So we have been doing a series on the drivers of fantasy production and finding out what snap level statistic has a correlation to their fantasy performance. In short, we are looking to find an indicator for a greater performance by each position and scoring format.
For example, in the wide receivers group we found the percentage of snaps played generated a higher fantasy score 85% of the time.
Whereas for tight ends the percentage of time a player was targeted on a pass, caught a pass, or attempted a pass or rush (also called utilization %) resulted in a higher fantasy score 77% of the time.
Now we turn to running backs, as mentioned above we will be breaking out standard and PPR scoring formats separately. Drafting a good running back who is involved in the passing game is crucial for PPR leagues. In standard leagues, I believe it is more important to focus on those teams who are often in the red zone, as TD production is more valuable.
In this article we are also introducing a new snap metric, touch percentage. Touch percentage is the percentage of times the player touched the football (pass attempts, rush attempts, and receptions). While this is similar to utilization percentage, utilization % also includes the times a player was targeted on a pass attempt.
STANDARD SCORING FORMAT
In standard we see a recurring theme, those players with the highest snaps played percentage were responsible for more points. These player should be at the top of all draft boards. Drafting running backs for standard leagues is even more difficult than PPR since there is no way to make up for days where a back fails to reach the end zone. If you haven’t already check out this article which focuses on the heaviest run teams that produced the most fantasy points for running backs.
Teams who often find themselves in the redzone, should be the main targets for folks in a standard scoring league. Individual players and their snap %’s will be shown after the PPR section below.
POINT PER RECEPTION SCORING FORMAT
The chart below shows all correlation data for running backs in PPR scoring formats: