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

  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)

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By Dave Caban | @DaveCabanFF | Archive