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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports
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analytics here on the Wharton podcast
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network. This is Kade Massie hosting
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today with the whole crew. Audi Winer is
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here, Eric Bradlo is here, Shane Jensen
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is here, and to add a little flavor, we
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are sitting in person in the Wharton
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podcast network studio. Our longtime
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home for years, our home for this show,
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and we're delighted to be not only in
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studio, but in person. something we only
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get to pull off, I don't know, once or
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twice a year for the last couple years
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since CO hit. It's been only once or
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twice a year. I would say we're looking
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out onto Penn's famous Locust Walk, but
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the studio is now kind of a video
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studio. So, we've got lights and the
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windows pulled and we could be anywhere
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really, but we have good sound, good
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video, and most importantly, we are all
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together. We are recording on Tuesday
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afternoon as we typically do. The show
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will go up on Wednesday. In this week's
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show, we are just gonna roll tape and
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talk about what caught our eye around
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the world of sports. We're gonna take
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advantage of being together and not have
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a guest, just find out where people are,
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what they're listening to, what they're
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paying attention to.
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>> Gentlemen, good to see you in person.
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>> Good to see you as well. How about that?
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>> It's been too long being uh kind of in
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the same physical space with each other.
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>> We, you know, we cross paths. We do
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every now and then see each other, but
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we don't get to record the show
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together. And in fact, Dion Simpkins,
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our longtime technical producer,
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associate producer of the show, is in
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house with us, which is also a delight.
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The whole crew is actually here. D
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Patel, the big boss. Aaron, the video
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producer. We've got more team members
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out there. It kind of takes a village,
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as D says, it takes a village
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surprisingly so to make this thing run.
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Gentlemen, where are we in the sports
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calendar? We are football season's
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getting mature.
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>> It feels like football weather out
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there. Certainly here in Philadelphia.
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Yeah.
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>> Phillyy's finally feeling like 12.
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>> I have to announce it's hot stove
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season.
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>> Oh gosh.
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>> Oh yeah.
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>> Which I just recently learned what what
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it refers to. It was it was the cold
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weather. Everyone would gather around a
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hot stove in the off season and talk
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baseball.
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>> Okay.
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>> And around and so we called hot stove
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season because there was no other
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sports. Historically baseball was the
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only substantive professional sport in
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America until about the 60s really. And
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then hot stove season was just what you
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do. gather around the hot stove in the
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cold winter months and talk baseball.
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>> You know, I I associate it with trades
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and front office transactions, but
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you're saying it's just like people
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>> anything that happens in the offseason
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is called the hot stove season.
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>> Okay. I haven't heard that phrase in a
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while.
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>> Where does Canada come from? Do you know
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that? Cuz that would that's a baseball
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one. I think it's like Yeah. Something I
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mean as far as these like you know, you
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know, I feel like
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>> so much of baseball's contributions to
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society sound like they came from like
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the 50s or something like that. But I
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think this is like
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>> 1950s. Yeah.
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But like I think this is one where it's
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like um I don't know. I I actually don't
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know. Somebody described to me it's like
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based on like you know how the stock
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boys used to like stalk the corn. It
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would fall off the sh like a can of corn
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and it's like kind of an easy fly ball
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or whatever.
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>> Yeah. Easy ground ball something easy
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that you know somehow it was based on
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like how the cans of corn fell off the I
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don't know. Somebody should look this up
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for us.
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>> Anyway, so I have my hot stove uh
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analyses to talk about.
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>> Oh. All right. Well, we let's just pitch
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it around and see where people are.
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Maybe we can do a few rounds of this.
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We'll go to the bottom of the hour, take
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a break, come back and wrap up with the
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second half of the show, but let's start
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off. Who's got something? Who's cut
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what's cut your eye in the world of
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sports? Audi W.
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>> I will I will do uh the the gold gloves
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came out. So, I don't need to dissect
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who won the gold glove here and there.
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That's all fun and but I want I want to
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get into that. What struck me is they're
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using now combination of analytics with
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basically the usual people's opinions. I
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think it's still 75% opinions and 25%
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the numbers. What numbers they use are
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outs above average and defensive run
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saves.
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>> Like is it is it kind of a public
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formula or is it kind of average out
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above average is not a public underlying
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formula.
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>> So you're saying it's not only 75 25 but
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of the 25 there are known weights
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component.
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>> Well and it depends on on the position.
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So catchers did not use outs above
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average they use a variety of pass ball
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saved. uh um runners caught above
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expectation, but it's the usual
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statistical stuff that's now really made
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its way into the Golden Glove um metric.
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So, I guess Derek Jeter probably would
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never have gotten one at the at, you
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know,
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>> the fans would have been so well. I
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mean, I get it 75% like humans still
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voting on it. He probably still Jeter
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outs above expected. My memory of the
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knock on his fielding was that he made
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easy plays look hard because he didn't
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have much range,
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>> right? So he he not only made easy pays
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look hard,
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>> uh he didn't have he wasn't prone for
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error. So he's can of corn he was fine
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with um he just didn't get to the center
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very well up the middle and this was
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before they would move the the player.
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So
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>> and the analytics would pick up on that
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now. Yes,
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>> they would recognize his limited range.
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I mean the average Shorstop would have
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gotten to that.
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>> Absolutely. I think he he was one that
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kind of he was an example. Mike L also
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in Boston I think was like this as well
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where he was very sure hand like he like
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on the plays where you saw him made he
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made an excellent
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play on. I see. He just didn't.
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>> It's just that there's this hidden
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denominator of plays that he couldn't
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make that he didn't, you know, wasn't
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two feet from in the factor and another
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player would have.
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>> Let me ask you a question then.
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>> Discretion is the better part of Valor.
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He's just living out the practical
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philos.
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So, let's assume he knows this at some
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level because he is a professional
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player. Did he for example, this would
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be a natural reaction. You move farther
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back on the field to give you more angle
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and you strengthen your arm. So that did
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he does anybody know cuz if I was
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talking to him those would be the things
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I know.
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>> I mean unfortunately he like you know I
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mean I I if if he played now we'd have
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the date on this and I because I mean he
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played most of his career kind of pre
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the big shifting crazy
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>> right way before the shift crazy. So
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which is interesting because today I
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still my reaction to a hard shot up the
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middle is a hit and I'm still not
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converted that to an out which is almost
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it's almost always an out now. It's it's
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it's it's terrifying because those were
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were hits.
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>> Let me ask another question. Do you know
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did the Yankees do whether it was
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cashman during most of that time did the
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Yankees do any I'll call it portfolio
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optimization which is look they can see
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that Jeter can't do that well moving to
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I guess his left get balls up the middle
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we got to get a second basement with
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range and maybe we'll trade that off for
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a little bit of
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>> back we we saw we saw what they they got
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a third they got a one of the best
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shorts stops in the field they actually
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traded for
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>> Yeah and they made him at third base
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>> but then they put him at third base I
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mean so they I mean they brought they
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actually brought in the personel how to
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correct the issue.
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>> You know, I'm not going to get all this,
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but Yankees were never known for
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particularly emphasizing fielding. So,
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what the other thing that I wanted to
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point out about the gold gloves now that
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they have these numbers, announce them.
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So, the the major one is outs above
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average, which is an MLB creation using
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their proprietary data, which knows
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where the fielders are standing at the
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time of the hit and how far exactly the
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trajectory of the ball. So, what they
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essentially calculate is the probability
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of an average player making that play
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and with what probability.
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>> Okay, but hold on. that doesn't give the
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player credit or debits for positioning.
