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 break down a topic related to regression to the mean. Some weeks, I'll explain what it is, how it works, why you hear so much about it, and how you can harness its power for yourself. In other weeks, I'll give 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 Jaxon Smith-Njigba is one of the top performers in my sample, then Jaxon Smith-Njigba goes into Group A, and may the fantasy gods have mercy on my predictions.
And then, because predictions are meaningless without accountability, I track and report my results. Here's last year's season-ending recap, which covered the outcome of every prediction made in our nine-year history, giving our top-line record (51-17, a 75% hit rate) and lessons learned along the way.
Our Year to Date
In Week 2, I laid out the four primary goals of Regression Alert. The first was to persuade you that regression to the mean is a real, actionable force in fantasy football. The core of that effort is making specific, concrete predictions based on nothing but regression to the mean and tracking the results over time. Here's a list of every prediction we have made so far:
- I predicted that high-volume, low-ypc running backs would outrush lower-volume, higher-ypc backs over the next four weeks (Week 4).
- I predicted that high-yardage, low-touchdown receivers would outscore low-yardage, high-touchdown receivers (Week 6).
- I predicted that low-interception teams would throw fewer total interceptions than high-interception teams (Week 8).
The Scorecard
| Statistic Being Tracked | Performance Before Prediction | Performance Since Prediction | Weeks Remaining |
|---|---|---|---|
| Yards per Carry | Group A averaged 29% more yards per game | Group A averages 43% more yards per game | None (Loss) |
| Yards per Touchdown | Group A averaged 14% more fantasy points per game | Group B averages 11% more fantasy points per game | 1 |
| Interceptions Thrown | Group A threw 59% as many interceptions | Group A has thrown 91% as many interceptions | 3 |
Our interception prediction would have had a solid start... had Michael Penix Jr. not thrown four picks in a deluge. There are three weeks left, though, so I'm not sweating a very small lead for Group A at this point.
Predicting Regression in an Unfamiliar World
I wrote at the beginning of the year that this column has four main goals:
- to persuade you that regression is real and reliable,
- to provide actionable examples to leverage in your fantasy league,
- to educate you on how and why regression is working, and
- to equip you with the tools to find usable examples on your own.
Having (hopefully) made some headway on the first three goals, I want to focus on the fourth.
It's hard enough as a football fan to know whether a "traditional" stat is meaningful or not. ("Meaningful", in this case, means both "correlates with something we actually care about" and "likely to remain stable going forward".) But we live in a world where new statistics are introduced seemingly every day. There was a time when receiving yards per game was the cutting edge for measuring receiver play. If you really wanted to dig deep, perhaps you'd look into yards per target.
After yards per target, people turned to yards per team pass attempt and yards per route run to evaluate receivers, and the options have only further multiplied from there—fans today who want to know how good a receiver is at getting open can consult NextGenStats' Average Separation, ESPN's Open Score, FantasyPoints Average Separation Score, and Matt Harmon's Reception Perception, just to name four different statistics that purport to measure the same thing but often return wildly divergent results.
In a world where we're inundated with ever-more-complicated statistics all claiming to be the newest and best measure of play—and especially when those stats frequently find themselves in strong disagreement with each other—how can anyone be an informed consumer?
I don't have a perfect answer to this. But I do have a quick trick that I use to help sort the wheat from the chaff.
When You Can't Have the Best...
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 and 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. (We've covered the long-run trends from our ten years of predictions often enough at this point that I won't repeat it here.)
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 a decade 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.)