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.

In Week 8, I lamented that interceptions weren't a bigger deal in fantasy football given that they're a tremendously good regression target, and then I predicted interceptions would regress.

In Week 9, I explained why the single greatest weapon for regression to the mean is large sample sizes. For individual players, individual games, or individual weeks, regression might only be a 55/45 bet, but if you aggregate enough of those bets, it becomes a statistical certainty. No specific prediction was made.

In Week 10, I explored the link between regression and luck, noting that the more something was dependent on luck, the more it would regress, and predicted that "schedule luck" in the Scott Fish Bowl would therefore regress completely going forward.

In Week 11, I broke down the very important distinction between "mean reversion" (the tendency of players to perform around their "true talent level" going forward, regardless of how they have performed to date) and "gambler's fallacy" (the idea that overperformers or underperformers are "due" for a correction).

In Week 12, I talked about how much of a team's identity was really just random noise and small samples and projected that some of the most rush-heavy teams would skew substantially more pass-heavy going forward.

In Week 13, explained why the optimal "hit rate" isn't anywhere close to 100% and suggested that fantasy players should be willing to press even marginal edges if they want to win in the long run.

Do Players Get Hot?

It's widely acknowledged that succeeding in the fantasy playoffs is largely about securing players who all "get hot" at the right time. But is "getting hot" a real, predictable phenomenon? Certainly, some players outscore other players in any given sample, but any time performance is randomly distributed you'd expect clusters of good games or clusters of bad games to occur by chance alone.

If a player has been putting up better games recently, does that indicate that he's "heating up" and will likely sustain that performance going forward? Or does it just mean that he just happened to string together a couple of good games, but you'd expect he'd be no more likely to do that again? The fantasy community often believes the former, but I'll venture that the truth is much closer to the latter.

Indeed, looking at how a player has performed over the last three, four, or five games is almost always worse than looking at how he's performed over the last nine, ten, or eleven games. As I keep saying around here, large samples are more predictable than small samples. Ignoring half or more of a player's games doesn't give you a better idea of how well that player will perform in the near future; it gives you a worse idea.

This is one of my favorite observations and I knew in advance that I'd be making predictions on it this week in preparation for the fantasy football playoffs, when all of the "hot" teams are riding high while the "cold" ones are starting to fret. Last year, I made this observation just as managers with Ja'Marr Chase were starting to fret about his ice-cold midseason stretch. During Week 17 championship games Chase, as you might recall, wound up posting the best game by a rookie wide receiver of all time, winning a lot of titles for a lot of teams. (Actually, he wound up scoring more fantasy points than any rookie at any position in any week in NFL history.)

And there it is, ladies and gentlemen. Ja'Marr Chase has just put up the biggest single-game total by a rookie WR in NFL history (PPR scoring). https://t.co/dQNOquxTOc

— Adam Harstad (@AdamHarstad) January 2, 2022

Now, it's just as easy to point to counterexamples. Amon-Ra St. Brown overperformed his season average by three points per game heading into the fantasy playoffs, yet he somehow managed to elevate his performance even more over the last four weeks of the fantasy season; he won a lot of titles for a lot of teams, too. And maybe Christian Watson is destined to be this year's Amon-Ra St. Brown.

While we know regression is real, it's easy to craft a story to the contrary, especially with rookies (who, it should be noted, are the only class of players who genuinely average more points per game over the last half of the season than they did over the first half). But plausible stories are a drug that lull us into complacency and cause us to accept without questioning. So let's put this idea to the test.

For starters, let's take the Top 200 PPR scorers this season and strip away anyone who's played in fewer than 10 games over the full season or fewer than 3 games since Week 9. Finally, let's drop anyone who is scoring fewer than 10 points per game over the last four weeks. (I don't think anyone would picture Cade Otton and his 14/131/2 receiving line over the last four weeks an example of a player "getting hot", even if it represent a 44% increase over his season average.) That leaves us with 96 names.

Of those 96 names, 27 are averaging at least 25% more points per game over the last four weeks than they are over the season as a whole. These 27 players are: Samaje Perine, Isiah Pacheco, Cole Kmet, Rachaad White, Christian Watson, Darius Slayton, Justin Fields, Noah Fant, Tony Pollard, Dalton Schultz, Amon-Ra St. Brown, DeAndre Carter, Ezekiel Elliott, Garrett Wilson, DK Metcalf, Juwan Johnson, Robbie Gould, Tyler Bass, CeeDee Lamb, Josh Jacobs, Jonathan Taylor, Davante Adams, Chris Godwin, Tee Higgins, Christian Kirk, and Nico Collins.

That... reads as a pretty solid list of players who we'd think of when we discussed who was "hot" right now. Maybe someone like Samaje Perine seems like a safe bet for regression. His "hot streak" coincided with Joe Mixon missing time to injury, and now Mixon is (presumably) back. On the other hand, someone like Isiah Pacheco seems like a safe bet to maintain his recent production with Clyde Edwards-Helaire on injured reserve. Collectively I'm assuming injuries to teammates wash out on average.

Anyway, if you for some reason played in a league that let you start 27 players a week, your team would be averaging 330.0 points per game over the full season, but a scorching 456.01 points per game over the last four weeks, an improvement of 38.2%. As you head into the playoffs, though, would you expect to score closer to 330 or 456? Are recent weeks a sign of things to come or just randomness being random again?

I'll wager that not only will that collection of players score closer to 330, their average over the next four weeks will be at least twice as close to their full-season average as it is to their last-four-games average. To reduce any unnecessary wonkiness, I'll exclude any player who doesn't play at least three games in the next four weeks (so that if, for example, Tony Pollard gets hurt on the first play of the game next week I don't get the benefit of counting his average as 0 points per game).

If every player meets the 3-game minimum, this means anything below 372 points per game will register as a win for me, while anything over that total counts as a loss.

Note that just because I'm tracking the high end of the scale doesn't mean the low end is immune, too. Jaylen Waddle's recent production is down 28% from his season average, Derrick Henry's is down 27%, Austin Ekeler's is down 22%. Josh Allen, A.J. Brown, Mark Andrews, and Nick Chubb are all down 20% or more as well. If these guys carried you to the playoffs, I wouldn't be losing sleep over their recent "cold" streak, either. At the end of the day, conclusions drawn from larger samples are just more reliable than conclusions drawn from smaller ones.