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The Best-Ball RB1 I’m Avoiding at All Costs

When Shawn Siegele penned the now legendary Zero RB, Antifragility, and the Myth of Value-Based Drafting, he talked about how a Zero RB lineup benefits from injuries and inaccurate projections. I’m not here to repeat or expand upon his great work, but I am here to tell you which RB1 is being projected too highly so you can avoid him in your redraft and Best-Ball leagues.

When you consider Todd Gurley‘s comparables, his 2016 schedule, and his game script dependency, I can’t possibly see a manner in which his median projection should be that of the season long RB2, which is where he’s currently going in Best-Ball formats according to the Best-Ball ADP App.

Gurley’s 2015 Season

Gurley’s rookie season was certainly impressive, as he amassed 1106 yards in only 13 games (starting the last 12 of those). If we consider only those 12 starts, he put up 91.4 yards per game on the ground, which would have placed him third on a per-game basis behind only Adrian Peterson and current Best-Ball RB1 Le’Veon Bell. That alone might convince you he’s worthy of a top two pick if you project Peterson to decline at 31 years of age.

However, if you’re in a PPR league then you’ll certainly want to consider his (lack of) receiving numbers. Gurley posted 20 catches in those 12 games, good for 1.67 receptions per game. That’s barely better than Carlos Hyde, who is an awful pass catcher. At least Gurley turned those catches into a 9.0 yards per reception average. All said, he averaged only 3.2 PPR points per game through the air.

On a per-game basis, Gurley’s PPR numbers ranked fifth behind Devonta Freeman, Jamaal CharlesArian Foster, and Bell. Again, you might expect Gurley to finish the year as RB2 if you think Charles and Foster are in decline. At least in the case of Charles, I don’t think it will be that dramatic.

Sim Scores

Using those 2015 numbers from his 12 games started, we can use the RB Sim Scores App to create a list of Gurley’s 25 closest comparables and see how they did the following year. Doing so gives him the following range of outcomes:

TGProj

What we see is that the Sim Score App thinks Gurley was close to his ceiling in production last year and actually predicts his median score will regress from last year. That high score of 18.5 PPR points per game would have landed him tied for third with Bell last year. And that’s the bullish projection. If we break it down to the individual player level, these are how his 25 closest comps fared in year-on-year production:

TGYOY

Most of those comps declined the following year. Those of you high on Gurley might gravitate toward that Peterson comp over on the far left from Peterson’s 2000-yard 2012 season. However, there are two more Peterson comps, one from his 2011 season and one from his 2008 season, both of which were declines in production from the season prior. If we look at the order of the Gurley comps in the image below, we see the 2008 season was the N+1 year that most similarly projects to Gurley’s N+1 year. The 2012 season is the lowest on the list of the three.

TGAP

Strength of Schedule

Gurley’s Rams, newly located on the west coast to match their NFC divisional alignment, finished 2015 with a 7-9 record. However, according to Pythagorean Expectation, the Rams were a bit lucky based off their scoring margins and were instead closer to a 6-10 team. According to Football Outsiders Almanac “teams that win a minimum of one full game more than their Pythagorean projection tend to regress the following year,” and while the Rams weren’t a full game better than expected, they were close (0.84 games better). So it’s possible the Rams regress next year, schedule aside.

In that same article, I projected 2016 wins based off Pythagorean Expectation, and found that the Rams project for 5.8 wins, or 1.2 fewer than they won in 2015. The reason? The Rams face the second toughest schedule in the league in 2016 via Pythagorean Expectation as they have six games against last year’s top five in Pythagorean wins (two each against Seattle and Arizona, and one against Carolina and New England). Yes, they faced a similarly tough schedule last year with opponents averaging 8.6 Pythagorean wins, but their 2016 opponents project to 8.8 Pythagorean wins. That could mean the potential for more negative game script for Gurley than he faced last year, or at best, it likely won’t improve.

Gurley and Game Script

With Gurley being a very rushing oriented back, game script would certainly seem to matter in his case. I used The Snap Report to evaluate Gurley’s percentage of snaps played from week 4 on, where he became the full-time starter.

TGSnap

In four games he played 52 percent of the snaps or less. The final margin of those games? 21, 24, 24, and 24. One of those (the 21-point margin) was a win, but that shouldn’t surprise us since coaches call fewer run plays in blowout wins as well. But focusing on the three blowout losses, Gurley had carry totals of 12, nine, and nine. I went through the game logs to find out how many fourth quarter carries Gurley had, and among all three games combined he had a grand total of one. ONE. That really goes to show how script-dependent Gurley’s production is. He isn’t even involved in the receiving game to make up for it.

Conclusion

Let’s recap. Gurley:

  • Is a true running back who isn’t really involved in the passing game
  • Who projects at best to improve slightly on his 2015 year numbers, or possibly decline
  • While playing in a slow-paced system
  • For a bad team that got (statistically) lucky to reach seven wins last year
  • That faces an even tougher schedule in 2016
  • All while being heavily dependent on game-script for his production

And I didn’t even mention the rookie QB part.

There’s just too many negative factors that point to his 2016 being, at best, a repeat of his 2015 on a per-game basis. Sure, he could finish the year as the RB1 or RB2, but I’m not banking on it.

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