It’s Manziel Time

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Published on: December 9, 2014

It’s Manziel time in Cleveland. I’ve discussed what I think of Cleveland in other posts. The short story is that I think the decisions of the Cleveland Browns front office resemble those a robot with a combination of dissociative identity disorder and schizophrenia. Once again we get to examine an important decision in Cleveland: the benching of Brian Hoyer in favor of Johnny Manziel. First, the facts.

Fact #1: Brian Hoyer has not been good this year

Not been good is putting it very mildly. Worst in the NFL is more correct. No quarterback has been allowed to be as inaccurate – 9.4% below league average – as Hoyer and still throw so many passes – 387 attempts. His inaccuracy is on par with Zach Mettenberger, Mike Glennon, Drew Stanton, and Ryan Mallett, all quarterbacks that began the year as back-ups and are unlikely to find permanent starting jobs any time soon. All in all, I don’t think much of Hoyer’s individual performance this year.

Fact #2: It is December and the Cleveland Browns have a winning record

At the time of this writing, Cleveland has a 7-6 record in the winningest division in football. By my quick reckoning, this has happened four other times in the last twenty years. Why does it matter if Brian Hoyer has been the worst quarterback in the league up to this point? You have won seven games with the worst quarterback, something must be working. Cleveland’s pass defense is coming up big week after week, they’ve got a group of receivers that showed they can spar with the best of them, and their running game has shown signs of life throughout the season. You got to seven wins doing something right. Why are you throwing that away now?

Fact #3: We don’t know what Manziel will do over three games

No one has any idea what Johnny Manziel will do during the final three games of the season. I have a prediction of Manziel’s quarterback rating of 76.6, but that’s over four years not three games. What is the purpose of upsetting everything that the Browns have built over the course of the season on the scant hope that Manziel can help you out? No one has any algorithm that says Manziel will be any better or worse than Hoyer over these final three games. And it is very possible that Manziel could be worse.

Fact #4: Coaching tenure in Cleveland

Perhaps the number that actually explains what is going on here. The average tenure over the last three head coaches in Cleveland has been a glorious 1.67 years. Mike Pettine likely doesn’t want to lower that average even further than it already is. He knows that sticking with Hoyer will likely get him a .500 team. Perhaps he sees the writing on the wall and knows that .500 won’t be enough to save his job. I don’t know that is the case, but I wouldn’t put it past the Cleveland management.

 photo blitzwing.jpg

Quarterback Carousel

First off, happy Veterans Day, Grandpas. I miss you both.

Now to football. The quarterback carousel continues to spin. Everyone wants to know about how their team will do now that the new quarterback is under center. I’m going to look at three teams this week, the Eagles, Cardinals, and Texans and discuss the probable futures of each team.

Eagles

I am not worried one bit about the Eagles. I’m writing this after the Monday night where Sanchez went crazy, but don’t think I’m overreacting to one game here. Sanchez currently has a crazy high completion percentage for him. I’m fully expecting him to regress. Mark Sanchez has consistently been 4-6% below league average in terms of completion percentage depending on the year. We shouldn’t expect that he suddenly learned some profound bit of information about how to complete more passes. We should expect that he will return to his 4-6% below league average completion percentage over the course of the rest of the time he’s a starter in Philadelphia. But, you know who else was about 4-6% below league average completion percentage? Nick Foles. Honestly, I don’t see Sanchez being a detriment to the Eagles offense. I think Chip Kelly has a plan and that he is nothing if not adaptable. Philly will get through this leaning on their defense and their receivers.

Cardinals

The Cardinals are going to have more of a problem. The drop from Palmer to Stanton is going to be a much bigger drop than the drop from Foles to Sanchez. Stanton is consistently 3-4% poorer in completion percentage and he’s also not as effective throwing the ball down field. Palmer is generally league average in down field throws, but Stanton is more like bottom of the league in that category. Couple that with the fact that the Cardinals have really been punching above their weight up to this point in the season and you have a situation ripe for regression. The Cardinals should be very very worried.

Texans

The Houston Texans are the wild card. I did not see a quarterback switch coming for them. I’m not saying that Fitzpatrick is great. I know I predicted him to be a wildcard to lead the league in passing yards this year, but that prediction wasn’t as much based on him as it was everyone else around him. I believe I called him “serviceable” at the beginning of the year. And I still stand behind that assessment. He’s a little below average in completion percentage, but in an offense that’s more about throwing downfield than the average team we would expect that. Hopkins seems to be having a reasonable season and the team has already won twice as many games as it did last season.

Which is why I was very surprised to see that they’ll be going with Ryan Mallett for the foreseeable future. What exactly can Mallett offer you that Fitzpatrick can’t? Mallett has never started an NFL game and has thrown a total of 4 passes in an NFL game, one of which was intercepted. What can we even know about him?

Actually, we can know something about him since he played his college career recently enough to be part of my data set. I’ve even got a prediction about his career passer rating after four years in the banner up top (2011 draft class). The prediction in the banner is for a passer rating around 65. However, that prediction was based on a model I call “Mk. I” (Everyone seems to name their models and I’m an Iron Man fan). That model worked, but was based on Linear Regression and a data set that wasn’t as expertly cleaned as it could be.