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>> It does not. As far as I know, it does
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not. Okay?
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>> And this isn't this is a So, in other
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words, it doesn't do that. And that's a
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major concern. There's also I have other
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major concerns. For one thing,
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outfielders are all treated as in one
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bucket. So, outfielders are all the same
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in terms of their ability to to make
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plays. And so, center fielders are
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fantastic and right and left tend to be
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pretty weak because
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>> the center fields are much faster. So an
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average outfielder, some mixture of a
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corner outfield and center.
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>> And I assume they do that structurally
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on the other side of things cuz like you
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know if you know a particular outfield
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is like moving around the out like you
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know differentiate you have to be tough
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to kind of grade them.
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>> So I've actually dealt and played around
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with this model and it's hard to you
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can't reconstruct it yourself because I
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don't have the starting position of the
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player. Now we could do what what we
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used to do which was we never would take
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that into account and we would just what
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when Shane and I we wrote a paper years
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ago uh using much more coarse data. We
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just look at the trajectory of the ball
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and we would say what's the average
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probability or what's the distribution.
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>> So either positioning or range would
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kind of confound those two together.
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>> Well, let me build on Kad's point
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though, which is if I'm trying to
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evaluate a player, you could argue it's
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a combination of positioning plus range.
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The reason only reason I'd want to
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decompose it is if I want to improve the
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player or to possibly manage the player
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in some way. But from a pure evaluation
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point of view, I don't know that that
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confound matters. Well, before the team
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got involved with positioning, it didn't
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matter. But now it matters.
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>> The team does you can see them out there
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with
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>> the positioning. You can apply across
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any players, right?
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>> I didn't say it doesn't matter for
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improving team outcome. I said it might
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not matter for evaluation. Meaning if
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I'm a player and I illosition myself, I
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should get knocked for that as part of
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the total value of fielding
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>> back in the day. For sure.
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>> Yeah. Yeah. And I mean I I totally
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agree. I feel like even even now if you
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kind of wanted an omnibus idea of their
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fielding, it would be fine to confound
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those two together. That's my
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>> point. And it must be the case also that
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the team when it's choosing optimum
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positioning
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>> knows the range of the players. So now
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we've got another little wrinkle in
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there like even they're going to
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prescribe different ranges for different
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people. They should and they'll give the
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more talented players more range to
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cover.
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>> Yeah. This is by this is this is
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historical. I mean, if you look, um, the
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in the old days, the center fielder had
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a lot of stature. The the other corner
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outfielders would back off and he'd get
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all these extra plays because he was the
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the star.
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>> Is that right?
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>> And not not so much anymore. Now they
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work about work at much more
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collectively. I mean, this is a big big
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daily task of the analytic staff to tell
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their outfielders how to be positioned
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for every player on the opposition.
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Produc optimally position the center
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fielder without knowing the optimal
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center,
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>> right? They do this for all the and this
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is a major task of the of the staff that
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travels with the team to produce a
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report tell them where to stand not only
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where to stand I mean how to like find
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landmarks in the ballpark to help them
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find their spot because there's no grid
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right so they have to like say you have
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to be here by the way the coaches do
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this too I just found this out from
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>> yeah you can see them kind of shoeing
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people over
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>> they move them over and you have to find
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sight lines and you can do this really
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well at your home ballpark because
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you're used to it you have a much harder
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time at the away
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>> ball park you get any sense cuz we all
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know fielders reposition themselves
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based on the batter. Do we have any
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ability to know that batters have the
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ability to hit differentially depending
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on who's fielding? Do we have any
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knowledge of whether they're able to?
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>> So, this is a big deal. Most most people
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would say no. We'd have to bring in a
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player or a coach to to tell us that
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some of the absolute best players were
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able to do that. It was no question um
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that the tremendous hitters of yester
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year were had that ability. I mean, one
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tool I think that even current players
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are underutilizing is the bunt. I would
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like to see the bunt make a big comeback
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because I do feel like
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>> you like an article.
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>> I mean, no, I mean, you know, you can
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personalize the bunt strategy, you know,
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if you wanted to. Manny Ramirez isn't
00:10:53
doing it or something like that. But no,
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I mean, I I when I was over in Japan
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watching baseball games, bunting is just
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a way bigger part of the game because
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they it's I think there's more of a
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focus just on kind of run generation and
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stuff like that and and moving people
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over and stuff like that. But it's also
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like I I think against some of these
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more extreme shifts, I mean, you can
00:11:09
kind of see if if if you could just put
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a a bunt down the third baseline, often
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the third baseman's like, you know,
00:11:15
halfway to second or something like
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that. It's like an easy if if you can
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execute that. Yeah.
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>> Then it's basically an Let me get to my
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my point. They have this thing called
00:11:23
outs above average.
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>> And what struck me is they calculate
00:11:27
this. It's all relative to your predict
00:11:29
position, but the outfield is thought of
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as one position. What struck me as the
00:11:33
the Gold Glove leaders at the corner out
00:11:36
outfields are smaller than centerfield
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and smaller than and centerfield is
00:11:40
about the same size as shortstop. But
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I'm going to ask a question. How many
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outs above average do you think the gold
00:11:46
glove winners are are making or earning
00:11:50
whatever the statistics per what?
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>> It's over the course of the season. It's
00:11:53
not on a per basis. It's outs above
00:11:54
average. I'm not sure what the magnitude
00:11:57
is, but I bet you it's about a quarter
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of what the you like the the top 10%
00:12:01
fielder is about a quarter like away
00:12:03
like in terms of runs I think adding to
00:12:05
his team compared to hitting.
00:12:07
>> All right. So
00:12:07
>> that that would be my I'm giving you a
00:12:09
relative you're you're on the right
00:12:11
ballpark but what and I was actually
00:12:13
struck by it. I thought I'll give you my
00:12:14
hint. I thought it was fairly low.
00:12:16
>> Um I thought fielding would matter.
00:12:18
>> Yeah. What do you think out? So, it's
00:12:20
outs above average AC across the season.
00:12:22
Okay.
00:12:22
>> For which which group that
00:12:24
>> So, start with center field. Center I'll
00:12:25
give you a hint. Centerfield and
00:12:26
shortstop are about the same. And the
00:12:28
leaders are um the center fielders are
00:12:31
uh uh Armstrong um what's his name? Um
00:12:34
Peter Armstrong um what's his last name?
00:12:37
Uh
00:12:37
>> PR Prow.
00:12:38
>> Pico Armstrong. He was he he won the
00:12:40
gold glove in center. Um Bobby Witer
00:12:43
didn't win it, but he was right up there
00:12:44
second. I mean, roughly I'm expecting
00:12:46
this to be two standard deviations or
00:12:49
something. I mean, just
00:12:51
>> Well, it's remember it's it's it's it's
00:12:53
not a rate stat. It's it's a total stat.
00:12:55
>> Yeah. So, I'm going to guess
00:12:57
>> I mean, you can just throw out numbers.
00:12:58
I can just tell you. We can end our
00:12:58
suspense.
00:12:59
>> My my initial guess was some total outs,
00:13:01
right?
00:13:02
>> Total outs above average.
00:13:03
>> I was going to guess somewhere around
00:13:04
eight. Eight to 10
00:13:06
>> over a season.
