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.

The Scorecard

The prediction is only halfway done, but it's looking very unlikely the Cowboys will salvage things at this point and finish the year better at home than on the road. If the prediction is to have any shot, a blowout win against the Giants on Thanksgiving would be a good place to start.

Our interception prediction is faring much better. Again, a mistake in my math led to making the prediction tougher than I intended, but I needn't have worried; Group B isn't just throwing fewer total interceptions; they're even averaging fewer per game.

Most Players Regress. Rookies Progress.

We've talked this season about how everyone regresses to the mean, but everyone's mean is different. Thinking of player performance as random fluctuations around a fixed "true mean" is useful. But it's not maximally accurate.

Just as means can vary from player to player, they can also change over time. Randall Cunningham was one of the most prolific rushing quarterbacks in history. In his 20s, he averaged 41.1 rushing yards per game at 7.0 yards per carry. This was his "true mean".

In his 30s, he averaged 14.7 rushing yards per game at 4.7 yards per carry. This was also his true mean. Quarterback rushing tends to age much like running back rushing, with even the most prolific runners finding themselves running less frequently and less successfully.