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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.

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 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.

In Week 17, I discussed how we can leverage the principles of regression in dynasty leagues by betting on talent and against situation.

Back in Week 7 I predicted that just because the passing production was a massive outlier didn't mean it was destined to regress. Teams did, in fact, finish with the lowest passing yard per game average since 2009, a full 8 yards per game below last year's average (which was already the second-lowest value since 2010). But I predicted not just that values would remain extremely low (compared to recent historical trends), but that they'd actually trend even lower than they were over the first half of the season, and that didn't happen, so this goes down as a loss.

Our last projection went much better for us. In Week 14, I produced the sample of players who were the "hottest" entering the fantasy playoffs. Collectively, they averaged 12.08 points per game over the full season but 16.09 over the last month, a 33% improvement. And then I predicted that the hot streak was a mirage and they group would perform at least twice as close to their full-season average as their recent level. How'd they do since the prediction? 12.07 points per game-- About as close to the target as we could have gotten.

18 out of the 31 players produced at or below their full-season average. 5 more produced slightly above their full-season average, but significantly below their hot streak, still. 6 players managed to mostly maintain their elevated level of play. And 2 players even managed to improve on their hot streak and take their game up another notch entirely. Unsurprisingly (given our penultimate prediction), those last two players were both rookies, the only class of players who consistently perform better late in the season.

Rashee Rice had a full-season average of 11.7 and a "hot streak" average of 14.3 points per game. Over the last month, that rose to 18.4. And Jayden Reed has a full-season average of 11.5 but a "hot streak" average of 14.8. Over the last month, that rose all the way to 21.5. Among receivers, only CeeDee Lamb and Amari Cooper averaged more fantasy points per game than Reed.

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 Christian McCaffrey or CeeDee Lamb, 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.

Top-line Record

• 2017: 6-2
• 2018: 5-1
• 2019: 7-2
• 2020: 6-1
• 2021: 8-1
• 2022: 4-3
• 2023: 5-3
• Overall: 41-13 (76%)

The One-Off Misses