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

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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

Rank
Team
First Name
Last Name
Projected 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

Rank
Team
First Name
Last Name
Projected 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

Rank
Team
First Name
Last Name
Projected 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.

2014 Draft Class – Wide Receivers and Tight Ends

Summer time is over for us up here in the Deep North. Those two 90 degree days were brutal, let me tell you. I’ve spent the summer cocooned in my office cooking up the latest and greatest that I can offer in predictive football statistics both at the college and professional levels.

If you’re new to the site during a football season, I’ll be talking wide receivers and quarterbacks here, both at the collegiate and professional level.

And to start, let’s talk NFL rookie wide receivers. I made some predictions about rookie wide receivers for the 2013 season and they were…well they were terrible. I spent most of my spring and summer ripping apart the model and figuring out what went wrong. I discovered two very important elements of predicting wide receivers that needed to be addressed.

#1) What’s your Dependent Variable?

One of the trickiest things about doing football analysis is figuring out exactly what you want to measure and what you want your wide receiver or pass catching tight end to do. Many people have tried to deal with this issue when it comes to pass receivers with varied success.

My approach in 2013 was to use an in-house metric I created that measures pass catching ability. This, it turns out, was a horrible mistake. The reason is that NFL teams typically use particular wide receivers in particular roles. One receiver goes deep, another goes across the middle, etc. This creates a problem because it confounds pass catching ability with depth of target. Deep passes are successful less often, but the large impact they create on the game can offset their lower success rate.

For the 2014 predictions, I have changed the DV I use to get around this problem. I start with NFL Yards per Target. Yards per Target is much less susceptible to the depth of target problem. It’s not perfect, but it’s less bad than what I was using last year. I also do a little hoo-doo with the numbers to make them more consistent year-over-year. The first thing I do is to subtract the league average Yards per Target for the given season. This corrects for changes in the passing game across seasons, rule changes, passing tendencies, etc. Next, I use a highly constrained (sort of) structural equation model to pull out the effect of quarterback and offensive system on yards per target. I call it a “sort of SEM” because the model is incredibly constrained due to the realities of the game I’m modeling. It’s so constrained that what I do cannot be called a true SEM. But the technical details are probably not why you’re here. The ultimate result is a metric I call Receiver Influence on Yards per Target (RIYPT; in my ears “ripped”). We’ll use this metric at the NFL level as our DV.

#2) Lower-Level Interactions

Issue #2 I didn’t appreciate when making the 2013 predictions is the importance of interactions. You see, RIYPT at the NFL level predicts productivity. In fact, it’s the only receiver-focused metric that predicts NFL level performance. I made a bad assumption that the same situation would exist at the college level. It doesn’t. At the NFL level, every receiver has great hands. If you don’t have great hands, you don’t get to be a receiver in the NFL. Once we account for depth of target, there’s no meaningful variance in ability to catch a football among NFL pass receivers.

This is not true at the NCAA level. You can be a college receiver without having exceptional hands. As long as you make up for it with lots of long gains, less-than-stellar hands aren’t the handicap that they can be in the NFL. To deal with this, you need an interaction term. Interactions find the receivers that have good enough hands to make it in the NFL while also having the ability to gain useful yardage on an efficient basis.

That’s enough details. Down to brass tacks. Who should we be looking at as far as success at the wide receiver position? Remember, our DV is yards per target, so our definition of success may be different than the actual outcomes obtained on a football field. You can have a large RIYPT, but if you don’t get a lot of targets you won’t gain a lot of yards (see Ladarius Green in 2013).

The full table can be found above in the web page header. My top five rookie wide receivers, according to Predicted NFL RIYPT are…

  1. Brandin Cooks – Oregon State – 2.56
  2. Jalen Saunders – Oklahoma – 2.33
  3. Cody Latimer – Indiana – 1.99
  4. Marqise Lee – USC – 1.97
  5. Cody Hoffman – BYU – 1.93

Here you can see I expect Brandin Cooks to be head and shoulders above the rest of the rookie receivers in the NFL. This is especially true now that he is a member of the New Orleans Saints. Jalen Saunders will be a stickier situation. He’s a lower round draft pick, so it will be more difficult for him to see the field compared to Cooks. Second, he has (at the moment) Geno Smith throwing to him. Past visitors to the site will know I’m not high on Geno Smith. Geno will likely improve next year compared to 2013, but I still expect him to be in the bottom fifth of the league in terms of completion percentage and passing yards.

