The wide receiver position is notorious for its vast amount of flavors and varying usage across different offenses in the league. These two facts make evaluating wide receivers much tougher since you’re comparing apples to oranges (a 6’5” 220 X receiver to a 5”10” 160 slot receiver) in a market where everyone has their own favorite fruit (NFL teams unique preferences from the WR position). So, how does one go about separating and sorting players to get a clearer evaluation? Enter clustering.
I won’t bore you by explaining what K-Means clustering is. Just know it’s math stuff that groups data into K numbers of clusters.