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 break down a topic related to regression to the mean. Some weeks, I'll explain what it is, how it works, why you hear so much about it, and how you can harness its power for yourself. In other weeks, I'll give 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 Ja'Marr Chase is one of the top performers in my sample, then Ja'Marr Chase goes into Group A, and may the fantasy gods show mercy on my predictions.

And then, because predictions are meaningless without accountability, I track and report my results. Here's last year's season-ending recap, which covered the outcome of every prediction made in our eight-year history, giving our top-line record (46-15, a 75% hit rate) and lessons learned along the way.

Our Year to Date

Sometimes, I use this column to explain the concept of regression to the mean. In Week 2, I discussed what it is and what this column's primary goals would be. In Week 3, I explained how we could use regression to predict changes in future performance-- who would improve, who would decline-- without knowing anything about the players themselves.

Other times, I use this column to make specific predictions. In Week 4, I explained that touchdowns tend to follow yards and predicted that the players with the highest yard-to-touchdown ratios would begin outscoring the players with the lowest.

The Scorecard

I've made the "yard-to-touchdown ratios will stabilize" prediction fourteen times in this column's history and tracked performance for four weeks each time, making last week the 57th. It was by far the worst week in this prediction's history; Group A maintained an 85-yards-to-touchdown ratio, Group B failed to reach the end zone, Group A saw its yardage increase, Group B saw its yardage crater; overall, eight receivers topped 15 fantasy points in standard scoring last week, and five of them came from Group A.

Group A remains likely to see its scoring decline going forward. Group B remains likely to see its scoring increase. This is a prediction that has proven successful in 13 out of 14 attempts, after all. With such a huge head start and only three weeks left to make up the gap, though, I'm skeptical that Group B will be able to completely flip the standings like originally predicted. We'll just have to follow along and see.

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.

I didn't realize at the time that this would turn Week 5 into my own personal Groundhog Day. 

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 sometimes results are surprising 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, it was time for a tiebreaker. 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. Like a dog chewing on a bone, when October rolled around in 2016, I revisited my revisitation of the revisited "Revisiting Preseason Expectations". And again in October 2017.

Now fully 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, and October 2023, and October 2024, 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 thirteenth annual edition of "Revisiting Preseason Expectations"! (Or as I prefer to call it, "Revisiting Revisiting Revisiting 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 2024 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 have always used PPR scoring for this exercise because that was easier for me to look up with the databases I had on hand a decade ago. For everyone who didn't miss significant time, 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".

For the sake of transparency, I'll post the raw data from last year. This largely isn't important; I'd recommend most readers skip down to the "Overall Conclusions" section below for the key takeaways.  

Quarterback

Running Back

Wide Receiver

Tight End

Overall Conclusions

We could cherry-pick individual names from those lists to argue for ADP or early-season performance. Tyreek Hill and Jaylen Waddle were 1st and 20th by preseason ADP, but disappointed over the first month, ranking just 26th and 48th. The Dolphins' offensive woes persisted, and they ranked 23rd and 56th down the stretch-- that's an argument in favor of trusting early-season performance.

On the other hand, Mark Andrews and Isaiah Likely were 3rd and 18th in preseason ADP, but 39th and 6th over the first month. Early signs of a changing of the guard? No; Andrews ranked 5th and Likely 23rd the rest of the way-- a point for preseason ADP.

But going name by name won't get us anywhere quickly, so here is the data on correlation between ADP and stretch performance, early-season performance and stretch performance, and a simple average of both factors and stretch performance.

Quarterback

Running Back