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.

In Week 6, I explained the concept of "face validity" and taught the "leaderboard test", my favorite quick-and-dirty way to tell how much a statistic is likely to regress. No specific prediction was made.

In Week 7, I talked about trends in average margin of victory and tried my hand at applying the concepts of regression to a statistic I'd never considered before, predicting that teams would win games by an average of between 9.0 and 10.5 points per game.

Interceptions Are Also Pseudoscience

Three years ago, I wrote about how interception rate was very nearly as unstable from one sample to the next as my favorite punching bag, yards per carry. I cited research, including findings by fellow Footballguy Danny Tuccito that while we only needed a sample of 396 pass attempts before a quarterback's yard per attempt average is as much a result of skill as luck, it takes a sample of 1681 pass attempts before interception rate stabilizes in the same way. (For context, yards per carry requires 1978 carries to stabilize and represent equal parts skill and luck.) People dramatically underestimate just how much luck is involved in whether a pass gets picked off or not.

In fact, given how unstable interception rates are, my biggest lament is simply that interceptions... don't really matter for fantasy football. In most scoring systems, quarterbacks are only penalized one or two points per turnover. Sometimes there's not any penalty at all. (My favorite scoring system penalizes players 4.5 points per turnover, which makes identifying regression much more valuable, but such setups are rare.)

Otherwise, we can predict that guys with a lot of interceptions will likely throw a lot fewer going forward, but this doesn't produce much actionable insight when it comes to fantasy football. With one notable exception.

You see, while interceptions play a virtually negligible role in quarterback scoring, they're a massive part of defensive scoring. And they're just as random for defenses as they are for quarterbacks, if not more so. (Justin Fields and Kenny Pickett will still be the same people a week from now, but their opponents will be different.) Which means fantasy defenses that have been excelling on the back of unsustainable interception rates are probably due for a reckoning, while solid units that have been good at getting sacks and keeping opposing teams out of the end zone but haven't been getting as many takeaways are due for a boost.

To this point of the season, the Buffalo Bills, Philadelphia Eagles, Chicago Bears, New York Jets, Baltimore Ravens, and Pittsburgh Steelers all have eight or more interceptions. Collectively, they've intercepted 51 passes in just 40 games, an average of 1.28 per game. This is our Group A.

On the other end, the Denver Broncos, Carolina Panthers, Miami Dolphins, Las Vegas Raiders, Indianapolis Colts, Kansas City Chiefs, Detroit Lions, Green Bay Packers, Washington Commanders, Cleveland Browns, New York Giants, and New Orleans Saints all have four or fewer interceptions. Collectively, they've intercepted 31 passes in 82 games, or 0.38 per game. This is our Group B.

To this point, Group A has a whopping 65% more interceptions than Group B. They might be better defenses, but that kind of advantage in such an unstable statistic isn't sustainable; I predict that through the magic of regression, Group B will intercept more passes than Group A over the next four weeks.