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 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.

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 seven-year history, giving our top-line record (41-13, a 76% 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 explained why large samples are our biggest asset when attempting to benefit from regression. 

In Week 9, I gave a quick trick for evaluating whether unfamiliar statistics are likely stable or unstable. In Week 11, I explained the difference between regression and the gambler's fallacy, or the idea that players are "due" to perform a certain way. And in Week 12, I showed how understanding regression can allow us to predict the past as easily as the future.

Sometimes, I point out broad trends. In Week 5, I shared twelve years worth of data demonstrating that preseason ADP held as much predictive power as performance to date through the first four weeks of the season.

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 explained that yards per carry was a step away from a random number generator and predicted the players with the lowest averages would outrush those with the highest going forward.

In Week 8, I broke down how teams with unusual home/road splits usually performed going forward and predicted the Cowboys would be better at home than on the road for the rest of the season. In Week 10, I explained why interceptions varied so much from sample to sample and predicted that the teams throwing the fewest interceptions would pass the teams throwing the most.

In Week 13, I explained that rookies were the only players whose production increased as the season went on and predicted that this year's rookie receivers would score more down the stretch.

The Scorecard

This week, we close the book on our interception prediction. Over the last month, the "low-interception" teams averaged 0.66 interceptions per game. The "high-interception" teams averaged 0.58. Eleven teams averaged at least one interception per game over the last month, and eight of those eleven came from Group A. (Two came from Group B, and one-- the Atlanta Falcons-- came from neither group.)

I didn't expect Group A to throw more interceptions per game, but I'm not especially surprised by it either-- interceptions are fairly close to random over small samples.

Our rookie receivers didn't get off to the best start in Week 13, but it's far too early for concern.

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. Three years ago, I made this observation just as managers with Ja'Marr Chase were starting to fret about his ice-cold midseason stretch. During 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.)

And there it is, ladies and gentlemen. Ja'Marr Chase has just put up the biggest single-game total by a rookie WR in NFL history (PPR scoring). https://t.co/dQNOquxTOc

— Adam Harstad (@AdamHarstad) January 2, 2022

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

Rather than trade anecdotes, let's create a hypothesis and test it. I took a list of the Top 200 PPR scorers this season and stripped out anyone who has played in fewer than 10 games over the full season. I've additionally removed anyone who missed more than one of their team's last four games, as well as anyone averaging fewer than 10 PPR points per game over their last four. (I don't think anyone feels great about their title odds because Andrei Iosivas' 9 catches for 120 yards and 2 TDs over the last month represent an 18% increase in fantasy points per game over his full-season averages.)

This leaves us with 105 qualifiers. Of those 105, 31 are averaging at least 20% more fantasy points over the last four weeks than the full season. These 31 players: Austin Hooper, Noah Gray, Jerry Jeudy, Nick Westbrook-Ikhine, Jonnu Smith, Jaxon Smith-Njigba, Chase Brown, Elijah Moore, Jaylen Warren, Courtland Sutton, Mike Gesicki, Jaylen Waddle, Mark Andrews, Bucky Irving, Keenan Allen, Cedric Tillman, Zach Ertz, Sam LaPorta, Brock Bowers, Joe Burrow, DJ Moore, Josh Jacobs, Matthew Stafford, Ray-Ray McCloud III, Ja'Marr Chase, Calvin Ridley, Tyreek Hill, Pat Freiermuth, Michael Pittman Jr., George Pickens, and Jason Sanders.

That... reads as a pretty solid list of players we'd think of when we discussed who was "hot" right now. Maybe someone like Westbrook-Ikhine seems like a safe bet for regression, given his absurd touchdown rate. Maybe the three Browns receivers seem like solid bets to maintain their heightened play, given the addition of Jameis Winston at quarterback. Collectively, I'm assuming injuries to teammates and changes to roles wash out on average.

Anyway, if you, for some reason, played in a league that let you start 31 players a week, your team would be averaging 366.0 points per game over the full season but a scorching 505.4 points per game over the last four weeks, an improvement of 38.1%. As you head into the playoffs, though, would you expect to score closer to 366 or 505? Are recent weeks a sign of things to come or just randomness being random again?

The Prediction

I'll wager that not only will that collection of players score closer to 366, but their average over the next four weeks will be at least twice as close to their full-season average as it is to their last-four-games average. To reduce any unnecessary wonkiness, I'll exclude any player who doesn't play at least three games in the next four weeks (so that if, for example, Ja'Marr Chase gets hurt on the first play of the game next week, I don't get the benefit of counting his average as 0 points per game).

If every player meets the 3-game minimum, this means anything below 412.5 points per game will register as a win for me, while anything over that total counts as a loss.

(Note that just because I'm tracking the high end of the scale doesn't mean the low end is immune, too. Derrick Henry and Justin Jefferson have seen their production drop by 33% over the last month. George Kittle and CeeDee Lamb are down 25%. If these guys carried you to the playoffs, I wouldn't be losing sleep over their recent "cold" streak, either. At the end of the day, conclusions drawn from larger samples are just more reliable than conclusions drawn from smaller ones.)