New England Patriots


I suspect  to a first approximation almost no one other than Baltimore fans, such as Brian Burke, and this blog really believed that Baltimore had much of a chance(+). Well, I should mention Aaron Freeman of Falc Fans, who was rooting for Baltimore but still felt Denver would win. Looking, his article is no longer on the Falcfans site. Pity..

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

After a double overtime victory by 3 points, it’s awfully tempting to say, “I predicted this”, and if you look at the teams I’ve  favored, to this point* the streak of picks is 6-0. Let me point out though, that you can make a limiting assumption and from that assumption figure out how accurate I should have been. The limiting assumption is to assume the playoff model is 100% accurate** and see how well it predicted play. If the model is 100% accurate, the real results and the predicted results should merge.

I can tell you without adding up anything that only one of my favored picks had more than a 70% chance, and at least two were around 52-53%. So 6 times 70 percent is 4.2, and my model, in a perfect world, should have picked no more than 4 winners and 2 losers. A perfect model in a probabilistic world, where teams rarely have 65% chances to win, much less 100%, should be wrong sometimes. Instead, so far it’s on a 6-0 run. That means that luck is driving my success so far.

Is it possible, as I have argued, that strength of schedule is an under appreciated playoff stat, a playoff “Moneyball” stat, that teams that go through tough times are better than their offense and defensive stats suggest? It’s possible at this point. It’s also without question that I’ve been lucky in both the 2012 playoffs and the 2013 playoffs so far.

Potential Championship Scenarios:

 

Conference Championship Possibilities
Home Team Visiting Team Home Win Pct Est. Point Spread
NE BAL 0.523 0.7
HOU BAL 0.383 -3.5
ATL SF 0.306 -6.1
SF SEA 0.745 7.9

 

