
(Bob Donnan, USA TODAY Sports)
This is a slightly different take on our consistency analysis. Rather than looking at just consistency, we’re examining how each player rates within specific categories leading to the production of fantasy points in both standard and PPR scoring.
The purpose is to pinpoint where players were most and least effective in relation to each other. We scored seven categories and created a composite score to illustrate how each player rates across the board. For example, some running backs thrive because of their ability to score high touchdown totals on the ground, whereas others excel via the passing game. Some are quite obvious, like LeGarrette Blount being an example of the former and Theo Riddick the latter. Others may not be so apparent.
The criteria required to evaluate the players began with a threshold of at least 120 offensive touches in 2016, leading to 42 running backs making the cut. Each category is measured against the average of these 42 players to show how much better or worse per category each back performed. The higher a player ranked in each category, the better his overall composite rating scores. This also allows fantasy gamers to see which running backs were more evenly efficient versus those inflated for doing well in just a few categories or, conversely, harmed by one terrible score.
The caveat to take away is this is just a tool. First of all, it is based on only one year, and 2017 is a new season. Don’t let what happened last year control what you expect to happen this year, but absorbing everything possible to create a bigger picture is the goal. It also helps if you’re stuck deciding between two players. As with any metric, situational elements cannot be ignored. Just because a part-time player fared well, it doesn’t mean fantasy gamers should draft said runner over a proven stud with a secure job.
Categories rated
Fantasy points per game (FP/G): Standard fantasy scored per game
PPR fantasy points per game (PPR/G): 1 point-per-game scored per game
Fantasy points per touch (FP/Tch): Standard fantasy points per offensive touch (rush+reception)
PPR points per touch (PPR/Tch): PPR points per offensive touch (rush+reception)
PPR points per reception (PPR/Rec): PPR points per catch
Yards per touch (Yds/Tch): Offensive yards (rush+receive) per touch (rush+reception)
Touchdown efficiency (Tch/TD): Number of offensive touches (rush+reception) per 1 TD scored
Highly efficient
The top-10 most efficient running backs of 2016
FP/G +/-
|
PPR/G
|
Yds/Tch
|
Tch/TD
|
FP/Tch
|
PPR/Tch
|
PPR/Rec
|
Rk
|
Player |
Tm
|
1
|
13
|
3
|
1
|
1
|
1
|
1
|
1
|
Tevin Coleman |
ATL
|
3
|
2
|
8
|
3
|
2
|
6
|
3
|
2
|
David Johnson |
ARI
|
9
|
3
|
9
|
9
|
4
|
13
|
6
|
3
|
Ezekiel Elliott |
DAL
|
6
|
4
|
7
|
6
|
3
|
8
|
30
|
4
|
LeSean McCoy |
BUF
|
8
|
7
|
12
|
8
|
5
|
9
|
15
|
5
|
Devonta Freeman |
ATL
|
10
|
8
|
17
|
10
|
12
|
4
|
9
|
6
|
Theo Riddick |
DET
|
13
|
10
|
13
|
13
|
8
|
10
|
11
|
7
|
Mark Ingram |
NO
|
11
|
5
|
21
|
11
|
16
|
17
|
7
|
8
|
Melvin Gordon |
SD
|
4
|
27
|
24
|
4
|
7
|
18
|
5
|
9
|
Ryan Mathews |
PHI
|
5
|
12
|
25
|
5
|
9
|
14
|
31
|
10
|
Latavius Murray |
OAK
|
This is all about showing which players make the most of their opportunities. It also can be helpful to dispel notions of perceived worth from PPR to non-PPR scoring, as well.
Notable: Aside from PPR points per game, Tevin Coleman dominated with each opportunity presented. LeSean McCoy and David Johnson were the only backs to register top-10 placement in every category but PPR points per reception, where it became obvious McCoy’s efficiency was at its worst. Despite receiving prowess being heavily weighted against him, Latavius Murray racked up a top-10 finish on the strength of his effectiveness around the goal line.
