski’s thesis: Eagles opponents this year have become one dimensional. One way to look at it is that opponents have a healthy respect for the Birds’ run defense. Another way to view it: they think they can attack the secondary. Somebody please inform the Green Goblin that he’s being disrespected.

Alternatively, perhaps this is an artifact of sampling bias – maybe the Eagles just happen to have faced teams who pass a lot (like Arizona).

Looking at it game-by-game, 6 out of 9 teams the Eagles played chose to pass more than they typically do, adjusted for game situation. There were three exceptions: the Cardinals 76.7% pass ratio, though 8.3% higher than expected, was a tad (0.5%) less pass heavy than Bruce Arians’ typical game; this was due to an extreme game script driven by a three touchdown first quarter by Carson Wentz. In the most recent two weeks, the Niners and Broncos both continued to run more than would be expected, despite falling behind by two scores in game script. Could this portend a change in opponent strategy, perhaps due to the absence of LB Jordan Hicks, whose season ended in the first series of Week 7? Or was this merely due to the injury to Joe Staley for the Niners  and the move to Brock Osweiler for the Broncos? The next few weeks should be interesting.

The analysis presented above demonstrates that Eagles opponents are 10% less likely to run the ball than average, given the game situation. If opponents indeed are choosing to attack the Eagles’ passing defense, they are picking a different, though still potent, poison. The Eagles passing defense is ranked 7th in ANY/A allowed, and ranked 8th in defensive passing DVOA.

Interestingly, the Rams’ and Jaguars’ average lead has been similar to the Eagles’, though their opponents are running more than typical, given such a deficit. Those teams are ranked 2nd and 1st against the pass, respectively, in DVOA, and are ranked 15th and 30th against the run. Though opponents of each team are falling behind during games at a similar rate, they are choosing to attack the Eagles differently, given the relative strengths of their defensive units.

Thanks to Eagles fan Noah Becker and MoK Editor-in-Chief Brian Solomon for discussion leading to this post. 

[1] The Y-intercept indicates the neutral pass/run ratio, 57.8%, which also mathematically corresponds to the league average pass-run ratio.

The following is a guest post by @sunset_shazz.

Carson Wentz’s start to the 2017 season has garnered national plaudits for his stewardship of the Eagles’ league-leading offense. But it being 2017, there lurks a coterie of skeptics who claim his underlying ability is “horrendous” like Blake Bortles or merely pedestrian like Andy Dalton. Even more emphatically, poor Jared Goff was confidently pronounced a bust after one season.

Is it fair to judge a quarterback solely on his rookie year? What about after the first nine weeks of his second season in the league? And how might one systematically evaluate a developing quarterback, relative to historical data?

Let us consider some advanced metrics that are used to evaluate quarterbacks:

• Adjusted Net Yards / Attempt (ANY/A) was developed by the great Chase Stuart, and accounts for sack yards, while providing a bonus for touchdowns and a penalty for interceptions. Both Stuart and Topher Doll have shown that ANY/A predicts wins. Danny Tuccitto has brilliantly used confirmatory factor analysis to show that ANY/A is a stable indicator of QB quality.
• Defense-adjusted Value over Average (DVOA), the brainchild of Aaron Schatz at Football Outsiders, is a success-based, opponent-adjusted per-play efficiency metric intended to both correlate with non-opponent adjusted wins (descriptive) and to predict future opponent-adjusted wins.
• Defense-adjusted Yards above Replacement (DYAR) uses similar success-rate inputs to DVOA, in order to compute an aggregate value for a player (combining volume and efficiency).
• Total QBR is ESPN Stats & Information’s proprietary efficiency metric that combines both passing and running contributions, adjusted for game situation, with charting to assign responsibility to a quarterback’s receivers and blockers.

Through nine weeks, the 2017 sophomore class is playing at an extraordinarily high level, as measured by each of these advanced stats:

Please note that nothing herein intends to argue for any of these quarterbacks to the detriment of the others. Though the data presented above is insufficiently precise to draw ordinal rankings, it is unequivocal:

Wentz is good. Goff is good. Prescott is good. All three of these things can simultaneously be true, pace internet trolls.

Some epistemic humility is in order: the first-nine-week sample size is obviously noisy, with varying degrees of luck, opponent quality, team injuries, coaching quality and supporting casts influencing the statistical performance of each QB. Danny Tuccitto warns us that ANY/A stabilizes at 326 dropbacks, and even at that sample size, 50% of the observation represents randomness/luck. Nonetheless, the broad takeaway should be that each sophomore QB has thus far performed at a top-quartile level, judged by a variety of different metrics. Is this good? And how confident can we be that such performance will continue?

Recently, Chase Stuart noted that three sophomores from the same class have not played this well since at least the NFL-AFL merger. Though ANY/A is less context-specific than the other measures, it has the advantage of being transparent and easy to calculate, permitting historical analysis. Stuart compared the first 8 weeks of 2017 for Goff, Prescott and Wentz to full seasons of prior 2nd year QBs. Comparing partial to full seasons isn’t quite neutral, due to the disparity in number of games sampled; we should expect some mean reversion of our reference QBs as sample size increases. Using pro-football-reference’s excellent query engine, I examined the first 9 weeks for each sophomore quarterback from 1999 through 2017. Historical comparisons need to be adjusted for era, due to the enormous change in average NFL passing efficiency over time. To account for this, I divided each quarterback’s ANY/A by the league average for that year. [1]

Top ANY/A vs Average since 1999, sophomore QBs, weeks 1-9

The 76 QB sample set in this study is itself a product of survivorship bias: only those QBs who were successful enough to throw 100 passes in the first 9 weeks of their second year in the league are included. On the other side of the distribution, successful QBs who rode the pine for their first few years (like Aaron Rodgers, Tony Romo or Philip Rivers) are not in this sample. The average age of the sample is 24, similar to our reference QBs.

The three 2017 sophomores are, as Stuart observed, performing extraordinarily well relative to their peer set (all are in the top quartile of the sample). Relative to their era, they are passing with greater efficiency than Tom Brady, Drew Brees, Matt Ryan or Andrew Luck did in their second seasons.

You will also note that the top ranked sophomore QBs include many future hits (Big Ben, Kurt Warner, P. Manning) and a few notable misses (Nick Foles, Derek Anderson). The last column I included is the Career Approximate Value (CAV), which is a (very) rough method developed by Doug Drinen that puts a single number on a player’s total career, encompassing both longevity and performance.