00:13:07
>> I'll tell you why I came up with that
00:13:08
number. So, this is just bad math, but
00:13:11
but it's rough math. Give us No, I'm
00:13:12
going to tell you how I thought about
00:13:13
it. So there's 27 outs in a game. Y okay
00:13:16
let's pret. It's a bad assumption. Let's
00:13:17
just say you uniformly distribute that
00:13:19
amongst the nine players. That means
00:13:21
each player makes three outs,
00:13:22
>> which is not true.
00:13:24
>> I didn't say it was true. And now I'm
00:13:26
thinking of a percentage of that number
00:13:29
of outs that I think they're going to
00:13:31
make an incremental play. And then when
00:13:33
they do make one, how many outs do they
00:13:36
actually save in terms of a probability
00:13:38
or expectation? That was the calculation
00:13:40
that got me to somewhere around 8 to 10.
00:13:42
>> All right. So, corner outfield. That's
00:13:43
exactly right. The leaders corner
00:13:45
outfield
00:13:47
uh short stop. Uh no, sorry. Corner
00:13:49
outfield, first base are right around
00:13:51
that mark. Nine, eight, eight to 10.
00:13:54
>> Out, uh center fielders are 20 to 24 and
00:13:57
short stops around the same amount.
00:13:59
>> By the way, they get more. It's
00:14:00
fascinating. That was my upper bound. I
00:14:02
would have I was thinking my two numbers
00:14:05
were in 8 to 10 or in the low 20s.
00:14:07
>> Good job. Good job. What what I was
00:14:09
talking about is like, you know, you
00:14:10
take like like the 90th percentile
00:14:12
fielder versus the median fielder. That
00:14:14
difference, I think, is about a quarter
00:14:16
what the difference would be for the
00:14:17
90th hitter versus the I think fielding
00:14:21
is maybe like a good way of thinking. We
00:14:22
can work it out. So, so here's the
00:14:25
problem. How do you convert outs into
00:14:26
runs and then runs we convert into wins?
00:14:29
The usual number for runs into runs into
00:14:31
war would be 10. 10 runs above
00:14:33
replacement, but these are outs above
00:14:34
average. But anyway, but Fielding is
00:14:37
roughly is fine. We don't have this this
00:14:39
I think the average replacement.
00:14:41
>> By the way, this is the wonderful thing
00:14:42
about this show for 11 plus years is
00:14:44
that I know in the instantaneous second
00:14:47
I had to think about it. I was thinking
00:14:49
about that route. The problem is I
00:14:51
didn't know how to compute go outs to
00:14:53
runs runs to war. I could have asked you
00:14:56
and you would have told me. I didn't
00:14:57
know how to do that first step. So I
00:14:59
abandoned that route just cuz I don't
00:15:01
know how to do that.
00:15:01
>> The thing is I don't never thought about
00:15:02
outs to runs. So, I'm just kind of
00:15:04
guessing that an outs to runs is
00:15:06
something like a quarter maybe of of
00:15:10
each out is about a quarter.
00:15:11
>> Well, we know the following, Audi. We
00:15:12
know how many runs are scored in a game,
00:15:15
which is about four and a half, right?
00:15:17
And so, 27 outs. So, maybe it's about
00:15:19
0.15.
00:15:20
>> So, that's that. So, the thing is an
00:15:22
extra out. It all depends on where it is
00:15:24
in the inning, right? So, the first
00:15:26
outro,
00:15:27
>> yeah, you're doing if you want to do a
00:15:28
context, I would go for 0.15. 0.15. And
00:15:30
so if you do that then you then you're
00:15:33
20 times point you're you're not looking
00:15:35
at very many runs. What are we looking
00:15:36
at? We're looking at five tops. No less
00:15:40
than that. Three to four runs. We're
00:15:41
looking at we're looking at about not
00:15:44
even a third of a win
00:15:46
>> and added by the best fielder seemed
00:15:49
low. Yeah, that does seem low because
00:15:51
again like you know I'm thinking of like
00:15:52
you know somebody like the best heel
00:15:54
hitters are giving you like say like six
00:15:58
wins above replacement or something like
00:15:59
that.
00:16:00
>> This was a very long critique of their
00:16:02
criteria
00:16:02
>> but not their defensive war. Their
00:16:04
defensive war is typically around two,
00:16:05
right?
00:16:06
>> Yeah. No, I'm kind of like I'm saying
00:16:07
like somebody like you know like like
00:16:09
let's assume it's almost all offensive
00:16:10
at the same
00:16:13
the best would be like this is above
00:16:15
average. So average is effectively the
00:16:18
me the middle and war is above the
00:16:20
replacement.
00:16:21
>> But I think your logic is like for
00:16:22
fielding at least
00:16:25
>> replacement but no but I think
00:16:26
replacement fielders are average close
00:16:29
up to average. But you this is a long
00:16:30
way of saying you don't love the
00:16:32
sophistication of their defensive
00:16:33
metric. Okay fine. Let me add some more
00:16:35
superficial evaluations to this. I
00:16:37
somewhere in my social media feed in the
00:16:39
last couple weeks I saw a clip on Greg
00:16:42
Maddox as a fielder.
00:16:43
>> Y'all know these things. I don't know
00:16:44
these things. 14 gold gloves or
00:16:46
something.
00:16:46
>> 14 gold gloves as a fielder and they
00:16:48
they were showing highlights of him
00:16:49
fielding and he was like wildly athletic
00:16:51
as a pitcher making all these plays. It
00:16:52
was really spectacular. I had no idea. I
00:16:54
don't think of Maddox as seeming like
00:16:56
that athletic of a guy, but apparently
00:16:59
>> you might also think not necessarily
00:17:00
correlate, but someone that's wildly
00:17:02
athletic might be able to pitch more
00:17:03
than 90 miles an hour. But but I'm
00:17:05
saying it turns out there might not be I
00:17:08
mean
00:17:08
>> well
00:17:08
>> I'm just saying you would think he
00:17:10
therefore might have pitched harder than
00:17:13
>> I don't Well, he's an averagesized guy.
00:17:14
Maybe he's a great athlete with an
00:17:16
average size.
00:17:16
>> Pedro and Pedro threw real hard.
00:17:18
>> Right. By the way, on this topic, who
00:17:21
are some of the canonical great center
00:17:24
fielders from a defensive perspective?
00:17:26
>> Andrew Jones kind of when I was sort of
00:17:28
paying attention in the early 2000s, I
00:17:30
think was like that. Andrew Jones,
00:17:31
Andrew Jones, especially moving in
00:17:33
>> ridiculous.
00:17:34
>> Well, we talking about historically like
00:17:35
Willie Mays was thought of as one of the
00:17:37
great center fielders.
00:17:38
>> Is that not because of the one catch? I
00:17:40
mean, there were other things that he
00:17:41
did. I mean that's I you know we that's
00:17:42
very so salient. So that's the first
00:17:44
verse I come up with.
00:17:45
>> But who else? Just give me
00:17:46
>> without a question.
00:17:48
>> Was considered also extraordinarily uh
00:17:50
graceful. He made things look incredibly
00:17:52
easy. Okay.
00:17:53
>> Um
00:17:54
>> Ken Griffy
00:17:54
>> Ken um and when he was in the original
00:17:57
>> Well, Trout was a great centerfielder
00:17:58
when he first came up. And
00:17:59
>> Jim Edmonds was also great early career.
00:18:02
>> Yeah, Jim Edmonds was really great.
00:18:04
>> I don't even know Jim Edmonds.
00:18:05
>> Paul Blair was the name I was thinking.
