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Should We Be Worried About Jeremy Langford’s Inefficient 2015?

Though we haven’t even gotten into the thick of free agency, and the NFL draft is months away, there is already some discussion about whether we should be worried about presumed lead back Jeremy Langford’s poor rushing efficiency last year.

Mike Clay at Pro Football Focus calculated that Langford was 47th, or seventh worst,1 in defense-adjusted yards per carry last year, which led to his succinct assessment of Langford’s current redraft value:

My initial thoughts were that yards per carry is a particularly noisy stat, and that we should be more focused on the available opportunity caused by Matt Forte’s departure, rather than Langford’s low YPC. But rather than go on complete faith in my gut assessment and accordingly draft Langford at will, I decided to dig into whether having a low previous-year YPC was worrisome for backs with newfound opportunity, and what other variables are more predictive of breakout success.

No surprise, the results of my analysis did not perfectly match my intuition. But this is what’s great about doing an analysis, you do it to win the game, not to validate your previous feelings.

For this study, I analyzed the 50 teams that lost their top-scoring running backs from 2001-2015, and looked to see what drove the “breakout” performance of the previous-year’s No. 2 scorer, who should be in best position to take the lead role.

First, let’s look at a simple plot of previous-year YPC versus PPR points in the potential breakout year.


Well, right off the bat it looks like I was wrong about the effect, or lack thereof, between previous-year YPC and subsequent performance. There’s a pretty clear positive linear relationship between the two, as you can see above. Why would this be the case when YPC has been shown to regress significantly towards the mean year-over-year? One reason might be that YPC is a more significant indicator for future team decision-making, in terms of which running back to play and whether to bring in competition through free agency and the draft, than it is an indicator of actual running back quality. We certainly can’t ignore YPC when assessing breakout chances, but we may want to be particularly focused on how it affects potential playing time.

The measure that I thought would correlate most strongly with breakout success is the amount of opportunity freed up by the departure of the lead back. I decided to use the departing back’s previous-year PPR points – what I’m calling “Missing PPR Points” — as a proxy for opportunity.


This makes me 0-for-2. Strangely, missing PPR points has negative correlation with breakout PPR points. We are using a smallish sample here (50 observations), so this doesn’t definitively say that missing PPR points doesn’t have an effect on breakout scoring, but it isn’t encouraging.

Rather than go through graphs for every possible variable, I put all the potentially relevant stats into a linear regression model aimed at solving for breakout PPR points. Then, I took out the non-significant variables one-by-one, leaving only three.

Screen Shot 2016-02-23 at 11.27.22 AM

*exp = experience by years in NFL; dpos = overall draft position; ry_aPre = previous year’s YPC

None of the previous-year production stats had statistical significance2 other than the one I thought wasn’t relevant: previous year’s YPC. The other two significant independent variables in the regression equation have nothing to do with on-field performance: experience and draft position. I’m not sure how exactly these are tied to running back performance, but I think the case can be made that each is a significant factor when a team decides who to make the heir-apparent when its lead back leaves. All else being equal, coaches and general managers are going to favor younger backs with higher draft positions that performed well the previous year. This equation could be most useful in solving for opportunity, which then is reflected in PPR points scored.

When we project 2016 PPR points for a few potential breakout running backs who could see their former lead-back teammate depart, these are the results.

Name Team Draft Position Experience Pre Yr YPC Predicted PPR
Charles Sims TB 69 3 5.0 150.8
Jay Ajayi MIA 149 2 4.4 116.0
Jeremy Langford CHI 106 2 3.7 112.9

Langford has the worst projection of the three, with Charles Sims leading and Jay Ajayi second. For context, Sims’ projected 150.8 PPR points would have made him the season-long RB28 last year.

It almost goes without saying that the probability that the Bears’ lead back will depart is certainly higher than for the Bucs or Dolphins, since Forte has already announced that he won’t be returning. But I think the results are interesting nonetheless. There might be a bigger chance that the Bears make a material free agent signing or higher capital draft pick than many assume, at least based on Langford’s relatively poor draft position and 2015 YPC.

I plan to revise the model in the future once we know the 2016 free agent signings and draft selections. Model accuracy should improve if we explicitly make those two events into independent variables. For now, it’s probably best to not fully ignore poor rushing efficiency when analyzing breakout potential.

  1. Minimum 80 carries.  (back)
  2. At the 0.1 p-value threshold, slightly worse than the other variables that were significant at the 0.05 threshold.  (back)

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