Here’s what we learn about Ryan Mallett. I have a measure of college arm strength that helps differentiate quarterbacks. Mallett has the fourth highest score on that metric in a dataset that goes back to 2007. The three above him are Robert Griffin III, Andrew Luck, and Russell Wilson. However, arm strength is icing on the cake of an effective quarterback, but the cake itself. The cake of effective quarterbacking is accuracy and in that category, Mallett falls woefully short compared to the three other quarterbacks mentioned. When Mallett actually completes a pass, it goes for a long long ways. But he has tremendous trouble actually completing those passes. Basically, I see Mallett as Zach Mettenberger amplified. He’s got a cannon arm, but no ability to control it. The “Mk. III” model predicts his passer rating to be somewhere around 71. I think Fitzpatrick might be able to do a slight bit better.

What Exactly Do We Know Part III: The Nightmare Edition

Categories: Decision Making, NFL
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Published on: October 28, 2014

This will be the final installment in my “What Exactly Do We Know” series. I think I’ve beat this horse enough that it’s about to fall over. But I need to talk about one final aspect of statistical reasoning and knowledge derived from data analysis. And this one is the creeping horror that should keep us all up at night. At the very least, this would keep me up at night if I were advising an actual NFL team. I’m going to being explaining the horror by having you imagine a job interview.

Actually, I’m going to ask you to imagine the lack of a job interview. How would you feel if you knew you were a top three candidate for a job, but the company called you one day saying “We don’t do interviews. We’ve already made our selection and we selected someone else because they had a higher college GPA.” When I ask my students to imagine this scenario, they say they’d be annoyed. They talk about how a particular score on a test doesn’t define them and if only they could get an interview they could prove their abilities and their worth.

However, if you’re trying to assess abilities and skills, evaluating on college GPA is actually the best way to get the skills and abilities you’re interested in. In fact, trying to assess abilities and skills with an unstructured conversation is one of the best ways to introduce unintended and significant bias into your decision making process. Most large, modern organizations don’t even use a conversation-style interview to assess skills anymore. Conversation-style interviews are done to only answer whether the person interviewing you could stand working with you for a day. But I digress.

I bring up job interviews because they are a fascinating point of the employee selection process. If used in the old let’s-chat-for-20-minutes way, the interviewer is unlikely to see the person’s worth with any form of accuracy. Which brings us back to football.

Football is an amazingly interesting game because of how interdependent all the action is. However, the interdependence leaves us with a fundamental problem. Can someone looking from outside the situation truly see what is actually happening in a quarterback-wide receiver connection? I looked in the published academic literature and couldn’t find the study that directly answers that question. I’m running the study in my lab right now, but I won’t have an answer for you for a long while. I haven’t looked at the data yet, but the tangentially related studies all seem to indicate that the answer is “No, we can’t see who is responsible for what when looking from the outside.” And, assuming I’m right, how then can we trust the opinion of any talent evaluator that doesn’t attempt to systematically control for such biases? Can even the most relevant talent evaluators, namely those that make personnel decisions for NFL teams, be trusted to make the right evaluation?

My top quarterback prospects from the 2014 draft were Nathan Scheelhaase and Keith Price. At the moment, neither of these players is on an NFL roster. The internet currently does not record what Scheelhaase is up to, but Prince is a quarterback – a backup for the Saskatchewan Roughriders in the Canadian Football League. So…what are we to make of this?

Keith Price 2013.jpg

Let’s say I’m right and we can’t trust talent evaluators that don’t use data to control the biases. This means that I can put out a list of prospect, those prospects can go out into the league and get evaluated. In the case of Price, he was evaluated by two of the best in the business – the Seahawks and Patriots.   Neither team desired his services which is how he ended up in Canada. But according to the theory we put out in the first paragraph, the fact that he didn’t get picked up doesn’t mean anything. We already believe that the evaluators can’t control a human bias. Hopefully you understand that this is a very advantageous rhetorical position to be in. How can you convince me that I’m wrong? What evidence would I accept if I won’t accept the pre-season evaluation of two teams who I have already stated I believe a two of the best in the league in evaluating talent?

The only evidence that the model will currently accept is on-field, regular season outcomes. And not only that, but I would need a lot of attempts to actually consider the notion I’m wrong. Which is why I would lose sleep if I worked for an NFL team. I’d have to trust the wings of Bayesian statistics to a degree I’ve never had to in the past. How terrifying would that be?

What Exactly Do You Know, Part II: The “Is He Your Cousin or Something?” Edition

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Published on: October 21, 2014

In last week’s post, I discussed the concept of what statistical inference actually tells you and how it’s boring and cumbersome to talk about it accurately, so analysts often shorten the conversation so they can actually talk with real people about something interesting. Today we take a slightly different tack regarding what exactly we know. Our example for this week is Minnesota Vikings quarterback Christian Ponder.

Christian Ponder close-up.jpg

If you’ve been reading for a while, you know that I was actually a fan of Ponder for a long time. Or, at the very least, I didn’t hate him with every fiber of my being like every other Vikings fan seemed to. I called him “not the problem in Minnesota” instead pointing to the largely ineffective receiving corps. I was talking to my neighbor before the season started. I said that Ponder is not the problem. We had this long and somewhat loud conversation about how I have to be wrong about him because everyone was giving up on Christian Ponder. Even Paul Allen – the radio play-by-play announcer for the Vikings – a guy who has never in his life given up on anyone in a purple jersey had given up on Christian Ponder. When I persisted that Ponder wasn’t the problem, my neighbor ended the conversation by saying, “You’re the only guy I know saying nice things about Ponder. Is he your cousin or something?” At the time the comment made me laugh. Then the Thursday night game against the Packers happened. I had to think more about this and examine what I know and what I don’t know about Christian Ponder in particular and the game of football in general.

