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Speaker 1: Hello everyone, and welcome on into the Betting Pros Podcast.

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Speaker 1: It is NFL Week eight. We are pretty much at

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Speaker 1: the halfway point of the season now and joining us

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Speaker 1: today to help break down the rankings, break down what

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Speaker 1: we're looking at at this point, and of course it

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Speaker 1: can't be an easy halfway clean because now we have

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Speaker 1: seventeen games on the slate. Still bothers me, Matt. But

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Speaker 1: joining us today we have a very special guest here.

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Speaker 1: He's got a dog's name because he's got that dog

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Speaker 1: in him. Rufus Peabody joining the show. Rufus, How are

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Speaker 1: you doing?

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Speaker 2: I'm doing well. How are you Tom?

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Speaker 1: I'm doing great. Just had a nice weekend, went back up,

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Speaker 1: saw the family, saw my goddaughter for the first time.

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Speaker 1: It was a nice time in the Bay area. And

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Speaker 1: as I left there was apparently an earthquake today, so

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Speaker 1: I got out at the right time. Matt, how are

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Speaker 1: you doing doing well?

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Speaker 3: It's another great week of NFL actions. So just happy

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Speaker 3: to be here and it's great to have Rufus on

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Speaker 3: the show. I'm a a longtime listener of his podcast,

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Speaker 3: I Bet the Process podcast with Jeff ma I think

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Speaker 3: one of the I mean.

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Speaker 4: It's not entertaining.

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Speaker 3: Let's be honest, it's not entertaining, but it's one of

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Speaker 3: those still one of the best sports betting podcasts out there.

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Speaker 1: Thanks a fantastic endorsement of It's not entertaining, but we'll

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Speaker 1: try and entertain you guys here today, and seriously, do

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Speaker 1: go check out bet the Process. Get that listener count

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Speaker 1: up to eight, because again, fantastic show, and it's great

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Speaker 1: to get a look inside your and Jeff Ma's thought

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Speaker 1: processes behind how you guys mostly just rag on each

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Speaker 1: other but also occasionally give out some betting advice on it.

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Speaker 1: And today, of course we're going to be talking about

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Speaker 1: plenty of stuff here. Power. When it comes to power rankings,

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Speaker 1: there is nothing better than the mass Epeabody system in

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Speaker 1: terms of just a guidebook that you can look to

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Speaker 1: to start basing a lot of what you're doing off of.

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Speaker 1: We're going to be breaking that down a little bit,

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Speaker 1: but first let's talk about a little bit about you Rufus.

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Speaker 1: First off, how did you get your start in gambling?

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Speaker 5: That's a good question, and it certainly was not intentional.

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Speaker 5: I talked my way into an internship at this company

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Speaker 5: called Las Veig Sports consultants after reading an article in

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Speaker 5: ESPN dot com and learning about that this company existed,

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Speaker 5: and I ended up doing my senior thesis on psychological

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Speaker 5: inefficiencies in the baseball betting market, getting a full time

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Speaker 5: job offer at LVSC that.

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Speaker 2: Paid me next to nothing, But I moved out.

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Speaker 5: To Vegas knew nobody slowly built a bankroll betting while

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Speaker 5: working and learning a lot from from you know, a

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Speaker 5: lot of old school book maker types, and you know,

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Speaker 5: happened to be in the right place, in the right time,

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Speaker 5: at the right people that were willing to take a

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Speaker 5: chance on me and have been betting for a living

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Speaker 5: since two thousand and nine.

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Speaker 4: No, rufus, I have a quick follow up.

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Speaker 3: I'm just going to by the way, I'm going to

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Speaker 3: ignore some of what Tom puts in the outline. He

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Speaker 3: can ask, he can ask the structured questions. But so

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Speaker 3: you have, you know, the statement there learned a lot

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Speaker 3: from old school book makers, and so you have a

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Speaker 3: very quantitative approach. But I'm imagining that there's a lot

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Speaker 3: that informs it that has to do with the way

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Speaker 3: that you know kind of like quote unquote, like old

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Speaker 3: school book makers would think, you know, like you're sort

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Speaker 3: of like quantifying some things that for them were maybe

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Speaker 3: more intuitive or things that had just been based on

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Speaker 3: years and years of knowledge. Can you talk about some

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Speaker 3: of the things that you did learn from old school

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Speaker 3: book makers and then like, how that's in your model?

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Speaker 4: Yeah?

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Speaker 5: I think I learned how to think in a lot

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Speaker 5: of ways. I mean they think about things creatively. And actually,

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Speaker 5: I remember one of the thing projects I did for

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Speaker 5: Kenny White was he wanted me to look and try

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Speaker 5: to find the value of different statistics in projecting out

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Speaker 5: some power rating system that he was that he had built.

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Speaker 5: You know, how much was a rebound worth relative to

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Speaker 5: you know, a mid range jumper blah blah blah. And

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Speaker 5: I don't think it was a specific as mid range jumper,

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Speaker 5: but I remember like running some regressions and basically coming

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Speaker 5: up with numbers that were like early close to what

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Speaker 5: he had figured out kind of intuitively on his own.

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Speaker 5: And I was just so impressed that, you know, he

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Speaker 5: could do that. Maybe he had had some other statistics person,

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Speaker 5: you know, a few years ago, do the same thing

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Speaker 5: and didn't tell me that, but I was like, how

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Speaker 5: the hell can you do that, like just just intuitively

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Speaker 5: on your own? But I think, I mean, I think

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Speaker 5: I came out there a little bit arrogant and thinking

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Speaker 5: that that I could solve everything just quantitatively, and I

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Speaker 5: think I learned a lot of the limits of that

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Speaker 5: and just I mean, these guys had made it so

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Speaker 5: long in this industry by coming up with I mean,

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Speaker 5: by asking the right questions and thinking about things in

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Speaker 5: a unique way, in a logical way, and I think

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Speaker 5: that that has been I think my biggest, you know,

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Speaker 5: source of alpha betting over my career has been asking

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Speaker 5: the right questions and thinking about things creatively.

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Speaker 2: Because there's a lot of people out there that.

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Speaker 5: Can, you know, do quantitative analysis that you have much

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Speaker 5: more advanced statistical backgrounds than I do. But it's about

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Speaker 5: asking the right questions of the data. And so you

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Speaker 5: can't just you know, you can't just toss it into

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Speaker 5: computer and have the computer you come up with all

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Speaker 5: the answers for you, like some people believe machine learning does.

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Speaker 2: But or if you do, you're probably not.

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Speaker 5: Going to do very well betting. But so so it's

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Speaker 5: it's it's a it's I think I learned a way

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Speaker 5: of thinking.

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Speaker 1: And that's an interesting point you bring up about the

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Speaker 1: limits of quantitative analysis. Where did you start to find

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Speaker 1: that you were pressing up against the ceiling there that

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Speaker 1: you had to start incorporating some of these other methods.

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Speaker 5: It's not a ceiling as much as it is that

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Speaker 5: there's certain things that you can't quantify, and and it's

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Speaker 5: really important to know what you don't know. And I

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Speaker 5: think sometimes people can be too confident in what in

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Speaker 5: the fact, you know that you're quantifying everything, or you

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Speaker 5: think you are. But if you don't understand the weaknesses

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Speaker 5: and the things you can't quantify, you're not gonna I mean,

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Speaker 5: you're you're going to get into a lot of trouble.

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Speaker 5: I mean you might be right in general, but there'll

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Speaker 5: be exceptions that you run into. And that's the case

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Speaker 5: with any modeling system. I mean there's every sort every

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Speaker 5: model has a flaw in some way.

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Speaker 2: You're you're not gonna be able to capture everything.

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Speaker 5: And if you know, if you understand the limits and

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Speaker 5: how the model's built, you know you can sort of

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Speaker 5: spot those weak points. I mean there's also I don't know,

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Speaker 5: it's there's also things like I mean injuries. Unless you know,

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Speaker 5: unless you're quantifying every player, which is really freaking hard

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Speaker 5: to do.

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Speaker 2: Especially in a sport like the NFL where you.

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Speaker 5: Don't have a huge amount of data overall, and and

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Speaker 5: you have all these players playing together and the value

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Speaker 5: of one depends on the value of the other in.

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Speaker 2: A way, and so it's it's.

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Speaker 5: A really really I mean not like footballs are really challenging.

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Speaker 5: Can temfort in that regard for sure?

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Speaker 1: Yeah, it's very true. Models They models can have so

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Speaker 1: many different flaws in them. You can put in just

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Speaker 1: bad data, you can have some sort of error in

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Speaker 1: the way that you're computing it. They can divorce you

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Speaker 1: be as you return to play your twenty first season

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Speaker 1: in the NFL. A lot of things can go wrong

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Speaker 1: with them, but specifically in the NFL market here you

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Speaker 1: don't actually handicap that much anymore. And it was a

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Speaker 1: really amusing Twitter conversation at the beginning of the season

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Speaker 1: that I saw between you and I forget who it was,

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Speaker 1: but someone was basically calling you out for being on

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Speaker 1: the golf course the day before the NFL season begins,

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Speaker 1: Steep Peasick. Yeah, and you were just like, well, I mean,

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Speaker 1: I'm not going to bet on the NFL, so I

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Speaker 1: don't have to care about it. I'm going to go

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Speaker 1: play golf and enjoy myself right now. But what is

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Speaker 1: it that makes the NFL market so hard for people

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Speaker 1: to bet into?

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Speaker 5: So, first off, it isn't I mean the way that

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Speaker 5: I do most of my work in a way, before

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Speaker 5: the season begins, the work is spent building the model,

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Speaker 5: and then during the season it's sort of babysitting and

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Speaker 5: implementing things, and you have these systems to do it.

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Speaker 5: So I'm not I don't make my money researching, reading

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Speaker 5: up on teams, anything like that. I try to capture

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Speaker 5: everything that I can in my model, and the things

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Speaker 5: I don't.

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Speaker 2: That then I don't.

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Speaker 5: And you know clearly, you know I try to pay

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Speaker 5: attention to injuries and stuff like that. But what your

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Speaker 5: question is what makes the NFL so difficult? It is

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Speaker 5: the most efficient. It's the biggest sports betting market in

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Speaker 5: the United States, and so it's the most efficient, at

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Speaker 5: least for sides and totals. And you know, we've I

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Speaker 5: think we've kind of we've bet less on the NFL

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Speaker 5: or fewer things within the NFL in previous years. I mean,

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Speaker 5: we were always betting sort of Sunday and Monday openers.

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Speaker 2: For the for the next week. You know, we've got

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Speaker 2: futures and things like that.

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Speaker 5: But it's it's just very it's because because of the

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Speaker 5: size of the market and the amount of data out there,

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Speaker 5: I mean it's and the amount of attention on it,

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Speaker 5: it's very, very, very difficult to beat the NFL on

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Speaker 5: game day, you know. It's it's a really freaking efficient market.

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Speaker 5: And there's not that many games either, So like if

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Speaker 5: you're attacking the NFL, there's a lot of there's a

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Speaker 5: lot of profitable areas to attack, but it's it just

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Speaker 5: wouldn't be the prime Like sides and totals. There's a

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Speaker 5: lot of great derivatives, there's props, there's you know, all

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Speaker 5: sorts of things.

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Speaker 4: You just mentioned there.

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Speaker 3: Historically betting the you know earlier lines, uh, Sunday and

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Speaker 3: Monday and uh. You know, so you as a professional

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Speaker 3: better you know, a a bank roll that is sizeable

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Speaker 3: enough to need to be deployed to where it makes

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Speaker 3: sense to do this professionally. My question would be sort

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Speaker 3: of like, if you're betting earlier, you always hear about

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Speaker 3: your limits earlier in the week, and that's especially the

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Speaker 3: case if you're betting even on the look ahead lines.

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Speaker 3: But is it feasible not for someone to be a

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Speaker 3: professional better betting on look aheads in early lines, but

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Speaker 3: you know, to be able to put down enough action

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Speaker 3: to where it's you know, maybe like a a source

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Speaker 3: of supplemental income.

