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. In Week 15, I offered sobering data on why the best team usually loses in the fantasy football playoffs, and in Week 16, I explained why what works in the long run doesn't always work in the short run. In Week 17, I looked at how even things like variations in the quality of incoming players are subject to regression.

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. And in Week 14, I noted that large samples were almost always more predictive than small ones, and therefore "hot" players would likely regress toward their full-season averages.

The Scorecard

We officially closed our rookie prediction last week, but I wanted to note that Week 17 saw the biggest rookie explosion yet; our sample's 14.7 points were nearly 80% higher than their pre-prediction average despite getting zeroes from Ja'Lynn Polk, Jermaine Burton, and Luke McCaffrey. 6 of the top 15 fantasy receivers of the week were rookies; if you won a championship, there's a good chance that a rookie receiver was a significant part of that.

Week 17 closed the book on our Cowboys prediction. We've known for a while that this would be a loss. They went 2-3 both at home and on the road after our prediction, but their two-game skid immediately after Dak Prescott's injury was insurmountable.

Our "hot" players finished more than three times closer to their full-season average than their scorching Week 10-13 pace. This wasn't driven by a few outliers, either; the median shift was nearly identical to the mean-- 74% of the way to the prior average.

Our Long-Term Report Card

To wrap up the season, I wanted to look back not just at this year's predictions but at every prediction since Regression Alert launched in 2017. Remember, I'm not picking individual players; I'm just identifying unstable statistics and predicting that the best and the worst players in those statistics will regress toward the mean, no matter who those best and worst players might be.

Sometimes this feels a bit scary. Predicting that stars like Saquon Barkley or Ja'Marr Chase, in the middle of league-winning seasons, are going to start falling off is an uncomfortable position. But looking back at our hit rate over time makes it a bit easier to swallow.

Top-line Record

• 2017: 6-2
• 2018: 5-1
• 2019: 7-2
• 2020: 6-1
• 2021: 8-1
• 2022: 4-3
• 2023: 5-3
• 2024: 5-2
• Overall: 46-15 (75%)

The One-Off Misses