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The Rant with Jeff Ratcliffe

Top Regression Candidates for the Second Half of the NFL Season

The Rant with Jeff Ratcliffe

Jeff Ratcliffe

Sports, Fantasy Sports, Football

4.81.3K Ratings

🗓️ 30 October 2025

⏱️ 14 minutes

🧾️ Download transcript

Summary

It's never a smooth ride in the NFL. Sometimes players get out of the gate hot only to come back to earth down the stretch. Other times it's the opposite. With 8 weeks in rearview mirror, Jeff gives you his top regression candidates, both downward and upward, for the second half of the NFL season.

Transcript

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0:00.0

Rap back where you at?

0:13.5

Yo, what's up?

0:15.3

It's Thursday, October 30th.

0:17.4

I'm Jeff.

0:17.9

This is the rant.

0:20.4

Tis the season, right? We are in November as of Saturday.

0:25.1

We are in November football. Things change in November. That's been the big theme for me this week.

0:30.1

And I also, at this time of year, like to look at the productivity over the first eight weeks.

0:35.5

And we are going to see some folks who are pretty much doing what we expected them to do. But we're also going to see guys who underproduce

0:42.2

or who overproduce. And so I wanted to take a look at those guys in particular today, the candidates

0:48.7

for regression. Now, before I actually get into this, I want to be really clear that a lot of times, like in casual

0:55.4

everyday speech, we would use regression as a way to say that things are going to get worse.

1:01.7

That's fine in everyday speech.

1:03.6

But remember, when we're talking about this in fantasy, we are not using that version of the word.

1:09.8

Words can mean different things, right? It's a statistical

1:12.7

concept. And, you know, usually, I mean, when we're doing regression analysis, we're trying to

1:17.7

figure out the relationship between two things. So, average depth of target and fantasy points scored,

1:24.6

or something like that, right? And what is the relationship between those two

1:27.7

things? Can we actually use one to predict the other? But we also can use this idea in terms of

1:33.8

regression to the mean, because there will be an average, essentially, between those two variables.

1:39.7

And when we see something that's an outlier from that average, it's way above or way below that

1:45.2

average, we know that's not a sustainable thing. Usually we'll see over the long term the

...

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