Below, I plotted log Career Approximate Value against ANY/A relative to league average for the first 9 weeks for second year QBs from 1999-2015 (I excluded QBs from 2016-2017 because recent QBs have not yet had sufficient time to accumulate CAV points).

The positive relationship shown above indicates that the first 9 weeks of a sophomore season predicts 37% of a QB’s future CAV. Do note that the correlation is sensitive to a few outliers. The odious Ryan Leaf and Akili Smith are on the bottom left, whereas Foles and Anderson are on the bottom right. I don’t want to ascribe an illusion of precision to this rough analysis – don’t fixate on the exact R-squared number, or the model coefficients. Both sample size and the extremely imprecise nature of CAV make me hesitant to draw definitive conclusions from the data. What is interesting to me is that the same plot using a QB’s full rookie season yields an R-squared of 0.224 – in other words, the first 9 weeks of a QB’s sophomore season tells you roughly 70% more about his future career than his entire rookie season does. Extending this analysis to full seasons since 1970, the R-squared is 0.083 and 0.2348 for rookie and sophomore years, respectively (n=155 & 204). My interpretation of this data: though rookie and second year passing efficiency predict only a small fraction of a quarterback’s career value, the sophomore year deserves 2.8x as much weight as the rookie year, in terms of confidence about predictive power. Rookie performance, in particular, is extremely noisy. One would have been wise to heavily discount Troy Aikman, Donovan McNabb and Terry Bradshaw’s dreadful rookie seasons. Rams fans should take note.

Relatedly, I didn’t find any predictive power when measuring the degree of era-adjusted-ANY/A improvement from rookie to sophomore season. This echoes Vincent Verhei’s study of second year improvement using DVOA. In hypothesis testing, a negative result can be an interesting result.

Quantitative analysis is not the only tool in an NFL researcher’s kit. Film study (though not my sphere of competence) is also valuable. Though Nick Foles had a magical sophomore season, the film showed reason for concern, as my friend Derek Sarley noted. I don’t personally see similar issues with Wentz – both his pre-snap adjustments and post-snap play appear to pass the “eye test”. No, he’s not perfect. Yes, he has flaws he needs to address. But so do all second year quarterbacks.

Moreover, our penchant for treating quarterbacks as static vessels of talent/ability shortchanges the importance of coaching and development. The installation of a new coaching regime in Los Angeles appears to be an interesting natural experiment, in terms of Goff’s maturation. Similarly, we can view Ezekiel Elliott’s probable(?) suspension as an instrumental variable when evaluating Prescott.

All inductive statements are, by their very nature, revisable. We don’t know the future; we can only use informed judgment to hazard a prediction. The false-positive rate for the top 20 QBs in table 2 above is 25% by my count [2], so let’s take that as the “base rate” of failure for the 2016 Sophomore QBs. It is therefore reasonable to expect that two – perhaps all three – of the 2016 sophomores will enjoy successful careers as NFL starters.

Finally, in these impatient times, let us remind ourselves that transcendent quarterbacks do not emerge, fully formed, from the forehead of Zeus. Each of these young, relatively inexperienced quarterbacks is playing the most technically and cognitively demanding position in sports at a very high level. Adjusted for experience and era, their achievements are even more astounding. The evidence suggests that the future of quarterback play is bright. Football fans, rejoice.

Thanks to Eagles fan / Data Scientist Sean J. Taylor for his insightful discussion on methodology. Any errors are mine alone.

[1] PFR’s partial season engine shows results from 1999 onward. Full season results go back before the merger, and also generate an era-adjusted ANY/A+ which uses a “Z-score” methodology, expressed in standard deviations above or below the population mean. My method is less sophisticated, though nonetheless robust.

[2] I excluded the reference QBs, as well as Marcus Mariota.

@sunset_shazz is an Eagles fan who lives in Marin County, California. He previously wrote about 4th down decisions.

The following is a guest post by @sunset_shazz.

With their NFL team celebrating a come-from-behind victory capped by a last-second, team-record 61-yard field goal by an unheralded rookie kicker, all of Philadelphia is understandably basking in reflected glory.

Just kidding.

Instead, the city is fulminating in collective outrage over Doug Pederson’s decision to eschew punting on 4th and 8 from the opponent’s 43-yard line, with 2:36 left in the 1st half and a 7-point lead. Numerate commentators Bo Wulf and Jimmy Kempski have demonstrated that Pederson’s decision was by no means incorrect (most likely it was a push). The case for more aggressive fourth down decisions is over a decade old; there is meagre profit in arguing with those who are impervious to evidence.

I am far more intrigued by the decision process itself. Sheil Kapadia quotes Jeffrey Lurie discussing the 4th down decision-making process, unprompted, in an informal chat with reporters after a recent presser:

“A lot of teams — our’s is one — where it’s all in the offseason done with mathematics,” Lurie said. “It’s not based on any form of instinct. If it’s going to be 50/50, 48/52, then a coach is going to have their instinctual predilection, right? But what we found is there’s been so many decisions over time that are too conservative for the odds of maximizing your chance to win that the opportunity. … I mean, you’ve seen certain coaches that are deemed more aggressive because the math leads them there. That’s all it is.”

Following some snickering that Pederson’s decision-making is being dictated by his superiors, the head coach clarified that he is the decider, with help from an analytics staff, including coaching assistant/linebackers coach Ryan Paganetti and Jon Ferrari (the latter’s title – “director of football compliance” – was obviously conceived by Oceania’s Ministry of Truth).

Is this decision-making set up weird? No, in fact it may be ideal.

I was struck how Pederson, during a press conference, corrected a reporter’s estimate of the historical probability of success (the “base rate”), citing his staff’s model estimate off the top of his head. His facility of recall regarding the base rate is textbook behavioral science. The seminal work of Daniel Kahneman and Amos Tversky showed that what they termed “base rate neglect” is common in poor decision making. Here is an example from Kahneman’s Thinking, Fast and Slow:

An individual has been described by a neighbor as follows: “Steve is very shy and withdrawn, invariably helpful but with little interest in people or in the world reality. A meek and tidy soul he has a need for order and structure, and a passion for detail.” Is Steve more likely to be a librarian or a farmer?

