Using Points Per Opportunity to Identify Regression RBs

As fantasy drafters, we are constantly striving to earn a positive return on our fantasy investments. This is for sure an impossible task, but nonetheless,  we must do our very best to come as close to achieving this goal as possible. Identifying players that are likely to regress is an important step in this process.

In an attempt to find running backs that are regression candidates, I did some digging through points per opportunity (PPO).  For the purposes of this post, ‘opportunity’ consists of rushing attempts as well as targets in the passing game. We will be reviewing the metric in an attempt to find unsustainable fantasy efficiency.  I pulled data for the 2013 through 2015 seasons and found the average points per opportunity (based on PPR scoring), for the top 40 RBs from each season, to be .83.

PPO rates for the top 40 running backs from last season are included in the table below:

Player Team Att Targets PPR Points PPO
James White NE 22 54 122.6 1.98
Theo Riddick DET 43 99 179 1.46
Danny Woodhead SD 97 107 244.1 1.37
Shane Vereen NYG 61 81 158.5 1.32
David Johnson ARI 125 57 209.8 1.30
Karlos Williams BUF 93 14 124.3 1.20
Bilal Powell NYJ 70 63 135.1 1.15
Charles Sims TB 107 70 180 1.14
Darren Sproles PHI 83 83 149.5 1.08
Duke Johnson CLE 104 74 164.3 1.00
Ryan Mathews PHI 107 28 124.5 0.98
DeAngelo Williams PIT 200 47 231.4 0.96
Lamar Miller MIA 194 57 231.9 0.96
Mark Ingram NO 166 60 203.4 0.94
Devonta Freeman ATL 264 97 316.9 0.94
Giovani Bernard CIN 154 66 181.2 0.89
James Starks GB 148 53 166.3 0.87
Jeremy Langford CHI 148 42 147.6 0.87
Todd Gurley LA 229 26 208.4 0.83
Matt Forte CHI 218 58 214.7 0.82
Thomas Rawls SEA 147 11 127.6 0.82
Javorius Allen BAL 137 62 145.7 0.80
CJ Anderson DEN 152 36 141.3 0.80
DeMarco Murray PHI 193 55 184.4 0.78
LeSean McCoy BUF 203 50 178.7 0.76
Charcandrick West KC 160 34 132.8 0.74
Rashad Jennings NYG 195 40 164.9 0.74
Chris Ivory NYJ 247 37 202.7 0.73
Adrian Peterson MIN 327 36 260.7 0.73
Doug Martin TB 288 44 232.3 0.72
LeGarrette Blount NE 165 7 122.6 0.72
Jeremy Hill CIN 223 19 170.3 0.72
TJ Yeldon JAX 182 46 155.9 0.72
Darren McFadden DAL 239 53 195.7 0.70
Ronnie Hillman DEN 207 35 161.4 0.70
Eddie Lacy GB 187 28 140.6 0.68
Isaiah Crowell CLE 185 22 137.8 0.68
Latavius Murray OAK 266 53 204.8 0.67
Frank Gore IND 260 58 193.4 0.66
Jonathan Stewart CAR 242 21 162.8 0.63

Notice anything? Pass catching backs have significantly better rates in terms of PPO.  This is further evidence as to why they are so useful in PPR formats.  Obviously, that’s a fairly intuitive concept; passing plays are more efficient and a higher percentage of these player’s production comes from a higher scoring play.   But it does help in driving home the argument that pass catching backs, such as Theo Riddick and Charles Sims, can be useful fantasy assets even with limited touches.  Though real-life efficiency doesn’t correlate strongly with fantasy success, measures such as PPO certainly have a place in fantasy analysis.

James White was extremely efficient in 2015, however, his overall usage was limited.  With Dion Lewis returning, his role will remain ancillary and as such, he doesn’t figure to be a major contributor in 2016.  In contrast, Theo Riddick has carved out a substantial role in the Detroit offense.  He finished the 2015 season with  179 PPR points and was the 18th ranked running back based on total points.  Is his rate of 1.46 points per opportunity sustainable?  Perhaps.  In 2014 (a season in which he saw only 20 attempts and 50 carries) he was good for a PPO rate of 1.35.

Of course, we need a little more context to evaluate the above data points.  Here’s how Danny Woodhead, a player of a similar profile, has fared the last five seasons, excluding a three game 2014 season:

Year Team Att Targets PPR Points PPO
2015 SD 97 107 244.1 1.20
2013 SD 106 87 223.4 1.16
2012 NE 76 55 156.7 1.20
2011 NE 77 31 78.8 0.73

So it looks like players of this mold are able to put up relatively consistent PPO rates, even when switching teams. So Riddick doesn’t look like a major regression candidate, especially as his volume may increase in 2016.

David Johnson would figure to regress on a per game basis. However, his overall opportunity should drastically increase, negating the impact of any negative regression he may experience.

We’ll be discussing Lamar Miller in great depth in a future podcast, so let’s instead consider a player that will see substantial usage in both facets of his offense, Mark Ingram.  His 2015 rate of .94 is well beyond the average of .83.  In 2014, he managed a rate of only .73 and in 2013 he was good for a dismal .66.  Ingram played in 12 games last season and was on pace for 271 PPR points, had he remained healthy for the duration.  This point accumulation seems unsustainable as his efficiency last season was high for his career and the league at large.

Devonta Freeman is the poster boy for players labeled as 2016 regression candidates.  This was to be expected as his production in the first half of 2015 was superhuman and his points per game dropped steadily after the mid point of the season.  If a smaller percentage of his overall production was not related to the run game, we might be able to make a case for his PPO remaining stagnant in the coming season.  However, rushing attempts accounted for over 73% of his 2015 opportunity.  A rate of .94 points per touch, with such a high volume of carries is definitely unsustainable.  I should note, however, that the market has corrected for this regression, baking it into his ADP.

Karlos Williams was a break out rookie in 2015.  He was able to find opportunity early in the season and had a surprisingly solid year, averaging over 11 points a game.  Granted, we only have one season of data to work with, but it seems highly unlikely that he will remain as efficient in 2016.  This is problematic as an expansion of his role in the Bills’ backfield is likely dependent on the health of Shady McCoy. Williams’ workload as a rusher dropped by 8 carries a game when McCoy was healthy.

Just to confirm that PPO rates, on an individual player basis, do not always remain stagnant I pulled the following table together:

Player Year Att Targets PPR Points PPO
Chris Ivory 2013 182 7 104 0.55
Chris Ivory 2014 198 27 152 0.68
Chris Ivory 2015 247 37 203 0.71
Adrian Peterson 2012 348 51 347 0.87
Adrian Peterson 2013 279 40 233 0.73
Adrian Peterson 2015 327 36 261 0.71
Doug Martin 2013 127 24 68 0.45
Doug Martin 2014 134 20 81 0.53
Doug Martin 2015 288 44 232 0.70

The important thing to remember when performing an analysis such as this, is that regression is okay.  It’s unavoidable.  Players can’t always have their best season.  We don’t need to avoid these players at all costs.  But, we do need to make sure that we factor this regression into the price tag we place on them.

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