The distribution of player rankings can affect the possible draft positions of a player. Hand drawn curves on a brand new pen tablet, so they’re not perfect curves. Imagine the purple curve with more extensive tails.

In the diagram above, there are three distributions, with different peaks, means and spreads. Player A, in black, has a tight distribution of values and barring any issues with uniqueness of position, there is a consensus where he will be drafted. Player B, in red, has a broader distribution, but is unlikely to suffer more than half a round of variance in draft position. Player C, in purple, has an envelope encompassing two whole rounds of the draft. It’s the player C types that create a lot of controversy.

Travis Frederick and ZZ Top: Separated at birth?

The player the Dallas Cowboys drafted in the first round, Travis Frederick, is exactly one of those types. He was highly ranked before the combine, but suffered because of his bad 40 time. People like Gil Brandt, who had him ranked 27th best at the time, dropped him because of his 40 time. Perusing various links, such as this one, you see rankings ranging from 31 (Gary Horton) into the 90s. Now please note that draft pundits really don’t count, NFL teams do. But for the sake of argument, we’re using media scouts as an estimator of the envelope of NFL opinions. And that envelope of values encompass two whole rounds of variance.

So, what happens when you must have a player whose valuation envelope is a broad distribution? This player must be taken pretty far from the mean, in the tails of the “high value” side, or else you risk losing him (3). What is guaranteed though, is that the pundits on the other side of the mean from you will undoubtedly scream bloody murder. That’s because a draft pundit’s opinion is his life’s blood, and they make their money validating and defending that opinion, usually in print, and sometimes on television. That it’s one of many doesn’t matter if that’s how you make your living. So of course pundits will scream.

2013 was a draft with few good players. If estimates are valid that there were only 16 or so players truly worth a first round pick, then by default you’re overdrafting your quality by a half round by the middle of the first round. If the span of Frederick’s valuations really ran from, say, mid second to the beginning of the fourth, then the so-called overdraft is not, it’s entirely the function of three things: first, the perceived need for the player and second, such a broad valuation envelope that Dallas had to draft him in the tails of the distribution. Third factor, the lack of talent overall in the draft that led to overdrafting in general.

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Footnotes

1. Jonathan Bales, before he became a New York Times contributor, favored this comment (common sense, IMO) and used it to help validate a pet drafting theory of his. I never saw enough rigor in his theory to separate it from the notions of BPA or need, as it was more a collective efficiency concept. IMO the notion hardly led to the invalidation of BPA or needs based drafting.

2. In the early 2000s, I wrote a Monte Carlo simulator of the draft, which explicitly used those distributions to estimate where players would be drafted. More discussion of that code, released as a Sourceforge project, is here.

3. Let me note that in “must have” situations, teams whose draft record no one complains about .. New England say .. will draft players above their worth. Belichick’s rationale, given in the link, is instructive. An excerpt is:

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.’

PS – tskyler, a Cowboys Zone forum contributor, has a very nuanced fan analysis of the Frederick draft here, one worth reading.

What you’ll see here in order is a link, perhaps a short summary of the scope of the link, and perhaps a short quote from the link. I might borrow a diagram or two from the link, if I think it will help the reader.

Obviously this is driven by the hire of Monte Kiffen and Rod Marinelli by the Dallas Cowboys. Rob Ryan was fired recently. The reasons are part of still heated discussions, but given subsequent hires, it’s easy enough to suggest that the powers that be among the Cowboys want better execution all round. That the Cowboys offense was more prone to penalties and miscues than the defense is a position many Cowboys fans take, and that faction treats the firing of Ron Ryan as a kind of scapegoating.

Whether true or false, we’re just trying to gather under one roof, resources on this defense, notes useful to fans and coaches alike.

Before beginning, we’ll mention some things that should be any any defense aficionado’s bag of tricks: the multipart articles on defense by Jene Bramel, a copy of the Jaworksi, Cosell and Plaut book, and of course, Tim Layden’s quality introduction to modern football concepts. If you have never seen the Smart Football blog, you should, and if you’re looking for information on stunts and modern zone blitzes, the Blitzology blog seldom disappoints.

A variety of other Xs and Os links are on the sidebar.

