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.

The Science of Intuition

One goal of this column is to convince you that regression to the mean is real, it is powerful, and it is everywhere. To explain what it is and how (and why) it works. Another goal is to give you lists of players who are underperforming and players who are overperforming so you can make informed decisions about what to do with them going forward.

But the most important goal is to equip you with the tools to spot regression in the wild on your own, to help you develop intuitions about what kinds of performances are sustainable and what kinds of performances are unsustainable. Obviously, I'll highlight certain stats and give you my opinions on them. Yards per carry: bad. Yards per touchdown: sustainable, but only within a narrow range from about 100-200. Interception rate: bad. (Sorry, spoiler alert.)

But as years go on, one fact of life in fantasy football is exposure to new statistics. If you listen to football commentary these days you might hear about things like Air Yards, Completion Percentage over Expectation (or CPOE), or Expected Points Added (or EPA). Some of these stats didn't even exist until a few years ago. Are they good? Are they bad?

The gold standard measure of how much a stat might regress is something called stability testing. By comparing performance in one sample to performance in another, we can determine how similar those performances are, how much of a player's performance carries over from one game to the next, from one season to the next. Something like broken tackles, it turns out, is pretty stable. The backs who break a lot of tackles in one year also tend to break a lot of tackles in the next year.

Something like yards per carry, on the other hand, is not stable at all. I've already run down some of the studies, but you can see the results in the predictions from this column, too. Year after year, prediction after prediction we see both high-ypc backs and low-ypc backs regress to right around the league average, and we frequently see the "low-ypc" backs pass the "high-ypc" backs entirely in yards per carry going forward. (Including, as of now, in our latest iteration of the prediction.)

But running stability testing is probably going to be beyond the abilities (or the inclinations) of most fantasy football players, and ordinarily, we can't just create six years' worth of prediction history to look back on. (Additionally, just because a statistic is stable doesn't necessarily mean it's useful. Sack rate is one of the most stable quarterback stats, but it's also useless for fantasy football purposes unless you're in the rare league that penalizes quarterbacks for sacks.)

So when you encounter a brand new stat, what can you do to tell if it's a useful stat or not? I'm a big fan of a concept that I call "the leaderboard test", that statisticians call "face validity", and that the rest of us call "the smell test". Just from looking at a list, how well does it match our intuitions of what that list should look like?

I like a statistic called Adjusted Net Yards per Attempt, or ANY/A. It's a quarterback's yards per attempt, but it gives a 20-point bonus for touchdowns, a 45-point penalty for interceptions, and includes sacks and yards lost to sacks. Why do I like it? Because I think the face validity is really high. Here are the top 10 quarterbacks heading into this season in era-adjusted ANY/A (with a 2500-attempt minimum):

1. Steve Young
2. Joe Montana
3. Roger Staubach
4. Peyton Manning
5. Dan Marino
6. Aaron Rodgers
7. Tom Brady
8. Dan Fouts
9. Drew Brees
10. Tony Romo

Maybe that's not a perfect list. Maybe you'd have Tom Brady higher or Tony Romo lower. But there are seven quarterbacks on the NFL's 100th-anniversary team who played the bulk of their career since the merger, and five of them are on that list, and four of the others (Young, Brees, Rodgers, Fouts) either were or will be first-ballot Hall of Famers. This list has a very high degree of face validity.

Here's the leaderboard for 2022 so far among players with at least 120 attempts:

1. Josh Allen (8.27)
2. Geno Smith (7.95)
3. Patrick Mahomes II (7.85)
4. Justin Herbert (7.64)
5. Jalen Hurts (7.58)
6. Jared Goff (6.92)
7. Tom Brady (6.79)
8. Lamar Jackson (6.67)
9. Trevor Lawrence (6.26)
10. Ryan Tannehill (6.20)

Again, is it perfect? Probably not. But odds are good whatever quarterback you think has been the best in the NFL this season is on that list. Is Geno Smith the second-best quarterback in the league? No way. Do I feel a lot more confident in his fast start knowing he's sitting right between Josh Allen and Patrick Mahomes II in one of the gold-standard measures of quarterback play? Absolutely.