As Kahneman wryly notes, it helps to know that there are roughly 20x more male farmers than male librarians in the United States. The base rate is very important information in making the right judgment.

I am struck by Pederson’s description of how he receives base rate and other key information, immediately prior to making an informed judgment in which he also will take non-quantitative measures into account (e.g. how the defense is playing, the weather, injuries, etc.) Here, the Eagles are harnessing another key behavioral tic – anchoring bias. Anchoring is the tendency to overweight proximate (sometimes irrelevant) information that is the starting point in making a decision under conditions of uncertainty.

Again, Kahneman and Tversky were among the first to describe and investigate anchoring, and by 2017 there exists a vast academic literature on it. My favorite study was conducted by James Montier, a financial economist (full disclosure: also a former colleague). Montier asked hundreds of subjects [1] (mainly fund managers and financial analysts) the following:

1) Please write down the last four digits of your telephone number

2) Is the number of physicians in London higher or lower than this number?

3) What is your best guess as to the number of physicians in London?

Montier loves shocking people with his results:

[T]hose with telephone numbers above 7000 believe there are on average just over 8000 doctors. Those with telephone numbers below 3000 think [there] are around 4000 doctors. This represents a very clear difference of opinion driven by the fact that investors are using their telephone numbers, albeit subconsciously, as inputs into their forecast.

Clearly, one’s personal telephone number should have no bearing on one’s estimate of the number of physicians in London. The fact that it does, consistently, for intelligent, educated, statistically-minded professionals speaks to the power of anchoring bias.

Over the last decade, hedge funds have paid attention to the behavioral science literature, and have sought to anchor their professionals to salient, predictive data. This has driven hybrid-quantitative trading strategies where a fund manager is augmented by an algorithmic or otherwise quantitative model. The outputs of the model are then used as anchors for further tweaking by a human that is aware of variables outside the model’s specification. Wall Street got this idea, in part, from the world of chess, where, by 2005 the best type of player was a hybrid expert + model, capable of beating Grandmasters and machines. Similarly, University of Pennsylvania professor Philip Tetlock [2] has found that expert forecasters can significantly improve their decision processes by relying on models to improve their calibration of variables such as base rates. The Intelligence Advanced Research Projects Activity (IARPA) has taken note of these results, and the CIA has been studying similar literature since 1999.

When Doug Pederson hears base rate data and associated variables over his headset, the Eagles organization is harnessing anchoring bias, and turning it from a bug into a feature. Moreover, the augmented expert approach is consistent with that of the more sophisticated analysts in the fields of finance, academia, chess, and government.

It’s nice to root for a team that pays attention to the world outside sports, rather than snickering at it dismissively.

@sunset_shazz is an Eagles fan who lives in Marin County, California. Check out his previous article during last year's Air Yards debate.

[1] His initial sample was 300 subjects, though I believe he has replicated this finding with subsequent samples.

[2] Since 2012, I have been a participant in Tetlock’s Good Judgment Project.

Despite disappointing results in the last two games, the Eagles are a good football team. Going into this week, they were first in the NFL in point differential and second in DVOA. Now, they remain third-best in the former and should stay near the top in the latter.

Two losses, by a combined score of eight points, do not end the season. But they do show us what kind of team the Eagles are: one that can't afford to make mistakes if they want to compete for the division title. 

The truth is that the Eagles don't have many difference-making players. Fletcher Cox and (old but still great) Jason Peters, perhaps. After those two, who can the team count on to consistently win individual match-ups? Carson Wentz has flashes of brilliance. Third down back extraordinaire Darren Sproles is the only explosive play maker on offense.

This roster isn't built to exploit mismatches in talent. It's built on competence. On defense, guys like Brandon Graham, Jordan Hicks, and Malcolm Jenkins form the core, but none of them are keeping offensive coordinators up at night trying to scheme around them. They are good because they do the right thing (most of the time). They won't get you killed and they can succeed within the scheme. Ditto on offense: Jordan Matthews and Zach Ertz are solid starters in the NFL as long as you're not counting on them to be the number one option.

The early season win streak was built on competence in all phases. The defense didn't do anything special with fancy blitzes; it just lined up and got pressure with four rushers. The offense took league-best field position and converted drives into points at the second-highest rate. They did so methodically, not gashing teams with big plays but marching down the field with a mix of efficient runs and short passes (part of the reason Carson Wentz scores so low in Air Yards). Limiting turnovers (to zero for the first three games) and capitalizing on opponent mistakes.

That strategy was effective until this started happening:

As far as I can tell, weeks five and six are the most penalties the Eagles have committed in consecutive weeks since 1989. And those penalties matter. According to friend of the blog Sean Taylor, each additional penalty a team has over its opponent is worth approximately -0.5 points. The Eagles have out-fouled their opponents by 16 in the last two weeks and, surprise, lost by a combined eight points. Add in a couple more unforced errors, like a rookie fifth round pick stumbling out of the gate and a veteran running back fumbling at the worst possible time, and you can see how the Eagles went from 3-0 to 3-2.

Again, this is not to bury them. They are still a good team that should be at least in the race all season. But it's not like teams we've seen in years past that could spot an opponent a three touchdown lead and roar back in the final minutes. There are too few #playmakers and not enough strengths. That means they either have to return to the suffocating competence of the early season—limiting turnovers and penalties, staying efficient on offense, and preventing big plays on defense—or come up with a new way to win... like putting more in Wentz's seemingly-capable hands.

The following is a guest post by @sunset_shazz.

This is a wonderful time to be an Eagles fan. Jim Schwartz’s Attack Nine defense is rapidly exorcizing the ghost of Juan Castillo. Doug Pederson has rejuvenated an offense that had become stale and predictable under Chip Kelly. And, of course, rookie quarterback Carson Wentz is turning heads across the league, not to mention in the oval office.

Eagles fans, unexpectedly blessed with success, look to the poet Browning to give voice to their collective sentiment:

The lark's on the wing; 
The snail's on the thorn: 
God's in His heaven— 
All's right with the world!

But wait. From his perch at the indispensable Football Outsiders, Scott Kacsmar has some discomfiting news: both Wentz and Cowboys rookie QB Dak Prescott are mere dink and dunkers, with lower than average air yards per attempt (defined as the average distance a football is thrown beyond the line of scrimmage). A low score on this metric is undesirable, in Kacsmar’s view.

