NCAA Quarterbacks: 2015 Draft Class

The quarterback situation for the 2015 draft class is looking very murky.  The 2012 draft class was a very unique class in that the highly talented players all managed to get drafted highly and to generate the needed playing time to demonstrate that talent.  I don’t see that happening with the 2015 draft class currently.  I see the 2015 class looking a lot more like the 2013 draft class.  Guys that can play exist in the pool, but who knows if they will get the playing time they need.

Revisiting the quarterbacks I’m watching section from earlier this year, I’m still high on Rakeem Cato.  I saw one feature story on him, but it had to do more with his background than with his ability as a passer.  Hopefully he gets some more attention for the latter.

One player we should keep a close eye on is Bo Wallace at Ole Miss.  I didn’t have him on my original list this year, but he’s having a very good season, both in terms of accuracy and in down the field throws.  That second part is important because he did not have a good season throwing down field last year.  Good to see him demonstrate that he actually has that ability.

Last but not least, someone to keep an eye on is Conner Halliday at Washington State.  Not saying I’m overly excited about him, but he does have something worth looking at.  I don’t know where he’ll end the season in my projections, but his current performance is at least elevating him from where he was.

Last but not least, looking far into the future, is anybody looking at this sophomore from Middle Tennessee?  His name is Austin Grammer.  A name you might want to get to know.

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
Comments: 1 Comment
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.

Final NFL Pre-Season Projections

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Published on: September 3, 2014

The NFL Season starts tomorrow.  As such, this will be my last revision of the preseason yardage projections.  To access the projections, either click on the link in the top banner or click on these links for

Quarterbacks

Wide Receivers

Tight Ends

A couple notes.

1)  Wes Welker is completely gone from these projections.  The news just came out that he’s out for 4 games due to some substance use, plus who knows how this concussion business will turn out.  Welker being gone actually helps Peyton Manning move into the top spot in yardage projections for quarterbacks.  Welker may be effective at getting you first downs, but he doesn’t do much for a quarterback’s YAC.  As such, we’re expecting Manning to get a few more yards with Welker out of the lineup than with him in.

2)  The Saints just resigned Robert Meachem (and surprisingly cut Ryan Griffin to do it).  Meachem has been a high quality receiver throughout his career, so his signing would be good news for Drew Brees.  He’s not on this list because there isn’t enough time to figure out where he finds a spot on the depth chart.  We will leave our conclusions about him to later in the season.

3) Very very very important to remember that the ability of this model to predict outsample date has not been validated.  We’re all going to be learning how this model does throughout the season.  Yay science.

 

NCAA Quarterbacks to Watch – 2014 Season

Categories: NCAA FBS, NFL Draft
Comments: 1 Comment
Published on: September 2, 2014

Week 1 of the college football season is in the books. I had intended to put together a post of the college quarterbacks I’m watching this year as NFL prospects. I did this last year and saw a lot of search interest in that poorly titled piece. Getting this piece out wasn’t near the top of the priority list, then the new semester started, I started working on a projection model for NFL production, and didn’t get around to putting out this piece until today. Sadly, this means I didn’t strike while the iron was hot in one specific case. I’ll take this as a lesson in timely publication.

Without further ado, the quarterbacks I’m watching for the 2014 NCAA season

Rakeem Cato, Marshall

Cato12.png

Cato is still my leader in the clubhouse for NFL quarterback prospects. I actually thought he declared for the draft last year. If he had, he would have been my #3 quarterback prospect then. Things are looking good for him and I’m excited to see what he’s got during his senior season.

Brent Hundley, UCLA

Brett Hundley.jpg

The first player on our list that many scouts will likely agree on. He’s currently second on my list of prospects, but had a really poor game by his standards last weekend. I’ll be watching this one to see which way it goes. Keep watching the season numbers page for more updates.

Shane Carden, East Carolina

Here’s someone you probably haven’t heard of. I’ve said it before, but one of the best things about using numbers to scout prospects is the ability to find gems that don’t get a large amount of T.V. time.  And the numbers say Carden definitely is worth a long look from professional scouts. I think you’ll be pleasantly surprised by what you see.

Sean Mannion, Oregon State

Sean Mannion.jpg

Mannion apparently really impressed some people over the summer at a Manning passing academy. I’m glad to hear it because I’ve been watching him with some interest since the beginning of last season. Hopefully he can continue the trend and keep trending up. I’ll be excited to see where he ends up.

Brandon Doughty, Western Kentucky

Yeah yeah, I know. I’m four days late and six touchdowns short on calling this one. Lesson learned, I suppose. I should probably also note that with last weekend’s performance Doughty launched himself from fifth to third on this list. Keep watching this guy.

So there you have it. My quick list of who I’m watching this season. As always, season numbers are available on the top ribbon.

Quick Update

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Published on: August 26, 2014

Nothing much new to report on the data front this week.  I updated the predictions for yardage totals recently.  The most important changes to the predictions are

1) Replace Sam Bradford with Shaun Hill as the Rams quarterback.  That change does not affect the predictions for the Rams receivers to a great degree.  My Twitter thoughts on the matter have not changed

What will change the predictions dramatically is getting some clarity as to who will get the lions share of the targets in St. Louis.  It seems as though the websites I access for information about depth charts have wildly different ideas about the Rams receiver depth chart.  This will probably update again before the season begins.

2)  Wes Welker completely removed.  I have no idea if Welker will come back or not.  The other day everything was “long-term health” and now today everything is “moving through the protocol.”  We’ll see, I guess.

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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