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 Jaxon Smith-Njigba is one of the top performers in my sample, then Jaxon Smith-Njigba goes into Group A, and may the fantasy gods have 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 nine-year history, giving our top-line record (51-17, a 75% hit rate) and lessons learned along the way.
Our Year to Date
In Week 2, I laid out the four primary goals of Regression Alert. The first was to persuade you that regression to the mean is a real, actionable force in fantasy football. The core of that effort is making specific, concrete predictions based on nothing but regression to the mean and tracking the results over time. Here's a list of every prediction we have made so far:
- I predicted that high-volume, low-ypc running backs would outrush lower-volume, higher-ypc backs over the next four weeks (Week 4).
- I predicted that high-yardage, low-touchdown receivers would outscore low-yardage, high-touchdown receivers (Week 6).
- I predicted that high-interception teams would throw fewer total interceptions than low-interception teams (Week 8).
The Scorecard
| Statistic Being Tracked | Performance Before Prediction | Performance Since Prediction | Weeks Remaining |
|---|---|---|---|
| Yards per Carry | Group A averaged 29% more yards per game | Group A averaged 43% more yards per game | None (Loss) |
| Yards per Touchdown | Group A averaged 20% more fantasy points per game | Group B averaged 5% more fantasy points per game | None (Win!) |
| Interceptions Thrown | Group A threw 59% as many interceptions | Group B has thrown 95% as many interceptions | 2 |
Our Yard-to-Touchdown prediction notched our first win of the season. Our "high-touchdown" receivers saw their touchdowns per game fall from 0.95 to 0.39, and their yard-to-touchdown ratio rise from 79.5 to 212.4. Our "low-touchdown" receivers saw their touchdowns per game rise from 0.23 to 0.61, and their yard-to-touchdown ratio fall from 438.0 to 120.3. Quite the turnaround!
Also, while the win was fairly narrow overall, the starting conditions were more difficult than we first supposed; Deebo Samuel Sr. didn't play a single game in the last month. He was the lowest-scoring wide receiver in Group A, and without him in the sample, their initial lead over Group B rose from 14% to 20%, which makes the total movement 25% in the direction of our high-yardage, low-touchdown producers.
Our interception prediction is halfway over, and right now Group B leads by the slimmest of margins; they have thrown 20 interceptions compared to 21 for Group A. Still, better to be leading than trailing!