Because most of the list makes pretty intuitive sense, we should pay extra attention to the surprise entries and omissions. Last year Matthew Stafford was the surprise leader on this list through five weeks, which heralded the offensive explosion that would lead the Rams to a championship. This year Stafford ranks dead last among the 25 qualifying quarterbacks, which is a worrying sign that the Rams' struggles are more serious than you might think.

Let's compare this to another stat. The NFL has been using its player tracking data to create a suite of "Next Gen Stats" to help fans evaluate the game. One stat they created is a measure of the average separation a receiver gets. Here's the Top 10 so far this year:

1. Hayden Hurst
2. Robert Tonyan Jr
3. Tyler Conklin
4. Kylen Granson
5. Greg Dortch
6. Dallas Goedert
7. Irv Smith
8. Parris Campbell
9. Will Dissly
10. Noah Fant

Here's the same list from 2021:

1. Rondale Moore
2. Gerald Everett
3. Byron Pringle
4. Mecole Hardman
5. Noah Fant
6. Braxton Berrios
7. Jonnu Smith
8. Dawson Knox
9. Cole Beasley
10. Robert Woods

and from 2020:

1. Deebo Samuel
2. Robert Tonyan Jr
3. Demarcus Robinson
4. David Moore
5. Dawson Knox
6. Dan Arnold
7. George Kittle
8. Drew Sample
9. Allen Lazard
10. Jordan Akins

You would think that a stat that showed how open players were getting would be a good stat, right? All else being equal, it's better to be four yards away from the covering defender than three yards away from the covering defender. But do these lists have face validity? Do they pass the smell test?

Not really. There are a few good players on these lists. There are several more bad players here. There are some one-dimensional deep threats, but mostly it's just a list of yards-after-the-catch specialists who get a bunch of schemed bubble screens, and tunnel screens and other gadget plays. I can probably invent a story to tie all of these guys together. Maybe the good players are here because they're really good at getting wide open. And maybe the bad players are here because the quarterback only looks their way when they're wide open. Maybe.

If you see that a quarterback is having a great season as measured by ANY/A, that should serve as compelling evidence to you that the quarterback is playing really well and you should be predisposed to believe that he'll be able to sustain his production to some extent or another. If you see a receiver is having a great season as measured by average separation, that... shouldn't really move the needle for you at all. That's not really evidence that the receiver is any good or that his level of play is in any way sustainable.

If someone tells you Parris Campbell is a hot sleeper because he ranks 8th in separation so far this year, or tells you Kyle Pitts and Terry McLaurin are busts because they're both in the Bottom 5 (which is true, by the way), I want your first instinct to be to ask to see the rest of the list.

Let's compare this to another stat from Next Gen Stats' list: TAY%, or percent of total air yards. TAY measures how many yards down the field each receiver was on each target (basically how many "air yards" the pass was thrown), totals them up, and then tracks which receivers are getting the highest percentage of their team's total. Here are the current leaders:

1. A.J. Brown
2. CeeDee Lamb
3. Darnell Mooney
4. Marquise Brown
5. Chris Olave
6. Justin Jefferson
7. Courtland Sutton
8. Davante Adams
9. Amari Cooper
10. Cooper Kupp

That's a much better list! A lot of the players are guys we know are great, and for the surprise entries (Marquise Brown, Chris Olave, Courtland Sutton), just seeing them in this company probably causes us to reevaluate how well they've been playing this season. (Or maybe it causes us to reflect on how bad their competition for targets has been. Or, in the case of Darnell Mooney, it's a reminder that it's easy to have a huge percentage of a team's air yards if... the team is not throwing for any air yards.) TAY% and average separation might both be "advanced stats," but one of them is obviously better than the other at identifying which receivers are actually playing well this year. We can tell just by looking at the respective leaderboards.

Intuitions are fallible, and at the end of the day, they're not as good as rigorous statistical analysis. But rigorous statistical analysis is hard and boring and a lot of work, and most of us have better things to do. There's no need to let the perfect be the enemy of the perfectly fine. Raw intuition is an underrated tool for separating the wheat from the chaff and, in a world with an ever-increasing number of new statistics to navigate, quickly settling on what we care about and what's just noise.