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. In Week 15, I examined evidence that the best teams were barely more than a coin flip to win any given game in the playoffs, and less than that to win the entire thing.

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. In Week 14, I explained that "hot streaks" were largely just random clustering and predicted that the "hottest" players would regress to their season averages.

The Scorecard

Despite closing with a bang (5 and 7 games with 300+ net passing yards in the final two weeks after only twelve such games in the first five), the league came up well short of the target and set a new record for fewest 300-yard passing performances in recent years. This solidifies the downward trend in passing that we've seen since 2020. Will this trend carry over into next year? It's hard to say for sure, but if pressed, I'd guess passing is very slightly more likely to go down than up again.

As for our rookie receivers, despite slumps from the two headliners (Emeka Egbuka and Tetairoa McMillan), the class as a whole saw significant gains over the last month. Luther Burden III doubled his per-game average. Kyle Williams, Isaac TeSlaa, and Jack Bech doubled their season-long total. While the gains weren't as broad-based as predicted, enough rookies had a late-season breakout to increase the class's overall scoring average even as the weather turned cold.

For our final prediction of the year, our "hot" players are currently on the very edge; they need to average 13.02 points per game or less for our prediction to win, and they're currently averaging 13.15. (This is partly a function of the way we are averaging—using an average of averages. If we instead used a straight average across all games, we'd be right at the 13.02 mark.) Either way, this will be a very close one.

Incoming Talent Regresses, Too

In 2018, I wrote about the perceptions that NFL careers were longer than ever before. Surprisingly, I discussed how they most certainly were not getting any longer (at least among the very oldest players), and how any perceptions to the contrary were mostly driven by a super-talented group of future Hall of Fame quarterbacks, headlined by Tom Brady.

In fact, there's no other position where careers are getting longer like they are at quarterback. In the last decade, eight different offensive linemen have started at least half a season at age 36 or older. Six different players did it in the year 2000 alone. There were seven different double-digit sack seasons by a 36-year-old player between 1997 and 2000. There has been one in the 17 years since. Even kickers aren't seeing any major improvements. From 2000-2009, the league averaged three kickers and punters per year over the age of 40. From 2010-2017, it averaged 2.5. (Old placekickers were slightly up, but old punters were way down.)

The evidence continued to mount against the "careers are getting longer" hypothesis to the point where, eventually, I wrote that the opposite was likely occurring: NFL careers were actually getting shorter. I think. Probably.

You see, studying trends in career lengths is devilishly tricky because of two simple facts: