Coefficient of Variation - A Draftmasters Key Fantasy Statistic?
Draft weeks are fast approaching, and who knows, you may have one this week. You may also be at a point of information overload and more than prepared to overthink your draft strategy. A key mistake all due to bombarding the frontal lobe of your brain with both fantasy football-specific reasoning and abstract thought essentially giving your neocortex the desire for a much-needed virtual reality vacation. And to think the regular football season has still yet to arrive. Rest assured we here at PFF Fantasy have more mind-numbing analytic data for your sponge to absorb.
As you approach draft day with strategy in hand regardless of whether you’re boarding the late round quarterback train or your stockpiling running backs early and often, at some point team owners will need to pull the trigger on a wide receiver. It has been proven of the “Big 3” positions wide receiver is the most volatile, fueling the fires of proponents who insist on waiting to draft wide receivers until later or at least grabbing the top-tier wideouts first.
Being fascinated by the wide receiver position, the specifics on each of last year’s top 50 wide receiver fantasy finishers in terms of both coefficient of variation and standard deviation was a paramount question in need of an answer. Hoping to discover which receivers had been the most reliable at their respected position over a defined span was the driving force in the need to find both the consistent and inconsistent. All this in hopes of possibly landing a later round diamond in the rough or even an established consistent playmaker at the position in Rounds 3-6.
Cowboys receiver Dez Bryant managed to drop his unitized risk by almost half over the last seven games of the 2012 season. Of veteran players, Carolina pass catcher Steve Smith proved to be the most consistent in terms of least risk over the span of last season. However a single season is likely too small a sample size, but as with any stat regarding both veterans and rookies, the data has to begin at some point. Therefore, if a rookie finished in the top 50 among wide receivers, the players’ numbers were analyzed.