Is Emmitt Smith's All-Time Rushing Record at Risk?

A few weeks ago, I completed a study detailing Emmitt Smith’s chances of breaking college football’s all-time rushing record (had he stayed in school).  Using statistical projections and the normal distribution, I concluded Smith had a 3-5 percent chance of breaking Tony Dorsett’s then all-time rushing record (and just a 0.1 chance of still being college’s all-time leading rusher).

The nature of college football and abundance of backs makes the record susceptible.  

Two running backs (Ricky Williams and Ron Dayne) have subsequently broken Dorsett’s mark, and a few generally recent runners have come pretty close as well (DeAngelo Williams, Cedric  Benson, LaDainian Tomlinson, Garrett Wolfe, Mike Hart, and Darren Sproles).

Emmitt Smith’s NFL rushing record of 18,355 yards seems much more unapproachable.  

For starters, the method by which running backs are employed has altered drastically even since Smith’s career.  Gone are the days of workhorse backs.  Today, it’s all about two and three-running back rotations.  There’s simply no way for a running back to approach Smith’s record with only 225 carries a season.

Second, running back is, even more so than ever, a young man’s position.  Rookie running backs are prepared to contribute immediately, replacing high priced veterans.

But are there any current running backs who possess even a glimmer of a hope of approaching 18,000+ yards? 

First, let’s take a look at Smith’s career numbers and the total for five current backs who I have deemed the most likely to break Smith’s record—Ray Rice, Adrian Peterson, Chris Johnson, Steven Jackson, and Maurice Jones-Drew.

Emmitt Smith
Years Pro: 15
Carries: 4,409
Yards: 18,355 (1,224/season)
YPC: 4.16

Ray Rice
Age: 23
Carries: 361
Yards: 1,793 (897/season)
YPC: 4.97

Adrian Peterson
Age: 25
Carries: 915
Yards: 4,484 (1,495/season)
YPC: 4.90

Chris Johnson
Age: 24
Carries: 609
Yards: 3,234 (1,617/season)
YPC:  5.31

Steven Jackson
Age: 27
Carries: 1,548
Yards: 6,707 (1,118/season)
YPC: 4.33

Maurice Jones-Drew
Age: 25
Carries: 842
Yards: 3,924 (981/season)
YPC: 4.66

Smith showed greatness isn’t flashing talent.  It is about longevity and consistency.  

There isn’t anything too special about his career 4.16 yards-per-carry average.  More amazing is the fact that he rushed for 1,000 yards in 11 straight seasons and 1,400 in five straight.

As we analyze the numbers, keep in mind that longevity and consistency are much more important than yards-per-carry.  

These projections assume near full health—a basic necessity to break any all-time record.

Projected Seasons Left (Projected “Prime” Seasons)
Ray Rice:  12 (9)
Adrian Peterson:  8 (5)
Chris Johnson: 10 (6)
Steven Jackson: 6 (3)
Maurice Jones-Drew: 10 (6)

These numbers will undoubtedly be the most important in our evaluation.  

I project Steven Jackson and Adrian Peterson to have less seasons left in the tank due to their running style.  Neither player will be able to hold up much past the age of 30 due to the hits they take.

Ray Rice is, in my opinion, the most likely to have a long career.  

He doesn’t rely solely on speed, like Chris Johnson, so he should still be effective in his 30s.  Remember, speed dissipates rather quickly in your late 20s.  Strength doesn’t.  He’s got workhorse running back potential without being an above average injury risk.

Rice is the most like Smith—not flashy, but consistent, utilizing vision and great balance.

Projected Carries/Season
Ray Rice:  275 in prime, 150 thereafter (2,925 total)
Adrian Peterson: 295 in prime, 200 thereafter (2,075 total)
Chris Johnson: 305 in prime, 200 thereafter (2,630 total)
Steven Jackson: 285 in prime, 175 thereafter (1,280 total)
Maurice Jones-Drew: 280 in prime, 200 thereafter (2,480 total)

Projected YPC
Ray Rice: 4.7 prime, 4.2 thereafter
Adrian Peterson: 4.9 prime, 4.4 thereafter
Chris Johnson: 5.1 prime, 4.5 thereafter
Steven Jackson:  4.25 prime, 3.8 thereafter
Maurice Jones-Drew: 4.7 prime, 4.2 thereafter

When determining yards-per-carry, I like to use a combination of past results and regression to the mean.  I use this same method in fantasy football to project a running back’s yards-per-carry.

For example, Chris Johnson’s career 5.31 yards-per-carry mark is stellar, one of the best ever, but I highly doubt he will be able to maintain it for even the next five years.  

His YPC will dip, despite his talent.

Meanwhile, Maurice Jones-Drew’s 4.66 career yards-per-carry mark is more established.  He’s played more seasons.  Expect him to maintain that mark for a little while.

Projected Yards Left (Total)
Ray Rice: 13,523 (15,316)
Adrian Peterson: 9,868 (14,352)
Chris Johnson: 12,933 (16,167)
Steven Jackson: 5,629 (12,336)
Maurice Jones-Drew: 11,256 (15,180)

Peterson figures to have a greater rushing average than Rice and Jones-Drew over his career, but he falls short in the projected career yardage mark because he’s unlikely to be able to sustain that level of play.

Longevity really is more important than short-term greatness.

