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Welcome to Regression Alert, your weekly guide to using regression to predict the future with uncanny accuracy.
For those who are new to the feature, here's the deal: every week, I dive into the topic of regression to the mean. Sometimes, I'll explain what it really is, why you hear so much about it, and how you can harness its power for yourself. Sometimes, I'll give some practical examples of regression at work.
In weeks where I'm giving practical examples, I will select a metric to focus on. I'll rank all players in the league according to that metric and separate the top players into Group A and the bottom players into Group B. I will verify that the players in Group A have outscored the players in Group B to that point in the season. And then I will predict that, by the magic of regression, Group B will outscore Group A going forward.
Crucially, I don't get to pick my samples (other than choosing which metric to focus on). If I'm looking at receivers and Justin Jefferson is one of the top performers in my sample, then Justin Jefferson goes into Group A, and may the fantasy gods show mercy on my predictions.
Most importantly, because predictions mean nothing without accountability, I report on all my results in real time and end each season with a summary. Here's a recap from last year detailing every prediction I made in 2021, along with all results from this column's six-year history (my predictions have gone 36-10, a 78% success rate). And here are similar roundups from 2021, 2020, 2019, 2018, and 2017.
An Easy Win to Start Us Off
If you read last week's column, you know that one of the keys to profiting off of regression to the mean is recognizing that everything regresses, but not everything regresses at the same rate. The more a statistic is dominated by luck, the more that statistic is going to swing wildly from one sample to the next.
Because I'm going to be making predictions and tracking their accuracy, I want to start the season off with my best prediction, the one I'm most confident in. And to make that prediction, I want to focus on the statistic that is more dominated by luck and random chance than any other statistic I know. I want to focus on yards per carry (ypc).
Yards per carry is one of the most beloved statistics for judging running backs. Jamaal Charles has never averaged below 5 yards per carry in a season where he's had at least 20 carries*, therefore Jamaal Charles is a star. Trent Richardson had 1300 yards from scrimmage and 12 touchdowns as a rookie, ranking as a top-10 fantasy back, but his 3.6 yards per carry was an early warning sign that he would eventually be regarded as a colossal bust.
*(Technically, Charles averaged 4.97 yards per carry in 2013, but what's a few hundredths of a yard among friends?)
I've written more about Trent Richardson before, back in 2014, when another young rookie had just had a high-volume, low-YPC season that had everyone drawing parallels and claiming he was destined to disappoint. I wrote that, based on history, maybe we shouldn't be writing off this Le'Veon Bell fellow quite so quickly.
Indeed, the list of high-volume, low-YPC rookie running backs was basically Trent Richardson and a who's who of Hall of Famers or almost Hall of Famers. In addition to Richardson (3.56 ypc) and Bell (3.52 ypc), there's LaDainian Tomlinson (3.65), Ricky Williams (3.49), Walter Peyton (3.46 ypc), Emmitt Smith (3.89 ypc), Matt Forte (3.92 ypc), and Marshawn Lynch (3.98 ypc).
Even the guys on the high-volume, low-YPC list who didn't go on to be All-Pros typically had several quality fantasy years in them. Karim Abdul-Jabbar, Travis Henry, Errict Rhett, and Joe Cribbs all followed up their "inefficient" rookie season with a top-12 fantasy campaign as a sophomore, Sammie Smith improved across the board and finished as RB18, and Jahvid Best looked (and produced) like a star before injuries derailed his career.
Since I wrote that article in 2014, Melvin Gordon III has also found himself on the "wrong" side of the ledger with an awful rookie ypc of 3.48. Fearing the shade of Trent Richardson, many owners sold low on the "inefficient" Gordon after a "disappointing" rookie season, only to see him rank 3rd in fantasy points (nearly 20% ahead of fourth place) from 2016-2018.
Indeed, other than Richardson himself, the only running back who had a high-volume, low "efficiency" rookie season and followed it up with a disappointing sophomore campaign was James Jackson, who also happens to be the only back in the sample to average below 3 yards per carry as a rookie, (2.84), and whose team thought so little of him that they drafted William Green in the first round to replace him.
What is going on here? Why is having a terrible rookie yard-per-carry average such a positive sign for a player's career? The truth is that a poor yard-per-carry average isn't a positive sign. It just isn't a negative one, either. I'm providing a list of high-workload rookies with low yards per carry, and the high-workload part is the real key.
Backs get a high workload because the coaching staff thinks they're good and wants to give them the ball. In the long run, backs who coaching staffs think are good and want to give the ball... tend to be pretty good. The low ypc, in the meantime, is just a meaningless fluke.
What Is Yards per Carry, Anyway?
To understand why yards per carry is a fluke, you have to understand something very important about it: it's not measuring how good a running back is. It's so thoroughly dominated by outlier runs that all it's really measuring is whether a back has had three long runs or merely two. For the majority of players who finish the season above the league average mark in yards per carry, you only have to remove one or two carries to drop them below the league average.
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