2018 Historical Projections: RB Volatility Scores

Within the Stat Explorer, I included historical projections. These projections use a player’s stat line from 2017 to identify players that recorded similar seasons in years past.1 By looking at the performance of these comparable players, in the season subsequent to their matching season, we can build a range of outcomes that will help us understand what we can reasonably expect from a player in the coming season.I overviewed this process in more detail within the Stat Explorer, so please check it out if you’re curious about the specifics.

The great thing about a set of projections such as these is that they are built using actual data and occurrences. As a result, they provide a historical and objective input into our player evaluation process. By comparing a player’s low-end outcome to his high-end outcome, we can get a sense of how broad his range of outcomes is.

Some drafters prefer to spend early round picks on players with narrow ranges of outcomes as, in theory, this allows them to have a better sense of the return they will get on their premium purchases. As the draft progresses, these drafters are more willing to spend their picks on players with wider ranges of outcomes. If the player’s upside of his high-end projection doesn’t hit, the impact of his low-end outcome will not be as heavy on their team.

With this in mind, Jonathan Bales developed what he called Volatility Scores in the early days of RotoViz. They are calculated with a simple formula (High Projection – Low Projection) / Median Projection. Let’s take a look at 2018 running backs.2

Running Back VOLATILITY SCORES

Player
Low
Median
High
Spread
Volatility Score
David Johnson15.318.922.47.10.38
Todd Gurley16.718.221.44.70.26
Le'Veon Bell15.717.220.54.80.28
Dalvin Cook15.016.920.45.40.32
Leonard Fournette13.316.520.37.00.42
Kareem Hunt12.116.420.58.40.52
Alvin Kamara11.916.219.87.90.49
Ezekiel Elliott13.415.820.67.20.45
Mark Ingram12.115.017.55.40.35
Melvin Gordon11.814.520.18.30.58
LeSean McCoy10.713.219.08.30.62
Devonta Freeman11.013.217.26.20.47
Carlos Hyde10.212.213.33.10.26
Christian McCaffrey9.211.615.96.70.58
Samaje Perine3.911.314.110.20.91
Alex Collins7.710.714.97.20.67
Jordan Howard7.510.512.95.40.51
Lamar Miller6.610.414.98.30.79
Frank Gore6.410.313.87.40.72
Jay Ajayi7.710.214.26.50.64
Chris Carson5.310.213.98.60.84
CJ Anderson7.310.113.15.80.58
Adrian Peterson5.310.113.07.70.76
Peyton Barber4.19.913.99.80.99
Chris Thompson7.19.813.76.60.67
Spencer Ware6.69.814.07.40.75
James White7.39.713.86.50.67
Duke Johnson7.69.711.84.20.43
Kapri Bibbs7.49.712.34.90.51
LeGarrette Blount6.09.710.54.50.46
Ameer Abdullah5.99.413.47.50.80
Tarik Cohen7.49.410.83.40.36
Aaron Jones5.59.413.68.10.85
Darren Sproles4.99.310.85.90.63
DeMarco Murray4.89.212.37.50.81
Rex Burkhead4.99.212.37.40.80
Derrick Henry5.19.111.76.60.72
Mike Gillislee6.59.110.54.00.43
Orleans Darkwa6.79.011.44.70.52
Jerick McKinnon7.48.911.44.00.45
Jalen Richard4.48.810.86.40.73
Doug Martin5.08.612.97.90.92
Bilal Powell6.08.411.05.00.60
Marshawn Lynch6.08.212.86.80.83
Deandre Washington3.98.110.97.00.86
Latavius Murray4.77.911.87.10.91
Giovani Bernard6.27.911.95.70.72
Ty Montgomery6.37.813.16.80.87
Wayne Gallman6.47.811.85.40.70
Dion Lewis5.97.812.56.60.84
Joe Mixon6.37.713.47.10.93
Theo Riddick4.37.713.59.21.20
Andre Ellington6.07.612.56.50.85
Terrance West4.97.511.06.10.81
Tevin Coleman6.37.49.53.20.43
Alfred Blue4.77.49.64.90.66
Kenyan Drake5.47.39.33.90.53
Isaiah Crowell3.37.212.28.91.23
Alfred Morris4.37.110.86.50.91
TJ Yeldon4.97.19.44.50.63
D'Onta Foreman4.36.910.15.80.84
Jamaal Williams4.56.89.24.70.69