The inimitable Jimmy Kempski responded to Kacsmar’s initial claim with a sardonic video rewind post, prompting Kacsmar, in an entertainingly vitriolic rant, to frame this argument as a contest between enlightened, statistically rigorous analysts on one side and straw-manning “numbers are for nerds” egg avatars on the other.

I don’t believe that view is correct.

As Brian Burke has explained:

A statistic that both correlates with winning and correlates with itself would be a reliable predictor of future wins.

First, you want your in-sample measure to have some predictive power in estimating out-of-sample future wins, because, hello, you play to win the game. Second, you want a metric to have some degree of statistical persistence over time, in order to be confident you are measuring a signal (in this case, an attribute of quality quarterbacking) rather than mere noise.

Regarding the latter, Kacsmar notes that in 2015, the correlation between air yards in the first three weeks of the year and the air yards for the entire season was 0.80. Well, that doesn’t seem quite fair, does it? After all, what we really care about is the correlation between the first 3 weeks of the season and the ensuing 14 weeks. Using his dataset, and using the Spearman rank correlation estimator rather than a standard Pearson estimator, which in this case would be considered less robust, I found that the correlation between the first 3 weeks and ensuing 14 weeks last year was 0.60. That’s pretty good, as far as football statistics go. However, do note that within a season a number of other factors surrounding the quarterback are, for the most part, held relatively constant: coaching scheme, strength of running game, defensive strength, etc.

When Chase Stuart examined the persistence of the Air Yards metric from year to year, he found that between 2006 and 2012 for 100 qualifying QBs the correlation between Year N and Year N+1 for Air Yards was 0.34. Both completion percentage and yards/attempt were “stickier” with N to N+1 correlations of 0.51.

Kacsmar, in his FO piece, assembles a smaller dataset (than Stuart, above) which he judges to be salient:

I gathered that yearly data on 21 quarterbacks with at least four years of starting experience, all of whom are still active starters this year except for the retired Peyton Manning. The following table shows their average air yards by year for the period of 2006 to 2015.

The first rule of Analytics Club is to plot your data, so I plotted Kacsmar’s data into a time series chart, in order to visualize the range and variability of the attribute, segregated by quarterback, over time:

Taking Kacsmar’s dataset (which, it is important to note, uses 21 quarterbacks who have experienced some career longevity rather than Stuart’s more comprehensive analysis of 100 QBs), and running a similar autocorrelative N to N+1 analysis, I found that the year-to-year correlation was 0.40. My friend, real-life data scientist Dr. Sean J. Taylor, was generous enough to both replicate my work and provide me with a scatterplot, complete with line of best fit and confidence interval shading:

The autocorrelation statistic, the scatterplot and time series visuals each show the same thing: we are measuring mostly noise, with a faintly detectable QB signal. The attributes I mentioned before—scheme, effectiveness of the running game, defensive efficiency which affects game script—are all likely to change the calculus of decision-making with regard to throwing shallow or deep.

In fact, Kacsmar himself gives us a good reason to doubt the validity of Air Yards in capturing an attribute of QB quality: it doesn’t improve as a player gains more experience. Quarterbacks, like all athletes, typically experience an age curve, reflecting both athletic maturation and decline, as well as the steep learning curve imposed by formidable NFL defenses. Chase Stuart has shown that the age curve for NFL quarterbacks is pronounced. The absence of an “age/experience curve” for Air Yards is yet another red flag.

Air Yards doesn’t appear to measure a persistent quarterback attribute over time, particularly when compared with a conventional statistic such as completion percentage or advanced statistics such as Adjusted Net Yards / Attempt (ANY/A, for which Danny Tuccitto brilliantly used confirmatory factor analysis to verify its validity) or Defensive Yards Above Replacement (DYAR, rigorously developed and tested by Aaron Schatz).

But does it predict wins?

My general model of the production function of football is as follows: runs and passes are inputs; completions and first downs are intermediate goods; points are outputs. Success rate metrics such as Defensive-Adjusted Value Over Average (DVOA), DYAR, and ANY/A are all measures of intermediate goods which are of interest to the analyst because they tend to reliably convert to points. And as Chip reminds us, if you (f__king) score points you are more likely to win. 

The scatterplot above shows the relationship between a QB’s average air yards over a season and the points scored by his team over that season. There is no statistically significant relationship between the two measures. Contrast this with ANY/A, which correlates 0.55 with wins. Or DYAR & DVOA, whose parameters were specified in order to predict future wins.

Kacsmar has been careful to note that he isn’t an advocate of maximizing Air Yards; he thinks middle is best. He elaborates in his FO piece:

Generally, air yards are a stat where you don't want to rank at the bottom, because that is where many ineffective passers dwell, including Blaine Gabbert. That preference for short throws often extends to crucial downs, which is why these quarterbacks tend to do poorly in ALEX and attacking the sticks. However, it is not preferable to rank at the very top in air yards either, because that is how "screw it, I'm going deep" players such as Michael Vick, Tim Tebow, Vince Young and Rex Grossman have earned their reputation as inefficient passers.

His claim, if I have understood it correctly, is that quarterbacks at the tails of the distribution are less likely to be successful in future. Our scatterplot above doesn’t show any relationship between the middle of the distribution and success, measured by points scored. But could Kacsmar’s anecdotal observation that “middle is best” be a mere artifact of sampling? If successful quarterbacks have longer careers, the law of large numbers dictates that they will, by mere virtue of larger samples, be less prone to the extremes in Air Yards. Taking a separate dataset evaluating quarterback air yards between 1992 and 2012, and plotting those against passes thrown, one arrives at the following:

You can see that the more passes a given quarterback throws, the less variance he exhibits with respect to his peer cohort. This needs to be examined further, in my view. I admit that I am not familiar with the nuances surrounding various measures of air yards (various observers have different estimates), but a longer, broader dataset would be desirable to plot air yards versus attempts. We don’t want to fall prey to the famous Bill and Melinda Gates Foundation misstep where it was initially claimed that small schools are consistently among the best performing schools, when it was merely the case that small schools experience more variance than larger schools, and therefore disproportionately comprise the tails of the distribution.