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Speaker 5: If you're doing it well for the NFL, you can

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Speaker 5: get down a good amount early in the week. Still, yes,

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Speaker 5: But I think the thing with the NFL that that

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Speaker 5: I think is worth mentioning is just how few games

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Speaker 5: there are in a given season, and so you have

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Speaker 5: so few and the fact that the lines are pretty

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Speaker 5: sharp means that it just is not you know, while

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Speaker 5: it drives a lot of content and stuff like that,

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Speaker 5: it's you know, betting NFL sides is even if I'm

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Speaker 5: doing it early in the week and getting great prices,

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Speaker 5: it's not going to be a huge source of income

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Speaker 5: for me just because there are only two hundred.

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Speaker 2: There were two hundred and fifty six games.

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Speaker 5: Now you have another what sixteen, so two hundred and

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Speaker 5: seventy two regular season games. It's just when when that

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Speaker 5: pales in comparison to the number of Major League Baseball

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Speaker 5: games you have NBA even college football and college basketball

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Speaker 5: is like insane.

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Speaker 1: So in college basketball, the amount of surface area there

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Speaker 1: to attack is just so incredible.

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Speaker 5: Yeah, I mean, but the thing is, it's it's you know,

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Speaker 5: I still watch the NFL. It's hard to it's hard

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Speaker 5: to not want to bet the NFL a little bit

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Speaker 5: just because it's I feel like, once I stopped betting

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Speaker 5: a sport, I kind of almost not paying attention to it.

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Speaker 2: Attention to it typically. That's kind of what happened with

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Speaker 2: me in baseball.

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Speaker 5: I don't miss it one bit, to be honest, but

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Speaker 5: you know, I feel like it would be very weird

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Speaker 5: to not not watch NFL games or care. But I

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Speaker 5: think people in my life would be very happy that I.

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Speaker 2: Would, you know, have more weekend time available.

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Speaker 1: But the nice thing about the NFL with those shortened

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Speaker 1: with the two hundred seventy two games, is that it's

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Speaker 1: not the same grind as the other sports of it's

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Speaker 1: this long, long season and so you know, it's seventeen

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Speaker 1: Sundays and change for us throughout the season, and then

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Speaker 1: we're back to normal life and you get to enjoy

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Speaker 1: your weekends again doing something that isn't watching folk watching golf.

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Speaker 2: Yeah, exactly.

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Speaker 1: Golf is absolutely the one sport I cannot handle on TV.

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Speaker 1: I went to the President's Cup when I was a kid,

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Speaker 1: amazing experience. Almost got run over by one of the golfers.

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Speaker 1: Mike Weird said Hi to May and we were two

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Speaker 1: feet away from Tiger Woods. You can't do that in

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Speaker 1: any other sport. Watching it on TV, the nonlinearity of

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Speaker 1: it just messes me up so badly that I get

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Speaker 1: frustrated with it.

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Speaker 2: It's gotten better in terms of like the.

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Speaker 5: Shot tracer technology and all that stuff, the top tracer

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Speaker 5: whatever it's called, so you can kind of see the

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Speaker 5: flight path of the ball, because I think that's the

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Speaker 5: coolest thing, be able to see how these gys shape

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Speaker 5: the balls.

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Speaker 1: But yeah, for me, the frustrating part is how they

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Speaker 1: bounce back and forth. Like I don't want to be

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Speaker 1: on the fifth hole and then go to the ninth.

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Speaker 1: I want to follow a golfer. But I digress here

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Speaker 1: because as much as I know that you would love

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Speaker 1: to talk about golf all day long here, let's get

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Speaker 1: into the NFL a little bit more here, and specifically

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Speaker 1: the Massypeabody system. It's of course available on unobated dot com.

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Speaker 1: The NFL power rankings that you update each and every

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Speaker 1: week here and what is it that they mean necessarily,

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Speaker 1: because it's not just the system of you going in

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Speaker 1: and saying the Bills are better than the Chiefs. So

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Speaker 1: the Bills are my number one team. And obviously this

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Speaker 1: is all below the Seahawks and Gino Smith. But still,

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Speaker 1: what is it that is going in? What's the secret

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Speaker 1: that's making the sauce here with this system when you

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Speaker 1: go to approach it every week?

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Speaker 5: So Kate and I developed this back or initially two

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Speaker 5: thousand before the twenty ten season. This guy Michael Solfino,

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Speaker 5: who we well, he wrote I think for the Metal

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Speaker 5: Lands Media group, and he and Kate talked and they

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Speaker 5: had this idea to build this rating system because the

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Speaker 5: Wall Street Journal was interested, and.

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Speaker 2: So we ended up doing that and.

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Speaker 5: It was very simple and its ambitions at the beginning

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Speaker 5: it was it was saying, and it still isn't a way.

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Speaker 2: I mean, I think the beauty of.

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Speaker 5: It is is the simplicity of the framework for it.

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Speaker 5: We take just a few statistics that that are that

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Speaker 5: we find to be meaningful predictively, and we can textualize

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Speaker 5: them very well. So you know, I guess a team

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Speaker 5: playing what's a good example, what was a bad weather

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Speaker 5: game or team.

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Speaker 1: I don't know, I mean Niners Week one.

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Speaker 5: Yeah, okay, the Bears, right, Like, like I wouldn't compare

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Speaker 5: like that weather. That's an extreme example, but but you

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Speaker 5: put any like every team is going to struggle on

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Speaker 5: offense in those particular weather conditions. Right, So you control

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Speaker 5: for you control for the strength of the opponent, you

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Speaker 5: control for the game situation, you control for you know,

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Speaker 5: on a particular play, like what is a team's down

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Speaker 5: by twenty seven points, the other team is playing a

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Speaker 5: soft zone in the late in the third quarter, they're

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Speaker 5: going to be able to get some yards there. But

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Speaker 5: it doesn't say they're a good team. It doesn't translate.

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Speaker 5: So it's it's it's about contextualizing performance dewaiting sort of

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Speaker 5: less predictive garbage time.

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Speaker 2: And so we have an algorithm that does that.

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Speaker 5: Controlling for strength of opponent, as I said in home field,

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Speaker 5: and and I guess waiting each metric by its predictive

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Speaker 5: value going forward. So a good example is something like

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Speaker 5: turnovers and especially fumble recovery luck or fumble recovery percentage.

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Speaker 2: Or whatever you want to call it.

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Speaker 5: Like fumbles who recovers them are almost entirely luck. There's

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Speaker 5: no there's no persistent skill. If you told me that

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Speaker 5: this team recovered, you know, a eighty five percent of

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Speaker 5: fumbles the first half of the year, I would still

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Speaker 5: expect that they would recover fifty percent.

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Speaker 2: Of fumbles the second half of the year. I mean, so.

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Speaker 5: That that kind of thing can really, I mean could

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Speaker 5: be implicated strongly in final scores and your assessment of

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Speaker 5: a team. And so we're we're not looking at final scores,

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Speaker 5: We're looking at the underlying metrics, the things that are predictive,

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Speaker 5: and that's that's yeah, that's.

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Speaker 2: What it is.

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Speaker 5: And it's we started out only using the current season.

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Speaker 5: It was a very dumb system in that regard, like

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Speaker 5: we were like, we don't want to put our finger

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Speaker 5: on the trigger, blah blah blah. But two years in

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Speaker 5: we were like, now we need priors, and so we

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Speaker 5: you know, we use the previous season's data as well,

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Speaker 5: and then we eventually incorporated We're like, you know, what

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Speaker 5: quarterbacks make such a difference the NFL, we.

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Speaker 2: Need you know, it was a team it was a

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Speaker 2: team based rating system. It still is, but.

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Speaker 5: We we modeled a quarterback element of it as well,

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Speaker 5: and so that's kind of what we have now, and

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Speaker 5: it is not you know now, there's just so much

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Speaker 5: amazing data out there available the you know where you

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Speaker 5: I mean, you see it on the Amazon telecasts and

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Speaker 5: you know this guy's shoulder pad was angled seven degrees

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Speaker 5: before he made this cut.

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Speaker 2: Like, I don't know what I would do with that data.

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Speaker 2: That's too advanced for me. But Massy Peabody we are

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Speaker 2: we are strictly a team based rating system. And so.

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Speaker 5: You know, back in twenty twelve, the market was much

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Speaker 5: less efficient overall, and you could blindly bet Massy Peabody

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Speaker 5: stuff and do quite well. I wouldn't say that's the

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Speaker 5: case now as much, but it's a good it's still

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Speaker 5: a good sort of framework and a starting point. And

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Speaker 5: as I mentioned earlier, knowing the weaknesses of your model,

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Speaker 5: that's that's one thing that I mean. You know that

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Speaker 5: this is a team based model, and so it's not

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Speaker 5: going tojust to you know, a team that has got

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Speaker 5: hit with injuries, right will probably be two on them

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Speaker 5: as a result. And so you can kind of use

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Speaker 5: it as a starting point knowing that what it's based

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Speaker 5: off of, and then make adjustments yourself if you want.

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Speaker 2: So.

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Speaker 5: A good example might be if you think that Tom Brady,

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Speaker 5: you know, use it like we had a prior on

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Speaker 5: Tom Brady based on how good he's been in the

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Speaker 5: past and all that stuff, and you know, he is old,

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Speaker 5: but he's been he was quite good last year and

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Speaker 5: he well, the Bucks have not been as good this

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Speaker 5: year on offense, and so you might say, okay, well

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Speaker 5: I'm holding on too strongly to that that prior on

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Speaker 5: Tom Brady, and so you know, you might say, oh, massive,

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Speaker 5: peobody's wrong there if you believe that. Personally, I think

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Speaker 5: that it's probably I think that the market is probably

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Speaker 5: overreacting a little bit there too. It's probably some company,

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Speaker 5: you know, partly the truth. But sorry, I mean, I'm

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Speaker 5: just you know, I go off on tangents. I just

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Speaker 5: keep talking, so you just got to cut me off sometimes.

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Speaker 1: So you are completely right like for me. And it

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Speaker 1: goes very much into qualitative analysis here, but just the

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Speaker 1: eye test and looking at Tom Brady's stats. It's not

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Speaker 1: like the Brett Farvr situation where you know, he goes

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Speaker 1: to the championship game and then the next season comes

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Speaker 1: back for one more ride and just gets beaten up

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Speaker 1: and it's all so add situation right exactly where his

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Speaker 1: carcass was dragged by that defense to a Super Bowl. Yeah,

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Speaker 1: Brady's still good. He's not the problem. He has no

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Speaker 1: problem with arm strength. It's not like he's really turning

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Speaker 1: the ball over more. It's just that the team has

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Speaker 1: been hurt.

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Speaker 5: He's like going to whoever's wedding it was, right Craft.

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Speaker 5: You know, He's going to Robert Craft's wedding on Friday

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Speaker 5: rather than going to practice, right, So, I don't know,

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Speaker 5: it feels like. It feels like, at least the narrative

390
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Speaker 5: is the fact that he hasn't been put in the

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Speaker 5: same amount of time and effort.

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Speaker 2: As he has in past seasons because he was trying.

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Speaker 5: He's trying to like save his marriage and do all

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Speaker 5: these other things, and you can't. You can't be great

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Speaker 5: unless you give it absolutely everything.

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Speaker 1: God, what an awful trade off blowing up your family

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Speaker 1: to lose to Mitched tru Whisky, Kenny Pickett and whoever.

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Speaker 1: PJ Walker, Yeah, PJ Walker.

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Speaker 2: John Walker.

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Speaker 3: Yeah, speaking of PJ Walker, I'm looking at the the

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Speaker 3: mass E Peabody rankings here, and you know, you see

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Speaker 3: that Carolina is number thirty, Washington is thirty one. The

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Speaker 3: Colts are number thirty two here, and you had mentioned

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Speaker 3: that there's a you guys have layered in a quarterback

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Speaker 3: component into the rankings. So even though it's a team

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Speaker 3: based model, there is a quarterback component. And so you know,

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Speaker 3: I'm looking here seeing these teams with you know, PJ.

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Speaker 3: Walker with backups basically at the bottom of the rankings here,

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Speaker 3: and so you know, I'm wondering, you know, the move

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Speaker 3: from Matt Ryan to Sam Ellinger. You know, you now

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Speaker 3: have the Colts. They were number twenty nine before, now

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Speaker 3: they're number thirty two. I'm assuming that the Ellnger news

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Speaker 3: has been taken into account in this and that is

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Speaker 3: why the Colts are at the very bottom of the

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Speaker 3: rankings here.

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Speaker 5: Yes, definitely, and maybe Ellinger is better than Matt Ryan

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Speaker 5: at this point I would probably would bet against it.

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Speaker 5: But you know, if I'm the Colts, you know, at

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Speaker 5: least he's he's different, he's got upside and all that.

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Speaker 5: But the thing is a guy making his first start,

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Speaker 5: Like we have data on this in the past to

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Speaker 5: say how much. I don't even remember what round Elander

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Speaker 5: was drafted in. But but guys typically, even even the

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Speaker 5: best guys that end up becoming all Pro quarterbacks don't

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Speaker 5: start there most of the time. Their first start isn't

426
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Speaker 5: always that way. And so like we remember the Russell

427
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Speaker 5: Wilson's the r G three is from that same season.

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Speaker 5: I guess the what's another good example of a guy

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Speaker 5: that just came out guns of blazing?

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Speaker 2: Did Se Watson?

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Speaker 4: Cam Newton was great his rookie year.

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Speaker 5: But there's so many guys that, I mean, even if

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Speaker 5: they and the thing is you might have a few

434
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Speaker 5: good starts and and people like even like Bailey z

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Speaker 5: Appy right, and people are like, oh, look, this guy

436
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Speaker 5: is actually really good, and it's like.

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Speaker 2: Well, no, I mean, in in one game, there's a

438
00:20:53,600 --> 00:20:56,080
Speaker 2: lot of randomness. In one game, there's a.

439
00:20:56,040 --> 00:20:58,880
Speaker 5: Lot of and there's a lot of in a weird

440
00:20:58,880 --> 00:21:01,560
Speaker 5: way correlated randomness, like was the game plan good or not?

441
00:21:01,840 --> 00:21:05,200
Speaker 5: You know, was this guy making plays within this framework

442
00:21:05,200 --> 00:21:07,119
Speaker 5: where it was very easy for him to do because

443
00:21:07,119 --> 00:21:10,440
Speaker 5: of you know, the this defense wasn't adjusting for this

444
00:21:10,480 --> 00:21:12,240
Speaker 5: one thing or something like that. You go into another game,

445
00:21:12,280 --> 00:21:16,280
Speaker 5: it could be a completely different circumstance. But but basically

446
00:21:16,320 --> 00:21:21,920
Speaker 5: there's a strong prior that is that basically says, this

447
00:21:21,960 --> 00:21:24,880
Speaker 5: is your first start, you're not a number one overall quarterback,

448
00:21:24,920 --> 00:21:28,000
Speaker 5: a really highly touted quarterback. You're probably not going to

449
00:21:28,040 --> 00:21:30,119
Speaker 5: be that good. And it takes a good amount of

450
00:21:30,200 --> 00:21:33,560
Speaker 5: data for us to sort of adjust that that guy's rating.

451
00:21:33,600 --> 00:21:36,320
Speaker 5: And so you know, even if Allinger lights it up,

452
00:21:37,280 --> 00:21:39,800
Speaker 5: you know, would he move up more than his would

453
00:21:39,800 --> 00:21:41,640
Speaker 5: his quarterback rating move up more than like a point

454
00:21:41,680 --> 00:21:41,840
Speaker 5: or so?

455
00:21:42,000 --> 00:21:42,399
Speaker 2: Probably not.

456
00:21:43,119 --> 00:21:46,040
Speaker 3: Yeah, you just mentioned there, you know, kind of the

457
00:21:46,119 --> 00:21:49,080
Speaker 3: idea of a prior built into this and strong priors,

458
00:21:49,200 --> 00:21:51,760
Speaker 3: and you mentioned Tom Brady too, and so you know,

459
00:21:51,840 --> 00:21:55,120
Speaker 3: looking at the Massy Peabody ratings, you know, I see that.

460
00:21:56,280 --> 00:21:58,080
Speaker 2: The table looks too high, don't they They look too

461
00:21:58,119 --> 00:21:58,720
Speaker 2: high to me too.

462
00:21:59,000 --> 00:22:01,560
Speaker 3: Well, no, I mean, actually I think they were too

463
00:22:01,600 --> 00:22:04,920
Speaker 3: high previously. But you know, there's what I would think

464
00:22:04,960 --> 00:22:08,160
Speaker 3: of as a pretty significant move from around six points

465
00:22:08,200 --> 00:22:10,440
Speaker 3: to around you know, four and a half points or

466
00:22:10,480 --> 00:22:14,440
Speaker 3: so this week. You know, Cincinnati moving up from three

467
00:22:14,480 --> 00:22:18,320
Speaker 3: points last week to four points this week. Like for

468
00:22:18,720 --> 00:22:21,720
Speaker 3: that kind of move, and you know, similar sized moves

469
00:22:21,720 --> 00:22:24,359
Speaker 3: for some of these other teams from one week to

470
00:22:24,440 --> 00:22:28,080
Speaker 3: the next. That feels like a kind of large move,

471
00:22:28,280 --> 00:22:31,399
Speaker 3: like to be able to jump up a point or

472
00:22:31,560 --> 00:22:34,520
Speaker 3: a point and a half, like Kansas City moving up

473
00:22:35,640 --> 00:22:38,040
Speaker 3: a point and a half from last week to this week,

474
00:22:38,280 --> 00:22:41,320
Speaker 3: San Francisco moving down about a point from last week

475
00:22:41,320 --> 00:22:42,000
Speaker 3: to this week.

476
00:22:42,520 --> 00:22:47,760
Speaker 4: That feels like a lot. And maybe I'm just drawn like.

477
00:22:47,720 --> 00:22:50,760
Speaker 3: There's obviously, but I'm just saying like it feels like

478
00:22:50,880 --> 00:22:53,359
Speaker 3: at this point of the season, like about the halfway point,

479
00:22:53,440 --> 00:22:56,560
Speaker 3: you know, entering week eight, for there to be a

480
00:22:56,600 --> 00:23:00,240
Speaker 3: system that has an adjustment of a point. Again, it's

481
00:23:00,320 --> 00:23:04,680
Speaker 3: this spread each week that feels extreme. Can you talk

482
00:23:04,720 --> 00:23:05,119
Speaker 3: about that?

483
00:23:05,520 --> 00:23:07,240
Speaker 5: So I would have thought most people would have thought

484
00:23:07,280 --> 00:23:09,560
Speaker 5: the opposite, like only a point, like they blew this

485
00:23:09,640 --> 00:23:10,120
Speaker 5: team out.

486
00:23:10,440 --> 00:23:14,640
Speaker 3: You know, will say, sorry to interrupt, I'll say, there's

487
00:23:14,680 --> 00:23:17,840
Speaker 3: a very strong chance that I when I'm adjusting my stuff,

488
00:23:17,840 --> 00:23:23,280
Speaker 3: that I am too slow and priors I think it's yeah, I.

489
00:23:23,200 --> 00:23:26,200
Speaker 5: Think the mistake people make is overreacting rather than underreacting.

490
00:23:26,200 --> 00:23:28,239
Speaker 5: And I think if I think, the one thing that

491
00:23:28,440 --> 00:23:31,000
Speaker 5: I think we've been criticized for, and we criticize ourselves

492
00:23:31,000 --> 00:23:34,399
Speaker 5: for it, but is that we are too slow, I

493
00:23:34,400 --> 00:23:37,560
Speaker 5: feel like to we're too slow to update our view

494
00:23:37,560 --> 00:23:40,680
Speaker 5: on quarterbacks because it is I'll tell you the reason

495
00:23:40,680 --> 00:23:44,080
Speaker 5: for that, though, is it's really hard to separate the quarterback.

496
00:23:43,640 --> 00:23:46,440
Speaker 2: From the rest of the offense in terms of value.

497
00:23:46,760 --> 00:23:50,959
Speaker 5: Yeah, and in a way, if like, in a way

498
00:23:51,160 --> 00:23:53,199
Speaker 5: we're going to think that Ellinger is going to be

499
00:23:53,359 --> 00:23:55,399
Speaker 5: were or the Colts are going to be worse with Ellinger,

500
00:23:55,480 --> 00:23:58,399
Speaker 5: it's going to maybe a bigger drop off because we're like, well,

501
00:23:59,200 --> 00:24:01,960
Speaker 5: the Colts played, the Colts were awful with Matt Ryan,

502
00:24:02,000 --> 00:24:03,639
Speaker 5: who we know as a track record of being at

503
00:24:03,720 --> 00:24:07,600
Speaker 5: least above at like an average too slightly above average quarterback.

504
00:24:08,040 --> 00:24:11,600
Speaker 2: Right, Like, we don't know, we don't know what was

505
00:24:11,640 --> 00:24:15,480
Speaker 2: causing the what was causing a team down to perform

506
00:24:15,480 --> 00:24:16,080
Speaker 2: there exactly.

507
00:24:16,119 --> 00:24:18,119
Speaker 5: We have the statistics, but we don't know if that

508
00:24:18,119 --> 00:24:20,479
Speaker 5: that pass you know it could We don't know if

509
00:24:20,520 --> 00:24:23,359
Speaker 5: the the anemic passing is due to the system, if

510
00:24:23,400 --> 00:24:25,280
Speaker 5: it's due to the players, it's due to Matt Ryan.

511
00:24:25,440 --> 00:24:28,359
Speaker 5: Like we can say according to our eyes that it's

512
00:24:28,400 --> 00:24:31,119
Speaker 5: definitely Matt Ryan. That's what I would have said a

513
00:24:31,160 --> 00:24:33,600
Speaker 5: lot of it's Matt Ryan. But but so in essence,

514
00:24:33,600 --> 00:24:36,840
Speaker 5: it's going to look like like we're not going to

515
00:24:36,960 --> 00:24:39,199
Speaker 5: sign all this We're not going to sign all the

516
00:24:39,200 --> 00:24:42,240
Speaker 5: blame Matt Ryan this season and so now when but

517
00:24:42,760 --> 00:24:45,439
Speaker 5: if we if it actually was Matt Ryan that's doing it,

518
00:24:45,480 --> 00:24:48,520
Speaker 5: then suddenly it's it's going to look like the rest

519
00:24:48,520 --> 00:24:51,760
Speaker 5: of the Colts team is very bad rather than just

520
00:24:51,760 --> 00:24:54,600
Speaker 5: being Matt Ryan. But I do think it's better to

521
00:24:54,680 --> 00:24:58,360
Speaker 5: adjust slowly and overall. Yes, Like I feel like some

522
00:24:58,400 --> 00:25:01,720
Speaker 5: of the some of the the actual nominal rating movements

523
00:25:01,760 --> 00:25:05,080
Speaker 5: are gonna like you can have a team that doesn't

524
00:25:05,119 --> 00:25:08,040
Speaker 5: actually move at all, but another team gets a starting

525
00:25:08,119 --> 00:25:11,400
Speaker 5: quarterback back and so that'll scale the ratings will change

526
00:25:11,400 --> 00:25:12,880
Speaker 5: a little bit of scale things that way too.

527
00:25:12,920 --> 00:25:15,520
Speaker 2: So I mean, yeah, that is that is the biggest

528
00:25:15,560 --> 00:25:17,240
Speaker 2: reason for movement and ratings.

529
00:25:17,240 --> 00:25:18,960
Speaker 5: And that's the hardest thing to deal with, this freaking

530
00:25:19,000 --> 00:25:20,919
Speaker 5: quarterbacks and quarterback injuries and.

531
00:25:21,760 --> 00:25:22,920
Speaker 4: Actually say that.