Javorius Allen4.86.89.74.90.72
Marlon Mack4.06.49.55.50.87
Eddie Lacy5.06.29.34.30.71
Damien Williams4.36.29.24.90.80
Matt Breida3.85.88.24.40.76
Shane Vereen3.55.88.24.70.80
Elijhaa Penny4.55.69.04.50.80
Jeremy Hill3.05.68.45.40.96
Chris Ivory4.35.68.54.20.75
De'Angelo Henderson4.25.412.88.61.60
Elijah McGuire4.05.49.45.40.99
Dwayne Washington2.65.47.44.80.88
Austin Ekeler2.25.38.86.61.23
Kenneth Dixon2.25.28.36.11.18
Devontae Booker3.35.19.96.61.29
Corey Grant3.65.05.92.30.46
Charles Sims3.84.78.54.70.98
CJ Prosise3.43.87.23.80.98
  • Carlos Hyde serves as a great reminder that historical projections do not account for changes in team or other external factors. In my opinion, he has one of the widest ranges of outcomes of all RBs this season. If he were to get all of the Browns’ rushing work, he could finish as an RB2, but if Nick Chubb wins the job he could quickly become irrelevant.
  • Todd Gurley and Le’Veon Bell, the consensus RB1 and RB2, have significantly lower scores than other Tier 1 RBs such as Ezekiel Elliot. Granted, Elliot’s score is top-12, but his spread of 7.2 points per game is much larger than those of Gurley and Bell who have ranges of less than five.
  • Dalvin Cook’s score is hard to rely on given the small number of games that contribute to his historical projections.
  • David Johnson’s 2016 season was used to create his projection. He owns the strongest high-end projection of all backs and despite a sizeable spread, his low-end outcome is top three.
  • The Lions’ backfield is crowded with rookie Kerryon Johnson joining Theo Riddick, LeGarrette Blount and Ameer Abdullah in the fight for the team’s RB duties. While Riddick projects with the majority of passing down work, he could lose some of his historical target share. Given that he has one of the largest spreads and a weak floor, he should be drafted when teams can absorb the risk in hopes of realizing his upside.
  • Peyton Barber has a wide range of outcomes and one of the highest volatility scores of all backs that are likely to get drafted. This makes him an attractive late-round pick. If Ronald Jones fails to live up to expectations, Barber has a real shot of being a weekly RB2.
  • Joe Mixon is one of the more divisive early-round RB options. The Cincinnati backfield should be his, but the jury is still out on just how good of an NFL back he can be. His historical projection only muddies the waters. His volatility score of 0.93 stems from the distance between his high-end outcome and his low/-median outcomes which are grouped closely together.
  • Tarik Cohen owns one of the smallest volatility scores. He projects as a reliable contributor that won’t win you your league but can certainly add depth to your RB corps.
  • Alvin Kamara, arguably 2018’s most likely regression candidate, forecasts with a wide range of outcomes, but is not as volatile as one might expect. This is because his median projection is not too far off from his high-end projection. This signals that he may not be as risky of a pick as it may seem. In fact, Melvin Gordon owns a wider range of outcomes and higher volatility score.
  • The 2018 Oakland Raiders are challenging to project, which makes Marshawn Lynch a difficult player to target. However, his high volatility score speaks to the upside he possesses, given an ADP in the mid-thirties, if he is able to control the backfield and shoulder a significant workload.

  1. In cases such David Johnson’s, where a player missed the majority of the season, I used data from 2016.  (back)
  2. Remember, historical projections are unaware of external factors such as team changes.  (back)