Football Times on the Tampa 2

Scope: introductory. Diagrams may not be accurate, as they display an even front.

From Football Times article. Yellow regions are holes in the zone of a stock cover 2. Tampa 2 lets MLB drop deeper, covering middle yellow, and allowing safeties to move zone coverage closer to end lines, helping close intermediate gaps.

Inside the Playbook on the Tampa 2

Scope: Introductory. Matt Bowen was a NFL defensive back, and knows coverages well.

Matt’s diagram of a Tampa 2 coverage, 4-3 over, with a stunt on the strong side.

Wikipedia on the Tampa 2

Scope: introductory. Article is self contradictory on the history of the defense.

The personnel used in the Tampa 2 are specific in position and required abilities. All positions in this defense place a premium on speed, and often the result is that they are all undersized by league standards. The defensive linemen in this scheme have to be quick and agile enough to create pressure on the quarterback without the aid of a blitz from either the linebackers or the secondary, with
the defensive tackle in the nose position having above-average tackling skills to help stop runs.

The Fifth Down Blog on the Cover 2 and Tampa 2

Scope: Introductory. Jene Bramel’s coverage of the Monte Kiffin system.

Daily Norsemen on Minnesota’s Tampa 2

Scope: nicely done introductory level discussion of the back 7 responsibilities in the Tampa 2

While they will generally all be tailored to one gap attacking from the 4-3 front … the back seven will have to display a wide variety of skills in order to execute the full defense.

This is distinct from some 3-4 systems (and other 4-3s), where the varied looks and confusing schemes imply a high degree of
flexibility from all players, but in fact does not require as much individual diversity at key positions.

ESPN News Article: fewer and fewer Cover 2 teams

Scope: news article, reporting teams moving away from Cover 2 because safeties can’t hit as hard anymore.

The Core Positions in the Tampa 2

Scope: Introductory, newspaper blog. Bears-centric. Reporter repeating what a coach has told him.

Stampede Blue on the NT position in the Tampa 2

Scope: Introductory, with emphasis on the Colts and their history with 1 technique DTs in the Tampa 2.

Bryan Broaddus on the Tampa 2

Scope: Introduction. Some discussion of where current Cowboys fit into a Tampa 2 style scheme. Historically accurate.

In terms of the Tampa Bay personnel compared to this current Cowboys squad, think of DeMarcus Ware as Simeon Rice, Bruce Carter
as Derrick Brooks, Sean Lee as Shelton Quarles and Barry Church as John Lynch, with Jay Ratliff as Warren Sapp. I don’t believe
the coverage part will be a problem for Carr and Claiborne, but how physical they can be trying to do those things I spoke of
in funneling runs inside or playing the run when he gets to the outside will be important.

How to coach the MLB drop in Tampa 2

Scope: coaching thread on message board.

That middle position isn’t manned by the true MLB type that the Miami 4-3 was predicated on. Like these guys have said, he’s 6 yards back and has different responsibilities too.

Pass Coverage in the Tampa 2

Scope: Coaching blog, and article. MLB, CB, and S responsibilities.

General coaching thread on the Tampa 2.

It is not a viable every-down coverage, due to the fact that you basically are giving up the entire 0-10 yd zone from hash-to-hash without resistance, so if a team just hits the TE & RB’s over the middle, they can kill you with 8 yd gains every snap.

Tampa 2 versus the spread

I was surprised when I found out that The Tampa 2 wasn’t the same thing as the 4-3 Over Cover 2. Went to a COY clinic in Orlando a few years ago and Monte Kiffen was the speaker. It looked more like a version of the WT-6(*) than a version of the 4-3 Over. I’m still not real sure about that thing.

Not Tampa 2 specific, but interesting in contrast

On Pete Carroll’s Seahawks 4-3

Scope: Introductory to intermediate.

Shakin’ the Southland on the Miami 4-3 and descendents.

Scope: Intermediate.

Offensive Football, busting the Cover 2

Shakin’ The Southland  (Clemson football blog) on the Cover 2.

Scope: intermediate, with plenty of video.

Almost any article by Chris Brown of Smart Football has football coaches as its primary audience. Advanced fans can glean some insight as well.