532
00:25:23,080 --> 00:25:25,080
Speaker 3: You say that, and that makes a lot of sense

533
00:25:25,160 --> 00:25:28,520
Speaker 3: to me because every week, you know, some quarterback will return,

534
00:25:28,720 --> 00:25:31,640
Speaker 3: a quarterback will get injured, and I'll have to adjust

535
00:25:31,760 --> 00:25:34,359
Speaker 3: ratings for all of the teams to get it to

536
00:25:34,440 --> 00:25:36,040
Speaker 3: sort of like the net recenter.

537
00:25:36,240 --> 00:25:39,520
Speaker 1: Yeah, yeah, and a couple of things there, because you

538
00:25:39,560 --> 00:25:42,920
Speaker 1: talk about quarterback injuries versus a situation like Matt Ryan

539
00:25:42,960 --> 00:25:47,719
Speaker 1: where he's just being replaced. That influences your rankings differently

540
00:25:48,080 --> 00:25:50,160
Speaker 1: when it's injury versus replacement, doesn't it.

541
00:25:50,520 --> 00:25:51,360
Speaker 2: Yeah, I mean we do.

542
00:25:51,680 --> 00:25:54,600
Speaker 5: As weird as this sounds, we have a marker, like

543
00:25:54,640 --> 00:25:58,919
Speaker 5: a designation that says like quarterback out, like this, this

544
00:25:58,960 --> 00:26:01,679
Speaker 5: guy's replacing an injury quarterback. And because we have you know,

545
00:26:01,800 --> 00:26:04,359
Speaker 5: back in that we have data since two thousand and

546
00:26:04,400 --> 00:26:06,800
Speaker 5: we've been able to like we've gone through and coded

547
00:26:06,840 --> 00:26:09,920
Speaker 5: out situations where a team a quarterback was out due

548
00:26:09,920 --> 00:26:12,760
Speaker 5: to injury. We have like historical injury reports, and so

549
00:26:14,119 --> 00:26:17,679
Speaker 5: we're able to sort of like knowing that, knowing that

550
00:26:18,240 --> 00:26:22,920
Speaker 5: Sam Ellinger is coming in to replace a bench Matt Ryan,

551
00:26:23,480 --> 00:26:25,399
Speaker 5: our number is going to be a little bit different

552
00:26:25,400 --> 00:26:28,720
Speaker 5: than if we if if Matt Ryan was injured and

553
00:26:28,720 --> 00:26:31,639
Speaker 5: Ellinger was forced to come in. It basically it says

554
00:26:31,680 --> 00:26:34,439
Speaker 5: something about the organization has enough confidence in him that

555
00:26:34,600 --> 00:26:37,159
Speaker 5: they want to bring him in. Love Matt Ryan, just

556
00:26:37,200 --> 00:26:40,520
Speaker 5: like you know it if if Gino Smith got hurt

557
00:26:40,560 --> 00:26:43,280
Speaker 5: and Drew Lock comes in, you know, our rating on

558
00:26:43,359 --> 00:26:45,040
Speaker 5: Drew Lock is going to be different than if Drew

559
00:26:45,080 --> 00:26:47,080
Speaker 5: Lock had won the starting job over Gino Smith.

560
00:26:47,359 --> 00:26:49,200
Speaker 2: In a way, like it's common sense if you think

561
00:26:49,200 --> 00:26:49,520
Speaker 2: about it.

562
00:26:49,520 --> 00:26:51,040
Speaker 5: It's like, well, I'm going to trust that the coach

563
00:26:51,080 --> 00:26:52,960
Speaker 5: in the organization knows what's best, knows.

564
00:26:52,800 --> 00:26:55,000
Speaker 2: Which player's best. Like, how how the hell am I going.

565
00:26:54,960 --> 00:26:57,439
Speaker 5: To say that this quarter? You know, they see these

566
00:26:57,480 --> 00:26:59,840
Speaker 5: guys in practice, et cetera. I mean that doesn't mean

567
00:26:59,840 --> 00:27:02,280
Speaker 5: they're always right and so that that's you know, but

568
00:27:02,520 --> 00:27:05,760
Speaker 5: it's I mean, like right now, Washington, we were pretty

569
00:27:05,800 --> 00:27:08,880
Speaker 5: low on Washington because I have the injury designation thing

570
00:27:08,880 --> 00:27:11,040
Speaker 5: on Heineke, even though I'm like, I feel awful doing that.

571
00:27:11,080 --> 00:27:12,840
Speaker 5: So there's a bit of a subjective component there too.

572
00:27:12,880 --> 00:27:17,000
Speaker 5: I'm like, well, Wentz his garbage, like like Heinick is

573
00:27:17,119 --> 00:27:19,800
Speaker 5: just as good at this point, probably which probably isn't true.

574
00:27:19,800 --> 00:27:24,800
Speaker 5: But I don't know, right, he's better, He's he's I

575
00:27:24,840 --> 00:27:27,760
Speaker 5: think he's better in a offense that he's better on

576
00:27:27,800 --> 00:27:30,600
Speaker 5: a bad team in a way. Yeah, he's more high

577
00:27:30,680 --> 00:27:33,320
Speaker 5: variance or he can he can make more plays himself

578
00:27:33,400 --> 00:27:36,919
Speaker 5: and do and improvise better than Wentz can's.

579
00:27:37,119 --> 00:27:39,360
Speaker 1: That's what I'm a little worried about with Ellinger going

580
00:27:39,359 --> 00:27:42,320
Speaker 1: into because he is much more mobile than Matt Ryan.

581
00:27:42,400 --> 00:27:43,920
Speaker 1: Given that Matt Ryan is eighty.

582
00:27:43,760 --> 00:27:46,919
Speaker 2: Years old, isn't anybody more mobile than Matt Ryan exactly.

583
00:27:47,400 --> 00:27:50,560
Speaker 1: It's the same thing with Joe Flacco versus Zach Wilson.

584
00:27:50,680 --> 00:27:53,639
Speaker 1: For me, where that offense saw a little bit of

585
00:27:53,680 --> 00:27:56,520
Speaker 1: an uptick from the fact that Flacco was throwing fifty

586
00:27:56,520 --> 00:27:58,800
Speaker 1: two times a game, but he was a statue in

587
00:27:58,840 --> 00:28:01,480
Speaker 1: the pocket. Zach Wilson able to get outside the pocket.

588
00:28:01,520 --> 00:28:02,119
Speaker 1: There's a little more more.

589
00:28:02,160 --> 00:28:05,080
Speaker 5: Bilis has done like nothing there though, well he just

590
00:28:05,119 --> 00:28:09,080
Speaker 5: looks so bad, but they just yeah.

591
00:28:07,680 --> 00:28:10,840
Speaker 1: I fully aware of that. But the offense has been

592
00:28:10,880 --> 00:28:15,439
Speaker 1: able to have a little more success. Yeah, they're no

593
00:28:15,480 --> 00:28:18,000
Speaker 1: longer passing fifty two times a game. Rest in peace,

594
00:28:18,040 --> 00:28:18,840
Speaker 1: Breeze Hall takeaway.

595
00:28:18,920 --> 00:28:20,080
Speaker 2: Wait, wait, they were passing.

596
00:28:20,200 --> 00:28:22,800
Speaker 5: They were passing, I believe largely because they were in

597
00:28:23,119 --> 00:28:25,800
Speaker 5: games negative game states where they were playing from behind.

598
00:28:26,520 --> 00:28:26,800
Speaker 1: Yeah.

599
00:28:27,119 --> 00:28:28,720
Speaker 2: Yeah, all right.

600
00:28:28,760 --> 00:28:31,640
Speaker 3: One one more question here I have about massive Peabody

601
00:28:31,720 --> 00:28:34,560
Speaker 3: ratings and kind of thinking about it's not even so

602
00:28:34,640 --> 00:28:37,560
Speaker 3: much about the ratings, but you know, I'm looking at

603
00:28:37,560 --> 00:28:40,400
Speaker 3: them now. One team obviously near the top, and this

604
00:28:40,440 --> 00:28:43,880
Speaker 3: would just be your your rate ratings, but everyone's ratings.

605
00:28:45,240 --> 00:28:48,640
Speaker 3: The Eagles are you know a team that you guys

606
00:28:48,680 --> 00:28:50,720
Speaker 3: have number six. I think for some people they would

607
00:28:50,760 --> 00:28:53,440
Speaker 3: think that's even a little bit low, but you know,

608
00:28:53,480 --> 00:28:55,880
Speaker 3: clearly one of the better teams in the league.

609
00:28:56,320 --> 00:28:56,560
Speaker 4: Uh.

610
00:28:56,600 --> 00:28:59,600
Speaker 3: And then you have the Steelers down at number twenty nine,

611
00:28:59,800 --> 00:29:03,040
Speaker 3: and we have those two teams going up against each

612
00:29:03,080 --> 00:29:07,200
Speaker 3: other this week. And if we think about the spread

613
00:29:07,480 --> 00:29:10,880
Speaker 3: in this game in the off season market, you know,

614
00:29:10,920 --> 00:29:14,760
Speaker 3: you have sports books that will release numbers months in advance.

615
00:29:15,520 --> 00:29:19,640
Speaker 3: This number was three and a half to four in

616
00:29:19,720 --> 00:29:23,760
Speaker 3: favor of the Eagles, and then you know opened in

617
00:29:23,800 --> 00:29:26,640
Speaker 3: the early line around ten and a half, and you know,

618
00:29:26,720 --> 00:29:30,320
Speaker 3: it feels like it could continue to move towards the

619
00:29:30,360 --> 00:29:33,400
Speaker 3: Eagles as we get towards kickoff. Can you talk a

620
00:29:33,400 --> 00:29:37,719
Speaker 3: little bit about the prior sort of like the strong

621
00:29:37,800 --> 00:29:40,960
Speaker 3: prior or the strength of the prior, where you can

622
00:29:40,960 --> 00:29:44,440
Speaker 3: look back to the number from the off season and say, okay,

623
00:29:44,880 --> 00:29:48,320
Speaker 3: these teams were in this position, and now, over the

624
00:29:48,360 --> 00:29:52,200
Speaker 3: course of two months of game action, we have seen

625
00:29:52,400 --> 00:29:55,040
Speaker 3: this line move in such a way that it's gone

626
00:29:55,040 --> 00:29:59,680
Speaker 3: through the seven, gone through the ten, and you know

627
00:29:59,760 --> 00:30:02,800
Speaker 3: it it might hit fourteen. You know, like there's a chance.

628
00:30:02,600 --> 00:30:04,080
Speaker 2: This happened, So that's fourteen.

629
00:30:04,960 --> 00:30:07,520
Speaker 3: You never know, it probably won't, but like there at

630
00:30:07,520 --> 00:30:09,840
Speaker 3: some point resistance has to happen, and so kind of

631
00:30:09,840 --> 00:30:12,760
Speaker 3: the question is like, how do you take that movement

632
00:30:12,920 --> 00:30:16,400
Speaker 3: into account and kind of know when to get off

633
00:30:16,520 --> 00:30:19,960
Speaker 3: of certain priors and when the point comes where it's

634
00:30:20,000 --> 00:30:22,720
Speaker 3: like this is just this line has moved too far.

635
00:30:23,640 --> 00:30:27,200
Speaker 5: So that's interes Okay, So you said when to get

636
00:30:27,200 --> 00:30:29,520
Speaker 5: off priors, And I think the thing is we gradually,

637
00:30:30,400 --> 00:30:33,720
Speaker 5: our numbers are gradually departing from the priors, Like in

638
00:30:33,760 --> 00:30:35,880
Speaker 5: season becomes more and more important every week, and it

639
00:30:36,680 --> 00:30:38,560
Speaker 5: kind of changes for different things in a way, like

640
00:30:38,640 --> 00:30:42,880
Speaker 5: so we tend to react more quickly to the team

641
00:30:43,040 --> 00:30:45,520
Speaker 5: than we do to like the quarterback play, because it's

642
00:30:45,600 --> 00:30:47,520
Speaker 5: very hard in season to sort of separate, you know,

643
00:30:47,600 --> 00:30:49,760
Speaker 5: especially if you have the quarterback and the offense, like

644
00:30:49,800 --> 00:30:52,440
Speaker 5: the quarterback, quarterback and that team the entire time.

