Wednesday, August 5, 2015

Six Sigma Drafting - 2015

   Hello and Welcome to Market Fantasy!

Floor drafting:

I love standard deviation and the principals of six sigma when looking at a player’s scoring. One of the most helpful things these two things can do for us is to show us a players’ floor.  By finding a player’s average weekly score and standard deviation, and using the idea of six sigma, we can determine a value for 84% of a player’s scores throughout the year. Looking at the normal distribution below, we get to 84 percent because 68.2% of all weekly scores will fall within 1 standard deviation on either side of the mean. Another 13.6% will fall two standard deviations above the mean, and another 2.1% will fall three standard deviations above the mean, and lastly, .1% of a player’s scores will fall over three standard deviations above the mean. This is what six sigma quality is based on, the idea that 99.9% of results will fall within three standard deviations of the mean. It is used for things like determining an expiration date of food. Take milk, you would get 100 different gallons of milk, let them go bad, log the amount of days each takes to go bad. Then you determine the average and standard deviation from that data set. Say the average is 20 days, with a standard deviation of 3. Since we know that 99.9% of all outcomes will fall right of three standard deviations left of the mean, we can say three standard deviations of three is nine. Subtract that from 20 is 11. So by putting an expiration date of 11 days after a gallon of milk is produced, we can know that on the 11th day, 99.9% will still be good, but will now start to go bad. That way there is an incredibly small chance that any will go bad before the expiration date.

So how does this all relate to fantasy football? I’ll tell you. A lot of us are used to looking at full season values and drafting players projected to score the most points throughout the season. Unfortunately, this isn’t roto and all that matters to us is what a player scores in a given week. Accumulation of points in all honestly doesn’t matter in fantasy football. Every week resets. So while it’s great that player X is projected to outscore player Y at the end of the year, that doesn’t really tell us how a player will do week to week, which is what we’re really concerned about. Case in point, would you rather have a running back who scores 20 points in back to back weeks, or one who scores 39 one week and 1 the other? They are both averaging 20 points over those two weeks, but in all likelihood, the second player killed you in the 1 point week, while the first player most likely helped you win both weeks. This is where establishing a floor comes in extremely handy. Say you have two QBs who both average 20 points per game, player A has a standard deviation of 4, while player B has a standard deviation of 7. Using the idea of the normal distribution like I outlined above, player a will score at least (or have a floor of) 16 points in 84% (or 13 in a 16 game season) of their games. Player B will score at least (or have a floor of) 13 points in at 84% (or 13 in a 16 game season) of their games. Player A will have a three point floor advantage over Player B. Three points might not seem like much, but now take that over an entire roster (say 1QB, 2RB, 2WR, 1TE, 1FLEX, 1K and 1DST). If your team has a three point floor advantage at each position, that is a 27 point (three points times nine players) higher floor than your opponent. While that is an extreme example, you can see the point here. If the idea is to score more points than your opponent each week, then it is imperative to SCORE MORE POINTS EACH WEEK! Having a roster of players projected to score more points over the course of a season is great, but if they have wide variations in their weekly scoring, they will leave you with a lower floor, or greater chance for disaster.

What is the flip side then? Well the flip side is in upside. Take the above scenario. If we can determine that 84% of player A’s  weekly point totals will be above 16 points, we can also take the reverse for player B. In this case, we can look at upside. Take the average of 20 points and add one standard deviation, or 7 points. What we can say now is that 14% of the time, player B will score above 27 points. Conversely, Player A will only exceed 24 points 14% of the time. This means player B has greater upside.