Here is the plot of the fourth-grade math scores versus number of students in the school:

The prior two sections showed that Air Yards as a measure is neither statistically persistent nor predictive of success, in terms of points scored. I did mention some alternative, robust metrics, two of which are generated by Football Outsiders. As of Week 3, FO has not applied opponent adjustments to their measures. On a raw Value Over Average and Yards Above Replacement measure, these young QBs have performed in the top quartile over the first 3 games.

Looking merely in the rearview mirror, without making any judgments about the future, they appear to have performed well.

Another measure I have mentioned, Adjusted Net Yards / Attempt (the “adjustment” gives a bonus for touchdowns and a penalty for interceptions, and the “net” deducts sack yards) is a persistent, predictive measure. With a hat tip to the excellent Derek Sarley, I prefer to plot this against completion %, to show both efficiency and consistency of per-play execution (weeks 1-3, minimum 46 attempts):

Once again, the rookies have played impressively: Wentz and Prescott are in the top quartile (4th and 8th, respectively) in ANY/A and the 2nd quartile (13th and 10th, respectively) in completion %.

As Bill Barnwell has noted, the statistics from 3 games tell us very little about how a QB will play in the future. A very small sample size disadvantages a purely statistical analysis; the comparative advantage shifts towards the film analyst. Ideally, one would combine both, but in this case, the stats aren’t meaningfully more robust than mere anecdotes. This is why I disagree with Kacsmar’s adversarial Michael Lewis-style “stats versus scouts” framing; the NFL stats on these two rookies don’t really tell you anything dispositive yet. From a purely Bayesian perspective, the eye test is just as likely as a mere three weeks of quantitative data to meaningfully update one’s priors. I have not yet enjoyed the privilege of watching Prescott, but I’ve seen every Wentz throw; moreover, I’ve seen astute film analysts such as Greg Cosell, Fran Duffy, Jimmy Kempski and Ryan from ChipWagon break his film down. Lastly, as Brent from EaglesRewind notes, one’s priors should be heavily influenced by draft position, which was the NFL auction market’s initial “revealed preference” view of value.

As for me, I’m on the Wentz Wagon. Dan McQuade reasons persuasively that Eagles fans should enjoy this run, because life is fleeting. Memento mori, football fans.

TL;DR:

• The early results from the credible advanced statistics, meaning those that tend to be both persistent and predictive, are that Wentz and Prescott have played well in their first three games.
• Looking at the numbers alone, a three game stretch is insufficient to give us high confidence that such success will continue in future. 
• The Air Yards statistic is neither persistent nor predictive, and reflects the aesthetic tastes of one particular writer, rather than a desirable quarterback attribute.

Thanks to Sean J. Taylor for his methodological insight and scatterplot work. Any errors are mine alone.

@sunset_shazz is a Philadelphia Eagles fan who lives in Marin County, California. He previously wrote about Chip Kelly's Oregon bias and other topics, and contributed to the 2015 Eagles Almanac.

The following is a guest post by @sunset_shazz.

In the aftermath of the Global Financial Crisis, one of the key lessons learned was that you cannot afford to ignore rare events, and that you should not assume that an event that rarely occurs is the same thing as one that never transpires. One of the key errors in subprime risk modeling was the assumption that there was zero probability of home price decline. As an old colleague of mine used to say, “never is a long time”.

Which brings us to our friend David Murphy, who, a few weeks ago, with the arrogance and overconfidence of a subprime bond salesman, declared:

Are the Eagles looking to make a play up the board to draft a Quarterback of the Future?

No. Hell no. It makes no sense. Unless Howie Roseman and Pederson are the two least rational people on the planet, everything that they have done thus far this offseason tells us that there is a ZERO percent chance that they are planning to make a play for Wentz or Goff. And everything we know about Roseman tells us he is the opposite of irrational. You do not survive as long as he has without being an extremely cold, calculating, meticulous decision-maker.

So . . . no. Hell no.

Now, I agreed, ex ante, that such a scenario was unlikely. Maybe 10-15% probability by my rough guess. But only a fool or somebody with no life experience says that a future event has 0% probability. And the easiest way to test such foolishness is to ask the fool to put his money where his mouth is. If he really thinks 0% probability is a fair bet, he should jump at the chance to take money from a sucker such as myself who offers him INFINITELY more than $0 should this impossible event occur. So I did.

To his credit, he didn’t demur. Instead, he backed his tough talk with real dollars. And, of course, today, the Eagles traded a king’s ransom to move up to the 2nd pick overall. Technically the bet has not been decided; if the Eagles don’t take a QB in the 1st round, I still lose. If they do take a QB, I assume that David is a man of his word, though I ask that rather than sending me the money, he instead donate $100 directly to Connor Barwin’s Make The World Better project. He can even take the tax deduction.

Bottom line: rare events occasionally occur. Nothing has zero probability – on any given Sunday (or Wednesday!) anything can happen. And that’s why we love this game.

@sunset_shazz is a Philadelphia Eagles fan who lives in Marin County, California. He previously wrote about Chip Kelly's Oregon bias and contributed to the 2015 Eagles Almanac.

Last March, Jeff Lurie told reporters he was was tired of waiting for the Eagles to be great. He’d seen sustained success, he’d been to a Super Bowl, and he’d watched his franchise post a solid 20-13 record (including a playoff loss) over the prior two seasons. It wasn’t enough.

“I’ve lived through a lot of division championships, a lot of playoff appearances, a lot of final four appearances, but our goal is we want to deliver a Super Bowl,” he said at the time. “And sometimes maybe I’m influenced by the notion of it’s very difficult to get from good to great, and you’ve got to take some serious looks at yourself when you want to try to make that step. It’s a gamble to go from good to great because you can go from good to mediocre with changes, but I decided it was important enough…”

On Wednesday, after firing Chip Kelly before the final game of the season, Lurie didn’t walk away from his words earlier in the year.

“I said, with Chip’s vision, it was an opportunity that he wanted to lead the way, to try to go from good to great,” he said. “In fact, I remember saying to all of you, there’s dangers in that, in terms of having two 10-6 seasons in a row, and when making significant changes, you can easily achieve mediocrity. I think it would be a shame not to try, but… that is the danger when you take a risk.”

I hope Lurie learned more than that, because his "strategy" was little more than a desperate hope. He handed over all power to Kelly, a moderately successful coach with zero experience running the intricacies of a NFL organization. 