My top five pass catching tight ends according to Predicted NFL RIYPT are…

  1. Eric Ebron – North Carolina – 1.37
  2. Richard Rodgers – California – 1.12
  3. Marcel Jensen – Fresno State – 1.04
  4. Blake Jackson – Oklahoma State – 1.02
  5. Jace Amaro – Texas Tech – 1.02

Once again, we have one prospect that is head and shoulders above the rest, that being the prospect everyone expected to be on top, Eric Ebron. The second name on that list probably surprises a few people. If you look up scouting profiles of Richard Rogers, they’re not that glowing of him. I guess we’ll see where we end up. Rodgers also ended up in a great place to succeed as a pass catching tight end – Green Bay – so hopefully we get an opportunity to see him succeed.

This wraps up our rookie preview.  Up next, we’ll predict 2014 yardage totals for veterans from 2013 data.

2014 Draft in Review

This will probably be my last post before I go into summer hibernation to work on my receiver model. I was going to review the first round pf the draft, but that’s been done to death at this point. There isn’t much to be gained from rehashing all the details. Instead, I only want to talk about two teams, the Minnesota Vikings and Cleveland Browns.

Minnesota Vikings

Regular readers know that the Vikings are my team. They’re the team I grew up watching and the logo  on the blanket currently draping my couch. So when the Vikings traded up back into the first round, I reacted emotionally. The reaction was about the trade-up, not the actual pick itself. I was worried the Vikings had learned the wrong lesson from their experience in the draft last year.

Last year, the Vikings gave up a number of picks to move back into the first round and make a third selection in the first round. For doing this, they were applauded, given high post-draft marks, and given general kudos all around. The problem is that the actual move was a terrible one. And the reason the 2013 trade was terrible was the number of picks they gave up to make it. Every single credible analyst has shown that the way to get the most value out of the draft is to make more picks (see this new, inventive analysis showing how number of picks predicts total draft value). So the psychological problem is a problem of reinforcement. The Vikings made a move. In the short term, they were told that this move was a great idea. However, the negative consequences of the move they made will either never be realized because the Patriots rarely play the Vikings, or they won’t be felt for three to four years down the road, which is far too long to learn something from reinforcement. And not only that, but the Vikings got incredibly lucky that their target in the trade up is showing promise as a wide receiver and kick returner. All this is positive reinforcement for making a trade up more likely in the future.

I was worried that the Vikings had learned the wrong lesson from their experience last year. I was worried they were going to give up a two to three picks to move back up into the first round as they did last year and then not recoup those picks in the later part of the draft. I’m very happy to say I was wrong. At the end of the draft the Vikings had made, by my count, the 4th most picks of any team in the draft. That’s a path to success through the draft if ever there was one.

As for the actual pick itself, I don’t mind the Vikings picking Bridgewater. My model isn’t precise enough to get upset about a difference between a predicted passer rating of 80 vs. 75. Good luck to the Vikings in the 2014 season.

Cleveland Browns

The Cleveland Browns are run by Blitzwing from Transformers: Animated. If you don’t know Transformers, Blitzwing is a triple-changer. He has forms of a jet, a tank, and a robot. To drive home the “triple” metaphor, the character has three distinct personalities. One of them is cunning, cold, and calculating, one of them is emotional and explosive, and one is completely wack-a-doodle. Or, as my 4-year-old nephew named them, Smart, Dumb, and Scary.

 photo blitzwing.jpgAny move the Browns make seems to fit into one of these three categories and you can never tell which personality is going to be in control for any specific decision. They achieve great outcomes, but project this image of ineptitude. They do really smart things to set up the situation in their favor, but then bungle the execution.  You could do Homer-Buying-the-Cursed-Krusty-Doll for an entire day with this team.

They commissioned a $100,000 study that (supposedly) said Bridgewater was the best quarterback in the draft.

That’s good!

They released the results of that study before the draft.

That’s bad.

They entered the draft with 10 selections.

That’s good!

They ended the draft selecting 6 players.

That’s bad.

They traded down with the Bills and secured picks in future drafts.

That’s good!

They gave up a pick to move one spot up.