How the top 10 scored against the field per category
Rk
|
Player |
Tm
|
FP/G +/-
|
PPR/G +/-
|
Yds/Tch +/-
|
Tch/TD +/-
|
FP/Tch +/-
|
PPR/Tch +/-
|
PPR/Rec +/-
|
1
|
Tevin Coleman |
ATL
|
100.0%
|
8.2%
|
22.7%
|
54%
|
35.4%
|
33.5%
|
30.6%
|
2
|
David Johnson |
ARI
|
46.9%
|
47.6%
|
14.0%
|
36%
|
21.9%
|
22.8%
|
14.9%
|
3
|
Ezekiel Elliott |
DAL
|
44.2%
|
38.2%
|
13.3%
|
24%
|
16.8%
|
7.8%
|
12.1%
|
4
|
LeSean McCoy |
BUF
|
33.0%
|
31.9%
|
14.2%
|
30%
|
19.7%
|
18.3%
|
-12.4%
|
5
|
Devonta Freeman |
ATL
|
24.2%
|
24.6%
|
10.9%
|
26%
|
15.9%
|
16.2%
|
1.8%
|
6
|
Theo Riddick |
DET
|
-0.8%
|
16.6%
|
2.7%
|
17%
|
7.5%
|
23.6%
|
10.0%
|
7
|
Mark Ingram |
NO
|
10.7%
|
10.9%
|
10.1%
|
14%
|
11.2%
|
11.7%
|
7.9%
|
8
|
Melvin Gordon |
SD
|
33.0%
|
31.1%
|
-1.8%
|
16%
|
4.1%
|
1.2%
|
11.8%
|
9
|
Ryan Mathews |
PHI
|
-8.8%
|
-21.3%
|
-5.7%
|
36%
|
11.4%
|
0.9%
|
13.0%
|
10
|
Latavius Murray |
OAK
|
13.5%
|
10.2%
|
-5.9%
|
35%
|
10.7%
|
7.5%
|
-13.4%
|
The idea here is to show on a percentage basis how much better than average each player performed. This is simply comparing raw stats and highlighting how much better or worse (-X%) a player fared in relation to his counterparts.
Notable: It goes to show how rare it is to be a consistently efficient elite running back. Only five of 42 players did not register a negative rating. Only three other players finished negatively in a single column.
Least efficient players scored
FP/G +/-
|
PPR/G
|
Yds/Tch
|
Tch/TD
|
FP/Tch
|
PPR/Tch
|
PPR/Rec
|
Rk
|
Player |
Tm
|
14
|
26
|
40
|
14
|
31
|
38
|
38
|
33
|
Jonathan Stewart |
CAR
|
29
|
30
|
28
|
29
|
33
|
30
|
27
|
34
|
Terrance West |
BAL
|
34
|
38
|
26
|
34
|
35
|
33
|
24
|
35
|
Chris Ivory |
JAC
|
32
|
35
|
34
|
32
|
36
|
35
|
20
|
36
|
Devontae Booker |
DEN
|
36
|
18
|
35
|
36
|
38
|
39
|
32
|
37
|
Lamar Miller |
HOU
|
37
|
32
|
38
|
37
|
39
|
36
|
26
|
38
|
Jerick McKinnon |
MIN
|
41
|
34
|
33
|
41
|
40
|
27
|
40
|
39
|
T.J. Yeldon |
JAC
|
40
|
39
|
23
|
40
|
37
|
41
|
37
|
40
|
Jacquizz Rodgers |
TB
|
38
|
22
|
41
|
38
|
41
|
42
|
35
|
41
|
Todd Gurley |
LAR
|
39
|
31
|
42
|
39
|
42
|
40
|
39
|
42
|
Rashad Jennings |
NYG
|
The results are hardly surprising — none of these guys are excellent receiving backs, nor are they particularly explosive. Most are overachievers and volume players.
Notable: Todd Gurley can be given a pass because of how poor the entire offense performed. Lamar Miller gets some sympathy since Houston pounded him into the dirt in the first half of the season. He was overused to the point of exhaustion.