Johnson leads the pack with a projected total of 16,167 yards–2,188 yards short of Smith’s total. 

However, those 2,000+ yards would be awfully difficult to gain at ages 34 and 35 and few running backs play until they are 35.

But what are the chances that, based on that projection, he would reach Smith’s record total simply by luck?  In other words, if we were to simulate 1,000 careers for Johnson and 16,167 was the average total, in how many of those 1,000 careers would he break the all-time mark?

I did a very similar analysis on Smith’s chances of passing Dorsett’s college total.  In both studies, we must consider the normal distribution, also known as the “bell curve”.

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The normal distribution is used to describe any set of variables that tend to cluster around the mean and gives us an excellent base for making probability-based predictions.

We see this all the time in football when there are a bunch of players with very comparable statistics and just a few players with “outlying” ones.   Of the 1,000 yards rushers in the NFL last season, for example, 14 of 15 rushed for within 220 yards of the 1,281 yard average.  

The lone outlier? Chris Johnson and his 2,006 yards.

By calculating the variance among the runners, we can determine the “standard deviation.”  If a set of data possesses a low standard deviation, we know that nearly all of the data clusters around the mean.  

A high standard deviation means just the opposite.

Calculating the standard deviation, or variance from the norm, is so important because the normal distribution is governed by standard deviations–even the distribution of football statistics.  In the example above, for example, we can determine that, of the 1,000 yard rushers, there is a standard deviation of about 160 yards.

Thus, according to the normal distribution, we would expect approximately 68 percent of 1,000 yard rushers to be within 160 yards, or one standard deviation of the mean.  In 2009, that would have been between 1,121 and 1,441 yards. In reality, only 9 of the 15 running backs were in this range (60 percent).

Over a larger sample size the numbers would level out—they always do.

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So, to more easily decipher Johnson’s chance of breaking the rushing record, we must determine how many career yards is equal to one standard deviation (as it relates to 1,000 simulated careers).  

A simpler way to put it is, “Within what range of yards is there a 68 percent chance that Chris Johnson’s career yardage falls?”

Why 68 percent?  

Well, if you look at the bell curve pictured above, you can see that in any given set of data which tend to cluster around the mean, about 68.2 percent will fall within one standard deviation of the average.  

If we know the standard deviation, we can determine the likelihood of future events quite precisely.  The answer to that question, however, tricky.  

The standard deviation of total yardage won’t be that great because, while totals can vary greatly from season to season, those fluctuations tend to level out over the course of a career.  

Since we don’t have any simulated season from which to gather data and because the statistics of others are basically irrelevant to Johnson’s future, we simply have to make an educated guess.

I would presume that Johnson has a 68 percent chance of falling with approximately a 3,000 yard range, between and 14,667 and 17,667 career rushing yards.

As you can see, the upper end of that estimate isn’t too far from Smith’s career yardage mark.  Actually, Smith’s record is less than 1.5 standard deviations away from Johnson’s average.

If an “average” Johnson career results in 16, 167 yards, then there is approximately a 5-7 percent chance that he eventually retires as the NFL’s all-time leading rusher. 

Not bad odds, really.

Of course, my estimates of his career rushing attempts are somewhat generous, as they assumed full health.  In reality, Johnson, nor any of the other backs on this list, will go through their entire careers unscathed.  

Smith was an anomaly.

If Johnson alone has a 5-7 percent chance of breaking the record, though, what are the chances that any of the running backs listed above will break it? 

Rice would have approximately a two percent chance.

Peterson a one percent chance.

Jackson almost a zero percent chance (we’ll say .001).

Jones-Drew comes in just under two percent.

The first thing that jumps out to me is that, despite less than 1,000 more projected yards than Rice or Jones-Drew, Johnson is about three times as likely to break the rushing record.  

This is because, as you get closer and closer to Smith’s record, the yards become “more valuable.”  

That is, the chances of falling two standard deviations from the mean  is exponentially lower than falling one standard deviation away, such that a small increase in projected average means big-time alterations in the probability of a player breaking the record.

Think about it this way: if one of the running back had a projected career rushing total of exactly 18,355, he’s have a 50 percent chance of breaking the record.  That’s a far great probability than even that of Johnson, whose mean projected total is just over 2,000 yards from Smith’s record.

Nonetheless, we can decipher the probability of any of these five running backs breaking Smith’s record using the following formula:

Let A=Rice’s chance of breaking the record, B=Johnson’s, C=Peterson’s, D=Jackson’s, and E=Jones-Drew’s

P(A or B)= P(A) + P(B) – P(A and B)= 7.88 percent
P(C or D)= P(C) + P(D) – P (C and D)=  about o percent
P(AorB or E)= P(AorB) + P(E) – P(AorB and E)=9.72 percent
P(AorBorE or CorD)= P(AorBorE) + P(CorD) – P(AorBorE and CorD)= 9.72 percent

Thus, the overall chance that one of these backs breaks Smith’s all-time mark is probably somewhere between 9-10 percent.  Johnson, of course, has the best shot at around 6 percent, while Jackson’s chances are basically nil.

The fact that perhaps the five best running back’s in the game today have just a one-in-10 chance combined of becoming the NFL’s all-time leading rusher is truly remarkable and speaks volumes about the magnitude of Smith’s achievement.

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