This will be imperfect (since it relies on last years’ data and projections to an extent), but so are all predictive models in fantasy sports. To make this work, you will need two sets of data. You will need weekly scoring from the previous year, and projected scoring from the upcoming year (to get a per game average). Better yet, take upcoming year projections, and try to apply them to projecting each game in the upcoming schedule. This will give you a current standard deviation to use. Take the average points per game and subtract one standard deviation. This will give you the player’s floor. In a couple of my leagues last year, the average points per team per week was around 95 using standard scoring, non-PPR. Using this drafting strategy, your draft should basically be a race to 96 points as your team’s average. We’re looking at floor, however. To get that, let’s take one standard deviation from each position on a standard roster away from the average score to get the floor we need to shoot for.  To do that, let’s just use the average positional standard deviation. (QB – 5.18, RB – 3.58, WR – 3.49, TE – 2.71 FLEX – 3.26, and for kicker and defense, we’ll just use 3) Remember, since there are 2 RB and WR, we have to use that number twice. Doing this, we get 31.29. Now we subtract 31.29 from 96 to get 64.71. This is the floor we need to shoot for in our draft. You can’t control your schedule, but you should be able to put together a team that has a good shot week in and week out.  You will probably be hard pressed to field a team with a projected 96 points as their floor, but remember, the floor is the expected WORST case scenario. Just get a couple players to perform at their mean or above and you’re quickly at or past your league’s mean score.

So now that we know how to draft a team with a good shot at winning week in and week out, there are a couple caveats to this approach. First off, standard deviation by definition is greatly affected by outlier performances. Stars are going to have larger standard deviations because they are more likely to go off, thus inflating their average and therefore, standard deviation. Don’t let this shy you away from them. The other side of this coin is that they have a huge ceiling. Remember, the player will outscore their floor 84% of the time, their average 50% of the time and their ceiling 14% of the time. The other thing to keep in mind is this strategy will leave you with a lot of boring old very good players. They tend to have the least amount of fluctuation in their game. These are the running backs who just go out and score 10 fantasy points per week one way or another. While they are unsexy, there is a lot to be said for this kind of player. As the numbers show, this is the kind of player who will rarely kill you. They are also less likely to give you the crazy week, but they will almost always help you win.
Below, you’ll see the average projected score per game per player for this year along with their standard deviation from last year. For rookies, I’ll just use last year’s average standard deviation for their position. Along with those numbers, I’ll post the +/- 1 standard deviation score to help you in your drafting. Take a look at the numbers, and work with them to find the best combination of safety vs. upside. Doing this will help take a lot of the guesswork out of your team and should win you a lot of games.

One last note on the numbers. When determining the standard deviation for each player, I took out games in which that player scored 0 points from sitting out for whatever reason. When you look at the weekly scoring at a lot of places, you’ll just see a 0 populated. I can see both sides of the argument for using a 0 for a game in which a player doesn’t play. On the one hand, they didn’t score any points, but on the other, a 0 will really throw off the numbers for what was essentially a non-event for the player. By and large, if a guy scores a 0, it was due to an issue you knew about ahead of time and could safely find someone else. You most likely weren’t using players on games they didn’t play. Make sense?

I can't fit the stats in one page, so those can be viewed here and here.

As always, thanks for reading, stay tuned for lots more pre-draft features and good luck!
File:Standard deviation diagram.svg

QB:

