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 Justin Jefferson is one of the top performers in my sample, then Justin Jefferson goes into Group A, and may the fantasy gods show mercy on my predictions.

Most importantly, because predictions mean nothing without accountability, I report on all my results in real time and end each season with a summary. Here's a recap from last year detailing every prediction I made in 2021, along with all results from this column's six-year history (my predictions have gone 36-10, a 78% success rate). And here are similar roundups from 2021, 2020, 2019, 2018, and 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 explained that touchdowns follow yards, but yards don't follow touchdowns, and predicted that high-yardage, low-touchdown receivers were going to start scoring a lot more going forward.

In Week 5, we revisited one of my favorite findings. We know that early-season overperformers and early-season underperformers tend to regress, but every year, I test the data and confirm that preseason ADP is still as predictive as early-season results even through four weeks of the season. I sliced the sample in several new ways to see if we could find some split where early-season performance was more predictive than ADP, but I failed in all instances.

Our yard-to-touchdown ratio prediction (which gave us its first-ever loss last year) has had no such issues. I mentioned that our "low-touchdown" Group B had a party in the end zone last week, scoring 7 touchdowns in 11 games after scoring just 6 in 33 games before the prediction. Well, last week, they took it to another level, again scoring 7 times, but this time in just 9 games, thanks to bye weeks. Group B's touchdown average last week (0.78 per game) was higher than Group A's "unsustainably high" touchdown rate at the time of the prediction (0.76).

As a result, Group B has staked a commanding two-week lead. Group B has been so dominant that you could remove the three highest-performing receivers (Puka Nacua, Ja'Marr Chase, and A.J. Brown), and it would still lead Group A in fantasy points per game by 7%. Any lead that is built in two weeks can be erased in two weeks, but so far, Group B has been cruising.

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. For starters, 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? There are too many statistics to cover them all. But a quick trick should help sort the wheat from the chaff.

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 and 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 virtually the same average. Even including the results from this year's thus-far failed prediction, across nearly 7,000 carries over 7 seasons, Group A averages 4.50 yards per carry, and Group B averages 4.52.

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 seven 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 high. Among all retired quarterbacks, here are the Top 10 passers 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. Tom Brady
7. Dan Fouts
8. Drew Brees
9. Tony Romo
10. Kurt Warner

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 other entrants (Young, Brees, Fouts, and Warner) 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. Brock Purdy (9.79)
2. Tua Tagovailoa (9.11)
3. C.J. Stroud (7.65)
4. Jared Goff (7.60)
5. Josh Allen (7.42)
6. Justin Herbert (7.29)
7. Baker Mayfield (7.04)
8. Russell Wilson (7.02)
9. Kirk Cousins (6.86)
10. Patrick Mahomes II (6.80)

That's probably not exactly the list you were expecting, and given the small sample size, we should expect a degree of movement over the rest of the season. But given that ANY/A has such a high degree of face validity, we should expect the players who are doing well to largely continue doing well in a way that we wouldn't expect with yards per carry. I'm not a huge believer in Baker Mayfield, but ranking right behind Josh Allen and Justin Herbert in a high-quality statistic like ANY/A at least prompts me to think twice.

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. Luke Musgrave
2. Cole Kmet
3. Kadarius Toney
4. Curtis Samuel
5. Rondale Moore
6. Jaxon Smith-Njigba
7. Noah Gray
8. Elijah Moore
9. Keenan Allen
10. Parris Campbell

Here's the same list from 2022:

1. Robert Tonyan Jr
2. Cole Kmet
3. Dawson Knox
4. Greg Dortch
5. Tyler Conklin
6. Noah Fant
7. Hunter Henry
8. Hayden Hurst
9. Rondale Moore
10. Jerry Jeudy

and from 2021:

1. Rondale Moore
2. Gerald Everett
3. Byron Pringle
4. Mecole Hardman Jr.
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 good, 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. Mostly, it's just a list of tight ends and gadget receivers who get a bunch of schemed bubble screens and the like.

I was a fan of Luke Musgrave and Jaxon Smith-Njigba coming out of this year's rookie draft. Does their presence on this list validate my optimism? Not at all; we might as well be looking at a list of players whose mothers have the longest maiden name, for all it seems to matter.

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 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 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.

This is always hard because we usually lack the necessary context in the wild. We might see someone claim Parris Campbell is a hot sleeper because he ranks 10th in separation so far this year or that Michael Thomas or Tee Higgins are washed up because they rank in the bottom 4 in the league (which is true, by the way). That argument might even sound convincing. But before accepting it, I want your instinct to always be a desire to first 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. Tyreek Hill
3. Davante Adams
4. Mike Evans
5. Garrett Wilson
6. George Pickens
7. Marquise Brown
8. DeAndre Hopkins
9. Chris Olave
10. Amari Cooper

That's a much better list! A lot of the players are guys we know are great, and for the surprise entries (Marquise Brown, George Pickens), just seeing them in this company probably causes us to reevaluate how well they've been playing this season.

The thing is, "air yards matter for a receiver, but separation doesn't" is kind of a weird conclusion. I wouldn't expect anyone to necessarily guess that that was the reality. It seems like separation should matter, and if a guy's getting a ton of targets, why is it important how far down the field they are? These are difficult positions to reach through reason alone.

But they're trivially easy to reach if we just look at the leaderboard for each. And as a bonus, we don't need a credible theory for why that's the case. I don't have any idea why players should be able to score a touchdown for every 140 yards but not one for every 80 yards-- but by looking at a lot of leaderboards over the years, I know with a high degree of certainty that it's true.

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.