How running back efficiency ratings should impact draft plans

How running back efficiency ratings should impact draft plans

Statistical Analysis

How running back efficiency ratings should impact draft plans

(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.

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