That’s bad.

They selected Manziel with the 22nd pick.

That’s good!

They outbid three other teams to trade up to get there.

That’s bad.

They signed Joe Haden, one of the best DB’s in the league to a big contract.

That’s good!

They also signed Miles Austin, a 30-year-old, oft injured wide receiver who hasn’t been productive since 2010.

That’s bad.

But he came with a free frogurt.

That’s good!

The frogurt is also cursed.

Can I go now?

How can a franchise make such good decisions at one time and such bad decisions 15 minutes later? Being run by a robot in disguise with dissociative identity disorder is the only possibility I could come up with.

Spaghetti & Advanced Analytics

Categories: General Info
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Published on: March 12, 2014

For being a German-Russian/Norwegian from North Dakota, I make one tasty spaghetti.  Cooking is one of the few hobbies that I get to indulge with my academic career as it’s difficult to claim that one is too busy to eat.  If you come to my department and ask my co-workers, they will tell you that they’ve all heard about my fabulous spaghetti.  I’m getting the feeling that they’re all getting a little tired of hearing about it without getting to eat any of it.

Now imagine I invited you over to my house for spaghetti.  Or rather, that I told you I would make my spaghetti for you if you would be willing to pay for all the ingredients that go into it.  You’ve heard me boast about my spaghetti.  You know that I talk about the time and care that goes into my spaghetti.  You decide that you are so intrigued that you’ll put down some cash to finally get the privilege of eating this wonderful spaghetti.  You might even start imagining what you’re going to get on the way over.  What could be in this oh-so-hyped spaghetti?  Is it a secret, homemade sauce?  Basil-infused deep-fried meatballs? (Sidebar:  mmmmm…basil-infused deep-fried meatballs)  You won’t know until the meal is prepared, but you’re thinking it must be something good.  Finally, the spaghetti is ready and I drop this in front of you. What would your reaction be?

At best, you’re justifiably annoyed.  At worst, you would curse my name and flee the house, using very forceful language about how much you’d rather be at Olive Garden right now.  You spent good money on something that a college sophomore creates every other day.  Beyond just being upset, you want to make sure that you never get tricked like that again.  What’s your solution?  How are you going to make sure you don’t get bamboozled into buying buttered noodles from a fast talker again?  The solution you propose is important because it calls into question the nature of proprietary knowledge and could ultimately dictate the ability of restaurateurs to make a profit on their business.  Do you want me to open the kitchen next time and show you how I made the spaghetti?  Do you simply not trust anything I say ever again, even after I’ve learned that buttered noodles are not what the rest of the world would call “tasty spaghetti?”

But I’m not a restaurateur (even though that spaghetti is really good, you guys).  I am an academic.  In my professional life, I don’t have a restaurant, but I do have research ideas that could get funded and pay my salary.  I may not have spaghetti, but I do have a model that predicts professional level success from college level inputs.  But I didn’t create the model to be exclusively about football.  It could apply to any interdependent situation.  Football just happens to be an interesting place for me to apply certain ideas.  The fact that the “field” of “sport analytics” is a growing area of interest is also nice because it makes it easier for me to spread interest in my research.  The more people that are interested in my research, the more valuable my ideas are, which means I could eat and pay my mortgage thanks to said ideas.  However, sports analytics as a field has a problem.

About 10 days ago, about 2,000 sports executives, academics, and analytically minded people all took a long and expensive trip to the Sloan Sports Analytics Conference in Boston.  They went there, by many accounts, for the express purpose of not talking to one another.  Sure, conversation was had, jokes were made, papers were presented, and libations were served but by all the accounts I’ve read or listened to nobody officially talked “spaghetti.”  Which is ultimately the problem that sports analytics is developing.

In a competitive environment, having knowledge that no one else has confers an advantage.  Sports teams have picked up on this notion and begun to make their data, their data analytic tools, and their methodologies private and proprietary.  The message to people like me is clear:  Unless the process is secret, it isn’t worth much.  This is a problem for me as I would very much like to use my brain to get money to eat.