645
00:30:53,840 --> 00:30:54,400
Speaker 2: I think in.

646
00:30:54,360 --> 00:30:59,240
Speaker 5: General, you they're like and I think in this case,

647
00:30:59,280 --> 00:31:02,640
Speaker 5: it's not that Hurts is a completely different quarterback than

648
00:31:02,640 --> 00:31:05,760
Speaker 5: he was last year. I think he's got a good system,

649
00:31:05,880 --> 00:31:08,080
Speaker 5: he's got good weapons around him, a good line, Like

650
00:31:08,160 --> 00:31:11,880
Speaker 5: I mean, it's it's a good situation for him. But

651
00:31:12,440 --> 00:31:15,400
Speaker 5: so there probably are times and Kate and I have

652
00:31:15,400 --> 00:31:17,280
Speaker 5: talked about this kid actually is like, you know, are

653
00:31:17,280 --> 00:31:19,360
Speaker 5: there ways that we has asked me, like, are there

654
00:31:19,360 --> 00:31:22,200
Speaker 5: ways we can try to identify like when we're wrong

655
00:31:22,280 --> 00:31:24,000
Speaker 5: on a prior where we're like, okay, we need to

656
00:31:24,040 --> 00:31:27,760
Speaker 5: just depart from this entirely because something has happened enough

657
00:31:27,800 --> 00:31:30,760
Speaker 5: to show us that this is this what we believed

658
00:31:30,840 --> 00:31:31,240
Speaker 5: was wrong.

659
00:31:33,040 --> 00:31:35,520
Speaker 2: But I think that's really hard to do.

660
00:31:35,880 --> 00:31:40,360
Speaker 5: And it's it's certainly the it's yep, it's at least

661
00:31:40,360 --> 00:31:42,120
Speaker 5: the way we've looked at it not really possible to

662
00:31:42,120 --> 00:31:44,840
Speaker 5: just say, okay, this is now, we're done with the prior.

663
00:31:45,280 --> 00:31:48,840
Speaker 5: It's just that the end season is outweighing it. And

664
00:31:48,880 --> 00:31:52,160
Speaker 5: so in this case, we started the season with Philadelphia

665
00:31:53,080 --> 00:31:56,480
Speaker 5: as a league average team or minus point zero five,

666
00:31:56,680 --> 00:32:00,239
Speaker 5: and Pittsburgh is exactly three points worse than that, and

667
00:32:00,280 --> 00:32:03,760
Speaker 5: so and now we're at a point where what's the number.

668
00:32:03,800 --> 00:32:07,120
Speaker 5: Now Philadelphia is improved by three and a half points

669
00:32:07,640 --> 00:32:11,240
Speaker 5: in Pittsburgh's worse by about a point and a half

670
00:32:11,360 --> 00:32:16,440
Speaker 5: or so, And so you know, they've they've they've gone

671
00:32:16,520 --> 00:32:18,480
Speaker 5: different way. But if if I made these numbers without

672
00:32:18,520 --> 00:32:21,640
Speaker 5: a prior, it would be like you would see Philadelphia

673
00:32:21,720 --> 00:32:26,400
Speaker 5: be way freaking better and Pittsburgh be way worse. I mean,

674
00:32:26,400 --> 00:32:30,240
Speaker 5: it's Philadelphia, Like I'm looking at the these sort of

675
00:32:30,280 --> 00:32:32,240
Speaker 5: unit grades, which I need to tweet out today at

676
00:32:32,240 --> 00:32:36,440
Speaker 5: some point. Philadelphia like their ninetieth percentile and pass offense

677
00:32:36,480 --> 00:32:40,240
Speaker 5: this year, they are eighty second percentile and scoring efficiency offense,

678
00:32:40,280 --> 00:32:45,520
Speaker 5: which is which is the ability to translate yards efficiently

679
00:32:45,520 --> 00:32:47,640
Speaker 5: into points so that that'll capture a lot of red

680
00:32:47,680 --> 00:32:51,200
Speaker 5: zone stuff, special teams. They're in fourth down, et cetera.

681
00:32:52,080 --> 00:32:54,320
Speaker 5: They're ninetieth and play success in offense, so they're you know,

682
00:32:54,440 --> 00:32:58,000
Speaker 5: the only thing they're averaging is rushing offense. And on

683
00:32:58,080 --> 00:33:01,240
Speaker 5: defense they're they're what ninety eight percentile on passing defense,

684
00:33:01,240 --> 00:33:03,480
Speaker 5: So the ninety percentile of passing offense, ninety eight percent

685
00:33:03,520 --> 00:33:06,320
Speaker 5: of passing defense. It's a passing league, Like that's pretty

686
00:33:06,320 --> 00:33:09,480
Speaker 5: freaking good. Seventy eight percent of scoring efficient efficiency defense,

687
00:33:09,760 --> 00:33:11,960
Speaker 5: eighty fifth percent tile play success defense.

688
00:33:11,960 --> 00:33:14,560
Speaker 2: They've been really, really good across the board.

689
00:33:14,800 --> 00:33:19,440
Speaker 5: And you know, the question is how much of that

690
00:33:19,600 --> 00:33:24,320
Speaker 5: is is sustainable and how much isn't. And so we

691
00:33:24,640 --> 00:33:26,680
Speaker 5: if we just went on that off of that, we'd

692
00:33:26,680 --> 00:33:28,960
Speaker 5: have them probably as the second best team in the league.

693
00:33:29,000 --> 00:33:30,400
Speaker 2: And I actually probably.

694
00:33:30,160 --> 00:33:33,520
Speaker 5: Should generate a I'm going to generate a no prior

695
00:33:33,600 --> 00:33:37,120
Speaker 5: mass Epeabody number. I gotta find code to do that somewhere,

696
00:33:37,160 --> 00:33:39,240
Speaker 5: just to kind of show how it would be how

697
00:33:39,320 --> 00:33:41,640
Speaker 5: it would be different if we had no if went

698
00:33:41,680 --> 00:33:43,640
Speaker 5: into a season without any beliefs on these teams, without

699
00:33:43,640 --> 00:33:46,240
Speaker 5: any information, we.

700
00:33:46,200 --> 00:33:48,560
Speaker 2: Would be yeah, a lot higher in Philadelphia.

701
00:33:49,520 --> 00:33:51,440
Speaker 1: And so with some of these teams too, like some

702
00:33:51,520 --> 00:33:54,720
Speaker 1: of the big changes that you're now starting to make.

703
00:33:54,880 --> 00:33:57,440
Speaker 1: You see the two teams that jumped the most spots

704
00:33:57,480 --> 00:34:01,360
Speaker 1: this week, the Seahawks and the Chargers going in opposite ways,

705
00:34:01,400 --> 00:34:02,520
Speaker 1: of course, but.

706
00:34:02,640 --> 00:34:04,560
Speaker 2: We've been low in the Chargers basically the whole time.

707
00:34:04,640 --> 00:34:07,720
Speaker 5: Like, yeah, we were low going into the season on

708
00:34:07,760 --> 00:34:10,040
Speaker 5: the Chargers, which you know, I know that they're a

709
00:34:10,080 --> 00:34:13,680
Speaker 5: trendy pick proving well, we thought they were half a

710
00:34:13,680 --> 00:34:15,640
Speaker 5: point better than the league average team going into the season,

711
00:34:15,680 --> 00:34:17,480
Speaker 5: and I know people thought they were a Super Bowl contender.

712
00:34:17,920 --> 00:34:20,880
Speaker 1: Yeah, but it turns out the old adage is true.

713
00:34:21,400 --> 00:34:23,400
Speaker 1: You can take the Chargers out of San Diego, but

714
00:34:23,440 --> 00:34:25,360
Speaker 1: you can't take the Chargers out of the Chargers.

715
00:34:25,680 --> 00:34:29,560
Speaker 2: Does any team have worse injury luck? Like year after year.

716
00:34:30,680 --> 00:34:35,280
Speaker 1: The Ravens recently. But it's just such a consistent trend

717
00:34:35,360 --> 00:34:37,640
Speaker 1: with this team. And then it's not even just the

718
00:34:37,640 --> 00:34:39,640
Speaker 1: injury luck. But it doesn't matter who the coach is,

719
00:34:39,680 --> 00:34:42,920
Speaker 1: It doesn't matter what happens. There almost has to be

720
00:34:42,960 --> 00:34:47,239
Speaker 1: a quantitative way to just milk it, mix in the

721
00:34:47,360 --> 00:34:50,080
Speaker 1: chargersness of this team because that no one is better

722
00:34:50,080 --> 00:34:52,839
Speaker 1: at snatching defeat from the jaws of victory. And it's

723
00:34:52,920 --> 00:34:55,520
Speaker 1: just proven time and time again.

724
00:34:55,680 --> 00:34:59,439
Speaker 2: Yeah, it wasn't Oh who is the North Turner? And wait,

725
00:34:59,480 --> 00:35:05,360
Speaker 2: who is the no recent coach got fired?

726
00:35:06,440 --> 00:35:07,319
Speaker 1: Oh the last guy?

727
00:35:08,160 --> 00:35:08,279
Speaker 2: Was it?

728
00:35:08,360 --> 00:35:09,399
Speaker 1: Vance Joseph.

729
00:35:10,800 --> 00:35:19,719
Speaker 2: Offensive guy? And I'm blak my, Yeah, the coach. Yeah,

730
00:35:19,719 --> 00:35:22,680
Speaker 2: I got the Anthony part. But Anthony Lynn there we go.

731
00:35:22,760 --> 00:35:25,239
Speaker 5: Yeah, An, I always thought Anthony Lynn got kind of

732
00:35:25,239 --> 00:35:29,240
Speaker 5: a raw deal because you know, exactly as you said,

733
00:35:29,320 --> 00:35:32,160
Speaker 5: it's it was the Charger part, not the Anthony Lynn part.

734
00:35:32,640 --> 00:35:34,880
Speaker 1: I mean, Turner got a raw deal. They went like

735
00:35:34,960 --> 00:35:37,000
Speaker 1: thirteen and three and he got fired.

736
00:35:38,120 --> 00:35:42,239
Speaker 5: Well yeah, I mean, well, the problem is it's the

737
00:35:42,320 --> 00:35:45,840
Speaker 5: curse of having former Washington coaches.

738
00:35:46,520 --> 00:35:48,359
Speaker 2: Yeah, Nor Turner and Marty Schottenheimer.

739
00:35:48,560 --> 00:35:52,280
Speaker 5: You bring that, you bring the curse of the Indian

740
00:35:52,280 --> 00:35:56,120
Speaker 5: burial ground that's underneath FedEx fields and it comes with

741
00:35:56,160 --> 00:35:57,840
Speaker 5: the coaches, I guess.

742
00:35:57,600 --> 00:35:59,520
Speaker 2: But I guess it didn't go with Kyle. No, it

743
00:35:59,520 --> 00:36:01,440
Speaker 2: probably did go with Kyle Shanahan to Atlanta.

744
00:36:02,520 --> 00:36:05,040
Speaker 1: It definitely want Kyle Shanahan, but then everybody else on

745
00:36:05,080 --> 00:36:08,200
Speaker 1: that coach. I mean, you had Sean McVay, Kyle Shanahan,

746
00:36:08,239 --> 00:36:09,120
Speaker 1: and who was the third guy?

747
00:36:09,160 --> 00:36:10,320
Speaker 2: Was it lafor?

748
00:36:10,480 --> 00:36:12,279
Speaker 4: Yeah, that is just.

