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 Ja'Marr Chase is one of the top performers in my sample, then Ja'Marr Chase 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 eight-year history, giving our top-line record (46-15, a 75% hit rate) and lessons learned along the way.

Our Year to Date

Sometimes, I use this column to explain the concept of regression to the mean. In Week 2, I discussed what it is and what this column's primary goals would be. In Week 3, I explained how we could use regression to predict changes in future performance-- who would improve, who would decline-- without knowing anything about the players themselves.

Sometimes, I use this column to point out general examples of regression without making specific, testable predictions. In Week 5, I looked at more than a decade worth of evidence showing how strongly early-season performances regressed toward preseason expectations.

Other times, I use this column to make specific predictions. In Week 4, I explained that touchdowns tend to follow yards and predicted that the players with the highest yard-to-touchdown ratios would begin outscoring the players with the lowest. In Week 6, I showed the evidence that yards per carry was predictively useless and predicted the lowest ypc backs would outrush the highest ypc backs going forward.

The Scorecard

After a catastrophic first week, I figured our "yard to touchdown ratio" prediction was dead in the water—even if the predicted regression arrived, it likely wouldn't be enough to overcome Group A's monster head start. Well, this is what I get for doubting the power of regression; Group B had yet another tremendous week and has almost completely erased the early lead. 

Perhaps most impressively, Group B is mounting its comeback despite significant headwinds. CeeDee Lamb, one of the most talented receivers in the group, has missed every game since the prediction, while his teammate George Pickens has been the highest-scoring receiver for Group A in his absence.

As for the yard per carry prediction, the first week brought mixed results. Group A maintained its rushing lead, which was bad. But Group A's yard per carry advantage completely vanished; Group A had a 1.98 ypc advantage at the time of the prediction, but that fell to just 0.02 last week (4.34 yards per carry vs. 4.32).

(In other news, this prediction offered a great lesson in why I don't pick my samples. If I wanted to stack the deck in Group A's favor by removing the worst running back, I would have struck Rico Dowdle from the list, but he wound up leading all Group A rushers by 60 yards.)

Regression and Large Samples

The arc of our first prediction this year highlights one of the key facts of regression to the mean: outlier performances are significantly more likely over small samples. In one week, Group A nearly doubled Group B's production. In another, Group B nearly doubled Group A. Neither week was representative of the true performance difference between the two groups.

This idea that weird stuff happens in small samples is the insight that drives the selection of our groups-- because of the small samples provided by an NFL season, the most extreme values in any given statistic are most likely chance-driven outliers. This also informs the nature of our predictions. If I flip a coin that's weighted to land on heads 60% of the time, that means there's still a 40% chance it lands on tails. Given those odds, landing on tails wouldn't be very surprising at all. But if I flipped the same coin a million times, the odds of seeing tails come up more often than heads dwindles down to nothing.

Similarly, if I single out one outlier player for regression, I'm more likely to be right than wrong... but I still might have a 40% chance of being wrong. If I bundle a bunch of outlier players together, though, the odds of being wrong fall substantially. Same idea behind running predictions for multiple weeks-- the longer the better, though I do like to have a clearly defined endpoint to make it easier to register our wins and losses for the year.

This idea that variance evens out over larger samples is one of the key insights of high-volume fantasy football players. Why do top DFS players compete with so many different lineups every week? The answer is not, as is commonly believed because it increases their expected return on investment. Indeed, every DFS player has a "best" lineup, a lineup that they think is most likely to win that week, and every other lineup that player submits actually decreases expected payout (because it's a worse lineup than the best lineup).