My model likes Seattle, which has the second best strength of schedule metric of all the playoff teams, but it absolutely loves San Francisco. It also likes Baltimore,  but not enough to say it has a free run throughout the playoffs. Like many modelers, I’m predicting that Atlanta and Seattle will be a close game.

~~~

+ I should also mention  that Bryan  Broaddus tweeted about a colleague of his who predicted a BAL victory.

* Sunday, January 13, 2013, about 10:00am.

** Such a limiting assumption is similar to assuming the NFL draft is rational; that the customers (NFL teams) have all the information they should, that they understand everything about the product they consume  (draft picks), and that their estimates of draft value thus form a normal distribution around the real value of draft picks, and that irrational exuberance, or trends, or GMs falling in love with players play no role in picking players. This, it turns out, makes model simulations much easier.

There were eight trades in the first day involving the first round of the 2012 NFL draft. Most of them involved small shifts in the primary pick, with third day picks added as additional compensation. The one outlying trade was that of the St Louis Rams and the Dallas Cowboys, which involved a substantial shift in  the #1 pick (from 6 to 14) and the secondary compensation was substantial. This high secondary compensation has led to criticism of the trade, most notably by Dan Graziano, whose argument, boiled to its essence, is that Dallas paid a 2 pick price for Morris Claiborne.

Counting  picks is a lousy method to judge trades. After all, Dallas paid a 4 pick price for Tony Dorsett. Was that trade twice as bad a trade as the Morris Claiborne trade?  The Fletcher Cox trade saw Philadelphia give up 3 picks for Fletcher Cox. Was that trade 50% worse than the Morris Claiborne trade?

In order to deal with the issues raised above, I will introduce a new analytic metric for analyzing trade risk, the risk ratio, which is the sum of the AV values of  the picks given, divided by the sum of the AV values of the picks received. For trades with a ratio of 1.0 or less, there is no risk at all. For trades with ratios approaching 2 or so, there is substantial risk. We are now aided in this kind of analysis by Pro Football Reference’s new average AV per draft pick chart. This is a superior tool to their old logarithmic fit, because while the data may be noisy, they avoid systematically overestimating the value of first round picks.

The eight first round trades of 2012, interpreted in terms of AV risk ratios.

The first thing to note about the 8  trades is that the risk ratio of 6 of them is approximately the same. There really is no difference, practically speaking, in the relative risk of the Trent Richardson  trade, or the Morris Claiborne trade,  or the Fletcher Cox trade. Of the two remaining trades, the Justin Blackmon trade was relatively risk free. Jacksonville assumed an extra value burden of 10% for moving up to draft the wide receiver. The other outlier, Harrison Smith, can be explained largely by the noisy data set and an unexpectedly high value of AV for draft pick 98. If you compensate by using 13 instead of 23 for pick #98, you get a risk ratio of approximately 1.48, more in line with the rest of the data sets.

Armed with this information, and picking on Morris Claiborne, how good does he  have to be for this trade to be break even? Well, if his career nets 54 AV, then the trade breaks even. If he has a HOF career (AV > 100), then Dallas wins big. The same applies to Trent Richardson. For the trade to break even, Trent has to net at least 64 AV throughout his career. Figuring out how much AV Doug Martin has to average is a little more complicated, since there were multiple picks on both sides, but Doug would carry his own weight if he gets 21*1.34 ≈ 28 AV.

Four historic trades and their associated risk ratios.

By historic measures, none of the 2012 first round trades were particularly risky. Looking at some trades that have played out in  the past, and one  that is still playing out, the diagram above shows the picks traded for Julio Jones, for Michael Vick, for Tony Dorsett, and also for Earl Campbell.

The Julio Jones trade has yet to play out, but Atlanta, more or less, assumed as much risk (93 AV) as they did for Michael Vick (94 AV), except for a #4 pick and a wide receiver. And although Michael is over 90 AV now, counting AV earned in Atlanta and Philadelphia, he didn’t earn the 90+ AV necessary to balance out the trade while in Atlanta.

Tony Dorsett, with his HOF career, paid off the 96 AV burden created by trading a 1st and three 2nd round choices for the #2 pick. Once again, the risk was high, the burden was considerable, but it gave value to Dallas in the end.

Perhaps the most interesting comparison is the assessment of the Earl Campbell trade. Just by the numbers, it was a bust. Jimmie Giles, the tight end that was part of the trade,  had a long and respectable career with Tampa Bay. That, along with the draft picks, set a bar so high that only the Ray Lewis’s of the world could possibly reach. And while Campbell was a top performer, his period of peak performance was short, perhaps 4   years. That said, I still wonder if Houston would still make the trade, if somehow someone could go back in to the past, with the understanding of what would happen into the relative future. Campbell’s peak was pretty phenomenal, and not entirely encompassed by a mere AV score.

Which of these players was drafted at a premium?

Sebastian Vollmer, drafted in the seond round in 2009.
Wikimedia image.

Derrick Burgess
Second round choice by Philadelphia in 2001.
Wikimedia image by BrokenSphere.

In my  mind, the answer is “both of them”.

One of the meatier passages in War Room comes in chapter 14, where Bill Belichick discusses the thought processes behind his selection of Sebastian Vollmer in 2009:

“Sebastian Vollmer is a good example”, he says of the Patriots’ starting right tackle, one of the team’s four second-rounders in ’09. “There’s no way he was really a second-round pick. Based on film or really based on the player he was at the end of the ’08 season. You know, East-West game and all  that. We knew there would be an undertow of Vollmer. And it was just a question of, ‘When’s this guy going to  go?’

“He should have been a fourth or fifth-round pick, by the film, by his performance. But  you saw him as an ascending player and he had rare size, and  there were a lot of things that you had to fix and all that. But it was clear the league liked him. Now,  the question is always, “How much do  they like him and where are they willing to buy?’ I’m sure for some teams it was the fourth round. For some teams it was the third round. But we just said, ‘Look we really want this guy. This is too high to pick him, but if we wait  we might not get him, so we’re going to  step up and take him.’

“And sometimes when you do that  you’re right and sometimes when you do  that  you’re wrong and everybody looks at you like, ‘Damn, you could have had him in the fourth.’

The Patriots aren’t the only team that practices the overdraft or the premium draft. If the Eagles really like someone, they tend to take them a round ahead of where he is commonly valued. Odd that teams that maintain plenty of draft picks practice this.  Offhand, I can recall the Eagles doing this for Derrick Burgess (generally viewed as a fourth rounder). The Steelers have done this as well;  they drafted Casey Hampton a full round above his common valuation.

In the 2012 draft class, players who appear to be attracting premium attention (we’re a day before the draft, mind you) are Ryan Tannehill (late first by talent, thought to be going to Miami at #8), Stephon Gilmore (drafted #3 in a mock draft by Greg Cosell), Fletcher Cox (mid first talent, seen as high as #5 in respectable mocks), Kevin Zeitner (mid second round talent, often in mocks with Pittsburgh in the first round), Chandler Jones (appearing in the first in some mocks), and Mark Barron (some people claim he’s the #7 now, often ranked as a mid first rounder).

If you feel you need the player, sometimes you have to just go out and get him.

I picked up this book after Greg Cosell gave it a big thumbs up on Rob Rang and Doug Farrar‘s radio show for KJR in Seattle, curious what it might actually say about the NFL draft.

Turns out this book is an update and rewrite of his earlier book, Patriot Reign, and for 11 of the chapters of this book, really has almost nothing to do about the draft, other than teasers spiked throughout the work. One interesting comment about the draft ranking system implemented by Belichick goes:

One of the things that made the system different was that it absolutely required a scout to know his college area or region of coverage in addition to each member of the Patriots’ fifth-three man roster. All reports, without exception, were comparative, and were based on what a given prospect could do vs. any current Patriot playing his position.

As a book, it initially has no sense of overarching storyline, content to wander about the narrative landscape the way a 60 year old grandfather would, telling one story in deep depth and then switching abruptly to another. It follows a variety of points of view. They all do not make much sense until you get to the end, where Michael actually starts talking more in depth about the draft in chapter 12. It finally becomes clear that he has three points of view, all intertwined, that of Belichick, that of Thomas Dimitroff, GM of the Atlanta Falcons, and that of Scott Pioli, GM of the Kansas City Chiefs. But to get there, to the three chapters of new material, he has you read about 11 chapters that I suspect were mostly all told in Patriot Reign.

Disturbing is the often myopic point of view of the book. Most notable in this regard is the coverage of Spygate, which can be summarized as (A) It was all Eric Mangini’s fault (B) Everybody does it and (C) People are picking on us needlessly and hurtfully. It’s in these segments where the book descends even from rambling history and becomes a fanboy lament. When you have to complain, in Poor Poor Pitiful Me fashion, about Gregg Easterbrook talking you down – in football terms, a comic, mind you – then you really do need to step out of the narrative a while and reexamine the facts. Tom, of the blog Residual Prolixity, puts it this way:

There are also a couple things Holley doesn’t seem to get, either from a Boston-centric viewpoint or they’re not obvious and nobody actually bothered to explain them to him, with the foremost example in my mind that Spygate (covered only briefly) exacerbated an existing anti-Boston sentiment arising from a belief that the Patriots were willing to push to the edge of the rules and beyond, if they could get away with it, which they could (see increase in illegal contact penalties, 2004, post Colts-Patriots).

All that said, once you get to Chapter 12, there are three chapters of useful insider stuff on how three teams conduct their draft. The background info on Dimitroff and Pioli are good enough to be useful to fans of the Falcons and Chiefs. Just, the new stuff isn’t substantial enough to be a book on its own – more like a long extended article in the New Yorker or the Washington Post. But, book sales being what they are, the new stuff was tacked onto the old stuff and sold as an entirely new product.

Up to you, whether you should read it. It can be interesting given the limitations of the material. Scaled in the measure of a draft pick, this is day two material.

When you try to think of the NFL playoffs as simply an extension of the regular season, you screw up. Advantages that reliably yield wins under regular season conditions – think of the dominance of the San Francisco 49ers defense, at times, in the NFC Championship game two weeks ago – aren’t consistent enough in the post season. A lot of games are decided by, well, small effects, perhaps intangibles, at this time of year.