The road to the Super Bowl is not paved with such gambles, with an outsider making a bunch of questionable bets that luckily pay off. You don’t win by cutting talent and building #culture. You don’t win with a rigid set of measurables that dictate player acquisition. You don’t win by ignoring critical positions and spending excessive guaranteed money on less important ones. You don’t win by overpaying for a subpar quarterback coming off two major knee injuries, or turning over half the roster in one offseason. That’s how you end up 7-9 in one of the worst divisions ever.

There are no shortcuts.

Remember Andy Reid’s binder? Reid came to Philadelphia with a detailed multi-year plan of how to build an organization. He arrived in 1999, overhauled the roster, brought in an impressive group of experienced coaches (including 6 eventual head coaches), drafted a quarterback in the first round, and went 5-11. In 2000 the team and young quarterback improved, overachieving to reach the playoffs. By 2001 they were one of the best teams in the conference. In 2002 and 2003 they missed the Super Bowl by inches. In 2004 they came minutes away from the trophy itself.

Lurie said he doesn’t want the middle steps, he just wants the Super Bowl, and he was willing to gamble to get there. But those middle steps are important. That’s how you build a champion—not overnight, but consistently, step by step.

The best teams in the NFL have the best talent. The best teams in the NFL have a top quarterback they drafted and groomed. The best teams in the NFL have smart, experienced coaches who adjust their schemes to the players they have. The best teams in the NFL have a front office structure that empowers multiple voices and balances scouting with analytics and financial understanding.

Going into 2016, the Eagles need to avoid the quick fix, or the allure of competing for the playoffs in year one of a new regime. That won't set the team up for long term success. (Plus, they'll likely be in the running anyway unless the putrid NFC East changes significantly in a year.)

The blueprint needs to be for a Super Bowl contender in 2018. Let's lay out what that looks like...

Front Office: Howie Roseman has a mixed reputation among fans and league sources, but he can succeed in the Joe Banner role. He has experience on the personnel side to pair with a firm grasp of the league's economics. Roseman does need to find a qualified general manager-type to run player personnel, someone less washed-up than Tom Donahoe, with more experience than Ed Marynowitz, who's not obvious idiot Ryan Grigson.

Coaching: A NFL team is a crazy thing to manage, and any head coach needs to be able to bring in the right people and command respect across the organization. He doesn't have to be a brilliant innovator on the cutting edge, but he does need to be flexible enough to adapt his schemes and techniques to get the most out of his players in each situation. The single most important skill set within that is the ability to find and develop a franchise quarterback, which is what makes a candidate like Adam Gase so attractive.

Quarterback: You cannot be a consistent Super Bowl contender until you have a quarterback. As such, you should exhaust every possible avenue to get one, especially the draft. I stand by my recipe for QB hunting laid out four years ago, on the eve of Andy Reid's firing:

Draft a quarterback early and late. Sign somebody in free agency. Trade for a promising backup. Rinse and repeat. You're never going to be able to compete for the Super Bowl until you find your one franchise guy. Might as well cycle through as many potentials as you can until you do. The financial cost of doing so is less than the opportunity cost of sitting pat with one player, [Bradford], who is statistically unlikely to ever become an elite quarterback.

In the Eagles' case, that means avoiding any multi-year guarantee to a still-unproven quantity like Sam Bradford, and perhaps letting him go entirely depending on the cost.

Roster Building: Outside of quarterback, which takes priority over everything else, the offensive line is next on the list of must-haves. Though I don't know many of the names, Jimmy Kempski's mock draft would thrill me based on the selection of two quarterbacks and three offensive linemen. Lane Johnson and Jason Kelce are the only long term building blocks you can count on there. Meanwhile, don't waste resources on the rest of the offensive skill positions, none of which will matter much until the offensive line and quarterback are fixed.

The Eagles defense needs more talent across the board, but it also likely needs a scheme that better takes advantage of the players in house. Kelly wanted a two-gapping 3-4 system, but it would be nice to see what Fletcher Cox and company could do in a one-gapping 4-3 instead.

Overall: Both the organization and its fans need patience. We were spoiled by Chip's quick turnaround, but where did that leave us? Let's plan for sustainability.

Read more: How The NFL Chewed Chip Kelly Up And Spit Him Back Out

The following is a guest post by @sunset_shazz.

Earlier today, DN columnist David Murphy published a contrarian piece purporting to show that the Eagles have neglected the offensive side of the ball in the draft under Chip Kelly. His lazy “analysis” consisted of counting up Eagles draft picks for the offensive and defensive units over the past 3 years. Even the dullest of readers can see the problem: this method equates the pick of Lane Johnson with that of Jordan Poyer. Or Zach Ertz and David King.

When pressed upon this obvious flaw by friend of the blog Noah Becker, Murphy accused his interlocutor of poor reading comprehension:

The Eagles spent 2 1sts, 3 2nds, 1 3rd round pick, and a 4th round pick on offensive players since 2013. https://t.co/avOT0i8dSu

— Noah Becker (@Noah_Becker) November 17, 2015

@Noah_Becker but i'm not going to annotate the piece for you. if you legitimately can't see it, then, yes, you are a poor reader.

— David Murphy (@ByDavidMurphy) November 17, 2015

Well, Mr. Murphy, we at McNabb or Kolb are both literate and numerate. One can easily assign values to the Eagles draft picks to determine the actual allocation of draft resources to each unit. The canonical draft value chart was developed by Jimmy Johnson in the early 1990s. For many years, this provided a sufficient first order approximation of relevant draft value. However, in 2012, the excellent Chase Stuart conducted an exhaustive analysis which used the approximate value provided by a player in his first five years with a team to construct a draft value curve; like all good scientists, he published his results.

Using these values, we can compute the approximate draft value allocated to each unit by the Eagles in the Chip Kelly era:

As is shown above, over the past 3 years, the Eagles picked 6 players on offense, at an average draft position of 48th overall. Although they picked 15 players on defense, these averaged at a draft position of 152nd overall. Using Chase Stuart’s draft value weights, the Eagles allocated 58% of their draft value to the offense and 42% to the defense; a balanced allocation, reflecting the front office’s desire to build a balanced team. Murphy’s claim that the offense was neglected in the draft is simply untrue (unless you believe a 1st round pick is equal to a 7th rounder).

