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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 dive into the topic of regression to the mean. Sometimes, I'll explain what it really is, why you hear so much about it, and how you can harness its power for yourself. Sometimes, I'll give some 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.
Most importantly, because predictions mean nothing without accountability, I report on all my results in real time and end each season with a summary. Here's a recap from last year detailing every prediction I made in 2022, along with all results from this column's six-year history (my predictions have gone 36-10, a 78% success rate). And here are similar roundups from 2021, 2020, 2019, 2018, and 2017.
In Week 2, I broke down what regression to the mean really is, what causes it, how we can benefit from it, and what the guiding philosophy of this column would be. No specific prediction was made.
In Week 3, I dove into the reasons why yards per carry is almost entirely noise, shared some research to that effect, and predicted that the sample of backs with lots of carries but a poor per-carry average would outrush the sample with fewer carries but more yards per carry.
In Week 4, I explained that touchdowns follow yards, but yards don't follow touchdowns, and predicted that high-yardage, low-touchdown receivers were going to start scoring a lot more going forward.
In Week 5, we revisited one of my favorite findings. We know that early-season overperformers and early-season underperformers tend to regress, but every year, I test the data and confirm that preseason ADP is still as predictive as early-season results even through four weeks of the season. I sliced the sample in several new ways to see if we could find some split where early-season performance was more predictive than ADP, but I failed in all instances.
In Week 6, I talked about how when we're confronted with an unfamiliar statistic, checking the leaderboard can be a quick and easy way to guess how prone that statistic will be to regression.
In Week 7, I discussed how just because something is an outlier doesn't mean it's destined to regress and predicted that this season's passing yardage per game total would remain significantly below recent levels.
In Week 8, I wrote about why statistics for quarterbacks don't tend to regress as much as statistics for receivers or running backs and why interception rate was the one big exception. I predicted that low-interception teams would start throwing more picks than high-interception teams going forward.
In Week 9, I explained the critical difference between regression to the mean (the tendency for players whose performance had deviated from their underlying average to return to that average) and the gambler's fallacy (the belief that players who deviate in one direction are "due" to deviate in the opposite direction to offset).
In Week 10, I discussed not only finding stats that were likely to regress to their "true mean", but also how we could estimate what that true mean might be.
In Week 11, I explained why larger samples work to regression's benefit and made another yards per carry prediction.
In Week 12, I used a simple model to demonstrate why outlier performances typically require a player to be both lucky and good.
In Week 13, I talked about how a player's mean wasn't a fixed target and predicted that rookie performance would improve later in the season.
In Week 14, I mentioned that hot and cold streaks are mostly a mirage and that all players tend to regress strongly toward their full-season averages.
In Week 15, I looked at the disheartening finding that even the best teams only win a title 30-40% of the time.
In Week 16, I explored the tension between predictions that were interesting and predictions that were likely and how I try to walk the line between both.
|Statistic Being Tracked
|Performance Before Prediction
|Performance Since Prediction
|Yards Per Carry
|Group A had 42% more rushing yards/game
|Group A has 10% more rushing yards/game
|Group A had 7% more points/game
|Group B has 38% more points/game
|Teams averaged 218.4 yards/game
|Teams average 219.9 yards/game
|Group A threw 25% fewer interceptions
|Group B has thrown 11% fewer interceptions
|Yards Per Carry
|Group A had 10% more rushing yards/game
|Group A has 19% more rushing yards/game
|Group A averaged 9.05 ppg
|Group A averages 9.42 ppg
|64% are beating their prior average
|"Hot" Players Regress
|Players were performing at an elevated level
|Players have regressed 111% to season avg.
Our passing yard per game prediction is down to the final week. I predicted that yards per game would be the lowest it had been in more than a decade, and it has been fairly handily. But I also predicted that it would finish below 218.4, and it's looking like it'll come up just short.
Our rookie production prediction cleared fairly handily, though, even with the deck stacked against it. Of the fifteen rookie receivers in the sample, nine saw their production improve over the last four weeks with a median gain of 2 points per game. Here are the six exceptions: Tank Dell, who was hurt in the first game and never registered a reception; Jordan Addison, who had improved by 3 points per game before getting injured on his first reception and leaving early in Week 16; plus Michael Wilson, Josh Downs, Jalin Hyatt, and Marvin Mims.
If we remove Dell from the sample, the rookies rose from 8.56 to 9.61 points per game, more than a full point improvement. Even among rookies who were already productive at the time of the prediction (Puka Nacua, Rashee Rice, Jayden Reed, Zay Flowers, and Josh Downs were all averaging at least 10 points per game) tended to see their production improve, with all but Downs improving by at least two points on their full-season average. As I said, rookies make phenomenal redraft picks because their production tends to peak when you need it the most.
Our "hot" players remain incredibly tepid since our prediction, performing even a hair below their full-season average. This isn't influenced by one or two outliers, either; 17 out of 31 players are underperforming their full season average, which is fairly close to the 50/50 split we'd expect to see if season-long average accurately represented each player's "true" performance level.
Regression and Dr. Ian Malcolm
My first love in fantasy football is dynasty, a format where once players are on your roster, you keep them indefinitely, drafting new rookies every year as a fresh batch of players enters the league. This column naturally focuses on shorter time scales with predictions that are testable over four-week windows, but late in the season, I like to deviate a bit and look at a way that regression impacts my favorite format. By now, the redraft cake has already been baked, so to speak; 80+% of teams are already eliminated from contention, and the ones that are still alive don't have much of a future to look forward to. But dynasty is forever.
In recent years, I've taken the opportunity to look at how incoming talent tended to regress over time, with positions getting younger once a strong crop of rookies entered the league and then older over time as that group aged. The 2017 running back class was the best in history, so top running backs were very young after 2017 and 2018 but much older in 2022 as Christian McCaffrey, Dalvin Cook, Austin Ekeler, Joe Mixon, Leonard Fournette, James Conner, Aaron Jones, and even fantasy-viable role players like DOnta Foreman and Jamaal Williams continued to age.
This year, I wanted to talk about one of my most important beliefs in dynasty and how that belief is shaped by my knowledge of regression. The belief is expressed in some quarters as "talented players tend to shine eventually" or "good players get theirs". Personally, I like to call it the Dr. Ian Malcolm Hypothesis after Jeff Goldblum's character in Jurrasic Park. Confronted by a scientist over his concern that the dinosaurs of Jurrasic Park might begin to breed despite all the dinosaurs in question being female, Dr. Malcolm is asked how that could happen. Malcolm replies that he doesn't know but that "life finds a way".
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