So why was I so adamant that Ponder wasn’t the problem? Because, for all his faults, Ponder has one singular but important ability. He is rather accurate for an NFL quarterback. He’s not super-star Peyton Manning accurate, but he can get a football into a receiver’s hands slightly better than the average NFL quarterback. And why do I care so much about accuracy and nothing else? Because it’s the only quarterback ability I’ve found at the NFL level that will predict useful outcomes. Nothing else comes back predictive. Not a quantification of arm-strength, not Wonderlich scores, nothing at the combine, nothing but accuracy predicts NFL level outcomes.

And now we have another trap that analysts can fall into, a trap that is particularly present and meaningful for the NFL. I can’t find a predictive effect of my in-house metric that I think measures arm strength (let’s ignore the measurement point of “how do we know this thing is really arm strength” for now. It’s important but not where we’re going here). So I don’t find this effect. There are a couple possibilities why. The first possibility is the one that brings the page views and the loud conversations – that Arm Strength isn’t an important thing. However, another interpretation is that the lack of data at the NFL level makes finding the effect of arm strength insanely difficult.

Think about it like this. Imagine I told you that there was gold to be found in the body of water closest to you. To me that body of water is a river, so for the rest of this example I’ll be talking about a river. But maybe for you it’s a lake or an ocean or your friend’s bathtub. Whatever. You want to find this gold because you think having gold would be better than not having gold. So you go out and buy all the equipment necessary to pan for gold. You get the sorter pieces and the dirt sucker and everything else and you go stand in the river for a few hours and try to find this gold. Now, if you stood in the same spot panning for gold for four hours and didn’t find gold, would it be reasonable for anyone to assume that I’m wrong and that there is no gold in the river?

 

Crude Drawing of Where Gold is in a Fictional River
Crude Drawing of Where Gold is in a Fictional River

No, it would be ridiculous to say that. Maybe you were panning in the wrong spot. Maybe the screen you were using was too big and all the gold was little and slipping through. There could be many reasons why you didn’t find gold in the river.

Analytical findings are like gold. Just because you don’t find one, doesn’t mean that they aren’t there. This is a concept called “statistical power” and in the NFL it’s a huge problem. Our ability to find effects generally increases the more data we have. Think of it like this – more data makes our gold panning screens smaller. It allows us to find ever smaller nuggets of gold. In the NFL, the data is very sparse. There are only 32 teams playing 16 games each with maybe 30 passing attempts in each game. This pales in comparison to basketball’s 82 games and baseball’s 162. Compared to other sports, an effect in the NFL has to be fairly large before our screens will catch it. There is so little data coming from the NFL that it’s possible an arm-strength effect exists but there just isn’t enough data to find it.

So, after the Thursday night Ponder debacle, I went on a quest for more power. And in football, if you want more statistical power you need to look at the college level. With many many more teams we suddenly have a lot more power in our data set. I spent most of my summer calculating the same arm-strength metric for every NCAA FBS level quarterback and I ran the same model to see if arm-strength, along with accuracy, can predict useful quarterback outcomes. Low and behold, it does (said the amazed analyst and no one else). Ponder fairs very well on accuracy, but he suffers horribly on arm-strength. With this lesson learned, it’s time to quit dying trying to take the Ponder hill. Ponder is a problem for the Vikings offense. One of many, many problems.

Bills Bench E.J. Manuel: What’s the Plan?

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Published on: September 30, 2014

The Buffalo Bills have benched their starting quarterback and hope of the franchise, 2013 edition, E.J. Manuel. We don’t know if we’ll ever see him on a professional football field again. As of this writing, Manuel has a career passer rating of 78.5 and a career adjusted net yards per attempt (ANY/A) of 5.9. But this post isn’t about E.J. Manuel and what he has or hasn’t done on the football field.

My prediction of where E.J. Manuel would be after four years in the league was a passer rating of 74.8 with an ANY/A around 4.8. By my estimation he’s performed right about where I expected, maybe even a little better on ANY/A. But one case doesn’t prove the worth of a process and this post isn’t about me.

This post is about the Bills’ front office. I’m very curious what they expected of E.J. Manuel. In fact, I’m curious what they expected of their entire football team. Did they believe that, in 2012 when they went 6-10 that they were just a few players away from contending for a playoff spot? Did they truly believe that when they haven’t had a winner record since 2004?

I wonder what they expected on draft day in 2013. They drafted a new quarterback, certainly, but they’d also drafted a couple wide receivers in Robert Woods and Marquise Goodwin. I wonder if they considered how difficult it would be to evaluate the performance of a new quarterback when both quarterback and receivers were changing. I wonder how much hope existed in the draft room on that day and on what evidence that hope rested. I wonder if they felt the same way at the end of the 2014 draft when they had given away their 2015 first round pick to Cleveland to draft another new wide receiver in Sammy Watkins.

I wonder if, when making the decision to bench E.J. Manuel, the decision makers in the Bills organization thought about benching Robert Woods instead, a player I have currently rated as the 203rd best pass receiver in the NFL. I wonder if someone is thinking, “We threw the ball Wood’s way 12 times this week and gained a total of 17 yards on those 12 attempts. I wonder if the receiver had any impact at all on such a stat line.”

