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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 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.
In Week 15, I discussed several methods of estimating your championship odds and explained why virtually every team is more likely to lose than to win.
In Week 16, I examined what happened to some of our failed predictions if you looked at them over longer timespans and found that while regression could be deferred, the bill eventually came due.
In Week 17, we finally threw our dynasty friends a bone, looking at how situation tends to regress over time, which makes betting on underlying talent a much safer play.
|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 had 4% more rushing TDs
|"Hot" Players Regress
|Players were performing at an elevated level
|Players have regressed 132% to season avg.
Don't read too much into the fact that our sample of "hot" players slightly underperformed their season average over the last four weeks. In the three times I've run this prediction, they've regressed 108%, 75%, and 133% of the way back. Ignoring the small fluctuations from prediction to prediction, they've been scoring pretty much right on their full-season average across the larger sample.
But absolutely notice that their hot streak disappeared immediately and dramatically. Of the 23 players who remained in the sample, 12 (more than half) averaged below their full-season average, to say nothing of their "hot streak" average. Three more regressed at least 90% of the way back to their full-season average. Only two players improved upon their recent hot stretch (Dalton Schultz and Juwan Johnson-- certainly not the two we'd have expected!), and only four more (CeeDee Lamb, Isiah Pacheco, Chris Godwin, and Rachaad White) finished closer to their hot average than their full-season average.
You could remove the ten players who underperformed their "hot" average by the most, and the sample still would have regressed more than 66% toward their season average.
"Hot streaks" aren't really a thing. If a player has a few good games in a row, that should raise our expectations for him because it raises his average over the entire season, but we shouldn't expect them to maintain their strong play beyond that. Full-season data is almost always significantly more useful than any recent sub-samples.
Our Final Report Card
To wrap up the season, I wanted to look back not just at this year's predictions, but at every prediction since Regression Alert launched in 2017. Remember, I'm not picking individual players, I'm just identifying unstable statistics and predicting that the best and the worst players in those statistics will regress toward the mean, no matter who those best and worst players might be.
Sometimes this feels a bit scary. Predicting that stars like Davante Adams or Josh Jacobs, in the middle of league-winning seasons, are going to start falling off is an uncomfortable position. But looking back at our hit rate over time makes it a bit easier to swallow.
- 2017: 6-2
- 2018: 5-1
- 2019: 7-2
- 2020: 6-1
- 2021: 8-1
- 2022: 4-3
- Overall: 36-10 (78%)
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