How they scored against the field
Rk
|
Player |
Team
|
FP/G +/-
|
PPR/G +/-
|
Yds/Tch +/-
|
Tch/TD +/-
|
FP/Tch +/-
|
PPR/Tch +/-
|
PPR/Rec +/-
|
33
|
Jonathan Stewart |
CAR
|
0.1%
|
-16.6%
|
-24.9%
|
14%
|
-10.2%
|
-28.1%
|
-16.6%
|
34
|
Terrance West |
BAL
|
-27.7%
|
-26.2%
|
-9.8%
|
-30%
|
-15.1%
|
-13.2%
|
-9.1%
|
35
|
Chris Ivory |
JAC
|
-50.5%
|
-47.6%
|
-7.1%
|
-57%
|
-18.2%
|
-16.2%
|
-5.7%
|
36
|
Devontae Booker |
DEN
|
-48.4%
|
-45.1%
|
-14.2%
|
-41%
|
-20.9%
|
-17.6%
|
0.4%
|
37
|
Lamar Miller |
HOU
|
5.3%
|
2.2%
|
-15.8%
|
-71%
|
-28.1%
|
-32.0%
|
-13.4%
|
38
|
Jerick McKinnon |
MIN
|
-59.2%
|
-38.2%
|
-24.3%
|
-73%
|
-35.7%
|
-17.7%
|
-9.0%
|
39
|
T.J. Yeldon |
JAC
|
-83.1%
|
-44.8%
|
-13.1%
|
-209%
|
-39.3%
|
-9.9%
|
-17.0%
|
40
|
Jacquizz Rodgers |
TB
|
-40.8%
|
-48.5%
|
-5.4%
|
-144%
|
-26.7%
|
-33.4%
|
-16.3%
|
41
|
Todd Gurley |
LAR
|
-12.1%
|
-7.8%
|
-29.4%
|
-84%
|
-41.8%
|
-36.7%
|
-15.9%
|
42
|
Rashad Jennings |
NYG
|
-37.3%
|
-26.7%
|
-32.9%
|
-85%
|
-45.1%
|
-33.1%
|
-16.9%
|
Stuck in the middle with you
Running backs from No. 11-32
FP/G +/-
|
PPR/G
|
Yds/Tch
|
Tch/TD
|
FP/Tch
|
PPR/Tch
|
PPR/Rec
|
Rk
|
Player |
Team
|
26
|
1
|
10
|
26
|
18
|
12
|
22
|
11
|
Le’Veon Bell |
PIT
|
25
|
29
|
4
|
25
|
10
|
5
|
18
|
12
|
Darren Sproles |
PHI
|
30
|
9
|
6
|
30
|
17
|
21
|
10
|
13
|
Jordan Howard |
CHI
|
19
|
11
|
22
|
19
|
20
|
26
|
8
|
14
|
Carlos Hyde |
SF
|
20
|
6
|
20
|
20
|
21
|
20
|
19
|
15
|
DeMarco Murray |
TEN
|
28
|
23
|
5
|
28
|
13
|
7
|
25
|
16
|
Bilal Powell |
NYJ
|
12
|
42
|
16
|
12
|
11
|
19
|
17
|
17
|
Derrick Henry |
TEN
|
15
|
15
|
27
|
15
|
23
|
24
|
14
|
18
|
C.J. Anderson |
DEN
|
31
|
28
|
2
|
31
|
6
|
2
|
34
|
19
|
Ty Montgomery |
GB
|
2
|
14
|
39
|
2
|
14
|
28
|
42
|
20
|
LeGarrette Blount |
NE
|
23
|
21
|
14
|
23
|
19
|
16
|
33
|
21
|
Isaiah Crowell |
CLE
|
35
|
17
|
11
|
35
|
25
|
25
|
2
|
22
|
Spencer Ware |
KC
|
21
|
40
|
19
|
21
|
24
|
22
|
13
|
23
|
Tim Hightower |
NO
|
FP/G +/-
|
PPR/G
|
Yds/Tch
|
Tch/TD
|
FP/Tch
|
PPR/Tch
|
PPR/Rec
|
Rk
|
Player |
Team
|
42
|
37
|
1
|
42
|
15
|
3
|
23
|
24
|
Duke Johnson |
CLE
|
33
|
24
|
15
|
33
|
29
|
11
|
21
|
25
|
Giovani Bernard |
CIN
|
17
|
33
|
29
|
17
|
26
|
32
|
12
|
26
|
Rob Kelley |
WAS
|
7
|
36
|
37
|
7
|
22
|
23
|
36
|
27
|
Christine Michael |
SEA
|
16
|
41
|
30
|
16
|
27
|
15
|
29
|
28
|
Matt Asiata |
MIN
|
22
|
20
|
31
|
22
|
32
|
31
|
16
|
29
|
Matt Forte |
NYJ
|
24
|
16
|
18
|
24
|
28
|
29
|
41
|
30
|
Jay Ajayi |
MIA
|
27
|
19
|
32
|
27
|
34
|
37
|
4
|
31
|
Frank Gore |
IND
|
18
|
25
|
36
|
18
|
30
|
34
|
28
|
32
|
Jeremy Hill |
CIN
|
As one would expect with such a wide range of ranked players, there is no clear-cut description of playing style. Large frames, small bodies, plodders, sprinters, stone hands, baby-bird catchers … you name it.
