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.

The Scorecard

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.

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.
-Billy Beane

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.

Nor is that someone me. I have three teams this year with at least eleven wins and the #1 overall seed. I'd love to think that I'm well-positioned to hang a couple more banners at the end of the year. Unfortunately, I'm cursed with knowledge; I know the true "expected value" for total titles this season is just a hair below one, and I'm significantly more likely to walk away with zero than I am to walk away with two.

I wrote a column last year about how understanding regression allowed us to reverse-engineer the present to make predictions about the past. I showed that if you took two teams that had very similar per-game averages and looked back over the weeks, you'd find that they virtually never had similar weekly scores.

I looked at one team that had scored 1258.81 points and another team that had scored 1251.86 points. The average point-per-game difference between them at the time was 0.6, but the average margin between them in any given week was 36.37 points. (The median was 34.23, and the teams featured more weeks with a gap larger than 50 points-- four times-- than a gap closer than 15 points-- just twice.)

This is a sobering reminder if your team is averaging, say, 10 points per game better than your playoff opponent, that 10-point lead might just mean you would have beaten them in eight out of the fourteen previous weeks instead of just seven. Even a 20-point margin is easily surmountable.

Instead of points per game, let's examine other metrics of team quality. My best team has an all-play winning percentage of 76.6% (118 wins against 36 losses); we've demonstrated that all-play records are relatively stable across samples. In order to have a 50/50 shot at winning back-to-back games, I'd need a 70.7% all-play winning percentage. (70.7% * 70.7% = 49.98%.) This team easily clears that threshold here; its chances of winning back-to-back games against random opponents would be around 58.7%. So it's at least the odds-on favorite for the title, right?

Not so fast; "random opponents" is doing a lot of lifting in that sentence. The average playoff team is better than the average team, and the average championship-game team is better than the average playoff team. How can we adjust for this? There's no perfect solution, but I do have a few quick hacks.

Collectively, the other eleven teams in the league have 806 combined all-play wins, with playoff teams combining for 466 of them (57.8%). Meanwhile, the rest of the league has 888 all-play losses, with the playoff teams comprising 304 (34.2%).

Since the playoff teams account for 34.2% of all the all-play losses, I'll assume that they account for 34.2% of my 118 all-play wins. And likewise, I'll assume they provided 57.8% of my 36 all-play losses. Multiplying out, my "playoff-adjusted all-play record" would be 40.4 and 20.8, or 66.0%. And if that represents my odds of winning a game against a playoff-caliber team, my chances of winning two such games back-to-back are just 43.6%. My chances of taking home a title are now noticeably below 50%.

And these odds are likely an overestimate. They assume playoff teams are roughly equal in quality; the bigger the differences between the best and the worst teams, the worse your title odds become. Imagine a league where two teams had the top two scores every week, both finishing with identical all-play winning percentages of 95.4%. Despite the tremendous record, neither team can have odds better than 50/50 because each team would almost certainly have to get past the other.

In fact, in the league in question, it'd be impossible for me to have a better than 50% chance of winning since I'm not even the best team; another squad has an all-play record of 121-33. Even if I were guaranteed to win my first playoff game and reach the championship (which I'm not), I'd still be a hair below 50/50 to win it all.

Additionally, these calculations assume that all-play records are a good predictor. They are in a broad sense, but this approach doesn't allow for chaos. Players get injured, surprise breakouts happen, and there's generally nothing more inevitable than the unexpected. To the extent that chaos has a bias, it favors weaker teams and cuts against stronger ones, because weaker teams have more to gain and stronger teams have more to lose.

This isn't to say that no team's chances at a title can ever top 50%. I've been running variations of this exercise for nearly a decade now, and in that time, I've seen one team that I believed was better than 50/50 to win a championship. (Not one of my own, unfortunately.) It had an all-play winning percentage of 87.7% and finished as the #1 overall scorer in 9 out of the 17 weeks of the season. It did, in fact, win a title, but even that wasn't inevitable; over the course of the year, it happened to lose four times in the eight weeks in which a loss was possible (despite an average weekly finish of 4th in those weeks).

For the rest of us, title odds somewhere in the neighborhood of 30-40% are the best we can hope for. I'm proud of the teams I've put together, and I'm proud of what they've achieved so far. I hope I don't lose sight of these accomplishments if the most likely thing happens and I lose in the next three weeks.

And I hope you don't lose sight of your accomplishments, either. When your playoff teams lose, let yourself off the hook. It's not your fault. Our s*** just doesn't work in the playoffs.

Most importantly, if you buck the odds and take home a title, don't take it for granted or view it as an inevitable outcome to a great season. Cherish it like the rare and unlikely event that it truly was.