I wonder what the decision makers in Buffalo expect from Kyle Orton, a player whose career per attempt statistics look remarkably similar to the guy he’s replacing. What exactly do they think is going to happen here? We have a pretty good idea what Kyle Orton is going to do with a football in his hands. And we also know that Orton is old enough and experienced enough that he probably isn’t going to get much better. Manuel, whatever his faults might have, at least has a chance to improve.

So much in the sports world comes down to expectations. We expect E.J. Manuel to be good because he was the first quarterback taken in the 2013 draft, and the only quarterback taken in the first round. But why did we expect so much of E.J. Manuel? Because of how much someone else valued him? Because of what we thought we saw during his college days? Sadly, human minds cannot correctly weigh those expectations. Data is needed so that we don’t let our expectations interfere with our ability to make the best decision we can at this exact moment.

Mostly, Buffalo Bills decision makers, I wonder…

 

Ryan Tannehill – Using Passer Rating to Evaluate Quarterbacks

Categories: NFL, Statistics
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Published on: September 24, 2014

Apparently Ryan Tannehill doesn’t have a high enough passer rating this season. At least, not high enough for his coach, Joe Philbin to commit to him being the starting quarterback for the Dolphins on two separate occations. I first saw this yesterday and assumed it was some thinly veiled motivational tool. But then it happened again today. Which doesn’t happen if you’re trying to use shame as a motivational tool…at least, not if you’re doing it right.

So let’s assume that Philbin is seriously considering benching Ryan Tannehill and putting in Matt Moore because Tannehill’s passer rating isn’t high enough. What are the implications of such a move?

First, Ryan Tannehill has not been good this year. In fact, he’s been one of the worst quarterbacks in the league over the first three games of the season, depending how you figure it. Philbin isn’t wrong that Tannehill’s passer rating is awful. However, and this is the really important part, who cares?

Whenever you start using numerical information to inform real world decisions, you must be incredibly careful to select the correct criterion variable. Criterion variables are the regression equivalent of dependent variables, a variable we care about but don’t mess with. We just allow it to vary naturally. Proper selection of your criterion variable is one of the most important decision you make when beginning a program of analysis. Every truth you uncover during your studies will only be true with respect to the criterion variable you chose. That single decision will color every single decision from the moment you make it on.

Returning to the last question I asked, who cares if Tannehill’s passer rating isn’t particularly good? Most people in the football analytics community would say no one should care. Pro Football Focus loves saying that passer rating tells you about the efficiency of the offense, but not the individual running it. Others like Chase Stuart at Football Perspective talk about how the weights assigned to passer rating are arbitrary and incorrect. These heavy hitters in the analytics community say that Philbin is using the wrong criterion variable to evaluate his quarterback. And I would largely agree with these perspectives.

So what does Philbin really want out of his quarterback? That’s the ultimate question in football. What criterion variable should we choose to actually understand the game and the people who play it? Those of us interested in understanding the game of football from an analytic perspective should be scrambling trying to understand everything we can about what different criterion variables tell us about the game. The correct selection of criterion variables is one of the most critical questions we face at this moment.  We know passer rating isn’t particularly good. ANY/A and ESPN’s QBR certainly make better cases as being good criterion variables, but the perfect variable doesn’t exist.

Ultimately, my guess is that Philbin would just like his offense to contribute to a win. But, if Joe Philbin is really going to use passer rating to evaluate quarterbacks, I could have told him to avoid Tannehill in the first place.

SeaWorld and the NFL

The orcas at SeaWorld are getting a new habitat. The new habitat will cost SeaWorld hundreds of millions of dollars and basically double the size of the habitat the orcas currently have.

Several events have conspired together to move SeaWorld toward building this habitat, but the catalyst can be traced back to a large male orca named Tilikum who murdered Dawn Brancheau, an experienced trainer who was working with him, in 2010.

A key point in the debate about whether or not “murdered” is an acceptable word for the events that transpired rests on whether or not any trainer at any level of experience is safe in the pool with an orca whale. In court, SeaWorld contended that trainers well versed in methods of animal learning and operant conditioning were perfectly capable of controlling a 3 ton whale. The Occupational Safety and Health Administration, on the other hand, contended that no amount of contact with orcas could be considered safe. After some legal wrangling, it was decided that any and all contact between orcas and trainers had to be done with a solid material, such as a concrete barrier, between the trainer and the whale. Direct contact was no longer allowed.

I’ve been returning to my feelings about the SeaWorld incident a lot during this week of terrible football fandom that just won’t end. I wonder about organized football, both in college and the NFL, from the perspective of a workplace. I think about the increased rates of brain damage, along with the recent instances of domestic violence, child abuse, and rape and I wonder what this game that I love to watch is doing to the individuals that get a chance to play it at a high level.

Last week I wrote about one of the great benefits to using statistical methods for employee selection. Basically, using math to find people means it is much easier to find other people should the ones you originally find not work out socially. That’s a great boon to employers in a typical workplace. They become less beholden to talent. Talented people no longer become insulated from the consequences of socially unacceptable acts. And, for any typical place of employment, that works quite well. However, I did not consider in that piece that the NFL is not a typical place of employment. Professional football has some troubling facts associated with it. As a psychology professor, I know all about what brain damage to the prefrontal cortex can do to an individual. Increased emotional reactivity and impulsivity are just the tip of the iceberg. I wonder if this game is destroying the lives of the people that play it. And I worry that anyone that works to identify and project success from one level of football to another, including myself, may be complicit in that destruction.

