Hello and Welcome to Market Fantasy!
Six Sigma 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 is what a player scores in a given week. 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 16% 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 – 9.7, RB – 6.54,
WR – 7.06, TE – 4.88, FLEX – 6.75 K – we’ll use 4, DST – we’ll use 5) Remember,
since there are 2 RB and WR, we have to use that number twice. Doing this, we
get 58.53. Now we subtract 58.53 from 96 to get 37.47. 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, I've posted 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. 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. Remember, the floor represents the players' floor for 84% of the games, and the ceiling is what they will exceed 16% of the time.
As always, thanks for reading and good luck!
QB:
|
Rank
|
Player
|
St Dev
|
Avg
|
Floor
|
Ceiling
|
|
1
|
Peyton Manning QB DEN
|
12.78
|
27.10
|
14.32
|
39.88
|
|
2
|
Drew Brees QB NO
|
12.20
|
24.97
|
12.77
|
37.18
|
|
3
|
Aaron Rodgers QB GB
|
12.20
|
24.01
|
11.81
|
36.21
|
|
4
|
Nick Foles QB PHI
|
15.20
|
23.05
|
7.85
|
38.25
|
|
5
|
Matthew Stafford QB DET
|
9.12
|
22.12
|
13.01
|
31.24
|
|
6
|
Colin Kaepernick QB SF
|
10.15
|
22.00
|
11.84
|
32.15
|
|
7
|
Matt Ryan QB ATL
|
6.92
|
21.40
|
14.47
|
28.32
|
|
8
|
Tony Romo QB DAL
|
10.73
|
21.06
|
10.33
|
31.79
|
|
9
|
Andrew Luck QB IND
|
8.94
|
20.97
|
12.03
|
29.91
|
|
10
|
Cam Newton QB CAR
|
9.06
|
20.94
|
11.89
|
30.00
|
|
11
|
Robert Griffin III QB WAS
|
9.70
|
20.79
|
11.09
|
30.49
|
|
12
|
Russell Wilson QB SEA
|
7.75
|
20.73
|
12.98
|
28.48
|
|
13
|
Tom Brady QB NE
|
10.78
|
20.55
|
9.77
|
31.33
|
|
14
|
Philip Rivers QB SD
|
9.19
|
20.49
|
11.30
|
29.68
|
|
15
|
Ben Roethlisberger QB PIT
|
9.82
|
20.02
|
10.19
|
29.84
|
|
16
|
Jay Cutler QB CHI
|
8.10
|
19.66
|
11.57
|
27.76
|
|
17
|
Ryan Tannehill QB MIA
|
7.33
|
18.56
|
11.23
|
25.89
|
|
18
|
Andy Dalton QB CIN
|
11.73
|
18.49
|
6.77
|
30.22
|
|
19
|
Sam Bradford QB STL
|
9.70
|
18.35
|
8.65
|
28.05
|
|
20
|
Alex Smith QB KC
|
21.11
|
18.05
|
-3.06
|
39.15
|
|
21
|
Josh McCown QB TB
|
14.09
|
18.04
|
3.95
|
32.14
|
|
22
|
Carson Palmer QB ARI
|
8.74
|
17.23
|
8.49
|
25.98
|
|
23
|