Name
Team
Bye
Average
Std Dev
Floor
Ceiling
Andrew Luck
IND
10
25.96
8.85
17.10
34.81
Aaron Rodgers
GB
7
25.05
8.24
16.81
33.29
Russell Wilson
SEA
9
22.81
9.20
13.61
32.01
Ben Roethlisberger
PIT
11
22.43
10.14
12.29
32.57
Cam Newton
CAR
5
21.44
7.59
13.85
29.03
Peyton Manning
DEN
7
21.36
7.91
13.45
29.27
Matt Ryan
ATL
10
21.18
6.78
14.40
27.97
Drew Brees
NO
11
21.03
5.66
15.36
26.69
Tony Romo
DAL
6
20.85
6.11
14.74
26.97
Matthew Stafford
DET
9
20.59
7.36
13.24
27.95
Eli Manning
NYG
11
20.51
7.50
13.01
28.01
Ryan Tannehill
MIA
5
20.44
6.61
13.83
27.04
Philip Rivers
SD
10
19.80
8.01
11.79
27.81
Colin Kaepernick
SF
10
19.44
6.54
12.91
25.98
Teddy Bridgewater
MIN
5
19.32
5.28
14.04
24.60
Jay Cutler
CHI
7
18.99
6.18
12.80
25.17
Jameis Winston
TB
6
18.63
5.18
13.45
23.80
Andy Dalton
CIN
7
18.55
7.46
11.09
26.01
Joe Flacco
BAL
9
18.15
7.01
11.14
25.16
Sam Bradford
PHI
8
17.98
5.18
12.81
23.16
Robert Griffin III
WAS
8
17.61
7.70
9.90
25.31
Alex Smith
KC
9
17.41
4.70
12.70
22.11
Carson Palmer
ARI
9
17.20
4.87
12.33
22.07
Blake Bortles
JAX
8
16.98
4.56
12.42
21.55
Derek Carr
OAK
6
16.93
6.18
10.75
23.11
Tom Brady
NE
4
16.86
8.74
8.12
25.60
Nick Foles
STL
6
16.78
7.32
9.46
24.11
Marcus Mariota
TEN
4
16.63
5.18
11.45
21.80
Geno Smith
NYJ
5
15.81
8.89
6.93
24.70
Josh McCown
CLE
11
12.14
7.28
4.86
19.42
Brian Hoyer
HOU
9
9.85
5.37
4.48
15.22
Tyrod Taylor
BUF
8
7.16
5.18
1.98
12.34
EJ Manuel
BUF
8
6.97
1.78
5.19
8.75
Ryan Mallett
HOU
9
6.36
7.08
-0.72
13.45
Johnny Manziel
CLE
11
4.41
3.74
0.66
8.15
Jimmy Garoppolo
NE
4
4.05
3.33
0.72
7.38
Mark Sanchez
PHI
8
3.98
5.60
-1.63
9.58
Ryan Fitzpatrick
NYJ
5
2.67
9.49
-6.82
12.15
Matt Cassel
BUF
8
2.55
5.45
-2.90
8.00
Kirk Cousins
WAS
8
2.34
8.28
-5.94
10.63

