Ahh, the sophomore slump. Is it real, or is it just our crazy fantasy fanatic brains just trying to explain the randomness of fantasy football? In an earlier piece, Mike Braude takes a look at Rookie vs. Sophomore Quarterbacks to see if there is any sort of pattern or drop-off. Here, we will be examining the perceived “sophomore slump” when it comes to running backs. Do they experience a decline in their second seasons? Let’s take a look.


Who Qualifies?

To get a solid sample size, I took every running back who got at least 170+ carries as a rookie from 2001-2012 and examined their statistics in their sophomore seasons. To get an idea of how they will compare in their second NFL season, here is a list of every qualifying running back and their stats in their rookie years:


PlayerGRush AttYdsY/ATDY/GRecYdsY/RTDY/G
Trent Richardson152679503.561163.3513677.2124.5
Doug Martin1631914544.561190.9494729.63129.5
Vick Ballard162118143.86250.9171528.9419.5
Alfred Morris1633516134.8113100.81177704.8
Ben Tate151759425.38462.813987.5406.5
Jahvid Best161715553.25434.7584878.4230.4
LeGarrette Blount1320110075.01677.55142.801.1
Knowshon Moreno162479473.83759.2282137.61213.3
Beanie Wells161767934.51749.61214311.9208.9
Matt Forte1631612383.92877.4634777.57429.8
Steve Slaton1626812824.78980.1503777.54123.6
Chris Johnson1525112284.89981.9432606.05117.3
Kevin Smith162389764.1861392867.33017.9
Jonathan Stewart161848364.541052.38475.8802.9
Ryan Grant151889565.09863.7301454.8309.7
Adrian Peterson1423813415.631295.81926814.11119.1
Marshawn Lynch1328011153.98785.81818410.22014.2
Joseph Addai1622610814.78767.6403258.13120.3
Laurence Maroney141757454.26653.2221948.82113.9
Ronnie Brown152079074.38460.5322327.25115.5
Cadillac Williams1429011784.06684.120814.0505.8
Kevin Jones1524111334.7575.5281806.43112
Willis McGahee1628411283.971370.5221697.68010.6
Julius Jones81978194.167102.4171096.41013.6
Domanick Williams1423810314.33873.6473517.47025.1
Clinton Portis1627315085.521594.33336411.03222.8
William Green162438873.65655.4161137.0607.1
Jonathan Wells161975292.69333.19485.3303
LaDainian Tomlinson1633912363.651077.3593676.22022.9
Dominic Rhodes1523311044.74973.6342246.59014.9
Michael Bennett131726823.97252.5292267.79117.4
Travis Henry132137293.42456.1221798.14013.8
Anthony Thomas1427811834.26784.5221788.09012.7
James Jackson111955542.84250.4756805.1

As I proved in my article about comparing Rookie vs. Veteran Running Backs, rookies are capable of putting up some excellent statistics when given the chance. But how do they follow up their remarkable rookie campaigns the following year?


Rookie vs. Sophomore: Season Averages

To answer the previous question, I took all of the qualifying running backs’ stats from their sophomore seasons and compared them to their rookie seasons to begin to test out whether or not a “sophomore slump” is a plausible theory. A “minus” indicates a sophomore regression, while a “plus” indicates an improvement:


Season TotalsGRush AttYdsY/ATDY/GRecYdsY/RTDY/G

So far, a “sophomore slump” seems like a very possible phenomenon when it comes to rushing statistics. Not only do sophomores decrease in productivity and games played, but their rushing efficiency regresses 4% from 4.26 to 4.11 yards per carry. Even more alarming for fantasy purposes, their scoring frequency takes a 22% nosedive by over a touchdown and a half from their rookie to sophomore seasons. Is this simply due to the semi-randomness of touchdowns, due to the 9% decrease in games played, or due to an actual sophomore slump?

 On the positive side, their receiving statistics obtained a significant boost – especially in efficiency. Even though their receptions increased a mere 1%, their yards per reception, scoring frequency, and yards per game all went up at least 10%. Even with the decrease in games played, sophomores seem to be doing much better catching the ball than in their rookie seasons.

However, it would be remiss not to take into account the decrease in games played. Only 15 out of the 34 qualifying running backs played in 15 or more games their sophomore seasons – down from 23 out of 34 playing 15+ games their respective rookie seasons. This could easily skew the data because it measures aggregate statistics over the course of a whole season and does not take into account injuries or those running backs who lost their starting jobs their sophomore season. To fix this problem, let’s compare their statistics on a per game basis:


Rookie Vs. Sophomore: Per Game Averages

Here are the qualifying running backs’ rookie statistics compared to their sophomore stats on a purely per-game basis. Remember, a “minus” indicates a sophomore regression, while a “plus” indicates an improvement:


Per Game AveragesRush Att/GY/GY/ATD/GRec/GRec Y/GY/RRTD/G

Even when we take the per-game averages into effect, sophomores clearly have a hard time improving upon or even matching their rookie season effectiveness when it comes to running the football. Without injuries playing a factor because of the per-game measurement, sophomores have no excuse for the statistical rushing drop-offs across the board. Simple regression to the mean is a possible explanation for the decreased effectiveness, but most puzzling are the drops in rushing efficiency and scoring frequency. It’s quite possible that after taking the league by storm their rookie seasons, many sophomores have been “figured out” to a point by defensive coordinators after having an entire season of game film on them. Not only do they get a look at the running backs themselves, they also get to see how exactly the coaches like to use their running backs and set out to stop specific plays that the running back was previously successful on. This obviously is not the case for sophomore explosions by players like LaDainian Tomlinson, Adrian Peterson, and Chris Johnson, but it is a very plausible explanation for the average rookie running back.

When it comes to receiving, however, it’s quite clear sophomores are far superior. On a per game basis, they experience double-digit percentage increases across the board, with an 11% boost in receiving efficiency and a huge 20% boost in receiving touchdown frequency. This is not a surprising statistic, as many rookie running backs struggle in their transition from simple spread or rush-heavy college offenses to the complicated passing offenses of the NFL. But by their second seasons, running backs clearly have a better grip on the big-league passing game. This is at least in part due to increased skill in blitz-pickup- something that many running backs only get exposed to in the NFL. After learning this for an entire season, they are trusted more in passing down situations, and as we know more passing downs played equals more passing down production.


In Conclusion: What does this mean for 2014?

It would be an egregious error to consider players like Giovani Bernard, Zac Stacy, Eddie Lacy, and Le’Veon Bell locks for regression in their sophomore campaigns. But our research here is at least a warning that a huge statistical jump in a promising rookie’s sophomore season is far from a lock. For every Chris Johnson-like progression, there is a Trent Richardson-like regression, with most falling somewhere in between. As this article shows, there is merit to the “sophomore slump” theory – possible explanations include regression to the mean, an entire year of game-tape on a player, and simple wear-and-tear from a player’s first NFL season.

On the other side of the coin, these 4-5% drops in rushing effectiveness, although somewhat alarming, should not scare owners away from taking a chance on players like Bernard, Stacy, Lacy, or Bell. Injuries are nearly impossible to predict, and all of these players project to be workhorse backs with plenty of talent and above-average offensive lines to work with. For whatever regression they may possible face on the rushing side, their improvement in receiving could be enough to balance it out.