Johnny Manziel QB CLE
|
9.70
|
16.80
|
7.10
|
26.50
|
|
24
|
Teddy Bridgewater QB MIN
|
9.70
|
15.87
|
6.17
|
25.57
|
|
25
|
Jake Locker QB TEN
|
9.70
|
15.73
|
6.03
|
25.43
|
|
26
|
Joe Flacco QB BAL
|
6.52
|
15.66
|
9.13
|
22.18
|
|
27
|
Eli Manning QB NYG
|
9.27
|
14.74
|
5.47
|
24.01
|
|
28
|
EJ Manuel QB BUF
|
7.64
|
14.68
|
7.04
|
22.31
|
|
29
|
Ryan Fitzpatrick QB HOU
|
11.22
|
11.66
|
0.44
|
22.88
|
|
30
|
Matt Schaub QB OAK
|
10.44
|
11.63
|
1.18
|
22.07
|
RB:
|
Rank
|
Player
|
Std Dev
|
Avg
|
Floor
|
Ceiling
|
|
1
|
Jamaal Charles RB KC
|
11.49
|
16.46
|
4.97
|
27.95
|
|
2
|
Matt Forte RB CHI
|
8.41
|
15.75
|
7.34
|
24.16
|
|
3
|
LeSean McCoy RB PHI
|
9.44
|
15.73
|
6.29
|
25.18
|
|
4
|
Adrian Peterson RB MIN
|
9.49
|
14.39
|
4.90
|
23.87
|
|
5
|
Eddie Lacy RB GB
|
7.33
|
13.55
|
6.22
|
20.88
|
|
6
|
DeMarco Murray RB DAL
|
8.03
|
13.41
|
5.38
|
21.44
|
|
7
|
Montee Ball RB DEN
|
5.27
|
13.22
|
7.95
|
18.49
|
|
8
|
Arian Foster RB HOU
|
6.54
|
13.15
|
6.61
|
19.69
|
|
9
|
Giovani Bernard RB CIN
|
5.76
|
12.92
|
7.17
|
18.68
|
|
10
|
Marshawn Lynch RB SEA
|
8.28
|
12.51
|
4.23
|
20.79
|
|
11
|
Le'Veon Bell RB PIT
|
4.74
|
12.43
|
7.69
|
17.17
|
|
12
|
Andre Ellington RB ARI
|
6.71
|
11.89
|
5.19
|
18.60
|
|
13
|
Alfred Morris RB WAS
|
4.75
|
11.83
|
7.08
|
16.57
|
|
14
|
Toby Gerhart RB JAC
|
6.54
|
11.74
|
5.20
|
18.28
|
|
15
|
Bishop Sankey RB TEN
|
6.54
|
11.52
|
4.98
|
18.06
|
|
16
|
Zac Stacy RB STL
|
8.25
|
11.46
|
3.21
|
19.70
|
|
17
|
Doug Martin RB TB
|
6.54
|
11.09
|
4.55
|
17.63
|
|
18
|
Joique Bell RB DET
|
7.10
|
10.86
|
3.75
|
17.96
|
|
19
|
Ryan Mathews RB SD
|
6.42
|
10.82
|
4.39
|
17.24
|
|
20
|
Reggie Bush RB DET
|
8.03
|
10.58
|
2.55
|
18.61
|
|
21
|
Rashad Jennings RB NYG
|
8.94
|
10.21
|
1.26
|
19.15
|
|
22
|
Chris Johnson RB NYJ
|
7.74
|
10.19
|
2.45
|
17.93
|
|
23
|
Shane Vereen RB NE
|
7.40
|
10.13
|
2.73
|
17.52
|
|
24
|
Stevan Ridley RB NE
|
7.31
|
9.81
|
2.50
|
17.12
|
|
25
|
C.J. Spiller RB BUF
|
6.88
|
9.69
|
2.81
|
16.56
|
|
26
|
Frank Gore RB SF
|
6.39
|
9.68
|
3.29
|
16.07
|
|
27
|
Trent Richardson RB IND
|
3.83
|
8.95
|
5.12
|
12.78
|
|
28
|
Ben Tate RB CLE
|
6.99
|
8.91
|
1.92
|
15.90
|
|
29
|
Fred Jackson RB BUF
|
5.61
|
8.84
|
3.23
|
14.44
