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The Gut Check No. 399: RB Production Shares, ADP, and Consistency

Matt Waldman examines running backs that quarterbacks lean on in the passing game, including the return of consistency data. 

Pairing statistics with insightful context can uncover fantasy value in pre-draft prep. Delivering the insightful context is the difficult part. Three data points that have potential value for fantasy owners are the percentage of targets, yards, and touchdowns that pass catchers earn from their quarterback during the year.

This week's Gut Check column will examine these three statistical outcomes for running backs between 2014-2016. This information will also be paired with ADP, consistency data, and other salient points that will help readers identify some of the safest options in 2017 fantasy drafts.


About Consistency Data

I began publishing Consistency Data in 2004. What differentiates my examination of "fantasy consistency" from many is that I don't use standard deviation as the method because this stat's purpose is to measure the variance of a process. When I was posting a lot of consistency analysis in the early-to-mid 2000s, the few others who were analyzing consistent performance were valuing low standard deviations because it proved to them that the process is under control. When applied to fantasy football, standard deviation doesn't help us see the type of consistency that a fantasy owner desires from a player.

Standard deviation doesn't take into account what we really want to know: How consistently a player reaches at least the desired amount of fantasy points per game. "At least" are the two key words. Strict adherence to standard deviation as the process punishes players who score a lot more than the baseline target—even when they consistency deliver to that baseline.

For example, here's a look at the 2016's top-35 PPR running backs with a minimum scoring average of 10 points per game and ranked by lowest to highest standard deviation:

Lowest Standard Deviation Among 2016 PPR Running Backs (Minimum of 10 fantasy points per game average)


The third row highlighted in yellow is the player's standard deviation. Frank Gore, Todd Gurley, Rashad Jennings, and Darren Sproles top the list. At or near the bottom is LeVeon Bell, Melvin Gordon, LeSean McCoy, and David Johnson. However, examine the columns labeled Sub Par, Elite, RB #1, RB #2, and RB #3 to the right fo the table. These columns calculate the frequency that these backs reached these tiers of performance in 2016.

What stands out immediately are the meaningful differences between backs like Jennings and Sproles—whose weekly performances fell to bye-week option (worse than an RB3 in a 12-team league) in at least 50 percent of their games—and studs like Bell and Johnson who scored worse than the average performance of an RB3 for no more than 20 percent of their games. In fact, Johnson was only worse than an RB3/flex-option 6% of the time. When weighting consistency analysis heavily on standard deviation, the most productive players get punished for the wrong reasons.    

Sproles and Jennings had standard deviations below 6 points and Bell had a 14-point figure, but when measuring the runner's performance against average value tiers of starter performance as shown in these last five columns on the table, you're rewarding runners for the optimal kind of deviation rather than punishing them. Even if we restricted the list to runners with at least 15 fantasy points per game and ranked solely by standard deviation, the information wouldn't have enough context because LeGarrette Blount and Theo Riddick would still be ranked higher the likes of Johnson, Bell, LeSean McCoy, and Melvin Gordon

I will examine player consistency in greater depth this summer. 

Running Backs Of Passing Game Note

Even before restricting this query to runners with at least 400 yards receiving and 8 games played, the list of backs heavily involved in their passing games is naturally smaller than receivers and tight ends. Let's begin with the runners earning the highest percentage of targets from their starting quarterbacks per season for the past three years. 

There's a lot of data here, but the insights of note are highlighted or in bold and discussed below. 


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