Chris Brown on Peyton Manning’s favorite play: Levels

Delayed Slant from the  Smash

Beating Cover 2 from Trips.

~~~
* presumably, wide tackle 6.

We got the Split-60 from Coach “Erk Russell” in 1984. It was the WT-6 on the Strongside and the Split-4 on the Weakside. They called it the “Junk Yard Dog” defense. Coach Russell said that it took the best of the WT-6 and the Split-4.

We got it, squeezed down the Strongside into a 50 that we were more used to and I’ve used it every since as a Gap 5-2. Wish I’d put it in print, but I was just a coaching pup then and just thought it wasn’t anything special.

Now they call it the 4-3 Under and say that Monte Kiffen is credited with it. Same defense that Coach Russell ran at Georgia with a few little wrinkles. I guess some things never change.

I suspect if Atlanta had won, we would be asking ourselves the question of whether SOS can be fooled. Advanced NFL Stats said, among other things, that Carolina was seriously underrated. If that were true of the whole NFC South, the Atlanta was actually playing better teams than their rankings suggested, and thus should have been more highly rated. But in the end, with 1:18 left to play, 3rd and 4 on the San Francisco 10 yard line, Atlanta was unable to get a first down, and San Francisco won a tough fought victory by 4 points. Two pivotal plays will markedly affect the narrative of this game.

Now to note, last year the New York Giants had the best strength of schedule of all the playoff teams, and they also won the Super Bowl. So I have to ask myself, at what point does this “coincidence” actually make it into the narrative of the average sports writer, or do they still keep talking about “teams of destiny” or other such vague language? Well, this kind of “sports journalist talk” hasn’t gone away in sports where analytics is an ever bigger factor in the game, sports like baseball or basketball. I suspect it doesn’t disappear here.

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:

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.

I suspect other systems will rank Seattle as stronger than mine does, and Baltimore as weaker. That said, the Vegas line as of this Sunday gives Atlanta a 2 point advantage over Seattle, and my system slightly favors Seattle. We can calculate odds and points via other mechanisms, say, Pythagoreans, SRS and median point spreads, and if we do, what do we get?

Certainly different systems yield different emphases. For me, the one lasting impression I had was the Washington Seattle game was an almost picture perfect demonstration that home field advantage is strongest in the first quarter.

Of all the teams playing, my system likes San Francisco the best. I suspect it likes it more than others. We’ll learn more as other analytics oriented folks post their odds for the divisional round.

Between a difficult work schedule this last December and a very welcome vacation (I keep my stats on a stay at home machine), I haven’t been giving weekly updates recently. Hopefully some of my various thoughts will begin to make up for that.

Though with SOS values, you could crunch all the playoff numbers yourselves, this set of data should help in working out the possibilities:

Odds as calculated by my formula, with home field advantage adjusted to 60%. Point spread calculated with formula 3.0*logit(win probability)/logit(0.60). Click on image twice to expand.

What I find interesting is the difference between Vegas style lines, and my numbers, and the numbers recently posted by Brian Burke on the New York Times Fifth Down blog. My model is very different from Brian’s, but in three of the four wild card games, our percentage odds to win are within 2-3 percent of each other.

Point spreads were estimated as follows: if an effect of 60% were valued at 3 points (i.e. playoff home field advantage is about 60% and home field advantage is usually judged to be worth 3 points), then two effects of that magnitude should be worth 6 points. But it’s only on a logit scale that these effects can be added, so it only makes sense to relate probabilities of winning through their logits. As the logit of 0.60 is about 0.405465, then an estimated point spread can be had with the formula

point spread = 3.0*logit(win probability)/0.405465

Update (1/9/2012) – even simpler is:

est. point spread = 7.4*logit(win probability)

A simplified table of the wild card games, with percentages and estimated point spreads is:

How many successes is a touchdown worth?

We’ve spoken about the potential relationships between success rates, adjusted yards per attempt, and stats like DVOA here, but to make any progress, you need to consider possible relationships between successes and yards. Let me point out the lower bound of the relationship is known, as 3 consecutive successes must yield at least 10 yards, and 30 consecutive successes must end up scoring a touchdown. In this case, the relationship is 1 success is equal to or greater than 3 1/3 yards.

