ADP is one of the most widely utilized tools for dynasty player valuation. Its purposes range from startup strategy development to starting basis for trade negotiations and much in between. As a key part of DLF’s industry-leading tools to help make you a better dynasty player, it’s important to understand all aspects of ADP to better utilize it.
Often, ADP is referred to as simply just a number. Mike Evans, for example, may have an ADP of 14.00, indicating he is, on average, expected to go at the beginning of the second round of startups. However, this may not necessarily be the case in all instances. One player may have a huge range of pick selections, while one may go in the same spot in every draft. So, how effective is ADP at understanding a specific player’s true value from league to league? In order to answer this question, we can use the Coefficient of Variation (Coef-Var) in our analysis.
Don’t be alarmed by what is potentially a new statistical term for you; it’s really easy to understand and use! The formula for the Coef-Var is simply a dataset’s standard deviation (Stdev) divided by its average (or mean). What this formula allows us to do is compare datasets with different averages. In our case, we can use it to compare two players with much different ADPs. For example, having a standard deviation of 5 when the mean in 10 is much different than if the mean was 150!
In this series, we’ll use the Coefficient of Variation to help us see which players in the dynasty landscape are the most and least consistently valued players. A low Coef-Var indicates a player is consistently valued, whereas a high Coef-Var indicates a player is inconsistently valued. Each month, we’ll dive into the top ten players in each category and see why that may be the case. Before we begin, there are two quick notes I want to mention!
- Only players in the top 200 of ADP qualify for this article, as passed that we may run into the “undrafted” issue.
- I’ve multiplied all the Coef-Vars by 100 to help distinguish between some pretty small numbers.