RB:
Name
Team
Bye
Average
Std Dev
Floor
Ceiling
Jamaal Charles
KC
9
14.57
8.83
5.74
23.40
LeVeon Bell
PIT
11
14.55
9.35
5.20
23.90
Eddie Lacy
GB
7
14.44
7.17
7.28
21.61
Adrian Peterson
MIN
5
14.40
3.58
10.82
17.98
Marshawn Lynch
SEA
9
14.11
8.91
5.20
23.02
DeMarco Murray
PHI
8
13.50
5.09
8.41
18.59
Matt Forte
CHI
7
13.34
7.18
6.16
20.52
C.J. Anderson
DEN
7
12.98
10.30
2.68
23.28
Jeremy Hill
CIN
7
11.99
7.99
4.00
19.98
LeSean McCoy
BUF
8
11.96
5.67
6.28
17.63
Justin Forsett
BAL
9
11.74
6.74
5.01
18.48
Joseph Randle
DAL
6
11.34
4.02
7.33
15.36
Mark Ingram
NO
11
11.11
6.19
4.92
17.29
Alfred Morris
WAS
8
11.05
6.31
4.74
17.36
Lamar Miller
MIA
5
10.94
5.85
5.08
16.79
Frank Gore
IND
10
10.83
6.53
4.29
17.36
Melvin Gordon
SD
10
10.54
3.58
6.97
14.12
Todd Gurley
STL
6
10.08
3.58
6.50
13.66
Latavius Murray
OAK
6
10.06
6.67
3.39
16.73
T.J. Yeldon
JAX
8
9.94
3.58
6.36
13.52
Carlos Hyde
SF
10
9.58
3.15
6.43
12.73
Andre Ellington
ARI
9
9.53
6.50
3.03
16.03
Jonathan Stewart
CAR
5
9.04
5.35
3.68
14.39
Joique Bell
DET
9
8.86
6.28
2.59
15.14
Chris Ivory
NYJ
5
8.76
4.85
3.91
13.61
Rashad Jennings
NYG
11
8.75
7.19
1.56
15.94
C.J. Spiller
NO
11
8.72
3.55
5.17
12.26
LeGarrette Blount
NE
4
8.66
5.53
3.13
14.19
Isaiah Crowell
CLE
11
8.55
5.68
2.87
14.23
Giovani Bernard
CIN
7
8.36
6.31
2.05
14.67
Tevin Coleman
ATL
10
8.23
3.58
4.65
11.80
Devonta Freeman
ATL
10
7.62
3.40
4.22
11.02
Ameer Abdullah
DET
9
7.46
3.58
3.88
11.04
Shane Vereen
NYG
11
7.29
5.50
1.78
12.79
Alfred Blue
HOU
9
6.75
4.53
2.22
11.28
Doug Martin
TB
6
6.62
4.07
2.55
10.69
Duke Johnson
CLE
11
6.54
3.58
2.96
10.12
David Johnson
ARI
9
6.41
3.58
2.83
9.98
Charles Sims
TB
6
6.38
3.48
2.89
9.86
David Cobb
TEN
4
5.93
3.58
2.35
9.51
Bishop Sankey
TEN
4
5.87
3.02
2.84
8.89
Danny Woodhead
SD
10
5.82
2.58
3.24
8.40
Darren Sproles
PHI
8
5.59
5.59
0.00
11.19
Tre Mason
STL
6
5.53
8.18
-2.65
13.70
Darren McFadden
DAL
6
5.38
3.54
1.84
8.92
Ryan Mathews
PHI
8
5.29
4.25
1.04
9.54
Fred Jackson
BUF
8
5.23
3.38
1.86
8.61
Montee Ball
DEN
7
5.21
4.75
0.46
9.96
Knile Davis
KC
9
5.06
7.81
-2.75
12.87
Arian Foster
HOU
9
5.05
7.86
-2.81
12.91
Roy Helu
OAK
6
4.86
3.56
1.30
8.41
Jay Ajayi
MIA
5
4.84
3.58
1.27
8.42
Lorenzo Taliaferro
BAL
9
4.59
6.22
-1.62
10.81
Reggie Bush
SF
10
4.56
4.81
-0.24
9.37
Pierre Thomas
HOU
9
4.48
5.86
-1.39
10.34
Theo Riddick
DET
9
4.37
4.85
-0.48
9.22
Dan Herron
IND
10
4.30
4.36
-0.06
8.66
Javorius Allen
BAL
9
4.15
3.58
0.57
7.73
James Starks
GB
7
4.15
4.10
0.05
8.25
DeAngelo Williams
PIT
11
4.09
1.58
2.51
5.67
Bilal Powell
NYJ
5
4.05
2.27
1.78
6.32
Denard Robinson
JAX
8
3.96
6.55
-2.60
10.51
Andre Williams
NYG
11
3.93
5.51
-1.58
9.44
Lance Dunbar
DAL
6
3.93
1.50
2.42
5.43
Matt Asiata
MIN
5
3.70
8.20
-4.50
11.90
Matt Jones
WAS
8
3.54
3.58
-0.04
7.12
Benny Cunningham
STL
6
3.49
3.46
0.04
6.95
James White
NE
4
3.37
1.17
2.20
4.54
Khiry Robinson
NO
11
3.35
4.61
-1.26
7.96
Travaris Cadet
NE
4
3.24
2.22
1.02
5.46
Jerick McKinnon
MIN
5
3.19
4.25
-1.06
7.44
Robert Turbin
SEA
9
3.08
3.06
0.02
6.13
Silas Redd
WAS
8
2.84
3.42
-0.58
6.26
Jacquizz Rodgers
CHI
7
2.84
2.62
0.22
5.45
DeAnthony Thomas
KC
9
2.66
1.97
0.69
4.63
Antone Smith
ATL
10
2.63
5.95
-3.32
8.58
Jonas Gray
NE
4
2.59
13.52
-10.92
16.11
Mike Tolbert
CAR
5
2.59
1.73
0.87
4.32
Toby Gerhart
JAX
8
2.52
3.47
-0.95
5.99
Christine Michael
SEA
9
2.47
2.07
0.40
4.54