I am well aware that this blog isn’t particularly popular. As of this writing, I have 45 followers on Twitter and I get about 6 readers a day, 3 of which are spam crawlers. I am, at the moment, insulated from a looming ethical dilemma. No one with actual decision making power is calling me to learn my opinion on whether or not a particular player should be granted the “privilege” of continuing to play the game. The minute that happens, though, I will have an important decision to make. I will need to choose whether or not I should use my intellect to grant someone else the opportunity to potentially destroy theirs in the name of a sporting contest. I will need to decide if I believe the game of football can be played in a way that doesn’t destroy the lives of its players or if I believe my ability to identity talented football players is the same as placing the best trainers in a pool with 3 tons of socially maladjusted rage.

I do know one thing. Talking about football has never seemed more hollow than in these last 10 days. The heart-rune of my fandom is ripped. I’m not sure it will ever heal.

Beholden to Talented Shitheads: Why We Need Analytics

Categories: General Info, NFL
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Published on: September 9, 2014

I hope everyone is enjoying the new football season. I’m glad to see the Vikings are 1-0 and the defense looked good, although, it was against the Rams so I’m not sure that means all that much.

I don’t have much to talk about in the way of numbers today. We’ve got one week worth of NFL data which will tell us largely nothing about how the rest of the season will play out and we’ve got two weeks of college football data which will tell us something so minor that we probably shouldn’t bother right now.

Instead, I thought I would talk about one of the more important social issues surrounding football right now. I want to talk about Ray Rice and, specifically, what Ray Rice shows us about the importance of adopting analytic strategies for selecting members of organizations.

Many people think that businesses use analytic strategies like skill testing and personality testing because the tests tell you which individual is the most talented, most productive, most useful potential employee and the business then selects the person who comes out on top of the most important tests. And if you think that, you’d be sort-of right about how the process works, but you’d also be sort of wrong.

Most businesses that use analytic strategies use their tests not to find a single individual, but instead to narrow the pool of possible individuals. Tests are used to cull the group, but they generally aren’t used to make a final decision. High scores are necessary to land the job, but they aren’t sufficient. Once the tests identify the proper pool of applications comes the next, and most vital question an interviewing team can ask, “Can we all work with this person?” Fit within the work culture and ability to get along with co-workers is critical to building a functional organization. Any business using this strategy needs to be very careful that their answers to whether they can work with different people are not biased in ways that violate Civil Rights laws or any moral principles that the company holds to, but in general that’s how companies use tests to select employees. Test them all, generate a pool, but don’t select based solely on high scores but rather on more human elements.

That’s the first way analytics helps you build your organization. You can be sure of selecting talented people that are actually the kind of people you want to work with. And that could be important if you’re trying to build a football team. Many coaches seem to have very high minded policies about avoiding players with domestic violence histories. And while they seem to stick to those principles to greater or lesser degrees depending on the talent of the player in question, we can at least see how this would work. If your analytic strategy returns two players as equally likely to succeed and one of them has a history of domestic violence, you probably go with the other one. But that’s not why NFL teams need to quickly adopt analytics.

Using analytics to select employees is critical when one of your talented and valuable employees makes a mistake so horrendous, so unspeakable that it makes you rethink whether or not you would be able to work with that person ever again. Enter our connection to Ray Rice.

What Ray Rice did was unspeakable. But how the Ravens and the NFL responded to the situation is just as unspeakable. And while I can’t speculate on what was going through Rice’s head when he committed his act, I have been associated with enough employee selection meetings to have a guess at what the Ravens were thinking prior to cutting him.

The Ravens, and all NFL teams, are in an industry where talent is incredibly difficult to identify. Highly trained NFL scouts get evaluations of talent wrong every season. It’s a terrible job to try to be good at because almost no one truly knows what it takes to be a great football player. If the organization can’t reliably identify talent, it becomes very guarded about the talent that has fallen into its lap. And when organizations have limited confidence in their ability to find new talent, they are more willing to forgive egregious actions from the talent they actually have. In essence, organizations can become beholden to talented shitheads.

Selecting players using analytic strategies can break that cycle. When a talented member of the organization moves into territory that the rest of the organization can’t follow, it is a simple matter to separate from that person, regenerate a new pool of potential applicants, and begin the selection process all over again. We don’t have to run our rationalizer ragged trying to find reasons why Action X might be morally repugnant, but doesn’t justify removal of the person from the organization. Instead, the incentives for talented individuals to act like a shitheads evaporate. The organization can afford to be less risk-averse when problems with talented players emerge. If the Ravens had a large scale analytics-based selection process they could have cut Rice in February and found two or three shiny new running backs. Instead, we have the nonsense we all saw this week. Honestly, I fail to see how the status quo is better.

2014 Passing Yardage Predictions – Part II

Categories: Fantasy, NFL, Statistics
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Published on: August 19, 2014

Welcome back everyone. I took last week off because some very important things were and still are happening in our country. I couldn’t bring myself to talk about a game overlaid on top of another game. It just seemed a little disconnected from the world at large. I think it’s important that we all stay reminded of the events happening in Ferguson, MO. That being said, it’s time to build an audience and nothing builds an audience like new content.