|
|
30
|
Steven Jackson RB ATL
|
5.33
|
8.76
|
3.43
|
14.09
|
|
31
|
Bernard Pierce RB BAL
|
6.54
|
8.72
|
2.18
|
15.26
|
|
32
|
Knowshon Moreno RB MIA
|
8.36
|
8.54
|
0.18
|
16.90
|
|
33
|
Maurice Jones-Drew RB OAK
|
4.92
|
8.39
|
3.48
|
13.31
|
|
34
|
Terrance West RB CLE
|
6.54
|
8.31
|
1.77
|
14.85
|
|
35
|
Pierre Thomas RB NO
|
5.96
|
8.25
|
2.29
|
14.20
|
|
36
|
Ray Rice RB BAL
|
6.15
|
8.22
|
2.06
|
14.37
|
|
37
|
DeAngelo Williams RB CAR
|
4.82
|
7.96
|
3.15
|
12.78
|
|
38
|
Danny Woodhead RB SD
|
5.41
|
7.65
|
2.24
|
13.05
|
|
39
|
Devonta Freeman RB ATL
|
6.54
|
7.56
|
1.02
|
14.10
|
|
40
|
Jeremy Hill RB CIN
|
6.54
|
7.05
|
0.52
|
13.59
|
|
41
|
Lamar Miller RB MIA
|
4.42
|
6.93
|
2.51
|
11.34
|
|
42
|
Tre Mason RB STL
|
6.54
|
6.92
|
0.38
|
13.46
|
|
43
|
LeGarrette Blount RB PIT
|
8.92
|
6.87
|
-2.05
|
15.79
|
|
44
|
Khiry Robinson RB NO
|
6.54
|
6.81
|
0.27
|
13.34
|
|
45
|
Darren Sproles RB PHI
|
6.96
|
6.70
|
-0.26
|
13.66
|
|
46
|
Christine Michael RB SEA
|
6.54
|
6.54
|
0.00
|
13.08
|
|
47
|
Stepfan Taylor RB ARI
|
6.54
|
6.52
|
-0.02
|
13.06
|
|
48
|
Charles Sims RB TB
|
6.54
|
6.06
|
-0.48
|
12.60
|
|
49
|
Chris Ivory RB NYJ
|
6.61
|
5.97
|
-0.64
|
12.59
|
|
50
|
Darren McFadden RB OAK
|
6.41
|
5.89
|
-0.52
|
12.29
|
|
51
|
Dexter McCluster RB TEN
|
6.54
|
5.71
|
-0.82
|
12.25
|
|
52
|
James White RB NE
|
6.54
|
5.43
|
-1.11
|
11.97
|
|
53
|
Carlos Hyde RB SF
|
6.54
|
5.36
|
-1.18
|
11.90
|
|
54
|
Ahmad Bradshaw RB IND
|
6.54
|
5.34
|
-1.20
|
11.88
|
|
55
|
Roy Helu RB WAS
|
6.54
|
5.27
|
-1.27
|
11.80
|
WR:
|
Rank
|
Player
|
Std Dev
|
Avg
|
Floor
|
Ceiling
|
|
1
|
Calvin Johnson WR DET
|
12.50
|
14.90
|
2.41
|
27.40
|
|
2
|
Demaryius Thomas WR DEN
|
8.70
|
14.15
|
5.45
|
22.84
|
|
3
|
Brandon Marshall WR CHI
|
7.00
|
12.79
|
5.79
|
19.79
|
|
4
|
Dez Bryant WR DAL
|
7.75
|
12.54
|
4.78
|
20.29
|
|
5
|
Randall Cobb WR GB
|
6.75
|
12.13
|
5.38
|
18.88
|
|
6
|
A.J. Green WR CIN
|
8.70
|
12.12
|
3.42
|
20.82
|
|
7
|
Antonio Brown WR PIT
|
8.70
|
11.50
|
2.80
|
20.21
|
|
8
|
Julio Jones WR ATL
|
6.34
|
11.23
|
4.90
|
17.57
|
|
9
|
Jordy Nelson WR GB
|
7.28
|
11.17
|
3.89
|
18.45
|
|
10
|
Alshon Jeffery WR CHI
|
11.23
|
11.11
|
-0.12
|
22.34
|
|
11
|
Larry Fitzgerald WR ARI