Notable: The first stat to catch the eye is Le’Veon Bell finishing first in PPR per game and 10th or worse in every other metric. Frank Gore was dynamic as a receiver out of the backfield, which makes sense from a preservation perspective. Don’t pound him into a wall of humanity and expect optimal results. Duke Johnson dominated in yardage per touch but failed to score a receiving TD, which crushed his overall rating. Some of that is being unlucky, the rest attributed to Cleveland’s offensive struggles.
And facing the field
Rk
|
Player |
Team
|
FP/G +/-
|
PPR/G +/-
|
Yds/Tch +/-
|
Tch/TD +/-
|
FP/Tch +/-
|
PPR/Tch +/-
|
PPR/Rec +/-
|
11
|
Le’Veon Bell |
PIT
|
45.6%
|
49.0%
|
12.9%
|
-28%
|
3.7%
|
9.7%
|
-3.0%
|
12
|
Darren Sproles |
PHI
|
-48.4%
|
-24.5%
|
17.6%
|
-25%
|
8.3%
|
23.4%
|
0.6%
|
13
|
Jordan Howard |
CHI
|
18.6%
|
12.8%
|
14.8%
|
-38%
|
3.9%
|
-3.2%
|
8.7%
|
14
|
Carlos Hyde |
SF
|
15.5%
|
10.6%
|
-3.5%
|
7%
|
-0.2%
|
-6.1%
|
10.1%
|
15
|
DeMarco Murray |
TEN
|
26.3%
|
25.9%
|
-1.6%
|
1%
|
-0.8%
|
-1.2%
|
0.5%
|
16
|
Bilal Powell |
NYJ
|
-24.8%
|
-8.4%
|
16.8%
|
-30%
|
6.9%
|
19.0%
|
-8.8%
|
17
|
Derrick Henry |
TEN
|
-66.4%
|
-78.6%
|
4.2%
|
16%
|
7.9%
|
0.8%
|
0.7%
|
18
|
C.J. Anderson |
DEN
|
11.4%
|
7.9%
|
-8.9%
|
13%
|
-1.1%
|
-4.8%
|
6.2%
|
19
|
Ty Montgomery |
GB
|
-44.5%
|
-23.0%
|
26.6%
|
-38%
|
14.7%
|
27.6%
|
-13.9%
|
20
|
LeGarrette Blount |
NE
|
22.6%
|
8.1%
|
-24.7%
|
42%
|
6.8%
|
-11.1%
|
-32.2%
|
21
|
Isaiah Crowell |
CLE
|
-4.6%
|
-3.6%
|
8.3%
|
-16%
|
2.2%
|
3.0%
|
-13.5%
|
22
|
Spencer Ware |
KC
|
7.7%
|
5.5%
|
11.9%
|
-70%
|
-2.8%
|
-5.4%
|
24.9%
|
23
|
Tim Hightower |
NO
|
-56.9%
|
-59.6%
|
-1.2%
|
-6%
|
-2.7%
|
-4.2%
|
6.5%
|
Rk
|
Player |
Team
|
FP/G +/-
|
PPR/G +/-
|
Yds/Tch +/-
|
Tch/TD +/-
|
FP/Tch +/-
|
PPR/Tch +/-
|
PPR/Rec +/-
|
24
|
Duke Johnson |
CLE
|
-86.2%
|
-46.8%
|
29.5%
|
-336%
|
6.2%
|
26.3%
|
-3.6%
|
25
|
Giovani Bernard |
CIN
|
-29.2%
|
-8.5%
|
5.7%
|
-49%
|
-5.8%
|
10.8%
|
-1.2%
|
26
|
Rob Kelley |
WAS
|
-27.7%
|
-42.4%
|
-11.9%
|
12%
|
-3.6%
|
-15.8%
|
6.5%
|
27
|
Christine Michael |
SEA
|
-40.8%
|
-45.6%
|
-20.3%
|
27%
|
-0.9%
|
-4.3%
|
-16.0%
|
28
|
Matt Asiata |
MIN
|
-71.6%
|
-60.5%
|
-12.4%
|
12%
|
-3.7%
|
3.0%
|
-12.0%
|
29
|
Matt Forte |
NYJ
|
1.0%
|
-1.7%
|
-12.6%
|
-6%
|
-10.7%
|
-13.9%
|
1.7%
|
30
|
Jay Ajayi |
MIA
|
13.5%
|
6.9%
|
1.5%
|
-23%
|
-4.7%
|
-12.6%
|
-30.9%
|
31
|
Frank Gore |
IND
|
1.0%
|
0.2%
|
-12.8%
|
-29%
|
-17.2%
|
-18.7%
|
13.6%
|
32
|
Jeremy Hill |
CIN
|
-5.6%
|
-14.8%
|
-17.2%
|
7%
|
-8.7%
|
-17.5%
|
-11.6%
|
Bell was deflated because he touched the ball so much. Check out the -28 percent rating for touchdowns per touch. Relatively low figures in PPR points per touch and PPR points per reception acknowledge his role as a checkdown weapon and shows that Pittsburgh often chose to extend the running game through short-area passing.
