Running Back Efficiency and the Perils of First Round Backs


Much debate has surrounded the idea of drafting a running back in the first round. Many analysts have succeeded in proving that the value of running backs pales in comparison to that of other positions. Additionally, the rookie wage scale makes teams more inclined to draft positions that tend to produce more value. Another problem that exists when drafting a running back is trying to parse the value of that running back versus the next best running back. Being able to confidently state that Running Back X should be one of the top 5 picks suggests that this player possesses more value than all but four other players in the draft, as well as being clearly superior to the other backs in the draft.

This analysis looks at the first four years of every running back drafted since 2002 up until 2011 (players from the 2012 NFL Draft will begin their fourth season this year). This ten year window seemed to be the most appropriate as passing became more prevalent in the early to mid-2000s. Recently, the league is seeing more 4000 and 5000 yard passers than ever, so the best thing to do was limit the sample to these backs. I examined how many rush attempts, rushing yards, targets, receiving yards each player had as well as the Approximate Value of every player. I excluded running backs who never made a rushing attempt during a regular season game from the data set, which left 162 players. The average yards per carry for these players was 4.3 yards, while the average number of receiving yards per target was 5.9 yards. Both the total number of yards as well as the Approximate Value were adjusted. That means that a player who rushed 100 times for 430 yards would be an average player. Similarly, a back who was targeted 100 times and gained 590 receiving yards would also be average. I elected not to incorporate touchdowns in might adjustment since the Approximate Value calculation does not include touchdowns in its calculation.

Now, with the Approximate Value adjusted, I determined what adjusted AV we could expect from a player based on their combined number of targets and carries:


The line of this graph reflects the average Adjusted Approximate Value. Essentially, any data point that falls on this line is a running back who did not produce any more or less AV than one could expect from an average player. Points above the line represent players who produced a greater AV than expected, while points below the line performed below the expectations for an average running back.

Categorizing the running backs by the round in which they were drafted shows that, on average, first round running backs have performed the worst compared to the average. The chart below shows the average difference between a running back’s Adjusted AV as compared to that of an average player with the same number of carries:

Figure 1. Net Difference from the Average Adjusted AV based on Round Selection


One explanation for the low figure for first round backs is that a high number of carries would lead to lower efficiency, and consequently, a lower Approximate Value. While I agree that a larger workload would lead to a decline in efficiency, this is not the issue here. The chart below categorizes the same players based on the number of carries and targets they had over their first four years:

Figure 2. Net Difference from the Average Adjusted AV based on Carries and Targets


Actually, players with over a thousand carries and targets saw the largest, positive deviation from the average. The worst range was the 600-799 carry/target group. The graph above shows that a good number of running backs in that carry or target range performed below the average. So why did first round running backs perform so low against the average?

There are two reasons that help explain why the figure is so low for 1st round running backs. The first is that despite my efforts to deflate the effects of receiving yards, it appears that running backs with lower rushes to target ratios tend to perform slightly better. Still, Laurence Maroney, who has one of the higher rush to target ratios with 91.37% of his opportunities coming from rushes, is in the top ten percent of running backs by this metric (Maroney was +4.94 points above the average). The second reason, which is primarily responsible for this, is the extremes we see from first round backs. Of the eight worst backs by this metric, six of them are first rounders. All six backs were -5.97 to -8.80 points below the average. Removing these six backs from the data set would increase the net difference for first rounders up to +1.15, significantly better than any other round.

Those six running backs are not necessarily as bad as the figure may make them out to be. In many instances like these, coaches and general managers insist on continually feeding these backs, despite their performance being average or subpar. Additionally, a team often selects a running back high in the draft to “fix” their running game, when many times, it is the offensive line that needs repair. Cedric Benson is a prime example of this. Benson had the lowest difference of the aforementioned six first rounders with production far below the average. Benson finally got his chance to start for the Chicago Bears in 2007, after sitting behind Thomas Jones his first two years. Unfortunately for him, he played behind the 30th ranked offensive line for run blocking, according to Football Outsiders’ Adjusted Line Yards metric. 2008 would prove to be even more frustrating as he moved to Cincinnati. There, he ran behind the worst offensive line according to that same metric. Benson would go on to rush for 1000 yards in three consecutive seasons as the Bengals continued to upgrade their line. Clearly, a major barrier in evaluating the skills of a running back is analyzing the abilities of his offensive line.

To help normalize the metric by workload, I divided the net difference in Adjusted AV by the number of targets and carries to see what results it would yield. We will call this metric “Efficiency,” as it tells us the value added for every carry/target the player receives. For running backs with a minimum of 100 carries, two former San Diego Charger running backs topped the list for efficiency. Both played for the Chargers at the same time. Most people would be quick to assume LaDainian Tomlinson would be one of the two running backs, but actually the two backs are Darren Sproles and Michael Turner (Tomlinson was drafted in 2001). Sproles and Turner could not have been more different in their backup roles as Michael Turner found most of his opportunities coming from rushing attempts (93.83%), while just over a third of Sproles opportunities came from targets. The attention that Tomlinson drew allowed for more efficient production from Turner and Sproles. In the same way that Peyton Manning made many of his wide receivers look better than they actually were, backs like Tomlinson, Adrian Peterson, and even DeMarco Murray this past year create the same effect.

While this does bolster the case for one back getting a larger share of the workload, there seems to be only a few backs who really make this type of impact on an offense. In 2011, Brian Burke wrote an article explaining why a team should not pay Chris Johnson top dollar. He approaches the case by showing the true worth of running backs relative to other positions and the tendency for teams to overvalue running backs. By 2011, Chris Johnson had a combined 1456 attempts/targets that produced a net difference of +2.18 in Adjusted Approximate Value. But let’s say the Titans never picked Chris Johnson, but rather traded that (24th) for two third rounders and a fourth rounder. Combined Jamaal Charles (73rd), Steve Slaton (89th), and Tashard Choice (122nd) had 1610 touches and carries in the same time frame as Johnson. Their aggregate net difference of +6.43 in Adjusted Approximate Value is almost triple the amount Chris Johnson produced. While Charles is a phenomenal running back, it goes to show that running back talent like Johnson is  1) replaceable when touches are distributed more evenly across different backs and 2) replaceable with “cheap” players.

Players like DeMarco Murray (+5.87), Brian Westbrook (+7.16), Ray Rice (+7.53), and Maurice Jones-Drew (+10.10) show us that running back talent is deeper than most front offices think as all four guys landed outside the top 50 picks of their respective drafts. As previously mentioned, a big reason that these running backs were even given the chance to succeed is ultimately tied to the talent of their offensive lines. For those who fail, the situation for each running back clearly affected their play. The chart below shows the running backs with the highest differences and lowest differences from adjusted AV. Marshawn Lynch is on the list, but after his tenure in Buffalo, he has become one of the best running backs in the game. Willis McGahee also found success when he became a backup for Ray Rice in Baltimore. All this being said, it appears that the talent difference among running backs is not only marginal, but very dependent on their team’s offensive structure and how frequently the coach opts to give them touches.

Figure 3. Ranking Running Backs by Difference from Adjusted AV (Min. 100 Carries/Targets)


Image of Willis McGahee against the New York Jets is from the New York Times.
All statistics are from Pro Football Reference, unless noted otherwise.

Nick Barton is a rising Junior in the McDonough School of Business
Twitter: @nbarton94

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