So here I sit, not quite sure where to go.  I have no incentive to give anyone details about the methodology, but won’t gain any credibility until I do.  This blog hasn’t particularly taken off in popularity.  I’ve tried to publish my methodology in academic journals but gotten three rejections, two of which were “Well, we think this is neat, but it’s not really a fit for our journal.” And I can’t exactly take it to a football team because 1) only secret knowledge is valuable and 2) NFL teams are not particularly incentivized to adopt useful advanced statistics in the first place.  Given this environment, exactly what is a quant to do?

How the Combine is Like Diagnosing Mental Disorders

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Published on: February 23, 2014

This is the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition – commonly known as the DSM-5.  It is the newest edition of the DSM and was just updated from the 4th edition in 2013.

This manual serves two purposes.  First, it provides a sort of dictionary for mental disorders.  By that I mean that it takes collections of symptoms and gives them a name.  This was one of the first purposes of the DSM.  It was handy to make sure that people that were researching different constellations of symptoms were calling those the same thing.  Without that common language, it’s very difficult to make progress in research on mental disorders.  The second purpose of this document is to provide insurance codes for clinicians so they can get reimbursed by insurance companies for their services.  If you are treating someone for a mental disorder, ostensibly they have something.  If you want an insurance company to give you money for a treatment, you have to tell the company what the patient has.

You might not think it, but the DSM-5 and the NFL Combine have a lot in common.

You see, the DSM-5 as a resource actually kind of sucks.  You don’t have to take my word for that.  The Director of the National Institute of Mental Health, the primary government funding body for mental health research, recently made a statement saying that “…patients with mental disorders deserve better [than DSM-5].”  You can read the full post, but to summarize, the director is saying that what is stated in the DSM-5 does not hold much water when we subject the assumptions and statements of fact contained in the book to serious, empirical, systematic testing.

In the same vein, the NFL Combine kind of sucks.  We drag 300 very large men to Indianapolis every year to collect a large amount of largely useless data.  In some cases, like the 40-yard dash, that data is useless because it doesn’t predict anything important that decision makers care about.  In other cases, like hand size for quarterbacks, we see a number, can come up with endless theories about what that number means, but really have no idea what to do with that number.

Epistemology

The DSM-5 and the NFL Combine both suck for the same reason – epistemology.  Epistemology is the notion of how we know what we think we know.  In other words, epistemology is the study of the criteria and factors we use to determine truth.  In the case of both the DSM-5 and the NFL Combine, we have decided to determine truth on the basis of authority.

In the case of the DSM-5, the book says there is a particular disorder called, for instance, borderline personality disorder and that disorder has a certain set of symptoms.  And the reason the book says that is because a high ranking and highly influential psychiatrist said so.  If you look in section three of the DSM-5 (the section on new and emerging trends) you will see a dramatically different picture of personality disorders based on evidence and collected data.  You can still get to a disorder called borderline personality disorder but the data paint a much different picture of the disorder and what to do about it than the “official” section of the book that clinicians must use to get reimbursed for their services.

In the case of the NFL Combine, we have decided that a small subset of tests will be the ones used at the Combine.  It doesn’t really matter that most everyone recognizes that the tests are largely worthless.  In modern times, even NFL teams don’t really use the 40-yard dash to evaluate wide receivers.  The correlation between draft position and 40-yard dash time for wide receivers since 2011 is 0.25 for relative draft position and 0.28 for absolute draft position.  Statistically significant correlations, but accounting for such a small percentage of the decision (about 5%) that they’re barely worth talking about.

So why do we continue this way?  Well, because authority tells us that this is the way things are.  Most NFL teams know that the Combine drills are useless, but the NFL itself wants to maintain a sense of authority.  And so everyone continues to talk about 3-cone drill times and hand sizes, and Wonderlic scores and all sorts of other useless data that should make no difference to anyone trying to find the best football player.  Change is unlikely to happen because that would mean the people that put on the event would have to admit that their publicly stated authority isn’t as correct and proper as they have indicated.

However, the data definitely show that the Combine is a large waste of everyone’s time.  Sometimes the wrong data is collected, sometimes the right data is used in the wrong way, and sometimes teams have no idea how to use the data they get.  In all cases, this amounts to useless data and incorrect evaluations.  And until the authority figures can admit that what we knew 30-40 years ago is different from what we know now, we will continue to get poor outcomes with both mental health treatment and evaluation of NFL players.

