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. In Week 7, I illustrated how small differences over large samples were more meaningful than large differences over small samples. In Week 9, I showed how merely looking at a leaderboard can give information on how useful and predictive an unfamiliar statistic might be. In Week 11, I explained the difference between anticipated regression and the so-called "Gambler's fallacy".

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. In Week 8, I discussed how most quarterback stats were fairly stable, but interceptions were the major exception.

In Week 10, we looked at how passing performances were trending down over the years and predicted this year would set new lows for 300-yard passing games. 

The Scorecard

Group B threw one more interception than Group A for the second time in the last two weeks, but had built enough of an edge early on that it didn't matter. Group A finished with 34 interceptions in 59 games (0.61 per game), while Group B finished with 28 in 37 games (0.79 per game)—about 30% more per game, which is a far cry from the initial 147% disparity.

Overall, the correlation between interceptions per game over the first seven weeks and the last four weeks was 0.31, which means a team's interception rate after Week 7 explained just 10% of their interception rate after Week 7. (The rest is a function of factors like schedule, changes in personnel, and the biggest of all, random chance.)

Our 300-yard game prediction remains strong so far. To match the long-term lows we predicted, teams would have to throw for 300 yards in fewer than 4.5 games per week on average. Through two weeks, they're managing it just 2.5 times per week, which provides us a bit of buffer against a surprise late-season passing explosion.

Predicting the Past

Understanding regression to the mean is not just useful for predicting the future-- it's also handy for predicting the past. "Predicting the past?" you may scoff, "Surely that's not a thing." Reader, your skepticism wounds me—predicting the past is called "retrodiction" and is very much a thing.

Why on earth would someone want to predict something that already happened? Because it's one of the easiest ways to check whether one's model is accurate.

For instance, in 1859, astronomer Urbain Le Verrier discovered that Mercury was "orbiting wrong". Under the standard Newtonian model of gravity, its orbit should be a fixed ellipse, but instead, the ellipse was itself slowly rotating around the sun over time.

(Le Verrier wasn't known to be shoddy with his calculations; thirteen years earlier, he had noticed several unexplained deviations in Uranus' orbit and, from those deviations, predicted not only the existence of Neptune—an as-yet-undiscovered planet—but also its precise location. This was an example of predicting the present, which unfortunately lacks a catchy name.)

Mercury's odd orbit remained a puzzle for more than 50 years until Albert Einstein began working on his own theory of gravity. One of the first tests of his new system was "predicting" what Mercury's orbit should look like. The fact that his model succeeded where Newton's failed helped convince him of its accuracy.

We may not be an Einstein or Le Verrier, but we also have a model, and we can also harness the power of retrodiction to reassure ourselves of its accuracy.

The Arrow of Time and Regression to the Mean