749
00:36:12,200 --> 00:36:16,440
Speaker 1: A ridiculous coaching tree to branch out there. But going

750
00:36:16,480 --> 00:36:19,400
Speaker 1: back to the Chargers, Yeah, they're snake bitten with the injuries,

751
00:36:19,440 --> 00:36:22,479
Speaker 1: but then it's still been I definitely think the play

752
00:36:22,520 --> 00:36:24,960
Speaker 1: calling hasn't been aggressive enough on this team. I think

753
00:36:25,000 --> 00:36:27,360
Speaker 1: you're you're rating for them is completely right. They have

754
00:36:27,440 --> 00:36:28,560
Speaker 1: been a real disappointment.

755
00:36:28,600 --> 00:36:30,279
Speaker 5: Well, so what's interesting is, I mean I was low

756
00:36:30,320 --> 00:36:32,319
Speaker 5: in them last year too, and I think that they

757
00:36:32,360 --> 00:36:35,760
Speaker 5: were They seemed, in my opinion, over achieved their peripherals,

758
00:36:35,760 --> 00:36:37,080
Speaker 5: and a lot of that might have been the play calling,

759
00:36:37,120 --> 00:36:39,759
Speaker 5: the fact that they just didn't let Justin Herbert throw

760
00:36:39,800 --> 00:36:43,719
Speaker 5: the ball more. But in terms of how good he

761
00:36:43,880 --> 00:36:46,239
Speaker 5: was on third down and went under pressure, it was

762
00:36:46,280 --> 00:36:49,440
Speaker 5: like he was really good in the high, highest leverage situations,

763
00:36:49,600 --> 00:36:52,400
Speaker 5: and so it made him, in my opinion, sort of

764
00:36:52,400 --> 00:36:54,200
Speaker 5: look better and made that team look better than.

765
00:36:54,120 --> 00:36:56,120
Speaker 2: They actually were if you just look at every play.

766
00:36:56,800 --> 00:37:00,320
Speaker 5: And so this year they i assume, has been the

767
00:37:00,360 --> 00:37:04,000
Speaker 5: same magic. I mean, they're fifty six percentile in passing offense,

768
00:37:04,640 --> 00:37:07,240
Speaker 5: but they're below average and everything else basically on offense.

769
00:37:07,320 --> 00:37:09,440
Speaker 5: So third percentile rushing offense.

770
00:37:09,440 --> 00:37:10,160
Speaker 2: That's really low.

771
00:37:11,160 --> 00:37:12,719
Speaker 1: Now, I want to talk to you a little bit

772
00:37:12,880 --> 00:37:15,040
Speaker 1: before before we let you out of here. I want

773
00:37:15,080 --> 00:37:16,920
Speaker 1: to talk to you a little bit about betting trends,

774
00:37:17,280 --> 00:37:21,279
Speaker 1: mostly because on this show, every week we hear we

775
00:37:21,320 --> 00:37:24,080
Speaker 1: hear about Kyle Shanahan as a road favorite, as a

776
00:37:24,120 --> 00:37:29,279
Speaker 1: home dog. Matt loves using trends in his analysis. And

777
00:37:29,360 --> 00:37:32,200
Speaker 1: I'm not trying to throw you under the bus here, Matt,

778
00:37:32,440 --> 00:37:35,400
Speaker 1: but I'm curious for your thoughts on how you parse

779
00:37:35,440 --> 00:37:38,360
Speaker 1: out signal versus noise when it comes to trends that

780
00:37:38,480 --> 00:37:40,440
Speaker 1: actually mean something like there are some that are just

781
00:37:40,760 --> 00:37:43,040
Speaker 1: a given that you can you can lay out a

782
00:37:43,120 --> 00:37:47,120
Speaker 1: rational reasoning for this. The Texas Longhorns six and one

783
00:37:48,120 --> 00:37:51,000
Speaker 1: straight up after on the weekend after a Taylor Swift

784
00:37:51,040 --> 00:37:53,560
Speaker 1: album release. We know that they're being powered on it

785
00:37:53,600 --> 00:37:56,480
Speaker 1: makes sense. Yeah, we know that they're being powered victory

786
00:37:56,560 --> 00:37:59,040
Speaker 1: by the music of Taylor Swift, but what about some

787
00:37:59,080 --> 00:38:02,200
Speaker 1: of these other ones. It's like a coach as a favorite,

788
00:38:02,280 --> 00:38:03,799
Speaker 1: a quarterback as a dog.

789
00:38:04,080 --> 00:38:05,960
Speaker 2: Can you reid off a bye that type of thing.

790
00:38:06,520 --> 00:38:09,400
Speaker 1: When does a trend like that become signal to you?

791
00:38:10,120 --> 00:38:13,280
Speaker 5: So I think you can come up with the narrative

792
00:38:13,320 --> 00:38:16,520
Speaker 5: for basically any trend, like any reasonable trend. I mean

793
00:38:16,840 --> 00:38:19,719
Speaker 5: the I think I've railed against trends a lot over

794
00:38:19,800 --> 00:38:22,719
Speaker 5: the years, and I believe one example I tweeted was

795
00:38:23,360 --> 00:38:26,480
Speaker 5: a few years back was that college football teams that

796
00:38:26,560 --> 00:38:30,160
Speaker 5: started with the letter OH were fifty eight percent against no.

797
00:38:30,239 --> 00:38:32,240
Speaker 5: I think it was fifty five percent against the spread

798
00:38:32,320 --> 00:38:35,320
Speaker 5: in the like previous twelve years spanning like seven hundred

799
00:38:35,360 --> 00:38:38,000
Speaker 5: and something games, which is like xanity to think, right,

800
00:38:38,040 --> 00:38:39,040
Speaker 5: Like you're like, how could you know?

801
00:38:39,200 --> 00:38:41,040
Speaker 2: If it was like, you could look at the same

802
00:38:41,040 --> 00:38:41,799
Speaker 2: thing and if there was.

803
00:38:41,760 --> 00:38:44,480
Speaker 5: A rational explanation, you'd be like, well, that's definitely something

804
00:38:44,480 --> 00:38:46,880
Speaker 5: real because it's fifty five percent over seven hundred games.

805
00:38:47,120 --> 00:38:49,600
Speaker 5: So I think part of it is you want to

806
00:38:49,640 --> 00:38:53,479
Speaker 5: be looking for something like have the narrative in mind

807
00:38:53,600 --> 00:38:55,080
Speaker 5: before you actually see the trend.

808
00:38:55,480 --> 00:38:58,000
Speaker 2: Like I agree with that, Like ask the question and

809
00:38:58,040 --> 00:38:58,760
Speaker 2: look for the answer.

810
00:38:58,760 --> 00:39:00,960
Speaker 5: I think you could find the same thing could exist.

811
00:39:00,960 --> 00:39:03,120
Speaker 5: But if you're if you're just mining all this data

812
00:39:03,239 --> 00:39:05,400
Speaker 5: and you find, oh, these are the significant things, can

813
00:39:05,440 --> 00:39:08,480
Speaker 5: I come up with the rational explanation for it? You've

814
00:39:08,520 --> 00:39:11,160
Speaker 5: seen that it's looks significant, you're gonna come up. You

815
00:39:11,160 --> 00:39:13,200
Speaker 5: can probably come up with some idea, but you have

816
00:39:13,239 --> 00:39:15,840
Speaker 5: no way of saying whether that idea is actually like

817
00:39:15,920 --> 00:39:18,839
Speaker 5: real or not. Like, I mean, most narratives tend to be,

818
00:39:19,960 --> 00:39:21,880
Speaker 5: you know, not backed up by numbers.

819
00:39:21,880 --> 00:39:22,520
Speaker 2: Probably, so.

820
00:39:24,000 --> 00:39:28,440
Speaker 5: I would say that, yeah, like just not just searching

821
00:39:28,440 --> 00:39:33,840
Speaker 5: brute force for things that turn up significance. But the

822
00:39:33,880 --> 00:39:35,799
Speaker 5: way I handle it is I look for things that

823
00:39:35,840 --> 00:39:37,959
Speaker 5: I can quantify, not in terms of like a trend

824
00:39:37,960 --> 00:39:40,560
Speaker 5: against the spread, but like if they're let's say, oh

825
00:39:40,760 --> 00:39:43,080
Speaker 5: like oh well, actually that's.

826
00:39:42,920 --> 00:39:44,600
Speaker 2: A bad example. But let's say that.

827
00:39:46,280 --> 00:39:50,720
Speaker 5: Andy Reid has Andy Reid teams have you know, they're

828
00:39:50,760 --> 00:39:52,520
Speaker 5: twelve and one against this. I have know the idea,

829
00:39:52,600 --> 00:39:54,880
Speaker 5: what the number is really really good against the spread

830
00:39:54,920 --> 00:39:59,799
Speaker 5: off the Bye weeks? Well, they're problem it's probably because

831
00:39:59,800 --> 00:40:02,000
Speaker 5: they're play Like i'd be like, well, are they is

832
00:40:02,000 --> 00:40:04,239
Speaker 5: it an effect on all offense? And then I would

833
00:40:04,280 --> 00:40:08,120
Speaker 5: look and see are there significant change? Are there significant differences?

834
00:40:08,400 --> 00:40:10,440
Speaker 5: Like if Andy Reid is really good off of bye weeks,

835
00:40:11,120 --> 00:40:12,920
Speaker 5: and for that to be true, I would assume that

836
00:40:12,920 --> 00:40:15,239
Speaker 5: there's other coaches that are less good, right, or that

837
00:40:15,320 --> 00:40:20,040
Speaker 5: there's this whole distribution of coaches effects off of bye weeks.

838
00:40:20,239 --> 00:40:22,279
Speaker 5: It's like if we found that Andy Reid is the

839
00:40:22,320 --> 00:40:25,279
Speaker 5: only one that is where there's any signal there to me,

840
00:40:25,360 --> 00:40:29,160
Speaker 5: that would that would say, Okay, that's maybe just an anomaly,

841
00:40:29,280 --> 00:40:30,320
Speaker 5: a statistical anomaly.

842
00:40:30,800 --> 00:40:32,279
Speaker 1: But I don't know.

843
00:40:32,280 --> 00:40:34,040
Speaker 5: Sorry, I'm not explaining this very well. I'd actually make

844
00:40:34,040 --> 00:40:36,359
Speaker 5: a mixed effects model to look at this, to get all.

845
00:40:36,280 --> 00:40:38,799
Speaker 6: Nerdy about it, but just to see what the distribution

846
00:40:38,920 --> 00:40:41,279
Speaker 6: of coach Sorry, I hit the mic, what the distribution

847
00:40:41,360 --> 00:40:43,960
Speaker 6: of coaching effects in terms of coming off a bye

848
00:40:43,960 --> 00:40:46,640
Speaker 6: week are not against the spread like looking at how

849
00:40:46,640 --> 00:40:47,920
Speaker 6: the team actually performed.

850
00:40:48,000 --> 00:40:50,799
Speaker 2: So I mean a lot of it is. I mean,

851
00:40:50,800 --> 00:40:53,120
Speaker 2: if you think right is thinking about.

852
00:40:52,840 --> 00:40:55,080
Speaker 5: Sort of adjacent effects, like if there's an effect for

853
00:40:55,120 --> 00:40:57,799
Speaker 5: this coach, that probably effects for other coaches. And if

854
00:40:57,840 --> 00:40:59,720
Speaker 5: we just see if we look at the whole spectrum

855
00:40:59,719 --> 00:41:01,439
Speaker 5: of other coaches, and it just looks like a reg

856
00:41:01,600 --> 00:41:02,440
Speaker 5: like just randomness.

857
00:41:02,440 --> 00:41:03,120
Speaker 2: If it looks.

858
00:41:02,880 --> 00:41:06,080
Speaker 5: Like like it's if it's indiscernible from if you just

859
00:41:06,200 --> 00:41:09,239
Speaker 5: randomly simulated, and you know, but you have this one

860
00:41:09,239 --> 00:41:12,440
Speaker 5: coach's way up here. Maybe maybe they're an outlier that

861
00:41:12,560 --> 00:41:16,200
Speaker 5: really may maybe they're the Wrigley Field of coaches. Who

862
00:41:16,239 --> 00:41:19,920
Speaker 5: knows in the sense that the wind has more of

863
00:41:19,960 --> 00:41:23,000
Speaker 5: an impact at Wrigley Field by like a factor.