This week I finished a full set of projections for yardage totals of quarterbacks, wide receivers, and tight ends. This new model makes two major corrections compared to the one I posed a couple weeks ago. First, it corrects for 2013 injuries. You’ll note that Julio Jones has a much higher predicted yardage total in this model compared to the previous one. Second, it corrects for changes to the offensive system. I’ll get to why this is important in a minute. For ease of viewing, I’ve added a new page to the banner so you can easily check these tables whenever you need to. Remember that these yardage totals assume the player in question plays all 16 games and any coaches that have changed jobs do not radically alter the schemes they’ve used in the past. I also want to throw out a big thank you to Jeff over at thefakefootball.com for the offensive coordinator history spreadsheet that made all these projections possible.

One very important caveat before we begin. I don’t have any historical data to check these predictions against. Jeff’s data on targets doesn’t go back far enough for me to do any historical checking on how accurate this model tends to be. So, I have no idea about the uncertainty inherent in this model. We’ll all be learning this together as the season goes on. After the season is over, we’ll check them together. Isn’t science fun?

Top Projected Wide Receivers – Receiving Yards

My first list of projections is for wide receivers, and that list doesn’t come with a lot of surprises. You’ve got your Andre Johnsons, your Dez Bryants, your Brandon Marshalls and your DeSean Jacksons at the top. I don’t really see a surprise on that list until I see Josh Gordan predicted at less than 1,000 yards – assuming he plays all 16 games. And even that is understandable given Cleveland’s quarterback situation. I’ll keep the list updated as depth charts change and injuries occur.

Top Projected Tight Ends – Receiving Yards

Once again, a lot of ho-hum on this list. Jimmy Graham will lead the league in tight end receiving yards, a Detroit Lion will follow him because Detroit will still throw the ball all over the place and defenses will try to lock down Calvin Johnson, blah-blah-blah. You’ll see Levine Toilolo third on that list, but I’m not sure I buy that specific prediction. The model is assuming that Toilolo will step in and take all of Tony Gonzalez’s targets which my human brain tells me isn’t going to happen. I have left that prediction as the model reports it for accuracy’s sake, but on that one, I think we have some justification to adjust it down a bit.

Top Projected Quarterbacks – Passing Yards

I went back and checked the results I’m about to tell you three different times. As I was doing that, I anthropomorphized the mathematical equation and called it a “little dickens” for trying to trick me. But there was no mistake. The inputs I fed into the model were all correct. Furthermore, all the other top five quarterbacks make perfect sense. Most of us expect Carson Palmer, Drew Brees, Tony Romo, and Peyton Manning to have high yardage totals at the end of the season. But I didn’t expect the guy at #1 by a long shot. And so, without further ado, your projected 2014 NFL leader in passing yards – edging Peyton Manning by 98 yards – is…Houston’s Ryan Fitzpatrick.

You’d call a mathematical equation a “little dickens” too if it tried to trick you with such nonsense. After I saw it I looked up the prop bet odds on Ryan Fitzpatrick leading the NFL in passing yards and found that it’s such a ludicrous notion that Vegas isn’t giving action on such a proposition. It seems insane, but let’s keep an open mind and consider this for a second.

Once you think about it, there are several reasons why it makes sense that Ryan Fitzpatrick could lead the league in passing yards this year. First, we know something about what Bill O’Brien likes to do on offense. We know he likes to throw the football and his system is very effective at gaining yards through the air. Any system that makes Matt McGloin look that good has got to have something going for it. We also know that O’Brien provides a lot of opportunities to his best receivers and seems to be able to adapt the passing game around what he has. Second, Houston has the best receiving corps you will find outside of Denver or Chicago. From top to bottom, the wide receivers in Houston know how to get open and know how to get yards after the catch. This will be a second huge bonus to Fitzpatrick’s passing yards. Third, nobody really knows what the status of Arian Foster is. We know he’s busy trying to be the best teammate he can be, but can he still be the productive running back he once was? I have my doubts. And finally, I don’t want to count out the man himself. Fitzpatrick is a serviceable quarterback. He’s not going to take a team on his back or anything, but he’s not horrific either. There’s a reason he’s stuck around in the NFL so long.

So there you go. Lots of fairly boring expectations for receiving yards and one super out-of-left field prediction. Let the season begin!

2014 NFL Passing and Receiving Yard Predictions

Last time I broke down which rookie wide receivers and pass catching tight ends have the most talent. Now it’s time to get to the heart of the matter. In this post I will present a preliminary model that predicts total receiving yards for wide receivers and tight ends and total passing yards for quarterbacks. In the first pass through the data, I’ll be strictly using information from the 2013 NFL season. I’m not readjusting for previous injuries that kept a player off the field (e.g. Julio Jones & Reggie Wayne) nor am I readjusting based on the difference between early and late season play (e.g. Cordarrelle Patterson, though the opposite of what you’re probably thinking). I’ll make a more robust prediction once the depth charts are a little more settled.

To make this prediction I entered variables that represent quarterback ability (broadly construed), receiver ability (broadly construed), and number of targets into a Bayesian multiple regression analysis. If you’re interested in some of the technical details of the Bayesian analysis, they can be found at the bottom of this post. For the general reader, in the past, those variables have explained about 33% of differences in yardage totals and been off by an average of 300 yards. Also be aware that this model constricts the range of the predictions. It tends to underestimate the high end and overestimate the low end. The reason for the constriction can be found in the technical details.