Thus, if the surplus value of a touchdown is 20 yards, that’s 6 successes. If a turnover is worth 45 yards, that’s about 13.5 successes.

A smarter way to get at the mean value of this kind of relationship, as opposed to a limiting value, would be to add up the yards of all successful plays in the NFL and divide by the number of those plays. For now, that’s something to be pursued later.

Adjusted yards per attempt, as we’ve shown, is derived from scoring models, in particular expected points models, and could be considered to be the linearization of a decidedly nonlinear EP curve. But if I wanted to, I could call AYA style stats the generalization of the yardage concept, one in which scoring and turnovers are all folded into a single number valued in terms of yards per attempt.

So, if I were to take AYA or its fancier cousin ANYA, and replace yards with success rate, and then refactor turnovers and scoring so that turnovers and scoring were scaled appropriately, I would end up with something like the “V” in DVOA. I could then add a SRS style defensive adjustment, and now I have “DV”. If I now calculate an average, and normalize all terms relative to my average, I’d end up with “Homemade DVOA”, wouldn’t I?

The point is, AYA or ANYA formulas are not really yardage stats, they are scoring stats whose units are in yards. So, if really, DVOA is ANYA in sheep’s clothing, where yardage has been replaced by success rate, with some after the fact defense adjustments and normalization from success rate “units”.. well, yes, then DVOA is a scoring stat, a kind of sophisticated and normalized “adjusted net success rate per attempt”.

Data from week 12

Data from week 13

The methodology of these stats is discussed in previous posts in this series. If you’re wondering where I’m getting odds to go into the playoffs, see this post. If you’re wondering what chance your team has of winning in the playoffs, see this post on my logistic regression methods, based on studies of playoff games. How would your ranking in the playoffs affect your chances of getting into the Super Bowl? We studied that here.

I am not a proponent of the notion that regular season offensive stats are predictive in the post season. My studies suggest p on the order of 0.15 for offensive stats in the post season, and thus aren’t predictive enough for my tastes ( p <= 0.05). That hasn't stopped Football Outsiders from pretending that their proprietary stats are predictive and calculating playoff odds with their tools.

But they’ve never outperformed their Pythagoreans as substantially as they have this year. It can’t be blamed on early season New Orleans collapse, as their only loss was inflicted by New Orleans. New Orleans has only hindered this process. Is it turnover that are causing all this? While the 2010 team had a +14 turnover ratio and the 2011 team had a +8 turnover ratio, the 2012 team has only a +5 turnover ratio at this point and the 2008 team had a -3 turnover ratio. No, it’s something else. For now, perhaps noting that this team tends to outperform its Pythagoreans is enough.

Week 11 scoring stats:

Chicago’s biggest weakness was on display this Monday night, as Aldon Smith had a career day. Aaron Schatz (@FO_Schatz) has sent digging into his archives for the biggest DVOA blowouts of all time. The 32-7 demolition of the Bears by the 49ers wasn’t the worst, but it clearly evoked the worst.

The game plan was heavy on traps and wham blocks, and would have warmed the hearts of anyone who ever played NFL Strategy against a blitz heavy opponent.

It does lead to the question of whether Chicago is in the same downward spiral they experienced last year. At this point, however, you would expect Jay Cutler to return and thus slow down the bleeding.

Cool Standings uses Pythagoreans to do their predictions (and for some reason in 2011, ignored home field advantage), FO uses their proprietary DVOA stats, and NFL Forecast uses Brian Burke’s predictive model.

Blogging the Beast has a terrific article on “the play”. If you watched any Dallas-Philadelphia games in 2011, you’ll know exactly what I mean, the way with a simple counter trap, LeSean McCoy treated the Cowboys line as if it were Swiss cheese.

Most important new link, perhaps, is a new Grantland article by Chris Brown of Smart Football. This article on Chip Kelly is really good. Not only is the writing good, but I love the photos:

Not my photo. This is from Chris Brown’s Chip Kelly article (see link in text).

as an example. Have you ever seen a better photo of the gap assignments of a defense?