864
00:41:22,640 --> 00:41:24,000
Speaker 2: Of three than any other stadium.

865
00:41:24,239 --> 00:41:28,440
Speaker 5: Yeah, you know that maybe they are, but you know, maybe, yeah,

866
00:41:28,719 --> 00:41:29,720
Speaker 5: maybe it's just randomness.

867
00:41:29,760 --> 00:41:32,040
Speaker 1: So yeah, guys, real quick, I want to talk to

868
00:41:32,080 --> 00:41:35,400
Speaker 1: you about Sleeper. Sleeper is the fastest growing fantasy platform

869
00:41:35,440 --> 00:41:38,120
Speaker 1: today with millions of players. You probably already have a

870
00:41:38,160 --> 00:41:40,600
Speaker 1: fantasy league on there. My friends and I changed over

871
00:41:40,719 --> 00:41:43,399
Speaker 1: this year and I absolutely love it. It's a game

872
00:41:43,560 --> 00:41:47,000
Speaker 1: changing product unlike anything else in the industry. And now

873
00:41:47,280 --> 00:41:50,000
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894
00:42:45,040 --> 00:42:49,120
Speaker 3: So you you mentioned I agree with everything that you

895
00:42:49,160 --> 00:42:51,920
Speaker 3: said there. I remember on I think like this was

896
00:42:51,960 --> 00:42:55,800
Speaker 3: an episode a long time ago of your podcast with

897
00:42:56,520 --> 00:42:59,560
Speaker 3: Jeff where I think I don't remember exactly. I think

898
00:42:59,600 --> 00:43:02,480
Speaker 3: it was doctor Bob You're talking because he was like

899
00:43:03,760 --> 00:43:04,640
Speaker 3: and so this is.

900
00:43:04,719 --> 00:43:07,439
Speaker 5: Like maybe our fifth podcast episode ever in like twenty

901
00:43:07,480 --> 00:43:11,160
Speaker 5: seventeen when we were like making enemies fast.

902
00:43:10,880 --> 00:43:14,560
Speaker 3: And yeah, so I feel like you said something like, okay,

903
00:43:14,680 --> 00:43:17,720
Speaker 3: like if you if you see something in this trend,

904
00:43:18,000 --> 00:43:22,399
Speaker 3: maybe there's something there that is actually real, but then

905
00:43:22,680 --> 00:43:25,400
Speaker 3: like quantify it and put it in your model.

906
00:43:25,680 --> 00:43:26,319
Speaker 2: Yeah that's it.

907
00:43:26,600 --> 00:43:29,560
Speaker 3: Yeah, yep, and so the so the idea, So the

908
00:43:29,640 --> 00:43:35,680
Speaker 3: question is, let's say that someone actually does notice something

909
00:43:35,800 --> 00:43:39,440
Speaker 3: in a trend, right And so let's say, for instance,

910
00:43:40,120 --> 00:43:44,520
Speaker 3: Kyle Shanahan horrible is a favorite great as an underdog, right,

911
00:43:44,600 --> 00:43:47,000
Speaker 3: like just in terms of like against the spread, Like

912
00:43:47,080 --> 00:43:50,239
Speaker 3: that's that's a real thing. And then let's say you

913
00:43:50,280 --> 00:43:54,160
Speaker 3: don't actually have the sophistication to build out a model

914
00:43:54,360 --> 00:43:56,200
Speaker 3: in terms of like the coding of kind of like

915
00:43:56,560 --> 00:44:00,680
Speaker 3: factoring in different things in terms of game screen in

916
00:44:00,719 --> 00:44:04,040
Speaker 3: like game state, but you actually do dig into the

917
00:44:04,160 --> 00:44:08,479
Speaker 3: numbers and you see, Okay, when he's a favorite, he

918
00:44:08,880 --> 00:44:12,840
Speaker 3: calls plays in this way, and when he's an underdog,

919
00:44:13,040 --> 00:44:15,920
Speaker 3: or like when the forty nine ers are trailing, he

920
00:44:16,040 --> 00:44:18,880
Speaker 3: calls plays in this way. And so you can actually

921
00:44:18,920 --> 00:44:21,680
Speaker 3: sort of even if you just sort of bucket things out,

922
00:44:21,760 --> 00:44:26,080
Speaker 3: you can see the difference between how plays are called

923
00:44:26,200 --> 00:44:29,160
Speaker 3: how the offense performs, but you actually don't have the

924
00:44:29,200 --> 00:44:33,040
Speaker 3: sophistication of incorporating that into your model. So if that

925
00:44:33,239 --> 00:44:36,360
Speaker 3: is the case, let me let me rephrase that incorporating

926
00:44:36,400 --> 00:44:40,719
Speaker 3: it in terms of like a like a prediction. But

927
00:44:41,160 --> 00:44:43,239
Speaker 3: if you have a model that is just based on

928
00:44:43,320 --> 00:44:45,719
Speaker 3: sort of like against the spread things, maybe you just

929
00:44:45,760 --> 00:44:47,719
Speaker 3: sort of try to quantify it by saying, I think

930
00:44:47,719 --> 00:44:50,160
Speaker 3: that's worth half a point or whatever it is. But

931
00:44:50,600 --> 00:44:55,200
Speaker 3: let's say someone actually does notice something, but they don't

932
00:44:55,200 --> 00:44:58,319
Speaker 3: have the model. In that case, how do you think

933
00:44:58,360 --> 00:44:59,440
Speaker 3: someone should approach it.

934
00:45:00,440 --> 00:45:02,399
Speaker 5: Well, if you don't have a model, what you're trying

935
00:45:02,440 --> 00:45:05,440
Speaker 5: to do is just find and you don't have access

936
00:45:05,440 --> 00:45:05,920
Speaker 5: to a model.

937
00:45:05,920 --> 00:45:07,560
Speaker 2: That's I don't know, right.

938
00:45:07,800 --> 00:45:08,960
Speaker 5: What you're trying to do if you don't have a

939
00:45:09,000 --> 00:45:12,520
Speaker 5: model is try to find something the market isn't accounting

940
00:45:12,560 --> 00:45:13,440
Speaker 5: for in a way.

941
00:45:13,760 --> 00:45:15,279
Speaker 2: That you think they're signal to.

942
00:45:16,160 --> 00:45:18,000
Speaker 5: Right, So if you think that the market is not

943
00:45:18,040 --> 00:45:21,720
Speaker 5: incorporating the fact that Kyle Shanahan tends to his teams

944
00:45:21,760 --> 00:45:24,160
Speaker 5: tend to play down to their opponents or play up

945
00:45:24,200 --> 00:45:26,799
Speaker 5: to their opponents, which is I assume what they would

946
00:45:26,800 --> 00:45:29,279
Speaker 5: be doing if if they tend to do better when

947
00:45:29,320 --> 00:45:33,320
Speaker 5: they're a dog, then when they in worse when their favorite,

948
00:45:34,840 --> 00:45:35,560
Speaker 5: they'd be playing or.

949
00:45:35,600 --> 00:45:37,240
Speaker 2: Up or down to their quality their opposition.

950
00:45:37,280 --> 00:45:39,160
Speaker 5: If you thought that, if you thought the market wasn't

951
00:45:39,160 --> 00:45:41,440
Speaker 5: incorporating that, then you would say, Okay, they're thirteen to

952
00:45:41,520 --> 00:45:44,880
Speaker 5: win favorite. I'd think that they're you know, the I

953
00:45:44,880 --> 00:45:47,160
Speaker 5: don't think the market's incorporating that. So I think there's

954
00:45:47,239 --> 00:45:50,480
Speaker 5: value betting the underdog, right, I mean, but in terms

955
00:45:50,560 --> 00:45:52,560
Speaker 5: of I can so, I don't. I don't know in

956
00:45:52,640 --> 00:45:56,640
Speaker 5: terms of if I didn't have a model, how I

957
00:45:56,640 --> 00:46:00,120
Speaker 5: would handle it. I would I mean, I get I

958
00:46:00,400 --> 00:46:02,600
Speaker 5: like what you're talking about in terms of like granularity

959
00:46:02,640 --> 00:46:06,080
Speaker 5: and saying, Okay, well, is it because of the style

960
00:46:06,120 --> 00:46:08,879
Speaker 5: of play at this point or I mean there could

961
00:46:08,880 --> 00:46:09,640
Speaker 5: also be.

962
00:46:10,920 --> 00:46:13,640
Speaker 2: Just the fact that it's like a preparation thing. Mm hmm,

963
00:46:14,200 --> 00:46:15,200
Speaker 2: I know how there.

964
00:46:15,080 --> 00:46:17,879
Speaker 4: Could be a lot of parts about why is the case? Yeah?

965
00:46:18,000 --> 00:46:19,240
Speaker 2: Is it because? I mean, because.

966
00:46:20,680 --> 00:46:22,560
Speaker 5: You know, if you're a three point underdog or a

967
00:46:22,560 --> 00:46:25,879
Speaker 5: three point favorite, you're not expected to have like drastically

968
00:46:25,920 --> 00:46:29,680
Speaker 5: different like amounts of time in different game states, like different,

969
00:46:29,680 --> 00:46:32,399
Speaker 5: but not like drastically drastically different versus like a ten

970
00:46:32,400 --> 00:46:35,799
Speaker 5: point favorite and a ten point underdog, and so just

971
00:46:35,840 --> 00:46:37,840
Speaker 5: in terms of like for props, just predicting like a

972
00:46:37,880 --> 00:46:41,160
Speaker 5: team's pass rate is a function of the point spread,

973
00:46:41,239 --> 00:46:44,640
Speaker 5: because yeah, the same team, I mean, the whole point

974
00:46:44,680 --> 00:46:48,560
Speaker 5: there is just that you're if you're trailing, you're going

975
00:46:48,600 --> 00:46:50,640
Speaker 5: to be throwing the ball more, and if you're up,

976
00:46:50,640 --> 00:46:52,480
Speaker 5: you're going to be running the ball more relative to

977
00:46:52,520 --> 00:46:56,480
Speaker 5: your baseline overall, And so certainly there could be an

978
00:46:56,520 --> 00:46:59,680
Speaker 5: impact in that regard though if like if the but

979
00:47:01,840 --> 00:47:04,000
Speaker 5: so I like that, I like the idea of approaching

980
00:47:04,040 --> 00:47:06,080
Speaker 5: it that way. But if there was an effect, I

981
00:47:06,120 --> 00:47:07,799
Speaker 5: would I would look at it in terms of like

982
00:47:10,440 --> 00:47:12,960
Speaker 5: trying to see if it's game state based or like

983
00:47:13,040 --> 00:47:17,000
Speaker 5: opponent skill based, like and how much is each and

984
00:47:17,040 --> 00:47:19,120
Speaker 5: then sort of and then again I would I'd be

985
00:47:19,120 --> 00:47:22,400
Speaker 5: interested in seeing if like, because presumably if Kyle Shanahan

986
00:47:22,800 --> 00:47:26,760
Speaker 5: is different in this regard than other coaches or other teams,

987
00:47:27,239 --> 00:47:29,680
Speaker 5: then there would be some other coaches that maybe are

988
00:47:30,000 --> 00:47:34,120
Speaker 5: the opposite too, like they're so for me being able

989
00:47:34,120 --> 00:47:36,400
Speaker 5: to sort of gauge the populate the effect that if

990
00:47:36,400 --> 00:47:39,840
Speaker 5: there's any other the effect in the whole population, would,

991
00:47:40,440 --> 00:47:44,440
Speaker 5: I guess give me greater clarity on whether how significant

992
00:47:44,440 --> 00:47:46,000
Speaker 5: the Kyle Shanahan effect really is.

993
00:47:47,800 --> 00:47:49,720
Speaker 2: Now, guys, sorry, I know I didn't answer your question.

