2014 Passing Yardage Predictions – Part II

Categories: Fantasy, NFL, Statistics
Comments: No Comments
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.

2014 NFL Draft Predictions: Quarterbacks

Categories: NCAA FBS, NFL Draft, Statistics
Comments: No Comments
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.

Quarterbacks to Watch – Mid-Season Edition

Categories: NCAA FBS, NFL Draft, Statistics
Comments: No Comments
Published on: October 24, 2013

We’re a little more than halfway through the college football season, so it’s time to share which quarterbacks have bubbled to the surface of my spreadsheet this season.

You can see the numbers for the 2013 season so far here, so I will focus on quarterbacks who could be draft eligible and are the most likely to succeed at the next level.  Also, I will avoid any quarterbacks I already talked about in my season preview.  Which leaves us with a rather short list.  I was going to at least be able to talk about two quarterbacks, but then this happened.

BYU UTAH STATE FOOTBALL
(AP Photo/The Herald Journal, Eli Lucero)

I was late to the Chuckie Keeton party anyway, so that name is probably not a shock to anyone.   I’m certainly on the Keeton bandwagon, assuming he can come back from injury and be as good as he’s shown in the past.  So let’s talk about a deep sleeper prospect who was just outside my top players to start the season and is having another solid year.

Troy’s Corey Robinson

Corey Robinson Quarterback Corey Robinson #6 of the Troy University Trojans throws a pass during the game against the Ohio University Bobcats during the R&L Carriers New Orleans Bowl at the Louisiana Superdome on December 18, 2010 in New Orleans, Louisiana.
(Chris Graythen/Getty Images North America)

Robinson has everything you want to see statistically from a prospective quarterback.  He is a four year starter, has over 1500 career attempts and is very accurate with his passes.  He’s never had a season with less than a 62% completion percentage.  He’s going to get razzed for having too many interceptions, but that’s not something the data is too terribly concerned with.

As of Saturday, my calculations predict he would have an NFL quarterback rating of right around 76 after four years in the league, earning him a solid backup status on many teams and a starting job on some teams that need help at quarterback.

So, if your team is in the market for a backup quarterback and doesn’t want to spend a draft pick to get him, you have a pretty good option in Corey Robinson.

Quarterbacks in Minnesota

Categories: Fantasy, NFL, Statistics
Comments: No Comments
Published on: October 16, 2013

The Vikings recently announced that Josh Freeman was going to be their starting quarterback.  This move really shouldn’t surprise anyone given the problems the Vikings have in the passing game.  However, is Josh Freeman “the answer” in Minnesota?  What can we expect from Josh Freeman now that he is the man of the hour on this endless carousel of starting quarterbacks in Minnesota? 

First, you’ve probably seen that Josh Freeman has an NFL low 45.7% completion percentage this year.  We care about completion percentage because it’s one of the few quarterback stats that travels reasonably well.  However, I am confident that Freeman’s completion percentage will go up as a member of the Vikings.  When that happens, people will tell you that it is because of the toxic relationship between Freeman and his coach in Tampa.  They will tell you that sometimes a change is scenery is necessary for a quarterback to get better.  These story lines are all nonsense of course, but people will tell them to you because so many are willing to believe them.  What’s actually going to happen is a much less interesting story called regression to the mean.  Extreme scores tend to be followed up by less extreme scores and performance tends to move toward the averages.  Josh Freeman has a career completion percentage of 58.2% and we would expect his completion percentage this season to move towards that number (This will be important later).    

We can also expect that Freeman’s completion percentage will go up because he moved from Tampa to Minnesota, but not by much.  Given the data we have, we can expect an increase in his completion percentage of about 1%.  This effect is statistically reliable, but not particularly meaningful because it’s going to be swamped by the change due to regression to the mean.

Let’s get to the heart of the question.  Are we going to see more production out of the Viking’s passing game now that Freeman is the starter compared to Matt Cassel or Christian Ponder?  We can actually answer that question better in this specific case than most times.  At one point or another last year, all three of these quarterbacks had starting jobs.  That means we have reasonably good data on production levels for each one.  This really good data gives us some idea of how well each would do in a starting role this year. 

The short answer is no, the Vikings are not better off with Josh Freeman starting.  In fact, I am projecting we will see much much less out the Viking’s already anemic passing attack now that Freeman has been placed in the starting role. 

Here are the numbers.  Pre-season passing yard projections are based on a regression equation that uses statistics in Year A to predict production in Year A+1.  It accounts for 33.5% of the variance in passing yards and has a standard error of the estimate of 314.9 yards.  This model assumes similar production and usage from 2012 to 2013.  Be aware that this model tends to underestimate yardage totals because yardage totals are not normally distributed.   

Some take away points from that table

·         I would not have recommended signing Greg Jennings.  At one time he was a productive wide receiver, but that production has faded with age.

·         Where has Jarius Wright been this season?  He had some good times in 2012.

·         Of the three quarterbacks currently on the Vikings roster, Ponder was the best choice preseason.  I’ve said it before and I’ll say it again.  Ponder is not the problem.  The problem for Minnesota’s passing game for the last three years has been and continues to be a wide-receivers-not-named-Percy-Harvin problem.

The decision to sign Freeman is not looking particularly good here.  However, the previous projections come from 2012 data.  We also have data from 2013 on all three quarterbacks.  Admittedly, at most it’s two or three games for each quarterback and five games for each receiver, but it’s at least something.  Let’s take what we know about each quarterback and receiver’s production and usage for 2013 and input that into our model instead of the 2012 numbers.  Here are the projections showing what I would expect with each of these quarterbacks throwing to these five receivers for a full season.

* Because Freeman’s completion percentage is so abysmal this year, the prediction of Freeman to Wright actually comes out negative. Freeman’s line has been changed to assume he will increase his 2013 completion percentage by 10% as a member of the Vikings, which would be more in line with his historical average.

Here are those same projections pro-rated for the 11 games the Vikings have remaining on their schedule. 

* See note in previous table

So there you go.  According to these projections, the Vikings paid $3 million to lose more than 1,000 yards of production through the air compared to what we would expect if Cassel or Ponder were still the starter.  Go Vikes.

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