00:18:07
>> Paul Blair was crazy. I remember. Who
00:18:09
did Edmonds and Blair play? Well,
00:18:10
Edwards played for the Cardinals mostly
00:18:12
>> and Angels for part of his career and
00:18:14
Paul Blair played for the Yankees like
00:18:17
Yankees 2000 to 2015. I guess
00:18:19
>> I think there was a time between a
00:18:20
little earlier maybe between grad school
00:18:22
and arriving here. I didn't pay
00:18:23
attention to baseball in grad school. My
00:18:24
adviser George Woo had me paying some
00:18:26
attention to baseball and when I got
00:18:28
here I got around you guys. I started
00:18:30
paying attention again. But I think
00:18:31
there was like a 12 year window where I
00:18:33
didn't Okay, let's that's a good one.
00:18:35
Odd, but let's keep going. Who's got
00:18:37
another caught your eye? Shane Jensen.
00:18:40
Well, uh, I guess I I should talk about
00:18:42
the fact that the Patriots are eight and
00:18:44
two.
00:18:44
>> Well, just a surp We do we do have to
00:18:46
talk about we we've been putting it off
00:18:49
because we've been keep saying, "Oh,
00:18:50
they're not they haven't been playing
00:18:52
any good teams. They haven't been doing
00:18:53
and now they they finally mean they
00:18:55
played two good teams now. Um, and
00:18:57
they've won it both." So, and they beat
00:18:58
the entire NFC South this season. Isn't
00:19:00
that fun?
00:19:01
>> That's
00:19:01
>> And the two teams they beat, by the way,
00:19:02
they beat on the road.
00:19:04
>> Yeah.
00:19:04
>> So, that is not easy. They beat the
00:19:06
Bills and the Bucks both on the road.
00:19:08
>> Although, hold on a second. Hasn't
00:19:09
homefield advantage become a lesser
00:19:11
thing over the years?
00:19:12
>> It has become a lesser thing. The
00:19:13
gamblers have pointed this out that it's
00:19:15
just monotonically decreased over the
00:19:17
last 10 15 years. And it used to be
00:19:20
three points. Everyone thought it was
00:19:21
three points and then we started
00:19:22
thinking maybe it's 2.25 but actually
00:19:24
it's lower than that. It's less than two
00:19:26
is the there obviously is differential
00:19:28
home field advantage. I know it's not
00:19:29
that much.
00:19:30
>> No, a differential home field advantage
00:19:32
is overrated,
00:19:32
>> right? No, but I was going to ask very
00:19:34
little predictive value. Well, what I
00:19:35
was going to ask was if you for worse
00:19:39
teams, is it harder for them? Like for
00:19:42
example, the fact that the Patriots have
00:19:44
won two road games against two really
00:19:48
strong teams. Is that worth more than
00:19:51
let's say a weaker team going on the
00:19:53
road?
00:19:54
>> Yeah. In our conversation is that a
00:19:56
worse team would struggle more on the
00:19:58
road.
00:19:59
>> Correct.
00:20:00
>> I think or at least that's kind of the
00:20:01
hypothesis.
00:20:02
>> Why I'm not following this for some
00:20:03
reason. What's the hypothesis
00:20:05
>> that to the extent that there is
00:20:07
heterogeneity in terms across teams in
00:20:10
terms of how they would play on the road
00:20:12
that a worse teams would be
00:20:14
>> you talking about interaction between
00:20:15
being on the road and quality? I don't
00:20:17
think that exists. No.
00:20:18
>> Do you think I mean you would agree that
00:20:20
an intera like a weather stadium
00:20:22
interaction exists or something like
00:20:23
that that or a stad a weather home team
00:20:26
interaction exists?
00:20:27
>> Uh there that's been studied and there
00:20:30
may be some. It's not as much as you
00:20:31
think. There's an east coast, west coast
00:20:33
thing and there's a division thing and
00:20:35
this is something that jumps out to me
00:20:36
like the Dolphins beat the Bills this
00:20:38
time.
00:20:38
>> Yeah,
00:20:39
>> it's surely the case. I think that I
00:20:41
know that home field is less for
00:20:43
division games,
00:20:45
>> which is interesting.
00:20:46
>> Is it because they travel less on
00:20:47
average?
00:20:48
>> No, but I think it's because they just
00:20:50
every every year they're there. Um, and
00:20:52
that's not the case at all. This is
00:20:53
football. This is football. But I wonder
00:20:55
if there's not more than that. If it's
00:20:57
more than just homefield, like in
00:20:58
general, power rankings matter less
00:21:01
among division rivals.
00:21:02
>> Yeah.
00:21:02
>> Does anybody know, did you look how much
00:21:04
did the Pats go up in the power
00:21:05
rankings? My guess is not a lot for
00:21:08
winning this game.
00:21:08
>> I haven't actually checked as of
00:21:10
yesterday, but but before this one in
00:21:12
the Bucks game, I was marveling that
00:21:14
they were kind of they were in like
00:21:15
still like around the median basically
00:21:17
and they had the for a while there up
00:21:19
until last week FBI had I think the
00:21:20
Giants above the Patriots.
00:21:22
>> The Giants were above the Patriots. We
00:21:23
talked about this on the show. But
00:21:24
again, the New York Giants are talking
00:21:27
above the New York Giants. Correct.
00:21:29
We're above FBI, I think, has a big
00:21:31
correction for strength of schedule,
00:21:32
which the pads do have again on where
00:21:35
they need.
00:21:35
>> I don't know if FBI FBI is not the end
00:21:37
all be all, but historically it's been
00:21:39
decent, but they are right now number
00:21:41
four in FBI
00:21:42
>> and they've moved a lot.
00:21:43
>> So, they might have moved a lot, but
00:21:44
they're sitting. Let me just review
00:21:45
these for you because it is interesting
00:21:47
and and it's hard for me to keep track
00:21:49
of the NFL this year. It just seems
00:21:50
wonky. Eagles number one, Rams, Colts,
00:21:53
Pats, Colts, Pats, three, four, Seahawks
00:21:56
five. The three the middle three, four,
00:21:59
five is Colts, Pats, Seahawks. Then
00:22:00
Lions, Bills, Bucks. Those not
00:22:02
surprising, but Colts, Pat, Seahawks.
00:22:04
>> That's very throwback power rank.
00:22:05
>> And by the way, you still you still
00:22:07
haven't mentioned the Chiefs.
00:22:08
>> The Chiefs are below that.
00:22:12
I don't care who they're going to be
00:22:13
much better.
00:22:13
>> I don't care where they are in the power
00:22:14
rankings. They're going to be in the AFC
00:22:16
Championship.
00:22:16
>> Well, that's a different matter. And if
00:22:17
you want to talk about it, I did run a
00:22:19
sim off of unabated and I used
00:22:22
inpredictables power rankings which are
00:22:24
marketbased and they decay and they
00:22:26
estimate the decay optimal for
00:22:28
predictive power. So
00:22:29
>> supposed quarterback and all that other
00:22:31
stuff.
00:22:31
>> So and injured quarterbacks and this is
00:22:33
this is so this is marketbased power
00:22:35
rankings run through the unabated sim
00:22:37
which has a lot of
00:22:38
>> question. You're just going to tell us
00:22:39
the results.
00:22:39
>> Well, let's do some questions. That's a
00:22:41
good idea. Who do you who do you think's
00:22:43
top in both conferences for winning the
00:22:46
conference? And what do you think the
00:22:48
what do you think the highest Super Bowl
00:22:51
probability is here after what is it 10
00:22:53
weeks of play?