994
00:47:49,920 --> 00:47:52,440
Speaker 3: No, no, I mean no, you answered it in the

995
00:47:52,480 --> 00:47:54,319
Speaker 3: exactly the way I thought you would like. It was

996
00:47:54,400 --> 00:47:55,560
Speaker 3: a very rufous answer.

997
00:47:56,360 --> 00:47:58,000
Speaker 5: I mean I didn't have any answer, So I just

998
00:47:58,120 --> 00:48:00,120
Speaker 5: like I just jerrymandered a little bit it.

999
00:48:00,560 --> 00:48:03,040
Speaker 2: No, it's not Jerry Matder. What did I say, Jerrymander.

1000
00:48:03,080 --> 00:48:06,800
Speaker 2: I didn't Jerrymander. You filibusters Jerrymander.

1001
00:48:08,680 --> 00:48:11,080
Speaker 1: Guys, rufus. We've kept you here more than long enough.

1002
00:48:11,120 --> 00:48:13,080
Speaker 1: We're gonna keep you here a tiny bit longer, if

1003
00:48:13,120 --> 00:48:13,640
Speaker 1: that's okay.

1004
00:48:13,920 --> 00:48:16,640
Speaker 2: I'm enjoy this. I'm enjoyed, you know, keep it going

1005
00:48:16,680 --> 00:48:17,560
Speaker 2: as long as you guys want.

1006
00:48:17,840 --> 00:48:19,600
Speaker 1: I'm glad most of our guests take the show. So

1007
00:48:21,440 --> 00:48:25,440
Speaker 1: now Thursday Night Football, it is a battle of mobile quarterbacks.

1008
00:48:25,480 --> 00:48:28,960
Speaker 1: Here is the Ravens take on the Buccaneers and Tampa.

1009
00:48:29,000 --> 00:48:31,000
Speaker 1: They're gonna be one point to one and a half

1010
00:48:31,080 --> 00:48:33,719
Speaker 1: point even two points, you can get. It's running the

1011
00:48:33,719 --> 00:48:36,640
Speaker 1: gamut here right now. Circa has it too, but our

1012
00:48:36,680 --> 00:48:38,880
Speaker 1: friends at bet MGM have it at one and a

1013
00:48:38,960 --> 00:48:41,920
Speaker 1: half right now, So if you know you wanted to

1014
00:48:41,960 --> 00:48:44,040
Speaker 1: take that one thousand dollars free bet that you get

1015
00:48:44,120 --> 00:48:46,399
Speaker 1: by signing up for bet MGM using the promo code

1016
00:48:46,440 --> 00:48:50,560
Speaker 1: Betting Pros, then you might be looking at taking the

1017
00:48:51,280 --> 00:48:53,040
Speaker 1: at taking the Bucks at one and a half, or

1018
00:48:53,560 --> 00:48:56,920
Speaker 1: you might be going to the Ravens, who are the

1019
00:48:57,000 --> 00:49:00,640
Speaker 1: road favorites here right now in this one, Matt, where

1020
00:49:00,640 --> 00:49:03,040
Speaker 1: are you going in this game? And this really brings

1021
00:49:03,040 --> 00:49:05,879
Speaker 1: it back to our adjustments here of the Tampa Bay

1022
00:49:05,920 --> 00:49:09,040
Speaker 1: Buccaneers and how far we are bringing them down? Rufus,

1023
00:49:09,400 --> 00:49:11,960
Speaker 1: what are you thinking about the Bucks one and a

1024
00:49:12,000 --> 00:49:14,280
Speaker 1: half point? Wrote home Dogs.

1025
00:49:14,360 --> 00:49:19,040
Speaker 5: Now, yeah, so my number would be Bucks minus two

1026
00:49:19,080 --> 00:49:23,880
Speaker 5: point nine actually so small favorite, which you know, you

1027
00:49:23,920 --> 00:49:27,200
Speaker 5: can call me insane or whatever if you want, that's fine,

1028
00:49:28,840 --> 00:49:32,520
Speaker 5: But I you know, I wouldn't say that that's the

1029
00:49:33,280 --> 00:49:35,160
Speaker 5: if I was if I was booking this game, I

1030
00:49:35,160 --> 00:49:36,120
Speaker 5: wouldn't book.

1031
00:49:35,920 --> 00:49:38,160
Speaker 2: It at minus two and a half or minus three

1032
00:49:38,200 --> 00:49:38,760
Speaker 2: to the Bucks.

1033
00:49:39,680 --> 00:49:42,480
Speaker 5: But I guess it comes down to whether you think,

1034
00:49:42,520 --> 00:49:45,279
Speaker 5: at least I'm direct like matth ecuybody's directionally correct there,

1035
00:49:46,160 --> 00:49:49,520
Speaker 5: and if people are a little bit overreacting to how

1036
00:49:49,560 --> 00:49:53,640
Speaker 5: the Bucks have looked so far, and I think I

1037
00:49:53,680 --> 00:49:56,040
Speaker 5: think they probably are.

1038
00:49:56,440 --> 00:49:57,799
Speaker 2: The market is overacting a little bit.

1039
00:49:57,840 --> 00:50:02,279
Speaker 5: But you know, so if forced to choose, I would

1040
00:50:02,320 --> 00:50:03,920
Speaker 5: I would choose the Bucks. But as I said, I have,

1041
00:50:04,200 --> 00:50:06,600
Speaker 5: I actually have not been betting NFL sides, which is

1042
00:50:06,680 --> 00:50:08,120
Speaker 5: kind of too bad because I feel like I would

1043
00:50:08,160 --> 00:50:10,479
Speaker 5: have crushed it last week. Every week I do. Every

1044
00:50:10,480 --> 00:50:15,399
Speaker 5: week I do the unabated pick them contest. All two

1045
00:50:15,400 --> 00:50:18,279
Speaker 5: weeks that I've actually done it, I've gone based on

1046
00:50:18,280 --> 00:50:20,000
Speaker 5: the strong mass Epeabody plays and I think I'm like

1047
00:50:20,040 --> 00:50:22,799
Speaker 5: eight and two, so I should.

1048
00:50:23,680 --> 00:50:24,680
Speaker 2: I mean, who knows, I might have.

1049
00:50:24,840 --> 00:50:26,480
Speaker 5: You know, I went in twenty the other weeks, but

1050
00:50:26,480 --> 00:50:28,960
Speaker 5: that's because I didn't do any put any picks in.

1051
00:50:29,040 --> 00:50:32,560
Speaker 1: But you miss every shot you don't take exactly what

1052
00:50:32,640 --> 00:50:33,080
Speaker 1: do you think?

1053
00:50:34,080 --> 00:50:35,400
Speaker 2: I was just saying, So I would, I would. I

1054
00:50:35,400 --> 00:50:36,759
Speaker 2: would be in Tampa here for sure.

1055
00:50:37,440 --> 00:50:38,160
Speaker 1: Now how about you?

1056
00:50:38,719 --> 00:50:42,120
Speaker 3: Yeah, directionally, I'm I'm with Rufus and so you know,

1057
00:50:42,160 --> 00:50:44,680
Speaker 3: I asked him earlier about you know, like, hey, you

1058
00:50:44,680 --> 00:50:47,319
Speaker 3: you guys one week from the next adjusted, you know,

1059
00:50:47,400 --> 00:50:49,240
Speaker 3: by a point or point and a half. It seems

1060
00:50:49,280 --> 00:50:51,560
Speaker 3: like a lot, like I I hardly ever adjust that

1061
00:50:51,640 --> 00:50:54,040
Speaker 3: much week to week, but last week or last week

1062
00:50:54,080 --> 00:50:55,759
Speaker 3: to this week, I did it for Tampa Bay. But

1063
00:50:55,800 --> 00:50:57,880
Speaker 3: I think it's because I stayed on them so long

1064
00:50:58,400 --> 00:51:01,160
Speaker 3: up to this point in the season, and so I'm

1065
00:51:01,200 --> 00:51:02,080
Speaker 3: still you know.

1066
00:51:02,239 --> 00:51:04,080
Speaker 4: Leaning Tampa Bay at this point.

1067
00:51:04,120 --> 00:51:08,000
Speaker 3: I have it Tampa Bay favored by point five points,

1068
00:51:08,040 --> 00:51:10,760
Speaker 3: and so you know, it's not like there's a massive difference,

1069
00:51:11,280 --> 00:51:13,400
Speaker 3: you know, going across zero, but you know, I do

1070
00:51:13,520 --> 00:51:16,399
Speaker 3: have them favored, you know, relative to where they are

1071
00:51:16,760 --> 00:51:19,120
Speaker 3: at this point, by you know, two or two and

1072
00:51:19,160 --> 00:51:22,160
Speaker 3: a half points. I will say, though, in the look

1073
00:51:22,200 --> 00:51:26,320
Speaker 3: ahead market, this was a Baltimore plus three and that's

1074
00:51:26,400 --> 00:51:28,520
Speaker 3: the bet that I made because I just I saw

1075
00:51:28,560 --> 00:51:30,960
Speaker 3: that number, and I you know, based on what I

1076
00:51:31,000 --> 00:51:33,920
Speaker 3: had at the time, uh and just based on market

1077
00:51:33,960 --> 00:51:36,279
Speaker 3: sentiment in the games last week, I thought, Okay, I

1078
00:51:36,719 --> 00:51:39,040
Speaker 3: think this number is going to move towards Baltimore, so

1079
00:51:39,080 --> 00:51:41,359
Speaker 3: I bet it. So I that's the position I have.

1080
00:51:41,920 --> 00:51:44,920
Speaker 3: But based on the numbers in the market now and

1081
00:51:45,080 --> 00:51:48,480
Speaker 3: the projections I have, I think Tampa Bay is the side.

1082
00:51:49,640 --> 00:51:51,480
Speaker 1: All right, Well, if you guys are both going to

1083
00:51:51,520 --> 00:51:53,239
Speaker 1: say that, I am going to get out of here

1084
00:51:53,320 --> 00:51:57,279
Speaker 1: and go rush to bet this line because that's that's

1085
00:51:57,280 --> 00:52:01,200
Speaker 1: a pretty good endorsement for me. But guys, thank you

1086
00:52:01,280 --> 00:52:03,680
Speaker 1: so much, Rufus, thank you so much for being here

1087
00:52:03,719 --> 00:52:06,360
Speaker 1: today one more time. Where can people find you in

1088
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Speaker 1: the great work you're doing around the internet.

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Speaker 5: You know, you can find at least the mediocre work

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Speaker 5: on I guess on my Twitter page Rufus Peabody, you

1091
00:52:16,760 --> 00:52:20,200
Speaker 5: can find let's you Bet the Process podcast and unobated

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00:52:20,239 --> 00:52:21,840
Speaker 5: dot com. If you haven't signed up, we have a

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Speaker 5: seven week free trial all these cool betting tools that

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Speaker 5: can help make you a smarter better.

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Speaker 4: And by the way, I should just I should give

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Speaker 4: the plug. Yeah.

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Speaker 3: Rufus is incredibly sharp, and he has Captain Jack there

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00:52:34,120 --> 00:52:38,319
Speaker 3: with him at Unobated and Dan Fabrizio shout out Dan,

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00:52:38,760 --> 00:52:43,560
Speaker 3: my former boss at one point at Fantasy Labs. So

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Speaker 3: a great group that they go out there, and Tom,

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Speaker 3: you are now part of the Unobated team, which is

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Speaker 3: very exciting.

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Speaker 1: That is true. I am working on getting the same

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Speaker 1: contract clause that Rufus has that allows us to not

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Speaker 1: go bald.

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Speaker 2: It's very important here.

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Speaker 5: Yeah, yeah, all right, guys.

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Speaker 2: I negotiate hard for that one episode.

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Speaker 1: Yeah, it's an important one to keep in there, but

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Speaker 1: for now, guys, that is going to do it for us.

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Speaker 1: We'll be back here on Thursday, breaking down the slate

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Speaker 1: as always, but for now, ladies and gentlemen, thank you

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Speaker 1: so much for being with us today. Don't forget to

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Speaker 1: hit that like button, and of course let's cash those tickets.