WR:
Name
Team
Bye
Average
Std Dev
Floor
Ceiling
Antonio Brown
PIT
11
13.64
5.69
7.95
19.33
Odell Beckham Jr
NYG
11
13.59
8.64
4.95
22.22
Demaryius Thomas
DEN
7
13.17
8.93
4.24
22.10
Dez Bryant
DAL
6
13.04
7.21
5.83
20.24
Jordy Nelson
GB
7
12.78
7.88
4.90
20.66
Julio Jones
ATL
10
12.59
8.22
4.37
20.82
Calvin Johnson
DET
9
12.52
8.85
3.67
21.37
A.J. Green
CIN
7
11.52
6.98
4.54
18.50
Randall Cobb
GB
7
11.43
5.45
5.98
16.87
T.Y. Hilton
IND
10
11.16
8.00
3.16
19.15
Alshon Jeffery
CHI
7
10.93
5.24
5.69
16.16
Mike Evans
TB
6
10.68
8.00
2.68
18.69
DeAndre Hopkins
HOU
9
10.62
8.10
2.52
18.72
Emmanuel Sanders
DEN
7
10.03
6.52
3.51
16.54
Kelvin Benjamin
CAR
5
9.58
5.57
4.01
15.15
Brandin Cooks
NO
11
9.41
6.19
3.21
15.60
Jordan Matthews
PHI
8
9.07
6.95
2.12
16.02
DeSean Jackson
WAS
8
8.89
6.90
1.99
15.80
Sammy Watkins
BUF
8
8.89
7.21
1.68
16.09
Keenan Allen
SD
10
8.84
6.66
2.19
15.50
Golden Tate
DET
9
8.79
5.68
3.11
14.48
Brandon Marshall
NYJ
5
8.78
6.96
1.83
15.74
Martavis Bryant
PIT
11
8.51
7.18
1.34
15.69
Amari Cooper
OAK
6
8.49
3.49
5.00
11.98
Nelson Agholor
PHI
8
8.41
3.49
4.91
11.90
Vincent Jackson
TB
6
8.41
4.15
4.25
12.56
Brandon LaFell
NE
4
8.28
5.95
2.33
14.22
Andre Johnson
IND
10
8.24
4.85
3.39
13.09
Allen Robinson
JAX
8
8.21
3.39
4.82
11.60
Mike Wallace
MIN
5
8.19
4.16
4.03
12.35
Anquan Boldin
SF
10
8.07
5.46
2.61
13.53
Roddy White
ATL
10
7.93
4.11
3.81
12.04
Julian Edelman
NE
4
7.85
5.45
2.40
13.30
Torrey Smith
SF
10
7.75
6.04
1.71
13.79
Michael Floyd
ARI
9
7.61
7.00
0.61
14.60
Charles Johnson
MIN
5
7.55
4.78
2.77
12.33
Jeremy Maclin
KC
9
7.44
8.15
-0.70
15.59
Larry Fitzgerald
ARI
9
7.39
5.70
1.70
13.09
Eric Decker
NYJ
5
7.36
6.46
0.90
13.82
Steve Smith
BAL
9
7.11
6.87
0.25
13.98
Jarvis Landry
MIA
5
6.96
4.18
2.78
11.15
Marques Colston
NO
11
6.90
3.20
3.70
10.10
John Brown
ARI
9
6.84
5.30
1.55
12.14
Terrance Williams
DAL
6
6.75
5.53
1.22
12.28
Victor Cruz
NYG
11
6.73
5.51
1.22
12.24
Pierre Garcon
WAS
8
6.71
5.17
1.54
11.88
Kenny Stills
MIA
5
6.59
5.37
1.22
11.95
Malcom Floyd
SD
10
6.53
3.67
2.86
10.19
Kendall Wright
TEN
4
6.45
5.76
0.69
12.21
