Photo: Ken Blaze, USA Today Sports

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.

If-- as I often claim-- yards per carry is a pseudorandom number generator, then we should occasionally expect the high ypc group to retain a high ypc and the low ypc to retain a low ypc, not because of anything innate to the groups themselves, simply because of a lucky (or unlucky) roll of the dice. So far that's what we've seen; five out of six backs in Group A have remained above 4.5 yards per carry, while five out of seven backs in Group B have remained below 4.0. Overall, Group B has maintained its workload advantage (averaging 25% more carries per game), but Group A remains stubbornly ahead because it's averaging 5.1 yards per carry while Group B is stuck way back at 3.7.

We're still in a great position to win this prediction. The workload advantage is the key, and Group B is doing fine there. The groups' yard-per-carry average might not have trended toward the league average over the last two weeks, but that doesn't mean it won't over the next two.

On my second prediction, I noticed an error in the math last week. I said that Group A was averaging 54.6 yards and 0.72 touchdowns per game; actually, I had failed to account for missed games by Brandon Aiyuk and Jakobi Meyers. I was dividing the group's total by 36 games when, in reality, they had only played 34. Correcting for this error, Group A averaged 57.8 yards and 0.76 touchdowns per game, and their advantage over Group B rose to 7%. (This is good; we wanted Group A's advantage to be larger.)

We predicted touchdown regression, and it arrived with a vengeance. Group A had scored 26 touchdowns in 34 games to start the season, but they combined for just two touchdowns in twelve games last week. Meanwhile, Group B started the year with 6 touchdowns in 33 games; they scored more than that last weekend alone, reaching the end zone 7 times in 11 games. As a result of that touchdown onslaught, Group B staked out a commanding 57% lead in fantasy points per game.

(If this sounds impressive, remember that before we made our prediction, we kicked the five top receivers out of Group B to make things more interesting-- Justin Jefferson, Stefon Diggs, Keenan Allen, Amon-Ra St. Brown, and Deebo Samuel. Those five receivers scored another 7 touchdowns between them.)

Revisiting Preseason Expectations

In October of 2013, I wondered just how many weeks it took before the early-season performance wasn't a fluke anymore. In "Revisiting Preseason Expectations", I looked back at the 2012 season and compared how well production in a player's first four games predicted production in his last 12 games. And since that number was meaningless without context, I compared how his preseason ADP predicted production in his last 12 games.

It was a fortuitous time to ask that question, as it turns out, because I discovered that after four weeks in 2012, preseason ADP still predicted performance going forward better than early-season production did.

This is the kind of surprising result that I love, but the thing about surprising results is that sometimes the reason they're surprising is really just because they're flukes. So, in October of 2014, I revisited "Revisiting Preseason Expectations". This time, I found that in the 2013 season, preseason ADP and week 1-4 performance held essentially identical predictive power for the rest of the season.

With two different results in two years, I decided to keep up my quest for a definitive answer about whether early-season results or preseason expectations were more predictive down the stretch. In October of 2015, I revisited my revisitation of "Revisiting Preseason Expectations". This time, I found that early-season performance held a slight predictive edge over preseason ADP.

With things still so inconclusive, in October of 2016, I decided to revisit my revisitation of the revisited "Revisiting Preseason Expectations". As in 2015, I found that this time, early-season performance carried slightly more predictive power than ADP.

To no one's surprise, I couldn't leave well enough alone in October 2017, once more revisiting the revisited revisitation of the revisited "Revisiting Preseason Expectations". This time I once again found that preseason ADP and early-season performance were roughly equally predictive, with a slight edge to preseason ADP.

And of course, as a creature of habit, when October 2018 rolled around, I simply had to revisit my revisitation of the revisited revisited revisitation of "Revisiting Preseason Expectations". And then in October 2019 and October 2020 and October 2021 and October 2022 I... well, you get the idea.

And now, as you've probably guessed, it's time for an autumn tradition as sacred as turning off the lights and pretending I'm not home on October 31st. It's time for the eleventh annual edition of "Revisiting Preseason Expectations"! (Or as I prefer to call it, "Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Preseason Expectations".)