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.
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
| Statistic Being Tracked | Performance Before Prediction | Performance Since Prediction | Weeks Remaining |
|---|---|---|---|
| Yard-to-TD Ratio | Group A averaged 17% more PPG | Group B averages 10% more PPG | None (Win!) |
| Yards per carry | Group A averaged 22% more yards per game | Group B averages 38% more yards per game | None (Win!) |
| Cowboys Point Differential | Cowboys were 90 points better on the road than at home | Cowboys are 62 points better on the road than at home | 1 |
| Team Interceptions | Group A threw 58% as many interceptions | Group B has thrown 66% as many interceptions | None (Win!) |
| Rookie PPG | Group A averaged 8.23ppg | Group A averaged 9.62 ppg | None (Win!) |
| Rookie Improvement | 40% are beating their prior average | None (Loss) | |
| Hot Players Regress | Players were performing at an elevated level | Players have regressed 85% to their season avg | 1 |
Our rookie receiver prediction closed with mixed results. On the one hand, the group as a whole saw a significant increase in its production and several young players have been veritable league-winners in recent weeks. Brian Thomas Jr. and Jalen McMillan have both scored more than 10 points per game higher than their prior average (Thomas has been the #4 fantasy receiver over the last four weeks). Rome Odunze, Xavier Worthy, and Ladd McConkey are also producing significantly more in recent weeks.
On the other hand, I predicted that this improvement would be broad-based, and it hasn't been. Ja'Lynn Polk, Adonai Mitchell, Malachi Corley, Jermaine Burton, and Luke McCaffrey haven't seen the uptick in playing time that is common among highly-drafted rookies late in the year; all five averaged 2.2 points per game or less.
As for our next prediction, some hot players have maintained their elevated pace and will likely be among the biggest league-winners in fantasy this year. Ja'Marr Chase, Keenan Allen, Josh Jacobs, Sam LaPorta, and Jason Sanders are all averaging more points over the last three weeks than they did over the four prior (their initial "hot" stretch). But more than twice as many of the "hot" players (12 out of 29) are underperforming their full-season average, so the group as a whole has regressed 85% of the way back to where they started.
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 last year, 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:
- Good players have longer careers than bad players.
- The number of good players entering the league in any given year is not constant.
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