00:22:55
>> I think it's probably the Chiefs. I
00:22:56
think I'm cheating because I think I saw
00:22:57
that they're still the Super Bowl
00:22:59
favorites on most of the
00:23:00
>> They are top AFC.
00:23:02
>> They are top AFC. I was going to say
00:23:04
that's
00:23:04
>> but not the Super Bowl faves at this
00:23:06
point.
00:23:06
>> Oh, is it the Eagles?
00:23:08
>> Eagles number one right now. So, but oh,
00:23:10
I just told you the numbers. So, it's
00:23:12
13.8% 14% likely to win the Super Bowl
00:23:16
is tops. The Rams, another NFC team,
00:23:19
>> is right behind them at 12.
00:23:21
>> And what I'm a little surprised by is
00:23:23
that they're given, you know, between
00:23:25
the two, they're given half the
00:23:26
probability of winning the NFC. So, does
00:23:28
that how does that feel? The Eagles and
00:23:30
Rams 50, who do you want, Eagles and
00:23:32
Rams or the rest of the NFC to make the
00:23:34
Super Bowl?
00:23:35
>> Oh, to make the Super Bowl?
00:23:37
>> To make the Super Bowl. Eagles and Rams
00:23:39
or everybody else?
00:23:40
>> I think there's I think I would probably
00:23:43
take the Eagles and the Rams because the
00:23:45
only other teams that I think are highly
00:23:47
highly competitive in the NFC. I think
00:23:49
the Lions definitely have to be
00:23:51
considered a serious threat.
00:23:52
>> Yeah.
00:23:53
>> Other than that, I don't see any
00:23:55
>> The Bucks I think can belong in that
00:23:57
conversation. I mean, with a little bit
00:23:58
more health, you know.
00:23:59
>> Well, that's
00:24:00
>> I mean I mean I don't know. I I I guess
00:24:01
an optimistic view of the Bucks would
00:24:03
have them in that conversation. If
00:24:04
you're telling me that by the end of the
00:24:06
season,
00:24:07
>> Mike Evans, Chris Godwin,
00:24:10
>> Bucky Irving, all of their and they've
00:24:12
got Igbuka who's turned out is going to
00:24:14
be the rookie of the year probably. If
00:24:15
they have all of that offensive power,
00:24:18
then they have an opportunity, but
00:24:20
they're not better than those other
00:24:21
three teams.
00:24:22
>> And I think I I might be with Eric. I
00:24:24
think I might. Rams Eagles, I think I
00:24:25
might.
00:24:26
>> Okay. So, the model gives them 47%
00:24:28
total. So, it's about
00:24:29
>> I was going to go with the with the
00:24:31
field only because I love the field.
00:24:33
>> We always bet. always make a field. So
00:24:35
just cuz I feel overestimate
00:24:38
>> say 50 years from now will you hope when
00:24:40
you're dead on your tombstone
00:24:41
>> it says
00:24:43
no we'll be looking at the results be
00:24:45
like a the field one finally won the
00:24:46
Super Bowl audi's happy
00:24:49
>> so Eric's intuition was very good the
00:24:51
next NFC team is Detroit and it's at 17%
00:24:54
to make the uh to make the Super Bowl
00:24:55
and then a big drop and after that so
00:24:57
who you're missing between the Lions and
00:25:00
the Bucks because there's a bunch of
00:25:02
them who you're missing is Seahawks at
00:25:04
13% % to make the Super Bowl. Packers
00:25:06
who didn't look great last night, 11% to
00:25:08
make the Super Bowl.
00:25:09
>> And I think I think we're questioning
00:25:12
like I mean you react to the Seahawks
00:25:14
and I think we're reacting like I would
00:25:16
say react to the Pats and Colts the same
00:25:17
way. It's like we have a lot of teams
00:25:19
where I think there's higher uncertainty
00:25:21
because they really are kind of
00:25:22
mismatching our priors coming into the
00:25:25
season. Like the Colts are are look like
00:25:27
I mean if you just looked at this season
00:25:28
you're like oh these guys are like you
00:25:30
know a powerhouse or whatever. But you
00:25:32
know it's like you knew where they were.
00:25:34
Same with the stats and you're unabated.
00:25:36
You use power ranks that you've
00:25:37
estimated somehow.
00:25:38
>> We grab them from anywhere. So you can
00:25:40
load in, you can put your own grab
00:25:41
somebody else,
00:25:42
>> right? Do you add a probability
00:25:43
distribution on the power ranks, a
00:25:45
posterior probability?
00:25:46
>> No, but we we put uncertainty into the
00:25:49
Sims.
00:25:50
>> And how do you do that? Specifically, we
00:25:53
simulate the outcome of a game
00:25:56
>> and then you add that into and we update
00:25:58
the
00:25:59
>> I should at a future date I will we'll
00:26:01
report the results of a study that one
00:26:03
of my students did to compare
00:26:06
>> putting a posterior on the on the
00:26:07
parameters which you don't do versus
00:26:10
adding the the noise into the through
00:26:12
the simulation.
00:26:14
It's it's an interesting result and it
00:26:16
is as I expected
00:26:17
>> but the trick is you need the the the
00:26:19
course to be to to match and so you want
00:26:22
to update as you go. So you need find to
00:26:25
put a poster I mean
00:26:26
>> you should take a draw you should have a
00:26:28
you should have a prior
00:26:30
>> you have to draw the next observation
00:26:32
and add that simulation error. Yeah.
00:26:35
>> The each week has to build on the
00:26:36
outcome. have to put the outcome of the
00:26:39
well the answer is techically no
00:26:43
if you simulated an infinite number of
00:26:45
games in any given week then in theory
00:26:48
the theta the parameter and y would give
00:26:51
the same amount of information. The
00:26:53
problem is most sims aren't doing it
00:26:55
that way but if you did it that way you
00:26:56
should get the same answer. In other
00:26:58
words whether you're doing
00:26:59
>> well
00:26:59
>> infinite simul not the same thing and
00:27:03
not even close. Well, let's we I'll be
00:27:05
interested in the paper to see how how
00:27:06
often it is, but often when you're
00:27:08
running sims like that, you want to know
00:27:10
a lot of internal metrics like what
00:27:11
happened, how often it happens, that
00:27:13
kind of thing. And so, you need a
00:27:14
realistic path through the sim.
00:27:17
>> But let's just be clear, what I'm
00:27:18
separating out is taking no at
00:27:21
>> there's two forms of infinity I'm
00:27:23
talking about. One is I do your forward
00:27:25
simulation an infinite number of times,
00:27:27
but that's not the one I'm talking
00:27:28
about. I'm talking about between week t
00:27:30
and t plus one. I do an infinite number
00:27:32
and then average that to even move on.
00:27:36
>> You can't run those sims. So you're not
00:27:38
can theoretic theoretically can but
00:27:40
you're like you like unabated is anybody
00:27:43
can log on here and get a 10,000 SIM
00:27:45
while they wait for it and you just
00:27:46
can't you can't do that with that.
00:27:48
Anyway, let's at least celebrate that
00:27:51
there is uncertainty baked in because
00:27:53
you you baked it in. Most of the
00:27:55
problems are that people don't and by
00:27:57
the way to be clear there's a
00:27:59
>> I don't know how this one's done but in
00:28:01
general when Massie Peabody does it we
00:28:02
have a prior and we we're going to draw
00:28:04
from that prior initially.
00:28:06
>> Yes.
00:28:06
>> Right. So that you're not going to get
00:28:08
you're not going to say everybody starts
00:28:09
at this number. It's like we we that's
00:28:10
the expectation but they might get a
00:28:12
different number even.
