Welcome to Regression Alert, your weekly guide to using regression to predict the future with uncanny accuracy.
The Scorecard
Returning readers, you know how this works by now, but for new readers here's the deal. Every week I take a look at a specific statistic that is prone to regression and identify high and low outliers in that statistic, and then I wave my hands in the air and shout “regression!”
But since predictions aren't any fun without someone holding your feet to the fire afterward, I don't stop there. I lump all of the high outliers into Group A. I lump all of the low outliers into Group B. I verify that Group A is outperforming Group B. And then I predict that Group B will outperform Group A over the next four weeks.
I don't get to pick and choose my groups, beyond being free to pick and choose what statistics are especially prone to regression. If I'm tracking yards per target, and Antonio Brown is one of the high outliers in yards per target, then Antonio Brown goes into Group A and may the fantasy gods show mercy on my predictions.
And then, groups chosen and predictions made, I track my progress. That's this.
In Week 2, I outlined what regression was, what it wasn't, and how it worked. No prediction was made.
In Week 3, I listed running backs with exceptionally high and low yards per carry averages and predicted that the low-ypc cohort would outperform the high-ypc cohort over the next four weeks.
In Week 4, I looked at receivers who were overperforming and underperforming in yards per target and predicted that the underperformers would outperform the overperformers over the next four weeks.
In Week 5, I compared the predictive accuracy of in-season results to the predictive accuracy of preseason ADP. Outside of a general prediction that players would tend to regress in the direction of their preseason ADP, no specific prediction was made.
In Week 6, I looked at quarterbacks who were throwing too many or too few touchdowns given the amount of passing yards they were accumulating, then predicted that the underperformers would score more fantasy points than the overperformers going forward.
In Week 7, I looked at receivers who were catching too many or too few touchdowns based on their yardage total, then predicted that the underperformers would score more fantasy points than the overperformers going forward.
| Statistic for regression | Performance before prediction | Performance since prediction | Weeks remaining |
|---|---|---|---|
| yards per carry | Group A had 60% more rushing yards per game | Group B has 16% more rushing yards per game | None (Win!) |
| yards per target | Group A had 16% more receiving yards per game | Group B has 11% more receiving yards per game | None (Win!) |
| passing yards per touchdown | Group A had 13% more fantasy points per game | Group A has 34% more fantasy points per game | 2 |
| receiving yards per touchdown | Group A had 28% more fantasy points per game | Group A has 13% more fantasy points per game | 3 |
Another week, another prediction closed out. Like our yards per carry prediction, our yards per target prediction played out spectacularly. Over the first three weeks, our Group A receivers collectively averaged 11.68 yards per target, while Group B averaged 7.14.
In the four weeks since, Group A's yards per target average fell to 7.89, while Group B's rose to... 7.86. And since Group B retained its target per game advantage like we predicted, Group B wound up averaging 61.7 yards per game to Group A's 55.5.
While it was a good week for the yards per target prediction, it was kind of a disastrous one for our touchdowns per passing yard one, as players like Prescott, Carr, and Wentz managed to smash the forces of regression. All told, six quarterbacks from Group A played last week, and five of them finished among the top seven fantasy passers. (The other two were Group B quarterbacks.)
As I've said before, regression to the mean operates over larger timescales, but with two weeks left to go and the Group A quarterbacks looking like they're heating up, things don't look great for Regression's undefeated streak at this point.
Anyway, on to the prediction.
Yards Per Carry Redux
Our first yards per carry prediction played out beautifully, but perhaps that was just because two weeks was too small of a sample size. Surely now, with the season nearly half over, yards per carry is a better predictor? Let's find out.
To change things up a bit, I just wanted to compare secondary options. By and large, players like Leonard Fournette and Ezekiel Elliott and Le'Veon Bell aren't going to be changing hands in fantasy football. But teams often do try to trade for a second or third running back to patch some holes. So let's see which backs are worth acquiring.
To create my list, I pulled up every running back with at least 50 carries, but fewer than 70 fantasy points in standard scoring. This gave me 27 names, which gets reduced to 26 when we remove Dalvin Cook, (who is done for the season).
Most of those backs are clustered in the 3.4-4.5 yard per carry range, which is relatively sustainable, but we have five high and five low outliers.
Collectively, Alex Collins, Aaron Jones, Orleans Darkwa, LeGarrette Blount, and Bilal Powell average 64 carries for 337 yards, a 5.3 yard per carry average. Say hello to your Group A.
On the other end, Joe Mixon, Isaiah Crowell, Ty Montgomery, Samaje Perine, and Jonathan Stewart average 76 carries for 234 yards. Here's your Group B.
Collectively, Group A averages 25% more fantasy points per game than Group B in standard scoring, and it's stocked full with trendy sleepers going forward. Other than Mixon, Group B is mostly unsexy veterans and players at risk of losing their jobs.
Nevertheless, the whole premise of this column is that I don't get to pick my groups, so I'll hold my breath and predict that Group B scores more fantasy points per game than Group A going forward.
(If you play in PPR, Group A's lead is just 5.5% instead, and Group B becomes an even more attractive play.)