|
6.25
|
10.27
|
4.02
|
16.53
|
|
12
|
Vincent Jackson WR TB
|
9.10
|
10.02
|
0.92
|
19.12
|
|
13
|
Michael Crabtree WR SF
|
3.88
|
9.92
|
6.03
|
13.80
|
|
14
|
Michael Floyd WR ARI
|
6.68
|
9.87
|
3.19
|
16.54
|
|
15
|
Andre Johnson WR HOU
|
10.77
|
9.71
|
-1.06
|
20.49
|
|
16
|
Keenan Allen WR SD
|
6.31
|
9.48
|
3.18
|
15.79
|
|
17
|
Wes Welker WR DEN
|
5.10
|
9.32
|
4.22
|
14.41
|
|
18
|
Pierre Garcon WR WAS
|
7.66
|
9.08
|
1.42
|
16.74
|
|
19
|
Cordarrelle Patterson WR MIN
|
6.64
|
9.04
|
2.40
|
15.68
|
|
20
|
Victor Cruz WR NYG
|
7.06
|
8.90
|
1.84
|
15.96
|
|
21
|
Terrance Williams WR DAL
|
6.78
|
8.89
|
2.11
|
15.67
|
|
22
|
Percy Harvin WR SEA
|
7.06
|
8.84
|
1.77
|
15.90
|
|
23
|
Golden Tate WR DET
|
6.93
|
8.67
|
1.74
|
15.60
|
|
24
|
DeSean Jackson WR WAS
|
9.77
|
8.67
|
-1.11
|
18.44
|
|
25
|
Eric Decker WR NYJ
|
11.93
|
8.58
|
-3.36
|
20.51
|
|
26
|
Torrey Smith WR BAL
|
8.93
|
8.55
|
-0.38
|
17.48
|
|
27
|
Roddy White WR ATL
|
6.70
|
8.45
|
1.75
|
15.15
|
|
28
|
Riley Cooper WR PHI
|
9.85
|
8.42
|
-1.43
|
18.28
|
|
29
|
Rueben Randle WR NYG
|
5.86
|
8.31
|
2.45
|
14.17
|
|
30
|
Emmanuel Sanders WR DEN
|
4.59
|
8.29
|
3.70
|
12.89
|
|
31
|
Julian Edelman WR NE
|
7.57
|
8.13
|
0.56
|
15.70
|
|
32
|
Mike Wallace WR MIA
|
7.08
|
8.02
|
0.95
|
15.10
|
|
33
|
Marvin Jones WR CIN
|
6.34
|
7.98
|
1.64
|
14.32
|
|
34
|
Marques Colston WR NO
|
7.16
|
7.98
|
0.82
|
15.13
|
|
35
|
Kenny Stills WR NO
|
7.46
|
7.68
|
0.22
|
15.14
|
|
36
|
Kendall Wright WR TEN
|
5.16
|
7.61
|
2.45
|
12.77
|
|
37
|
Sammy Watkins WR BUF
|
7.06
|
7.52
|
0.46
|
14.59
|
|
38
|
Justin Hunter WR TEN
|
7.06
|
7.46
|
0.40
|
14.53
|
|
39
|
T.Y. Hilton WR IND
|
9.78
|
7.35
|
-2.44
|
17.13
|
|
40
|
Mike Evans WR TB
|
7.06
|
7.33
|
0.27
|
14.39
|
|
41
|
Jeremy Maclin WR PHI
|
7.06
|
7.29
|
0.23
|
14.35
|
|
42
|
Tavon Austin WR STL
|
7.06
|
7.21
|
0.14
|
14.27
|
|
43
|
Dwayne Bowe WR KC
|
4.18
|
7.15
|
2.98
|
11.33
|
|
44
|
Kelvin Benjamin WR CAR
|
7.06
|
7.08
|
0.01
|
14.14
|
|
45
|
Reggie Wayne WR IND
|
7.06
|
7.07
|
0.01
|
14.14
|
|
46
|
Doug Baldwin WR SEA
|
4.47
|
7.02
|
2.55
|
11.49
|
|
47
|
Brandin Cooks WR NO
|
7.06
|
6.93
|
-0.13
|
13.99
|
|
48
|
Brian Hartline WR MIA
|
5.36
|
6.78
|
1.42
|
