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. In Week 7, I illustrated how small differences over large samples were more meaningful than large differences over small samples. In Week 9, I showed how merely looking at a leaderboard can give information on how useful and predictive an unfamiliar statistic might be.

In Week 11, I explained the difference between anticipated regression and the so-called "Gambler's fallacy", and in Week 12, I talked about retrodiction, or "predicting" the past as a means of testing your model.

Sometimes, I use this column to point out general examples of regression without making specific, testable predictions. In Week 5, I looked at more than a decade worth of evidence showing how strongly early-season performances regressed toward preseason expectations.

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. In Week 6, I showed the evidence that yards per carry was predictively useless and predicted the lowest ypc backs would outrush the highest ypc backs going forward. In Week 8, I discussed how most quarterback stats were fairly stable, but interceptions were the major exception.

In Week 10, we looked at how passing performances were trending down over the years and predicted this year would set new lows for 300-yard passing games. In Week 13, we discussed how most players declined slightly late in the year, but predicted that rookies would improve.

The Scorecard

The NFL continues to smash even my pessimistic projection for 300-yard passing games, averaging just 2.5 per week over the last month.

Meanwhile, our rookie improvement is coming along nicely. Only 8 of the 15 active receivers last week beat their average, which falls short of our predicted 60%, but only by a single player, and three of our rookies failed to record a statistic. Give them a few more weeks, and everyone will be on the board.

Do Players Get Hot?

It's widely acknowledged that succeeding in the fantasy playoffs is largely about securing players who all "get hot" at the right time. But is "getting hot" a real, predictable phenomenon? Certainly, some players outscore other players in any given sample, but any time performance is randomly distributed, you'd expect clusters of good games or clusters of bad games to occur by chance alone.

If a player has been putting up better games recently, does that indicate that he's "heating up" and will likely sustain that performance going forward? Or does it just mean that he just happened to string together a couple of good games, but you'd expect he'd be no more likely to do that again? The fantasy community often believes the former, but I'll venture that the truth is much closer to the latter.

Indeed, looking at how a player has performed over the last three, four, or five games is almost always worse than looking at how he's performed over the last nine, ten, or eleven games. As I keep saying around here, large samples are more predictable than small samples. Ignoring half or more of a player's games doesn't give you a better idea of how well that player will perform in the near future; it gives you a worse idea.

This is one of my favorite observations, and I knew in advance that I'd be making predictions on it this week in preparation for the fantasy football playoffs, when all of the "hot" teams are riding high while the "cold" ones are starting to fret. Four years ago, I made this observation just as managers with Ja'Marr Chase were starting to fret about his ice-cold midseason stretch. During the Week 17 championship games, Chase, as you might recall, wound up posting the best game by a rookie wide receiver of all time, winning a lot of titles for a lot of teams. (Actually, he wound up scoring more fantasy points than any rookie at any position in any week in NFL history.)

Now, it's just as easy to point to counterexamples. That same year, Amon-Ra St. Brown overperformed his season average by three points per game heading into the fantasy playoffs, yet he somehow managed to elevate his performance even more over the last four weeks of the fantasy season; he won a lot of titles for a lot of teams, too.

The Hypothesis