It's really easy to look at fantasy football results from one season and use them as the basis of your rankings for the next. This is especially true of top players -- if Jamaal Charles goes nuts one year, the chance that he'll be ranked highly across the industry during the upcoming one is pretty high.

The problem with this line of thinking is that great seasons are great because something spectacular happened. Perhaps a player scored a lot of touchdowns. Maybe he saw an unreasonable amount of volume. Or there's a chance the player was super efficient.

Automatically slotting the best players from one year as the best ones during the next in fantasy football ignores regression. And ignoring regression can get you in a lot of trouble in this addicting game we play.

Take C.J. Spiller's 2012 season. That year, he was a top-five running back in fantasy, rushing for 1,244 yards and adding another 459 through the air. He averaged 6.0 yards per carry, becoming just the sixth player in NFL history to rush 200 or more times and average such a ridiculous number of yards per tote.

Entering 2013, Spiller was understandably a top-three fantasy selection. 

He finished as the 27th best running back in fantasy football, playing 15 of 16 games.

You may think this is a dramatic example, but it's really just another instance where regression -- a return to a former or less developed state -- is in play. And as you'll see, if you don't pay attention to regression in fantasy football -- specifically at the running back position -- you could put yourself in an undesirable situation.

The Study

Exactly how real is regression at the running back position? That's the high-level question I wanted to find an answer to.

So I dug into the data. More specifically, I looked at numberFire's Net Expected Points (NEP) data. If you're unfamiliar with NEP, it's an advanced metric we use to determine what actually happens on the football field, rather than using simple counting stats. A 10-yard gain on 3rd-and-10 is much more important than a 10-yard gain on 3rd-and-15, right? Well, it should be -- one results in a first down, while the other puts the team in a not-so-great 4th-and-5 scenario. One contributes positively towards NEP, while the other may not.

To get more info on Net Expected Points, check out our Marion Barber and his 2006 season. If you forgot, the man scored 14 rushing touchdowns that year...on 135 carries. That's a fantasy football owner's dream.

But let this serve as a quick glance to the relationship between Rushing NEP per rush and a dip in fantasy points per touch. The average difference from one year to the next in fantasy points per touch among this group was -0.27, far worse than what we saw from the sample as a whole. In other words, the higher the efficiency, the bigger the drop during the following season.

What This Means for 2015

The more I thought about this study, the more I realized it's sort of like baseball's BABIP (batting average on balls in play) metric.

If you're not familiar, BABIP measures, well, it measures exactly what it says -- the batting average a player accumulates on balls hit in play.

Generally, this BABIP number will hover around .300. Anything significantly higher may mean the player is getting lucky, while anything lower means the batter may have some positive regression upcoming.

But some players are just good. Some can hit line drives consistently, while others are fast and can beat out slow grounders to third. Good players can sustain higher BABIP averages.

The same can probably be said for a study like this. Across the board, names like Jamaal Charles, Maurice Jones-Drew, LeSean McCoy and Marshawn Lynch appeared. And that's because these players are really good at their jobs. They're efficient more years than not because they're incredibly gifted athletes who can maintain a high level of effectiveness on the ground.

So when I show you the seven running backs who hit the highly-efficient mold from 2014, don't assume each one of these players is doomed, especially when you consider who some of them are.

Remember, we found two big things with the study above: (1) higher Rushing NEP per rush rates tend to see larger fantasy points per touch drop-offs and (2) lower Success Rates have a mild correlation to larger fantasy point per touch dips.

Because Charles and Lynch have proven to be hyper efficient for multiple years, we can probably ignore them on this list. Meanwhile, Le'Veon Bell and Lamar Miller have both metrics favoring them -- their Rushing NEP per rush is almost as low as it can be to even make the study, and their Success Rates are about average among the sample size. They're probably safe.

That leaves us with Justin Forsett, Jeremy Hill and C.J. Anderson. Interestingly enough, Anderson and Hill were both players who Trestman has a history of helping running backs catch passes out of the backfield. If Forsett is able to gobble up targets in the Ravens' passing attack -- which could easily happen given the team's weapons -- he shouldn't be in awful shape in PPR formats.

But as a rusher, given this data, it seems we should be a little hesitant with Forsett this season. And it's all because of regression.