Catch Passes to Beat the Masses: Why Pass-Catching is the Key to Identifying Late-Round RB Breakouts
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Image Credit: Jordon Kelly/Icon Sportswire. Pictured: Alvin Kamara.

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Player evaluation is hard. That’s a truth we’ll have to deal with a lot over the next week, as the 2020 NFL Draft begins on Thursday. Sticking with the draft idea for a minute, we know that there are certain thresholds prospects have to meet to have a respectable likelihood of success at the next level. For example, if a wide receiver doesn’t break out in college, their professional prospects are usually pretty dire. These thresholds make it a lot easier for us because we can simply look at which players meet them and know that they have a better chance of succeeding than if they didn’t. Of course, there are always outliers, but it’s just a relatively straightforward way for us to identify which players have a good shot at being a valuable fantasy asset. 

Today, we will take that idea and use it to figure out how we can increase our odds of hitting on late-round running backs in best ball drafts. We’ll look at each RB based on their role (i.e., is this RB a pass-catcher, between-the-tackles grinder, or something in between?) and identify which backs you should target in the final rounds. Picking the right RB late in drafts is often the difference between a championship team and one that doesn’t even finish in the money. While there weren’t many late-round breakouts last season — I explained as much back in January — just ask fantasy owners who had James Conner in 2018 or Alvin Kamara in 2017 how much it helps to get RB1 production from a late-round pick. Let’s look at how you can maximize your probability of picking one of those league-winners.

Methodology

To be included in the sample, an RB had to meet the following criteria:

  • Have an ADP in Round 10 or later
  • Be selected in at least 50.0% of best ball drafts
  • Play at least one game

This exercise only includes players from the last five seasons because that’s as far back as best ball data is available.

There ended up being 137 RBs who met all three criteria. Next, I took each player’s ratio of receiving expected points (reEP) to rushing expected points (ruEP) and split the sample into groups so we could compare RBs in different roles and analyze which roles appeared to be the most fantasy-friendly.

Results

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