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 Cooper Kupp is one of the top performers in my sample, then Cooper Kupp goes into Group A and may the fantasy gods show mercy on my predictions.
Most importantly, because predictions mean nothing without accountability, I track the results of my predictions over the course of the season and highlight when they prove correct and also when they prove incorrect. At the end of last season, I provided a recap of the first half-decade of Regression Alert's predictions. The executive summary is we have a 32-7 lifetime record, which is an 82% success rate.
If you want even more details, here's a list of my predictions from 2020 and their final results. Here's the same list from 2019 and their final results, here's the list from 2018, and here's the list from 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 discussed the tendency for touchdowns to follow yards and predicted that players scoring a disproportionately high or low amount relative to their yardage total would see significant regression going forward.
In Week 5, I revisited an old finding that preseason ADP tells us as much about rest-of-year outcomes as fantasy production to date does, even a quarter of the way through a new season. No specific prediction was made.
In Week 6, I explained the concept of "face validity" and taught the "leaderboard test", my favorite quick-and-dirty way to tell how much a statistic is likely to regress. No specific prediction was made.
In Week 7, I talked about trends in average margin of victory and tried my hand at applying the concepts of regression to a statistic I'd never considered before, predicting that teams would win games by an average of between 9.0 and 10.5 points per game.
In Week 8, I lamented that interceptions weren't a bigger deal in fantasy football, given that they're a tremendously good regression target, and then I predicted interceptions would regress.
In Week 9, I explained why the single greatest weapon for regression to the mean is large sample sizes. For individual players, individual games, or individual weeks, regression might only be a 55/45 bet, but if you aggregate enough of those bets, it becomes a statistical certainty. No specific prediction was made.
In Week 10, I explored the link between regression and luck, noting that the more something was dependent on luck, the more it would regress, and predicted that "schedule luck" in the Scott Fish Bowl would therefore regress completely going forward.
In Week 11, I broke down the very important distinction between "mean reversion" (the tendency of players to perform around their "true talent level" going forward, regardless of how they have performed to date) and "gambler's fallacy" (the idea that overperformers or underperformers are "due" for a correction).
In Week 12, I talked about how much of a team's identity was really just random noise and small samples and projected that some of the most rush-heavy teams would skew substantially more pass-heavy going forward.
In Week 13, explained why the optimal "hit rate" isn't anywhere close to 100% and suggested that fantasy players should be willing to press even marginal edges if they want to win in the long run.
In Week 14, I sympathized with how tempting it is to assume that players on a hot streak can maintain that level of play but discussed how larger (full-season) samples were almost always more accurate. I predicted that the hottest players in fantasy would all cool down substantially toward their full-season averages.
|STATISTIC FOR REGRESSION
|PERFORMANCE BEFORE PREDICTION
|PERFORMANCE SINCE PREDICTION
|Yards per Carry
|Group A had 24% more rushing yards per game
|Group B has 25% more rushing yards per game
|Yards per Touchdown
|Group A scored 3% more fantasy points per game
|Group A has 12% more fantasy points per game
|Margin of Victory
|Average margins were 9.0 points per game
|Average margins are 9.9 points per game
|Group A had 65% more interceptions
|Group B has 50% more interceptions
|Group A had 38% more wins
|Group A had 4% more wins
|Group A had 12% more rushing TDs
|Group A has the same number of each
|"Hot" Players Regress
|Players were performing at an elevated level
|Players have regressed 132% to season avg.
While they've regressed a hair, our run-heavy teams remain stubbornly run-heavy. We find ourselves once again in a do-or-die situation; if the teams in our sample throw for more touchdowns than they rush for next week, our prediction succeeds. If they tie or rush for more, our prediction fails.
As for our hot players, our running backs actually had a great week. Tony Pollard, Ezekiel Elliott, Isiah Pacheco, and Rachaad White all performed within 15% of their recent "hot" average. But all other positions crashed down hard, underperforming not only their recent hot streak but their season-to-date average. As a result, the total sample regressed 30% past their season average. And this is excluding Tee Higgins, who technically put up a zero for the week but only played a single snap. I have no interest in winning on a technicality. (Nor do I expect I'll need a technicality to win.)
Playoff Teams Regress
My s*** doesn't work in the playoffs. My job is to get us to the playoffs. What happens after that is f****** luck.
We spend all year working to get our teams into the playoffs. We seek out every edge we can exploit. We learn about regression to the mean, and we harness the forces of randomness to carry us onward.
But randomness does not hold a harness well. It is wild, and it is chaotic. And so, despite our efforts, our most likely reward for reaching the postseason is a season-ending loss.
It's important to realize that this is not a failure on our part. It's tempting to think that we can control the chaos; we can find the exact right sleeper against the exact right matchup and push ourselves over the finish line. Or maybe we're focused on the order and not the chaos. Maybe most teams are more likely than not to lose, but certainly not our best ones. Certainly, our 12-2 team, armed with a bye and outscoring all competition by 10 points per game, has at least a better-than-a-coinflip shot at the title.
I've written several columns with an eye toward demonstrating that that's simply not the case. Sure, someone is going to end the season holding a trophy. But for 99.9% of fantasy teams out there, it's more likely than not that someone isn't you.
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