Tennessee’s backfield is an obvious example of how player roles factor into production. Compare DeMarco Murray’s numbers (good in PPR, not so much in the non-receiving columns) to Derrick Henry’s. It is no secret Murray is the superior third-down weapon, which was proven based on each player’s role and corresponding statistics.
Opportunity knocks
This table shows which players made the most of their PPR chances. In other words, how did the player produce based on targets plus rushing attempts in relation to the 41 other backs studied.
Rk
|
Player |
Team
|
PPR/Opp
|
Rk
|
Player |
Team
|
PPR/Opp
|
1
|
Tevin Coleman |
ATL
|
32.9%
|
22
|
Christine Michael |
SEA
|
-3.4%
|
2
|
Ty Montgomery |
GB
|
24.2%
|
23
|
Carlos Hyde |
SF
|
-3.5%
|
3
|
LeSean McCoy |
BUF
|
20.2%
|
24
|
Spencer Ware |
KC
|
-4.0%
|
4
|
Theo Riddick |
DET
|
20.2%
|
25
|
Jordan Howard |
CHI
|
-5.7%
|
5
|
David Johnson |
ARI
|
18.6%
|
26
|
LeGarrette Blount |
NE
|
-6.1%
|
6
|
Duke Johnson |
CLE
|
18.2%
|
27
|
C.J. Anderson |
DEN
|
-6.2%
|
7
|
Darren Sproles |
PHI
|
17.5%
|
28
|
Jay Ajayi |
MIA
|
-10.2%
|
8
|
Devonta Freeman |
ATL
|
17.1%
|
29
|
Terrance West |
BAL
|
-13.0%
|
9
|
Bilal Powell |
NYJ
|
15.9%
|
30
|
Rob Kelley |
WAS
|
-13.9%
|
10
|
Mark Ingram |
NO
|
11.9%
|
31
|
Matt Forte |
NYJ
|
-14.2%
|
11
|
Ezekiel Elliott |
DAL
|
10.2%
|
32
|
Jeremy Hill |
CIN
|
-14.7%
|
12
|
Le’Veon Bell |
PIT
|
9.2%
|
33
|
T.J. Yeldon |
JAC
|
-15.1%
|
13
|
Latavius Murray |
OAK
|
8.0%
|
34
|
Frank Gore |
IND
|
-16.4%
|
14
|
Giovani Bernard |
CIN
|
7.2%
|
35
|
Chris Ivory |
JAC
|
-17.2%
|
15
|
Ryan Mathews |
PHI
|
5.1%
|
36
|
Jerick McKinnon |
MIN
|
-17.6%
|
16
|
Matt Asiata |
MIN
|
4.0%
|
37
|
Devontae Booker |
DEN
|
-20.1%
|
17
|
Derrick Henry |
TEN
|
4.0%
|
38
|
Jonathan Stewart |
CAR
|
-29.1%
|
18
|
Isaiah Crowell |
CLE
|
2.5%
|
39
|
Lamar Miller |
HOU
|
-29.1%
|
19
|
Melvin Gordon |
SD
|
0.8%
|
40
|
Jacquizz Rodgers |
TB
|
-29.7%
|
20
|
DeMarco Murray |
TEN
|
-0.3%
|
41
|
Rashad Jennings |
NYG
|
-30.9%
|
21
|
Tim Hightower |
NO
|
-1.8%
|
42
|
Todd Gurley |
LAR
|
-36.3%
|
For the most part, we’re looking at the same faces from the least efficient table above. It is interesting to see, though, just how far below or above water each player finished on both ends of the spectrum.
As previously mentioned, all of this data should be used as part of the equation and not a direct cheat to solving the problem.