Doug Baldwin
SEA
9
6.44
4.89
1.55
11.33
DeVante Parker
MIA
5
6.08
0.00
6.08
6.08
Rueben Randle
NYG
11
6.05
5.07
0.98
11.12
Marvin Jones
CIN
7
5.96
3.49
2.46
9.45
Brian Quick
STL
6
5.86
6.58
-0.72
12.44
Breshad Perriman
BAL
9
5.58
3.49
2.09
9.07
Dwayne Bowe
CLE
11
5.45
2.31
3.14
7.76
Kenny Britt
STL
6
5.42
4.66
0.75
10.08
Allen Hurns
JAX
8
5.37
7.07
-1.70
12.44
Steve Johnson
SD
10
5.30
3.62
1.68
8.92
Davante Adams
GB
7
5.25
3.65
1.60
8.90
Taylor Gabriel
CLE
11
5.24
4.11
1.14
9.35
Cody Latimer
DEN
7
5.23
0.25
4.98
5.48
Michael Crabtree
OAK
6
5.17
4.74
0.43
9.91
Kevin White
CHI
7
5.08
3.49
1.59
8.57
Kamar Aiken
BAL
9
4.81
3.58
1.23
8.39
Dorial Green-Beckham
TEN
4
4.62
3.49
1.13
8.11
Greg Jennings
MIA
5
4.58
3.62
0.96
8.19
Percy Harvin
BUF
8
4.56
5.39
-0.83
9.95
Robert Woods
BUF
8
4.50
5.05
-0.55
9.55
Markus Wheaton
PIT
11
4.48
3.50
0.98
7.98
Brian Hartline
CLE
11
4.43
2.79
1.64
7.22
Marqise Lee
JAX
8
4.43
3.64
0.79
8.06
Jermaine Kearse
SEA
9
4.42
2.51
1.91
6.93
Stedman Bailey
STL
6
4.41
4.38
0.04
8.79
Cecil Shorts
HOU
9
4.35
3.94
0.41
8.29
Phillip Dorsett
IND
10
4.33
3.49
0.84
7.82
Mohamed Sanu
CIN
7
4.29
6.26
-1.97
10.55
Harry Douglas
TEN
4
4.28
3.42
0.86
7.69
Cordarrelle Patterson
MIN
5
4.23
4.76
-0.54
8.99
Josh Huff
PHI
8
4.18
1.37
2.80
5.55
Chris Matthews
SEA
9
4.13
3.49
0.64
7.62
Andrew Hawkins
CLE
11
4.10
4.64
-0.54
8.74
Nick Toon
NO
11
4.06
1.57
2.49
5.62
Devin Funchess
CAR
5
4.03
3.49
0.53
7.52
Cole Beasley
DAL
6
3.98
4.54
-0.56
8.52
Albert Wilson
KC
9
3.97
2.86
1.11
6.82
Eddie Royal
CHI
7
3.88
6.18
-2.30
10.06
Donte Moncrief
IND
10
3.81
7.69
-3.87
11.50
Tavon Austin
STL
6
3.78
2.58
1.19
6.36
Rod Streater
OAK
6
3.71
4.24
-0.52
7.95
Andre Roberts
WAS
8
3.71
2.88
0.82
6.59
Nate Washington
HOU
9
3.58
4.08
-0.51
7.66
Tyler Lockett
SEA
9
3.48
3.49
-0.01
6.97
Chris Conley
KC
9
3.23
3.49
-0.27
6.72
Jerricho Cotchery
CAR
5
3.15
2.51
0.64
5.66
Quinton Patton
SF
10
3.04
0.30
2.74
3.34
Danny Amendola
NE
4
2.93
2.43
0.50
5.36
Riley Cooper
PHI
8
2.88
4.07
-1.19
6.94
Jeremy Kerley
NYJ
5
2.84
3.39
-0.55
6.23
Marquess Wilson
CHI
7
2.81
2.70
0.11
5.51