00:28:13
>> So the reason why I brought it up is
00:28:14
that NFL this year does seem to have
00:28:17
more uncertainty in the rankings the
00:28:19
power rankings than traditionally. I
00:28:21
mean, in other words, if you put in
00:28:22
typically what you do is you put in two
00:28:24
power ranks and outcomes a probability
00:28:26
of victory. That's usually a logistic
00:28:28
function of some kind. What's happening
00:28:30
this year is it seems to be that that
00:28:32
spread in the power ranking is not
00:28:34
matching the usual probability that
00:28:36
comes out of it.
00:28:37
>> Yeah. I mean, I mean,
00:28:37
>> it's a scale factor.
00:28:38
>> I I don't know. I mean, you could you
00:28:40
could see right my hypothesis is that
00:28:42
like there's more of a truncation in
00:28:44
that. I think most power rankings when
00:28:45
we think about park power ranking
00:28:47
consistency, we're really thinking about
00:28:48
like the top 10. I mean, who really
00:28:50
cares about the 10th team versus the
00:28:51
20th team or something like that? And
00:28:53
the top 10, I think because I think
00:28:54
things have kind of truncated, we don't
00:28:56
have enough like I I I think there's not
00:28:58
a lot of standout.
00:29:00
>> And so I think our we've got
00:29:02
let me draw an analogy between what AI
00:29:04
is saying and my you know what I spent
00:29:06
20 years on in an educational testing.
00:29:09
So I've got a ability parameter of a
00:29:11
person and I've got a difficulty
00:29:13
parameter of an item. There's a distance
00:29:15
between the two. If you want in your
00:29:16
language, there's two teams and I take
00:29:18
the difference between the two. But
00:29:19
there's another parameter which is a
00:29:21
slope parameter that multiplies that
00:29:23
difference. That's called an item
00:29:25
discrimination parameter. Some items
00:29:27
have a steeper slope or what we might
00:29:29
call in statistics factor loading on
00:29:31
true ability than others. What Audi's
00:29:33
saying is if my guess is if you did a
00:29:35
time series of that that discrimination
00:29:37
is going down, which means the ability
00:29:40
of strength parameters to predict
00:29:43
outcomes is getting worse. Why would
00:29:45
that be going down?
00:29:47
>> There's not as much. So that's a
00:29:48
different question about as to why, but
00:29:50
I'm guessing the empirical result. Well,
00:29:52
a couple reason.
00:29:54
I'll give you a couple reasons. One
00:29:56
reason is there's error in the estimates
00:29:59
of those. So that could be one possible
00:30:01
reason.
00:30:01
>> Of course raises the question of why
00:30:02
that would be.
00:30:03
>> Another possibility is that's possible,
00:30:05
but the error, the stochastic error that
00:30:09
I'm drawing on each game is going up. So
00:30:12
that's flattening the distribution of
00:30:14
probabilities. That's another possible
00:30:16
reason. So it's not that measured. It's
00:30:18
not that your game itself has changed.
00:30:22
>> You can you can argue this and there are
00:30:23
new rule changes and it does look like
00:30:24
it makes an impact. I watched so many
00:30:27
punts yesterday. I'm sick of it. But
00:30:29
they land in different spots.
00:30:30
>> I assume by the way in Elo models or the
00:30:32
type of paired comparison models I know
00:30:33
in marketing we can do this. You can
00:30:36
estimate that scale factor. And so
00:30:39
>> you could probably answer it
00:30:40
empirically. Does it appear that the
00:30:42
stochasticity of games is going
00:30:45
>> but we would have to see it over
00:30:46
multiple years of course just to put it
00:30:49
straight straight in front of you. Let's
00:30:50
put it in football language. An 8-2 team
00:30:52
with a certain win differential might
00:30:54
not be the same and from year to year
00:30:56
>> possibly but Shane but Shane that's
00:30:59
underlying both of the whole thing
00:31:00
>> but Shane has a more parsimmonious
00:31:02
explanation I think or at least
00:31:03
hypothesis.
00:31:03
>> Sure that there's Go ahead. Well, no,
00:31:05
that well think
00:31:07
>> that yeah, that that I think we we tend
00:31:09
to when we think about consistency
00:31:10
across year to year, we're really kind
00:31:12
of thinking about the top teams. And I
00:31:13
do think that there's this year it seems
00:31:16
like, you know, the top teams don't seem
00:31:18
to stand out as much in my mind as as
00:31:20
you know the other ones. I I I think
00:31:22
there's more parody I think like say 1
00:31:24
to 10. I know that
00:31:27
usual. I think this is my thought is
00:31:29
that conceptually this scale factor is
00:31:33
meant to be invariant to the locations
00:31:36
of the teams on that scale. Conceptually
00:31:39
it's meant to be.
00:31:40
>> Okay. Interesting. All right. Well,
00:31:41
estimate the scale factors please and
00:31:43
report to us.
00:31:44
>> It just has to do with these this
00:31:45
distribution of the probabilities and
00:31:46
why but but but Eric has offer Eric has
00:31:50
asked the question is it changing over
00:31:52
time systematically in one direction? I
00:31:54
think that's fair.
00:31:55
>> And I'm wondering if there's a mechanism
00:31:56
and just you more afficados of the game.
00:31:59
I I notice the game looks different.
00:32:02
>> Just there's more
00:32:03
>> a hell of a lot more gambling on it than
00:32:04
there used to be. I don't want to be
00:32:06
like cynical, but maybe that's the
00:32:08
stoastic element we're looking for is
00:32:09
gambling.
00:32:10
>> Clearly more uh kick returns. I mean,
00:32:12
that's a huge
00:32:13
>> I'll give you two I'll give it one you
00:32:14
gave, which is rule changes could change
00:32:16
that. Here's another one. This is the
00:32:18
classic uh parody argument. Everyone I'm
00:32:21
making this up, but not entirely.
00:32:23
Everyone's using analytics today and
00:32:25
what that's causing it's causing a
00:32:27
compression
00:32:28
>> and that compression is actually going
00:32:31
to cause this scale. I'm just coming up
00:32:33
with theory. I like the theory. I like
00:32:34
it's possible that theories the style of
00:32:37
play has homogenized. Correct.
00:32:39
>> That's another possibility.
00:32:41
>> Maybe analytics related. It was very
00:32:43
entertaining last night when they were
00:32:44
when they were going for it at midfield
00:32:46
at fourth and 20 or fourth and whatever.
00:32:48
And uh and that's an obvious I mean not
00:32:50
that's such an it's an obvious analytics
00:32:52
move and and the the playby-play
00:32:54
announcers saying I can't believe
00:32:55
they're doing this but that's what they
00:32:57
all do today and what who are we
00:32:59
>> those teams that want to win games
00:33:00
that's what they all do today. We we we
00:33:02
need to wrap up and take a break, but I
00:33:04
just want to push it one step further
00:33:06
and make sure I understand is it true
00:33:07
that so let's just take that hypothesis
00:33:09
and say let's say this is true that
00:33:11
something about the game has homogenized
00:33:12
whether it's analytics base or just you
00:33:14
know wisdom or whatever that everyone's
00:33:16
playing more similarly now are you still
00:33:19
going to get this result where you for
00:33:21
the same difference in power rankings
00:33:23
it'll be less diagnostic you would get
00:33:25
more similar power rankings
00:33:26
>> correct yes it's a different it's a
00:33:28
different thing
00:33:29
>> it's a different dynamic that the
00:33:30
locations are getting scrunched, but
00:33:32
that doesn't mean that the differences
00:33:34
are less predictive of outcome.
