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 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.
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 seven-year history, giving our top-line record (41-13, a 76% hit rate) and lessons learned along the way.
What Is Regression To The Mean
For our first article of the year, I think it's important to define regression to the mean and explain why it is so powerful. I'd like to illustrate this with an example from basketball.
The free throw attempt might be the purest act in all of sports. There's no defense. There's no weather. The distance and angle never change. It is exactly the same every time: one player, one ball, one hoop, one shot.
For his career, Steph Curry shoots 91% on free throws, but on a game-to-game level, there's a little bit of variance. Imagine that in the opening game of the 2024-2025 season, Curry makes 3 of 6 free throws. (This would be a wildly uncharacteristic game, but it's not impossible; Curry once shot 1-of-4 and has twice gone 4-of-7.)
Nobody in their right mind would look at this game and conclude that Curry was suddenly a 50% free throw shooter, right? Instead, we'd think this game was an outlier and expect him to go back to hitting 91% the rest of the way. Because 91% is Curry's long-term mean (or average), and we expect him to regress (or return) to it.
Just like Steph Curry has an innate average free throw percentage, so does every player have an innate average production level. And just as Curry's game-by-game results can deviate from that average, so can every player's results deviate from their own true mean.
In fact, most tasks in sports are not free throws. Most tasks are heavily influenced by teammates, opponents, weather, location, and a host of other factors entirely outside the player's control that vary significantly from attempt to attempt. Because of this, results likewise tend to vary much more significantly. But just like we'd expect Curry to return to his average, we should expect all players to return to theirs, as well.
That's regression to the mean in a nutshell. It's a concept we tend to intuitively understand, even if we don't talk about it in so many words.
What is Regression Alert
This column has four main goals.
Persuade
I strongly believe in the power and applicability of regression. But I also know there's a lot of skepticism-- many who are quick to note that football isn't played on a spreadsheet or ask if I even watch the games. (Because of how these predictions work, I wouldn't need to watch to write this column -- but I do anyway because football is great.)
This is largely why we track our results as we go. I find the best way to convince people that regression is real is to first show them that it works.
Provide
Footballguys is a part of the fantasy football advice industry, so what sort of column would this be if we didn't include any fantasy football advice? Our predictions show who is most likely to sustain their production, who will likely decline, and who is a good bet to improve. I find that this tends to be useful information whenever confronted with a decision to buy, sell, hold, start, or bench a player. Basically, any time it's time to make a decision.
(I also find that it's useful information if my team starts the year slowly; usually, I can convince myself that I've just been unlucky and things will turn around any day now. Often, they don't, but at least I feel better in the meantime.)
Educate
Once we've established that regression works, we can cover the "why" and the "how" of it. Why do some stats regress more than others? Is there anything that doesn't regress? Given its strengths and limitations, how can we best leverage what we know about regression? How can we make useful predictions around stats we've never encountered before?
Equip
Thinking is for doing; the purpose of gaining knowledge is to put it into practice. Once we know the why and the how of regression, we're equipped to spot it in the wild.
My ultimate goal is to equip regular readers with all the tools they need to see the tell-tale signs of regression on their own-- to look at their leagues and decide for themselves what sorts of performances are sustainable and which are destined to regress-- and to become more and more accurate with those judgments over time.
And maybe to apply the knowledge to other areas of their lives, as well; after all, regression to the mean isn't specific to football, it's a basic mathematical property of the universe itself (or at least of the ways we make meaning of it); when you draw an extreme result from a distribution, your subsequent draws are likely to be less extreme.
If any or all of this sounds interesting to you, I hope you follow along throughout the season.
