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.