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.

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 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 tenth annual edition of "Revisiting Preseason Expectations"! (Or as I prefer to call it, "Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Revisiting Preseason Expectations".)

METHODOLOGY

If you've read the previous pieces, you have a rough idea of how this works, but here's a quick rundown of the methodology. I have compiled a list of the top 24 quarterbacks, 36 running backs, 48 wide receivers, and 24 tight ends by 2021 preseason ADP.

From that list, I have removed any player who missed more than one of his team’s first four games or more than two of his team’s last thirteen games so that any fluctuations represent performance and not injury. As always, we’re looking by team games rather than by week, so players with an early bye aren't skewing the comparisons (though this wasn't a factor last year as all byes happened after Week 4).

I’ve used PPR scoring for this exercise because that was easier for me to look up with the databases I had on hand. For the remaining players, I tracked where they ranked at their position over the first four games and over the final thirteen games. Finally, I’ve calculated the correlation between preseason ADP and stretch performance, as well as the correlation between early performance and stretch performance.

Correlation is a measure of how strongly one list resembles another list. The highest possible correlation is 1.000, which is what you get when two lists are identical. The lowest possible correlation is 0.000, which is what you get when you compare one list of numbers to a second list that has no relationship whatsoever. (Correlations can actually go down to -1.000, which means the higher something ranks in one list, the lower it tends to rank in the other, but negative correlations aren’t really relevant for this exercise.)

So if guys who were drafted high in preseason tend to score a lot of points from weeks 5-18, and this tendency is strong, we’ll see correlations closer to 1. If they don’t tend to score more points, or they do but the tendency is very weak, we’ll see correlations closer to zero. The numbers themselves don’t matter beyond “higher = more predictable”.

Here's the raw data for anyone curious. If you're willing to take my word for it, I'd recommend just skipping ahead to the "Overall Correlations" section below for averages and key takeaways.

Quarterback

Running Back

Wide Receiver

Tight End

Overall Correlations

(A technical note: if early-season results have historically performed better then preseason ADP at quarterback, running back, and tight end, then why does ADP perform slightly better overall? Because there are a lot more wide receivers in the sample than any other position, so performance there is the most impactful. We start with many fewer quarterbacks and tight ends, and running backs are more likely to get weeded out thanks to injuries in one sample or the other. This year, for example, wide receivers represented 42% of all qualifying players.)

Two years ago, I noticed that early-season results had outperformed preseason ADP for all five seasons I had measured at tight end. I tentatively declared that maybe tight end was different than the other positions and performance stabilized quicker. So, of course, the next three seasons were the three best seasons on file for performance of ADP relative to early-season results.

Last year I noted that the quarterback position had seen early results outperform preseason ADP for five straight years. I rhetorically asked if this was a sign that early-season performance mattered more at quarterback, and then rhetorically answered that it was more likely just a sign that random variation tends to vary randomly. Sure enough, 2021 saw preseason ADP correlate better with rest-of-year performance than any subsample at any position since I've started tracking these results.

Overall, I've run this study ten times and preseason ADP has been more predictive five of those times while early-season results have been more predictive the other five. On average, the gap in correlation between the two is just 0.015, a minuscule difference that's basically indistinguishable from zero.

Over the biggest sample available and across all four positions, preseason ADP predicts stretch performance almost exactly as well as early-season performance does. The first four weeks of the season feel like they're incredibly meaningful, but the truth is they only tell us as much as we already knew over the offseason.

Of course, just comparing preseason ADP to early-season results is a false dilemma; in truth, we should be basing our expectations on a blend of the two. A strict 50/50 mix of both sources predicts rest-of-year performance substantially better than either source alone at every position.

There's no testable prediction this week other than just a general reminder that player performance to date will tend to regress strongly in the direction of our initial preseason expectations.