2014 NFL Draft Predictions: Quarterbacks

Categories: NCAA FBS, NFL Draft, Statistics
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Published on: February 12, 2014

Welcome back everyone.  I took a couple weeks off to recharge and enjoy the Super Bowl.  Now it’s time to get to the heart of the matter.

This post marks my predictions for the 2014 NFL Draft Quarterback class.  On the Draft Numbers page you will see predictions for both NFL Passer Rating and ANY/A for every draft eligible prospect.  This class is a very interesting one.  It’s very similar to the 2011 class in its number of potential starters, one.  Unlike the 2011 class, though, the one player that has the potential to start in the NFL isn’t getting very much buzz, is unlikely to be drafted highly, and probably won’t be a first year starter.  That player is Keith Price from Washington.  Many of the other potential prospects will get playing time, some have potential to be career backups in the league, but this class will be very short on quality starters.  All the numbers are available here.

Prediction Model Details

These predictions are generated using Bayesian analysis procedures.  If you would like details on the priors, you can ask in the comments.  The data set used to generate the equation includes all quarterbacks that played FBS football for at least one season from 2007-2012 and threw at least one pass in the NFL during the 2008-2013 seasons.

First off, the analysis finds that Career Completions Away from Average effectively predicts both 4-year passer rating and 4-year ANY/A.

When we make predictions like this, it’s important to evaluate the model to see how precise it is.  When I tell you that Aaron Murray is predicted to have a Passer Rating of 77.5 after four years in the NFL, how much uncertainty is there in that prediction?  Below you will see a plot showing how much we can reasonably expect the predictions to be off.

The plot gives you an indication of how much the predictions based on CAA can be expected to be off.  The circles represent one quarterback in the data set that was used to generate the prediction model.  The vertical blue lines represent the region that is 95% likely that the prediction will fall into.  You might look at that plot and rightly say that there is a lot of uncertainty in these predictions.  And you would be right.  There is a lot that isn’t accounted for by this single number.  However, let’s make a comparison.  One of the best ways to gauge the general league’s opinion of a prospect’s chances of being successful is using relative draft rank broken down by position.  In other words, was this quarterback the 1st, 2nd, 3rd, etc. quarterback selected.  The plot below uses the same data set that was used to generate the CAA plot, but uses positional draft rank to generate the regions.

The direction of the effect is reversed, so it might be difficult to see, but the length of those blue lines is almost exactly the same as with CAA.  Which I like to see.  It tells me I’m on the right track with this thing.  For completeness sake, here is the same plot predicting ANY/A.

Player Profile: Derek Carr

Categories: NCAA FBS, NFL Draft, Statistics
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Published on: January 22, 2014

Player:  Derek Carr

School:  Fresno State

Year:  Senior

Career CAA:  35.1

Predicted 4-year NFL Passer Rating:  75.3

Predicted 4-year ANY/A:  4.9

The Senior Bowl is just now wrapping up, and by all accounts Derek Carr was the quarterback that everyone came away impressed with.  Certainly there were some detractors, but it seems like overall, everyone had much more positive things to say about Carr than they said about any of the other quarterbacks in the mix.

But then there are always whispers and rumors out there.  One person compared Carr to Christian Ponder.  They meant it to be a frowny face sort of thing.  I would say that’s a better comparison than most people think, but then again I don’t get to be an arbiter of Ponder opinions.

So, what do we think about Derek Carr?  In a word, meh.  At least for the first four years he’ll be okay but not going to set the world on fire.  This is actually going to be a theme as we advance through the player profiles.  So much hype is surrounding the current class of quarterbacks and almost to a man the model shrugs at them and says “Yeah I guess.”  So that’s what I think will happen to Derek Carr.  I guess it could happen.  I wouldn’t expect him to set the world on fire though.

Take Home Point

Cool story, bro

Do you draft him?

Much like Manziel, probably not where he’s going to go.

Career CAA Numbers – 2014 Draft Class

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Published on: January 15, 2014

I’ve updated the Career CAA numbers for the 2014 draft class.  The table does not include predictions at this moment.  Those will go up after the Super Bowl when all the professional data is available.

The only thing I will say about the data right now is that there is one player on the list that is head and shoulders above the other prospects, and truly the only viable NFL starting quarterback of the bunch – Keith Price.  If you want to argue with me about Manziel, I’ll listen, but it will be tough sledding for him.

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