Moreover, during our research we also discovered the earth-shattering news that the NFL has a salary cap. In 2015, the Eagles allocated $69.4 million to the offense, the highest(!) number in the league. To argue that the Eagles have neglected the offense in their allocation of resources is either lazy or disingenuous. Or both.

The Eagles’ woes are more prosaic: rather than being inattentive to the offense, the front office suffered from poor execution. Allowing the offensive line to age while failing to build adequate depth, using three high picks on one position in two years, guaranteeing money to the insipid Riley Cooper, over-allocating salary to an aging running back who has carried more than 400 times the previous season, cutting their best receiver for #footballreasons without recompense, trading a draft pick for a speculative upgrade at QB – these are all legitimate criticisms of the front office strategy. But accusing Chip Kelly of neglecting to spend resources on the offense… the evidence doesn’t support that extraordinary claim.

It’s one thing to mail in a column. It’s quite another to insult your readers’ intelligence when your obvious shortcomings are pointed out. Eagles fans and Daily News readers aren’t as dumb as some writers make us out to be. 

@sunset_shazz is a Philadelphia Eagles fan who lives in Marin County, California. He previously wrote about Chip Kelly's Oregon bias and also contributed to the 2015 Eagles Almanac.

It's amazing what a win over Dallas will do for your spirits. The Eagles went into the bye week at 3-4, reeling from a loss to Carolina that wasn't particularly close. Post-bye, post-victory over a hated rival, and they're 4-4, with the wind at their back, a rejuvenated team.

All it took was a few fourth down tries, a revived running game, and a kicker that finally made a clutch field goal longer than 50 yards. Suddenly it's "the biggest win of the Chip Kelly era" (which is really saying something about the Chip Kelly era, unfortunately).

Sam Bradford is fixed. He's "continuing to flip the script on his legion of detractors". Apparently, the main issue was footwork, with QB coach Ryan Day noticing a problem three weeks back. "The results have been evident," writes the Inquirer.

Have they?

The funny thing about the win on Sunday night is that on paper, not a lot looks different from the losses earlier in the season. If you're going to give the Eagles credit for any improvement, it has to be in the run game, where they've reeled off four straight games with more than 150 rushing yards. Kelly also seems to be mixing up the play calling a bit better. 

But Bradford suddenly proving he's the guy of the future? This team, suddenly a legit playoff contender? Seems like wishful thinking.

Take Bradford on Sunday. His stats were solid: 25 for 36, 69% completion rate, 295 yards, 8.2 yards per attempt, 1 TD, 0 INTs. But the funny thing about those numbers is how much they were influenced by one great overtime drive. In regulation, his stats weren't quite so good: 20 for 31, 65% completion rate, 239 yards, 7.7 yards per attempt, 0 TD, 0 INT. That's arguably worse than Matt Cassel, who completed a higher percentage of passes for more yards and 3 TDs (plus one bad INT).

The point is not that Bradford's game-winning drive has no value. But just remember that he was one Caleb Sturgis screw up or one heads-tails controversy from being the same just-good-enough-to-lose Bradford we've seen all season. If Jordan Hicks tears his pectoral muscle before grabbing that pick six, the Eagles don't make it to overtime and there is no "Sam Bradford has fixed it" narrative worth telling.

Nor, really, is there one for the Eagles, who had two weeks to prepare and narrowly avoided falling to a Cowboys team that lost 5 straight games coming in and had posted just 91 passing yards against Seattle a week earlier. This is a 4-4 team that has played a bunch of bad teams in a bad division. Luckily, they play more going forward: their next three opponents are a combined 7-17.

Maybe this group has finally turned a corner, and will cruise to 7-4 before heading to New England. I hope so. But don't be surprised if this turnaround is a mirage, and inconsistency dooms the Eagles to underperform down the stretch. That would simply be par for the course this season.

I'm starting to think Chip Kelly, our beloved head coach and offensive mastermind, may not be on the same page with the Eagles general manager. In fact, hot take alert here, the GM may be actively sabotaging Chip. Let's examine the evidence.

In 2013, Kelly proved he could take a collection of players drafted by other people and mold them into a successful team. The defense, sure, was a bit rough. But Kelly inherited an offense of Andy Reid's making and turned it into something special. The offensive line dominated, with the left side anchored by two all-pro players, an emerging athletic center, wily veteran at right guard, and a top pick at right tackle. Meanwhile, Kelly's frenetic, quick-strike offensive style was matched perfectly by the dynamic big play duo of DeSean Jackson and LeSean McCoy, who combined for 3,480 yards on the season—including 42 plays of more than 20 yards. Kelly even made Nick Foles, an immobile quarterback with a questionable arm, look like a star.

But I look at the 2015 Eagles, and it seems the GM actively tried to rid Chip of all his best players. He swapped the Foles for Sam Bradford, who costs more and has a terrible injury history. He ditched both of the Eagles' homegrown, in-their-prime dynamic wide receivers for a bunch of unproven youngsters supplemented with empty threats (this week notwithstanding) Riley Cooper and Miles Austin. He flipped McCoy, a shifty home run hitter, for north-south runners, then refused—over Chip's strenuous protests, I'm sure—to put any resources into the aging offensive line that made the whole thing work.

What Kelly's left with is an offense that looks nothing like the type of team he's been successful with in the past. Foles was never a running quarterback, but the current starter can't even execute read option plays for fear he won't survive the next hit. And Bradford doesn't even have the confidence, despite a decent arm, to throw downfield, wasting Chip's proven ability to scheme players open. His twice-a-game realization that there might be open players beyond 5 yards has corresponded with a brief one-quarter offensive resurgence in each game.

The coach probably could have told the GM that the running back position wasn't his limiting factor in the first two years. When defenders shot into the backfield in 2014, McCoy actually made many of them miss and got positive yards. Spending big to get shiny new running backs was much less important than making sure the blockers ahead of them weren't just terrible backups and journeymen. The Eagles GM even had the gall to extend both Allen Barbre and Andrew Gardner, moves which can only be interpreted as a straight up middle finger to his run-first coach.

To be fair, the GM has added talent on the defensive side, bringing it up to average or perhaps even slightly above. But even against weak quarterbacks like Kirk Cousins, the unit's flaws are obvious. While the defensive line is shutting out running backs, giving up the 3rd-fewest yards per carry in the NFL, this is a passing league. The pass rush is nonexistent, with a sack rate of just 3.4%, 5th-worst. Fletcher Cox and Bennie Logan are disruptive run defenders, but can't get to the quarterback. Connor Barwin and Brandon Graham are solid players but not sack artists. That meant that in the final minutes on Sunday, with Washington marching down the field, the Eagles failed to get any pressure. It doesn't help, of course, that the secondary is still suspect.

When you look at all of it together, it's clear that something needs to be done before the Eagles GM torpedoes our head coach's obvious talent. Fire this idiot, I say, and hire someone who knows what he's doing. Heck, maybe give Chip Kelly a chance to take over player personnel. He could probably do just as well.

Last week, I placed the blame for the Eagles' 0-2 start squarely at the feet of the offense. Despite this week's win over the Jets (yay!), Chip Kelly's favorite unit continued to perform woefully.

Yes, Ryan Mathews, subbing in for DeMarco Murray, rushed for 108 yards on 25 carries. But his numbers hide the fact that there were still far too many negative or no gain rushes (10 total). Between Mathews and Darren Sproles, the rushing game averaged just 3.47 yards per carry, a heady improvement from the prior weeks, but nothing to get excited about. Everyone on the offensive line, including Kelce, Peters, and Johnson, are struggling to run block consistently.

Meanwhile, despite being sacked just once, Sam Bradford played another horrible game. His final numbers were 14 for 28 (50% completion rate) for just 118 yards. One touchdown and zero turnovers, if you want to focus on that. But his accuracy was poor, his ball placement terrible, and he has zero guts to throw the ball downfield, one of the only things that might give the running game enough room to breathe. See more at ChipWagon, as always.

All of which leads to...

The #Eagles offense is regressing. 2013: 6.3 yards per play 2014: 5.6 yards per play 2015: 4.5 yards per play

— Phil Bicking (@p_Red) September 28, 2015

Really bad. So the question is, until the time the offense recovers (assuming that's in the cards?), can the Eagles defense carry the team? And on that front, at least, there are some promising signs. To the stats!

Through three games, the Eagles have given up the 5th-fewest points per drive (1.42), the 9th-fewest yards per drive (26.9) and the 10th-fewest plays per drive (5.6). The run defense has been fantastic—allowing a league-low of 3.1 yards per rush and zero rushing touchdowns. Most important, they've forced turnovers on one out of every five opposing possessions. To keep it going, we'll need more Walter Thurmond ball-hawking and disruption at the line of scrimmage (Brandon Bair sighting FTW).

The worrisome note is that the Eagles haven't gotten consistent pressure on quarterbacks. The defense ranks 8th-worst in sack rate so far. Overall, this isn't terribly surprising. The two-gapping scheme means players like Fletcher Cox are responsible for the run first, then the pass second. Neither Connor Barwin nor Brandon Graham are consistent man-beaters on the outside. And now the top two middle linebackers are hurt. Billy Davis needs to mix in some good blitz concepts to get pressure if this defense is going to sustain itself.

Three games is also quite a small sample size, especially when about half of that time the Eagles faced Brandon Weeden and Ryan Fitzpatrick. Luckily, due to a combination of easy schedule and injuries, the Eagles aren't scheduled to face many top quarterbacks down the stretch. The next three games match up against Kirk Cousins, Luke McCown, and Eli Manning, with Cam Newton as the pre-bye week chaser.

Still, that we're having this conversation at all is a good sign. The defense was atrocious two years ago, and improved to average or slightly above last year. If it can take another leap, that's a huge deal for this team—especially if the offense can't turn it around.

If you watched the putrid Thursday Night Football game between the Giants and the Redskins last night, there was some solace for Philly fans. Mainly that everyone else in this division is pretty much just as #terrible as the Eagles.

That does leave room for optimism, despite two of the worst Eagles games in recent memory, strung back to back at the start of this season. The truth is that as bad as the Eagles have been, they still can turn things around. The question is whether their awful play so far is the result of fundamental, uncorrectable problems or something more fixable.

The answer to that question is in the eyes of the beholder. The offensive run blocking has been so bad that it's the worst (through two game sample size, of course) of any offensive line I can find in Football Outsiders' history. Their Adjusted Line Yards (aka roughly the yardage that the line is responsible for) is just 1.11 yards. To put that in perspective, last year's poor line was ranked 29th in ALY with 3.52 yards. Ryan over at ChipWagon can give you both the execution and scheme reasons for such failure. 

On one hand, maybe this is a sign that with a historically-bad offensive line and questionable play calling, the Eagles will never get their run game together. On the other hand, we can probably expect the Eagles to revert back at least somewhat to the mean, making the run offense moderately usable at some point. (Optimism!)

Ditto for Sam Bradford. This is a quarterback who has endured very little pressure from defenses (just 23% of the time through two games). The offensive line, for all of its run blocking woes, has allowed just one sack, good for the 3rd-best Adjusted Sack Rate in the NFL of 1.5%. Yet Bradford continues to be unable to throw the ball down the field.

From @PFF, Bradford has attempted just 5 passes (5.6% of att.) of 20+ yards. 0 completions, 2 drops.

— EaglesRewind (@EaglesRewind) September 25, 2015

Bradford has averaged just 6.3 yards per attempt so far, 28th best in the league. To go with that, he's thrown 2 touchdowns against 4 interceptions and has looked out of sync with his receivers the whole time. Maybe Bradford is just bad, and Chip Kelly bet on the wrong quarterback. Maybe Mark Sanchez (barf) is actually the better choice right now. Or maybe Bradford hasn't played much in two years and is still adjusting to a new offense, new personnel, and being hit. Maybe he'll get better.

The defense, meanwhile, has shown promising signs (Malcolm Jenkins, Walter Thurmond, Bennie Logan) and worrisome ones (Byron Maxwell). The stats are slightly above average through the first two games against two potentially decent offenses. The defense certainly isn't good enough to carry an offense this bad, but it isn't the reason the Eagles have lost, either. More tests to come, though.

This weekend's game against the Jets is a must win. You don't go 0-3 and make the playoffs in this league. According to the chart below from FiveThirtyEight, that would leave them with just a 2% chance. But if they go 1-2, that's 25% and a tie for second place in a historically weak division. Then just maybe, there's room for hope...