12.14
|
|
49
|
Anquan Boldin WR SF
|
8.95
|
6.66
|
-2.29
|
15.61
|
|
50
|
Jerricho Cotchery WR CAR
|
7.00
|
6.58
|
-0.41
|
13.58
|
|
51
|
Rod Streater WR OAK
|
5.45
|
6.54
|
1.09
|
11.99
|
|
52
|
DeAndre Hopkins WR HOU
|
5.03
|
6.38
|
1.35
|
11.42
|
|
53
|
Hakeem Nicks WR IND
|
5.05
|
6.35
|
1.30
|
11.39
|
|
54
|
Malcom Floyd WR SD
|
6.68
|
6.27
|
-0.41
|
12.94
|
|
55
|
Jarrett Boykin WR GB
|
5.51
|
6.21
|
0.69
|
11.72
|
|
56
|
James Jones WR OAK
|
6.34
|
6.04
|
-0.29
|
12.38
|
|
57
|
Robert Woods WR BUF
|
4.42
|
6.01
|
1.59
|
10.43
|
|
58
|
Harry Douglas WR ATL
|
6.90
|
5.91
|
-0.98
|
12.81
|
|
59
|
Cecil Shorts WR JAC
|
7.06
|
5.86
|
-1.20
|
12.92
|
|
60
|
Danny Amendola WR NE
|
6.41
|
5.68
|
-0.73
|
12.09
|
TE:
|
Rank
|
Player
|
Std Dev
|
Avg
|
Floor
|
Ceiling
|
|
1
|
Jimmy Graham TE NO
|
8.29
|
13.24
|
4.95
|
21.53
|
|
2
|
Julius Thomas TE DEN
|
7.23
|
10.48
|
3.25
|
17.71
|
|
3
|
Vernon Davis TE SF
|
8.27
|
9.46
|
1.19
|
17.73
|
|
4
|
Rob Gronkowski TE NE
|
4.88
|
8.81
|
3.93
|
13.69
|
|
5
|
Jason Witten TE DAL
|
7.01
|
8.61
|
1.60
|
15.62
|
|
6
|
Jordan Cameron TE CLE
|
7.31
|
7.59
|
0.28
|
14.90
|
|
7
|
Ladarius Green TE SD
|
4.88
|
7.35
|
2.47
|
12.23
|
|
8
|
Greg Olsen TE CAR
|
3.65
|
7.30
|
3.65
|
10.96
|
|
9
|
Zach Ertz TE PHI
|
4.98
|
7.05
|
2.07
|
12.03
|
|
10
|
Dennis Pitta TE BAL
|
3.26
|
6.79
|
3.53
|
10.05
|
|
11
|
Kyle Rudolph TE MIN
|
4.88
|
6.78
|
1.90
|
11.66
|
|
12
|
Eric Ebron TE DET
|
4.88
|
6.78
|
1.90
|
11.66
|
|
13
|
Jordan Reed TE WAS
|
4.88
|
6.72
|
1.84
|
11.60
|
|
14
|
Dwayne Allen TE IND
|
4.88
|
6.23
|
1.35
|
11.11
|
|
15
|
Martellus Bennett TE CHI
|
4.66
|
6.21
|
1.56
|
10.87
|
|
16
|
Antonio Gates TE SD
|
5.30
|
6.05
|
0.75
|
11.35
|
|
17
|
Delanie Walker TE TEN
|
4.22
|
5.77
|
1.55
|
9.98
|
|
18
|
Jared Cook TE STL
|
6.37
|
5.44
|
-0.93
|
11.81
|
|
19
|
Charles Clay TE MIA
|
6.18
|
5.35
|
-0.83
|
11.52
|
|
20
|
Heath Miller TE PIT
|
6.95
|
5.33
|
-1.61
|
12.28
|
|
21
|
Brent Celek TE PHI
|
4.26
|
5.24
|
0.98
|
9.50
|
|
22
|
Jace Amaro TE NYJ
|
4.88
|
5.13
|
0.25
|
10.01
|
|
23
|
Garrett Graham TE HOU
|
5.67
|
5.11
|
-0.56
|
10.77
|
|
24
|
Marcedes Lewis TE JAC
|
4.22
|
5.09
|
0.87
|
9.31
|
|
25
|
Mychal Rivera TE OAK
|
3.46
|
4.63
|
1.17
|
8.10
|