00:33:36
>> Okay, so we need we need more we need
00:33:38
more evidence on. Um, but great
00:33:39
question. All right, guys. That's the
00:33:41
first half of Wharton Moneyball. We
00:33:43
still have a half to go. Come back and
00:33:44
join us.
00:33:45
>> Welcome back. Welcome back to Wharton
00:33:48
Moneyball. Welcome back to a very
00:33:50
special episode in that we are in
00:33:51
person. We are in studio and all four of
00:33:53
us are here. This happens about once a
00:33:55
year these days. We always enjoy it.
00:33:57
We're playing a little What caught your
00:33:59
eye? We're going guest free this week.
00:34:01
Give us a more chance. Give us more of a
00:34:03
chance to kick things around. Um, we
00:34:05
made it through two in the first half of
00:34:06
the show. Two. And it was going to be a
00:34:08
shorter second half of the show. We're
00:34:10
going to kick it off with Eric Bradlo.
00:34:12
If we have any time left, I'll add one
00:34:14
at the end. Eric Bradlo.
00:34:15
>> Well, ours might be the same one. So,
00:34:17
you know, I was looking at college
00:34:18
football and I was thinking about the
00:34:21
number of teams that are essentially,
00:34:23
you call it in the playoffs now. And I
00:34:25
don't mean the teams that are in the
00:34:26
playoffs. I mean, let's take our friend
00:34:29
Cade Massiey's team, Texas. Like, the
00:34:31
playoffs have started for them. Like,
00:34:33
they have two losses. Like, they have to
00:34:35
win now and every game left to make the
00:34:38
playoffs.
00:34:38
>> Oh, you're talking your define play like
00:34:40
teams that are like in like have to win
00:34:42
out.
00:34:42
>> Have to win out to make it. And I was
00:34:44
thinking, you know, Texas is playing
00:34:46
Georgia this week. I think it's at
00:34:47
Georgia. And I'm thinking, wow, what sim
00:34:50
is going to have Texas right now having
00:34:53
any significant probability of like
00:34:55
they've got to win at Georgia? My guess
00:34:57
is they're not favored at Georgia.
00:34:59
>> Six and a half point underdog.
00:35:00
>> Okay. So, but they have to win that
00:35:03
game. Like if they get there's there's
00:35:04
no way this year the way it's going, I
00:35:06
don't think the three loss Texas is
00:35:07
making
00:35:08
>> speaking with too much confidence. And
00:35:09
one of our missions on this show, you
00:35:12
think if Texas loses the game, they have
00:35:14
a chance to make the playoffs
00:35:16
>> if they Yes. depends on what happens
00:35:18
>> because well one it heavily depends on
00:35:20
what happens to other teams
00:35:21
>> but they can't they're in the I forget
00:35:23
what they're in the SEC they can't win
00:35:24
the SEC if they lose this game to
00:35:26
Georgia
00:35:26
>> they have a hard time winning it even as
00:35:28
it is but they but Eric they would have
00:35:29
still in front of them&m who's a top
00:35:32
three team in the country so if they
00:35:33
beat them they'll be Arkansas if they
00:35:34
lose Georgia beat Arkansas beat&m they
00:35:37
>> be nine and three
00:35:38
>> and they would have beaten an undefeated
00:35:40
number three team in the country on the
00:35:43
last week of the year and their two two
00:35:45
of their three losses would be to Ohio
00:35:46
state and Georgia who would probably be
00:35:48
top five teams. So, I'm not saying
00:35:50
they're in. I'm not saying they're in,
00:35:52
but it's not zero.
00:35:53
>> I guess a follow up empirical question.
00:35:55
The extent that anybody's sim
00:35:59
forward, how many times does like a
00:36:01
three win or three loss team get in? I
00:36:04
mean, I guess it sounds improbable to me
00:36:06
under the old rules, but you know, we're
00:36:08
all still getting used to kind of what
00:36:09
what I guess what bubble this kind of 12
00:36:12
team structure
00:36:13
>> provides. I wasn't putting just Texas in
00:36:14
that bucket. I personally think I mean K
00:36:16
to correctly I think Notre Dame's in
00:36:17
that bucket. I think Notre Dame has two
00:36:19
losses. They have to win out.
00:36:20
>> Oh, that that people take that as a
00:36:22
given.
00:36:22
>> Okay. I think Oklahoma
00:36:24
>> almost certainly would have to win out.
00:36:26
>> Georgia Tech.
00:36:27
>> Yeah.
00:36:28
>> Okay. Utah.
00:36:30
>> Oh, yeah. For sure.
00:36:30
>> Okay. Well, so
00:36:31
>> but they don't have they don't have the
00:36:32
I'm Look, I'm not I would defend
00:36:34
>> they don't have the degrees of freedom
00:36:35
that they don't have the schedule. All
00:36:37
I'm commenting on is I'm fascinated by
00:36:39
this bucket of teams that with three
00:36:42
games remaining essentially to win the
00:36:45
national title,
00:36:46
>> they have to start winning now. Every
00:36:49
game now
00:36:50
>> they're essentially elimination.
00:36:51
>> Every game is an elimination game for
00:36:53
them. And for some reason this week I
00:36:55
was looking through this more than
00:36:56
normal.
00:36:57
>> Well, I mean in the old days every game
00:36:58
was like an elimination game. It was
00:37:00
like one loss and you're out of the
00:37:01
playoff out of the two game playoff.
00:37:03
>> That is my point. Yeah,
00:37:04
>> I'm loving the fact that there's more
00:37:06
teams now. Like I I just I'm finding
00:37:09
games exciting because I mean obviously
00:37:11
I would probably watch the Texas Georgia
00:37:13
game anyway, but the fact that it's in
00:37:15
my mind an elimination game, not with
00:37:17
certainty for Texas makes it even a
00:37:21
stronger game to watch. The same thing
00:37:23
for Notre Dame and this whole set of
00:37:25
teams now.
00:37:26
>> No, and I mean your observation is true
00:37:28
that that game would be essentially
00:37:29
meaningless in the old scheme because
00:37:31
Texas would definitely be out of like a
00:37:33
two team
00:37:34
Georgia would still be in the mix, you
00:37:35
know, long shot kind of thing. But I
00:37:37
mean, we've gone from two teams
00:37:40
>> at the end to four teams at the end to
00:37:42
now 12 teams at the end. Of course, that
00:37:44
just broadens the number of meaningful
00:37:45
games late in the season and and you're
00:37:47
talking about something that I think is
00:37:48
exactly right and fun. Um, that there's
00:37:50
still that many in the mix. Um, and that
00:37:53
we're looking at, you know, early round
00:37:54
playoffs kind of already. Certainly many
00:37:56
many people, by the way. So, I mean, I
00:37:58
I'll I'll kibbitz on yours in the same
00:38:00
way I kibbist on Shane's and maybe we'll
00:38:03
skip any actual what caught my eye, but
00:38:06
I ran the I looked at the athletics
00:38:09
college sim and this is the first time
00:38:12
I've ever looked at it and you know the
00:38:14
athletics part of the New York Times
00:38:15
now. So, this is what they everyone
00:38:16
points them to and you know they're
00:38:18
doing the same thing. They're grabbing
00:38:19
power rankings and putting them in and I
00:38:21
think it's an example of a sim that
00:38:22
doesn't have enough uncertainty into it.
