Photo: Imagn Images

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 Cooper Kupp is one of the top performers in my sample, then Cooper Kupp goes into Group A and may the fantasy gods show mercy on my predictions.

Most importantly, because predictions mean nothing without accountability, I track the results of my predictions over the course of the season and highlight when they prove correct and also when they prove incorrect. At the end of last season, I provided a recap of the first half-decade of Regression Alert's predictions. The executive summary is we have a 32-7 lifetime record, which is an 82% success rate.

If you want even more details here's a list of my predictions from 2020 and their final results. Here's the same list from 2019 and their final results, here's the list from 2018, and here's the list from 2017.

The Scorecard

In Week 2, I broke down what regression to the mean really is, what causes it, how we can benefit from it, and what the guiding philosophy of this column would be. No specific prediction was made.

In Week 3, I dove into the reasons why yards per carry is almost entirely noise, shared some research to that effect, and predicted that the sample of backs with lots of carries but a poor per-carry average would outrush the sample with fewer carries but more yards per carry.

In Week 4 I discussed the tendency for touchdowns to follow yards and predicted that players scoring a disproportionately high or low amount relative to their yardage total would see significant regression going forward.

In Week 5, I revisited an old finding that preseason ADP tells us as much about rest-of-year outcomes as fantasy production to date does, even a quarter of the way through a new season. No specific prediction was made.

The Science of Intuition

One goal of this column is to convince you that regression to the mean is real, it is powerful, and it is everywhere. To explain what it is and how (and why) it works. Another goal is to give you lists of players who are underperforming and players who are overperforming so you can make informed decisions about what to do with them going forward.

But the most important goal is to equip you with the tools to spot regression in the wild on your own, to help you develop intuitions about what kinds of performances are sustainable and what kinds of performances are unsustainable. Obviously, I'll highlight certain stats and give you my opinions on them. Yards per carry: bad. Yards per touchdown: sustainable, but only within a narrow range from about 100-200. Interception rate: bad. (Sorry, spoiler alert.)

But as years go on, one fact of life in fantasy football is exposure to new statistics. If you listen to football commentary these days you might hear about things like Air Yards, Completion Percentage over Expectation (or CPOE), or Expected Points Added (or EPA). Some of these stats didn't even exist until a few years ago. Are they good? Are they bad?

The gold standard measure of how much a stat might regress is something called stability testing. By comparing performance in one sample to performance in another, we can determine how similar those performances are, how much of a player's performance carries over from one game to the next, from one season to the next. Something like broken tackles, it turns out, is pretty stable. The backs who break a lot of tackles in one year also tend to break a lot of tackles in the next year.

Something like yards per carry, on the other hand, is not stable at all. I've already run down some of the studies, but you can see the results in the predictions from this column, too. Year after year, prediction after prediction we see both high-ypc backs and low-ypc backs regress to right around the league average, and we frequently see the "low-ypc" backs pass the "high-ypc" backs entirely in yards per carry going forward. (Including, as of now, in our latest iteration of the prediction.)

But running stability testing is probably going to be beyond the abilities (or the inclinations) of most fantasy football players, and ordinarily, we can't just create six years' worth of prediction history to look back on. (Additionally, just because a statistic is stable doesn't necessarily mean it's useful. Sack rate is one of the most stable quarterback stats, but it's also useless for fantasy football purposes unless you're in the rare league that penalizes quarterbacks for sacks.)

So when you encounter a brand new stat, what can you do to tell if it's a useful stat or not? I'm a big fan of a concept that I call "the leaderboard test", that statisticians call "face validity", and that the rest of us call "the smell test". Just from looking at a list, how well does it match our intuitions of what that list should look like?