The first table shows predictions for wide receivers. To get into this table, a wide receiver had to have been the target of at least one pass during the 2013 season and be listed as at least the 4th wide receiver on the projected depth chart according to CBS Sports as of July 24th. You can argue with me about that decision all you want. I made it entirely because I only have so many hours in my day and somebody else had projected some line-ups.

Wide Receivers

RankTeamFirst NameLast NameProjected Yards
1PittsburghAntonioBrown1014.3
2DenverDemaryiusThomas1012.7
3Tampa BayVincentJackson989.9
4HoustonAndreJohnson981.2
5ChicagoAlshonJeffery973.7
6CincinnatiA.J.Green954.4
7San DiegoKeenanAllen948.8
8Green BayJordyNelson947.7
9AtlantaHarryDouglas940.7
10WashingtonPierreGarcon940.2
11ChicagoBrandonMarshall930.6
12New OrleansKennyStills914.8
13DetroitCalvinJohnson898.2
14New OrleansMarquesColston886
15SeattlePercyHarvin876.8
16San DiegoMalcomFloyd873.8
17DallasDezBryant857.9
18ArizonaMichaelFloyd826.9
19BaltimoreTorreySmith804.7
20New EnglandJulianEdelman803.9
21IndianapolisT.Y.Hilton801
22WashingtonDeSeanJackson800.1
23AtlantaJulioJones795.1
24San DiegoEddieRoyal791.7
25AtlantaRoddyWhite781.2
26ArizonaLarryFitzgerald779
27DenverEmmanuelSanders768.1
28MiamiBrianHartline766.5
29MiamiMikeWallace758.1
30San FranciscoAnquanBoldin757.6
31HoustonDeAndreHopkins748
32DenverWesWelker747.5
33New OrleansRobertMeachem740.4
34PhiladelphiaRileyCooper730.5
35Green BayJarrettBoykin715.2
36OaklandRodStreater713.2
37IndianapolisHakeemNicks709.9
38JacksonvilleCecilShorts699.4
39DallasTerranceWilliams699.1
40SeattleDougBaldwin697.1
41New York GiantsVictorCruz691.4
42TennesseeKendallWright684.7
43New York JetsEricDecker677.2
44CincinnatiMarvinJones676.5
45San DiegoVincentBrown658.6
46Kansas CityDwayneBowe647.1
47Kansas CityDonnieAvery641
48DetroitGoldenTate632.7
49OaklandJamesJones631.6
50TennesseeNateWashington629.3
51MinnesotaGregJennings622.6
52New EnglandDannyAmendola612
53ArizonaTedGinn610.2
54MinnesotaJariusWright601.7
55OaklandDenariusMoore596
56CarolinaTiquanUnderwood596
57Green BayRandallCobb595.5
58New EnglandBrandonLaFell592.2
59MinnesotaJeromeSimpson591
60CarolinaJerrichoCotchery588.9
61BaltimoreSteveSmith586.6
62PittsburghLanceMoore586.1
63IndianapolisReggieWayne578.1
64New EnglandAaronDobson553.1
65San FranciscoSteveJohnson544.2
66JacksonvilleAceSanders536.5
67ChicagoMarquessWilson530.7
68New York GiantsRuebenRandle528.9
69BaltimoreMarlonBrown524.4
70MiamiRishardMatthews520.2
71SeattleJermaineKearse519.3
72New EnglandKenbrellThompkins518.9
73WashingtonSantanaMoss512.2
74CincinnatiMohamedSanu506.6
75OaklandGregLittle505.5
76BuffaloRobertWoods504.9
77WashingtonAndreRoberts502.3
78DallasColeBeasley499.8
79BaltimoreJacobyJones495.1
80St. LouisChrisGivens484.2
81AtlantaDariusJohnson477.3
82ClevelandAndrewHawkins470.4
83DetroitKrisDurham460.2
84MinnesotaCordarrellePatterson454.3
85ClevelandNateBurleson446.7
86ChicagoJoshMorgan445.6
87St. LouisBrianQuick441.1
88HoustonKeshawnMartin425.3
89New York JetsJeremyKerley423.8
90Tampa BayChrisOwusu422.4
91San FranciscoMichaelCrabtree410.3
92ArizonaJaronBrown408.8
93St. LouisTavonAustin398.2
94St. LouisAustinPettis395.1
95BuffaloT.J.Graham393.4
96Kansas CityA.J.Jenkins385.6
97New York GiantsJerrelJernigan384.3
98HoustonDeVierPosey380.4
99CincinnatiDaneSanzenbacher377.3
100New York JetsDavidNelson366.6
101TennesseeJustinHunter353.4
102Kansas CityJuniorHemingway345.6
103CarolinaJasonAvant342.1
104New York JetsStephenHill311.5
105PittsburghMarkusWheaton300.7
106DallasDwayneHarris298
107Tampa BayLouisMurphy256.5
108DetroitRyanBroyles252.3
109San FranciscoQuintonPatton249.2
110BuffaloMikeWilliams246.8
111ClevelandMilesAustin230.6

Remember that this table is entirely derived from last year’s data. Some of those predictions may be too high (Harry Douglas, T.Y. Hilton, possibly Vincent Jackson) and others too low, but all in all, it’s a decent list.

