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.
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
| Statistic Being Tracked | Performance Before Prediction | Performance Since Prediction | Weeks Remaining |
|---|---|---|---|
| Yard-to-TD Ratio | Group A averaged 25% more PPG | Group B averaged 12% more PPG | None (Win!) |
| Yards per Carry | Group A averaged 39% more rushing yards per game | Group A averages 33% more rushing yards per game | None (Loss) |
| Interceptions Thrown | Group A threw 69% as many interceptions | Group B has thrown 82% as many interceptions | None (Win!) |
| 300-Yard Games | Teams had 30 games in 9 weeks | Teams have 12 games in 5 weeks | 2 |
| Rookie PPG | Group A averaged 4.94 points per game | Group A averages 5.55 points per game | 2 |
| Rookie Improvement | 59% are beating their average | 2 | |
| Hot Players Regress | Players were performing at an elevated level | Players have regressed 31.2% toward their season average | 3 |
The league added just two more 300-yard passing games last week (Dak Prescott and Shedeur Sanders), continuing to significantly underperform what I thought was even a pessimistic projection. (For those who are wondering: Jared Goff threw for 309 yards, but he also took a 10-yard sack, which means the Lions as a team only had 299 net passing yards in Week 14. I could just as easily use gross passing yards, but the site I use to look up historical data always returns net passing yards, so that's what we've been using. The story is the same either way, only the scale changes.)
Our rookies continue to up their game. Only 59% are beating their prior average, and we need 60% to win, but that's largely because Matthew Golden played five snaps in his return from injury last week, so his game goes down as a zero. Remove him from the data, and 62.5% of rookies are beating their prior average.
Finally, despite several salient datapoints to the contrary (Michael Wilson, Jahmyr Gibbs), our hot players have started reverting to their full-season averages. They're still closer to their "hot" performance than their typical performance, but we have three more weeks for that production to come down a little bit more still.