Part of the reason is that  the gap in the classical offensive and defensive metrics is much more narrowed in the post season; you’re looking at such small differences in net offensive potential that other elements come into play.  The other component, as far as I can  tell, is that traditional analysts, focused on the analysis of the regular season, are loathe to abandon tools that worked so well  on the 16 regular season games. If it’s 66-75% accurate during the regular season, isn’t that enough in the post season?

In my  opinion, the answer is no. Regular tools fail because the playoff system has already selected for teams  that are good at scoring and preventing scoring. Those teams are, to a first approximation, already well matched. You can’t use regular season tools reliably.  You have to  analyze  for playoff specific causes of wins and losses.

This is the only reason I can  come up with for the recent analyses of the strength of schedule metric. Analysts have  noted (see here and here) that it is negatively correlated with winning. This year has particularly potent effects, using Football Outsider’s definition of the SOS metric. Jim Glass, in the FO article, nails the effect on the head when he states:

The fact that stronger teams play easier schedules and weaker teams play tougher ones results trivially from the fact that teams cannot play themselves. As teams cannot play themselves, in lieu of doing so the strongest teams must play the weaker and the weakest the stronger.

This,  of course, begs the question that my playoff results pose: if strength of schedule correlates with losing, then why do playoff teams with advantages in the strength of schedule metric win? The confidence limit  of this effect is larger than the one for playoff experience, in my measurements. Given the right experimental design, this is pretty much a given.

Back in  the early 1990s, I used to call this  the “NFC East effect” and it seemed as obvious to me as the  nose on my face. The NFC East was the toughest division  in football. Whatever team won the NFC East was bound to win the Super Bowl because they had faced such incredibly  hard competition, that anyone else was a patsy by comparison (with the possible exception of the San Francisco 49ers). And whether any division could again gain such dominance, I don’t know. The salary cap has made it hard to hold such powerful teams together.

I’m posting now because the 2007 (and now 2011) New York Giants are a poster child for this phenomenon. My formula gave the New York Giants a 61% advantage in the 2007 Super Bowl. It is giving the Giants an advantage in this Super Bowl as well, by 66%. By traditional metrics, the 2011 Giants shouldn’t have survived so much as  their first playoff game. They managed, this year, to win three. The largest  measurable advantage they had  in this year’s playoffs is their exceptional strength of schedule.

So, win or lose, the question is still out there. If regular season stats are so important, why are the Giants winning? And if you’re using a “regular season” model to  predict playoffs, perhaps you need to step back and start analyzing the playoffs on their own, without preconception.

After the Giants victory over the Packers, I finally got up the nerve to say what my system has been saying from the start, that my predictive system markedly favors the Giants throughout the entire playoffs.

Going all the way?

The deal, of course, is a heavily favored team can lose. A team seeded 1 or 2 and favored by 70% in every game only has a 34% chance of making it through 3 games. The nature of the playoffs make it difficult for any team, even a really good team, to win it all.

That said, the Giants are favored by 75% over the San Francisco 49ers. The only advantage the 49ers hold is home field advantage. The Giants have to be considered a playoff experienced team, and they have a massive strength of schedule advantage, the same advantage that will give them precendence over either New England or Baltimore. If you choose to treat the Giants as having no playoff experience, that lowers their odds to win to a mere 58%.

Favored in the Conference Championship Round:

Giants over 49ers: 75%
NE over Ravens: 59%

Favored in the Super Bowl:

Giants over NE: 66%
Giants over Ravens: 64%
NE over 49ers: 64%
Ravens over 49ers: 65%

Odds of winning the Super Bowl:

Giants: 49%
NE: 24%
Ravens: 18%
49ers: 9%

For contrast, we’ll calculate the Pythagorean odds for these teams as well, ignoring the effects of strength of schedule, and playoff experience.

49ers over Giants: 86%

NE over Ravens: 61%

49ers over NE: 61%

And the 49ers are favored to win the Super Bowl, via Pythagoreans, by 52%.

Of course, if you’re taking these kinds of offensive metrics seriously, please note the odds of the Giants having made it this far was only 7.4% (Originally calculated as 5.4%). Consider those odds, please, before writing my little predictive system off.

Both the Giants and Denver have won today, eliminating all wild cards and leading to two #4 seeds playing at the #1 seeds. In the case of the Giants, using my formula, we have the question of whether they truly have playoff experience. If they do not, then Green Bay is favored, on average, by 56%, though the relative error of strength of schedule results allow for Green Bay being favored by as much as 73% to the Giants being favored by 63%. If the Giants are treated as if they have playoff experience, then there is a wide range of results, from Green Bay being favored by 55% to the Giants being favored by 78%, with the average result being the Giants favored by 63%. Note that home field plus Pythagoreans would favor Green Bay by 83%.

In the Case of Denver versus New England, New England has playoff experience and home field in their favor, and Denver played a tougher schedule. New England is favored by my scheme by 69%. Home field plus Pythagoreans would favor New England by 88%.

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