TE:
Name
Team
Bye
Average
Std Dev
Floor
Ceiling
Rob Gronkowski
NE
4
11.89
6.58
5.31
18.48
Jimmy Graham
SEA
9
9.04
6.33
2.71
15.38
Travis Kelce
KC
9
8.06
4.23
3.83
12.28
Greg Olsen
CAR
5
7.93
5.14
2.80
13.07
Martellus Bennett
CHI
7
7.44
5.03
2.40
12.47
Zach Ertz
PHI
8
7.13
3.92
3.21
11.05
Delanie Walker
TEN
4
6.41
5.38
1.02
11.79
Dwayne Allen
IND
10
6.36
3.49
2.87
9.84
Jason Witten
DAL
6
6.26
3.84
2.41
10.10
Julius Thomas
JAX
8
6.04
8.36
-2.31
14.40
Josh Hill
NO
11
5.94
4.40
1.54
10.33
Heath Miller
PIT
11
5.86
4.32
1.54
10.18
Jordan Cameron
MIA
5
5.83
5.23
0.59
11.06
Austin Seferian-Jenkins
TB
6
5.56
3.16
2.40
8.72
Coby Fleener
IND
10
5.37
6.59
-1.22
11.95
Charles Clay
BUF
8
5.14
3.97
1.17
9.12
Kyle Rudolph
MIN
5
5.06
2.25
2.81
7.31
Antonio Gates
SD
10
4.92
7.61
-2.69
12.53
Tyler Eifert
CIN
7
4.92
2.71
2.21
7.63
Owen Daniels
DEN
7
4.87
4.29
0.57
9.16
Larry Donnell
NYG
11
4.66
5.68
-1.02
10.33
Jared Cook
STL
6
4.62
4.70
-0.08
9.32
Jace Amaro
NYJ
5
4.59
3.36
1.22
7.95
Eric Ebron
DET
9
4.44
2.30
2.15
6.74
Jordan Reed
WAS
8
4.31
3.69
0.62
7.99
Vernon Davis
SF
10
4.18
4.12
0.06
8.30
Ladarius Green
SD
10
4.16
2.17
1.98
6.33
Clive Walford
OAK
6
3.96
2.71
1.25
6.66
Richard Rodgers
GB
7
3.76
2.94
0.83
6.70
Mychal Rivera
OAK
6
3.43
5.33
-1.90
8.76
Rob Housler
CLE
11
3.39
0.72
2.67
4.12
Garrett Graham
HOU
9
3.19
2.62
0.57
5.81
Crockett Gillmore
BAL
9
2.84
1.66
1.18
4.50
Jacob Tamme
ATL
10
2.78
2.67
0.11
5.44
Lance Kendricks
STL
6
2.59
3.21
-0.63
5.80
Jermaine Gresham
ARI
9
2.59
3.73
-1.14
6.32
Virgil Green
DEN
7
2.44
3.94
-1.49
6.38
Niles Paul
WAS
8
2.40
4.09
-1.69
6.49
Andrew Quarless
GB
7
2.36
2.81
-0.45
5.18
Gavin Escobar
DAL
6
2.35
6.06
-3.71
8.41
Scott Chandler
NE
4
2.34
3.44
-1.10
5.77
Brent Celek
PHI
8
2.32
3.14
-0.82
5.46
Ben Watson
NO
11
2.31
2.45
-0.13
4.76
Marcedes Lewis
JAX
8
2.16
4.29
-2.13
6.45
Jeff Cumberland
NYJ
5
1.82
3.89
-2.07
5.71

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