Tight Ends

RankTeamFirst NameLast NameProjected Yards
1New OrleansJimmyGraham992.1
2San DiegoLadariusGreen855.4
3San DiegoAntonioGates853.5
4DenverJuliusThomas768.8
5ChicagoMartellusBennett717.3
6CarolinaGregOlsen689.9
7MiamiCharlesClay676
8DallasJasonWitten670.4
9Tampa BayBrandonMyers667.2
10San FranciscoVernonDavis660.6
11ClevelandJordanCameron651.7
12PittsburghHeathMiller646.1
13PhiladelphiaBrentCelek639.6
14New EnglandRobGronkowski627.5
15IndianapolisCobyFleener584.3
16HoustonGarrettGraham579.2
17ArizonaRobHousler568.1
18New OrleansBenjaminWatson565.6
19WashingtonJordanReed561.7
20BuffaloScottChandler557.2
21St. LouisJaredCook546.8
22CincinnatiTylerEifert530.8
23CincinnatiJermaineGresham523.3
24ArizonaJohnCarlson495.4
25JacksonvilleMarcedesLewis493.2
26Green BayAndrewQuarless479.9
27HoustonRyanGriffin477.5
28CarolinaEdDickson470.8
29SeattleZachMiller461.6
30TennesseeDelanieWalker447.6
31JacksonvilleClayHarbor447.4
32OaklandMychalRivera445.7
33DallasGavinEscobar441.8
34IndianapolisDwayneAllen438.5
35DetroitBrandonPettigrew432
36New York JetsJeffCumberland428.4
37MinnesotaKyleRudolph395.9
38Kansas CityAnthonyFasano389.9
39BaltimoreOwenDaniels363.6
40AtlantaBearPascoe345.2
41PhiladelphiaJamesCasey333.4
42AtlantaLevineToilolo319.2
43PittsburghMattSpaeth295
44ClevelandJimDray293.3
45BaltimoreDennisPitta292.9
46ChicagoMatthewMulligan287.5
47WashingtonNilesPaul285.5
48MiamiMichaelEgnew278.8
49DenverVirgilGreen254
50MinnesotaRhettEllison251.8
51San FranciscoVanceMcDonald238.5
52St. LouisCoryHarkey202.7
53New York GiantsKellenDavis202.5
54TennesseeCraigStevens-136.3

Couple weird things about this table. First is the insane yardage totals for both Antonio Gates and Ladarius Green. Green was very good last year in limited usage. Gates has been very productive for a very long time. The question for 2014 production among those two individuals is who is going to get the targets. Green’s prediction is driven by assuming great talent but limited targets. Gate’s prediction is driven by assuming many targets, but declining ability due to age. It will be very interesting to see how the tight end battle in San Diego sorts itself out. You’ll also see that one prediction is negative. The model is unimpressed with everything associated with Tennessee’s offense and makes no apologies for it.

Quarterbacks

RankTeamFirst NameLast NameProjected Passing Yards
1San DiegoPhilipRivers5067.0
2New OrleansDrewBrees4746.6
3DenverPeytonManning4181.0
4ChicagoJayCutler3954.7
5Green BayAaronRodgers3943.2
6New EnglandTomBrady3774.6
7SeattleRussellWilson3773.2
8ArizonaCarsonPalmer3758.1
9IndianapolisAndrewLuck3754.0
10AtlantaMattRyan3720.9
11WashingtonRobertGriffin III3666.6
12HoustonRyanFitzpatrick3661.6
13CincinnatiAndyDalton3633.4
14MiamiRyanTannehill3632.7
15DallasTonyRomo3535.7
16Tampa BayJoshMcCown3535.0
17OaklandMattSchaub3525.7
18PhiladelphiaNickFoles3477.3
19PittsburghBenRoethlisberger3474.6
20JacksonvilleChadHenne3381.3
21CarolinaCamNewton3321.1
22DetroitMatthewStafford3306.0
23BaltimoreJoeFlacco3130.7
24Kansas CityAlexSmith3045.4
25New York GiantsEliManning3009.3
26MinnesotaMattCassel2984.6
27San FranciscoColinKaepernick2926.1
28BuffaloE.J.Manuel2905.8
29New York JetsGenoSmith2843.3
30ClevelandBrianHoyer2727.6
31TennesseeJakeLocker2604.6
32St. LouisSamBradford2534.0

Philip Rivers is the top of this list, largely because the model is unsure what is going on with the tight end situation in San Diego and is just sort of throwing up its hands. That being said, Rivers was really, really good last year and has an impressive cast to throw to. Antonio Gates and Malcom Floyd are quality veterans while Ladarius Green and Keenan Allen are up and coming rookies that had remarkable seasons last year. I’d be high on Philip Rivers.

That’s all for our first jump into predicting the 2014 season. Near the end of the pre-season, I’ll have a more robust prediction based on more accurate depth charts.

Technical details

The Bayesian model began with weakly informative priors. Receiver yardage totals follow a gamma distribution. In an attempt to simulate a long-tailed distribution, the dependent variable was assumed to be t-distributed. The equation was trained on data from the 2011 season predicting 2012 yardage totals and then evaluated with 2012 season data predicting 2013 yardage totals. Average deviation of the prediction from the actual in the evaluation data was 314 yards.

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