00:38:24
They have it's just they're I feel way
00:38:27
too sure about teams making playoffs and
00:38:31
and way too sure teams aren't going to
00:38:32
make playoffs and they give for what for
00:38:34
example what do you think Eric you're
00:38:36
paying some attention to college
00:38:37
football this year. What do you think is
00:38:38
the highest probability any one team has
00:38:41
or should have for winning the national
00:38:43
championship. Right now there's three
00:38:45
regular season
00:38:46
>> a champion point. Three regular season
00:38:48
games left a conference championship
00:38:50
round and then depending on how many
00:38:52
times you have to play whether you get a
00:38:53
buy or not three or four games in the
00:38:54
>> Okay. So I'll I'll do my I'll go around
00:38:56
the room here. Okay.
00:38:57
>> What's the maximum?
00:38:58
>> You want me to give a number or do you
00:39:00
want me to tell you how I'm thinking
00:39:00
about it?
00:39:01
>> Both.
00:39:01
>> Okay. So I'll tell you how I'm thinking
00:39:03
about it and that will lead me to a
00:39:04
number. So there's going to be some set
00:39:06
of teams that win the power conferences
00:39:08
that get a buy in the first round.
00:39:10
There'll be another set of teams 9
00:39:12
through 12 that have to play five
00:39:14
through eight. But let's get to the
00:39:15
final eight at. So there's 12 and a
00:39:17
half% on average probability given to
00:39:20
each of those eight teams. Okay. Let's
00:39:22
assume a team is the favorite in that
00:39:24
round. How much pro what's the most
00:39:26
probability I'd give to any of those
00:39:28
final eight teams? I would say
00:39:32
I I was going to say 25%.
00:39:34
>> I'd jump to the same place
00:39:36
>> somewhere like that. So that's my
00:39:37
prediction.
00:39:37
>> Double double. But so you think but that
00:39:40
>> but that's giving by the way very low
00:39:42
probability to the five through eights.
00:39:44
It's not giving zero probability because
00:39:45
I actually I'm not going to count Ohio
00:39:47
State at one the same I'm going to
00:39:50
count.
00:39:51
>> But but but but Eric you're but you're
00:39:52
giving them a pass to that point. I
00:39:54
don't think that's fair. I don't think
00:39:55
there's any team you can say right now
00:39:57
is guaranteed to be final eight. I mean
00:39:59
happens.
00:39:59
>> Well Ohio State just has to win the Big
00:40:01
10, right? because then if
00:40:04
>> to get a to get a pass that's true they
00:40:07
so that's that's true
00:40:09
>> four teams four teams will get it we'll
00:40:11
get you I'm just catch I'm catching up
00:40:13
with you
00:40:13
>> I think they'll be one of the top four
00:40:15
remaining
00:40:15
>> so you are they guaranteed I mean no
00:40:18
they have to beat Indiana in the Big 10
00:40:20
championship
00:40:21
>> and then maybe maybe in some world both
00:40:24
of those teams get a buy but some that's
00:40:27
not given
00:40:28
>> can't happen now right it's not based on
00:40:30
rankings like I think the top It can
00:40:32
happen now. Yes. It's it's based on
00:40:34
rankings. Last year it was it was
00:40:36
conference champions.
00:40:37
>> Oh, it's the opposite.
00:40:38
>> The only the only wrinkle they changed
00:40:40
in the college football playoff setup is
00:40:41
that they went away. The top four teams
00:40:45
get buys and they're the top four
00:40:46
ranked.
00:40:47
>> Oh, there's no guarantee then that Ohio
00:40:48
State's the top four. No, definitely
00:40:50
not. Now that I thought it was the other
00:40:51
way around. You're right. They switch it
00:40:52
from conference ranking.
00:40:54
>> But that improves your chances. It
00:40:55
doesn't hurt your chances because some
00:40:56
there's some chance that the Big 10
00:40:58
loser still gets a buy and there was no
00:41:00
chance before. Either way, I watched
00:41:01
Indiana since my wife's a Penn Stater.
00:41:04
>> That's not a good football team. Come
00:41:06
on,
00:41:06
>> Eric. No, you're Eric, you're speaking
00:41:08
with too much confidence on these
00:41:09
college football.
00:41:10
>> They're not a great football team. How
00:41:12
about that?
00:41:12
>> They've got They've got possibly the
00:41:13
best quarterback in the country. And in
00:41:15
college football, the best quarterback
00:41:16
is going to go along.
00:41:17
>> If they played Ohio State right now,
00:41:20
what would be the point spread in that
00:41:21
game?
00:41:22
>> On a neutral field, I'm not looking at
00:41:24
anything, but their power rankings are
00:41:26
close. Um, no more than three and
00:41:29
possibly less. Wow.
00:41:30
>> No more than three.
00:41:31
>> We don't have the the
00:41:32
>> I'm certain of that,
00:41:33
>> Kate. We don't have the distance that we
00:41:34
used to have back in the day
00:41:36
>> when Alabama would be like six points
00:41:38
above somebody else.
00:41:39
>> That is correct. And also, it's the
00:41:41
thing that Shane was saying a minute ago
00:41:42
about the about the Colts. We can't get
00:41:44
it into our head that the Colts are
00:41:45
decent. They may not be decent, but it's
00:41:47
the same thing with college football. We
00:41:48
can't get same state, by the way. Same
00:41:50
state. We can't get our head around
00:41:52
these Indiana football teams.
00:41:54
>> They don't even do the time difference
00:41:55
uh the time change. It's very confusing.
00:41:57
But I mean, this is a
00:41:58
>> What are they doing over there?
00:42:00
>> I mean, it's a big deal because
00:42:01
historically, I I don't know the ins and
00:42:03
outs of the tournament structures and
00:42:05
leagues and the power conferences the
00:42:06
way you guys do cuz I don't watch
00:42:07
college football. But what I would have
00:42:10
done was looked historically at how
00:42:12
often the, you know, the top ranked team
00:42:15
in the season was categorically
00:42:18
different.
00:42:18
>> I just think I can't do that.
00:42:19
>> Categorically different. And I think
00:42:20
it's and and because of this diffuseness
00:42:23
of talent, you don't have this this
00:42:25
elomeration in just a couple teams who
00:42:27
just walk all over everyone because
00:42:29
everyone has to be at that team because
00:42:30
you're not getting paid to be somewhere
00:42:32
else and a lot too.
00:42:33
>> Is Indiana too
00:42:34
>> in the power rankings? They're the
00:42:35
closest to
00:42:36
>> they were going into the games last
00:42:38
weekend, I think.
00:42:38
>> So, it's possible that uh&m will jump
00:42:41
them. Um Ohio State be one and then
00:42:43
it'll be Indiana or&M because Indiana
00:42:45
struggled with Penn State. Um which was
00:42:47
an that Did y'all see the catch?
00:42:50
That's
00:42:50
>> I heard about that.
00:42:51
>> I think it's the best catch. It's It's
00:42:52
certainly one of the best catches. It's
00:42:54
a that I've seen in my lifetime of
00:42:56
watching C of football, period. I mean,
00:42:58
name me a better catch. It would have to
00:42:59
be a diving thing. This wasn't a diving
00:43:01
thing. This was a foot in the back of
00:43:02
the endzone thing. But between the
00:43:04
quality of the catch and the moment
00:43:06
because it was a game-winning catch, I
00:43:07
think I made the
00:43:08
>> There's no greater combination.
00:43:10
>> The greatest catch I've ever seen is the
00:43:12
Giants against the Patriots. What was
00:43:13
the guy's name that the helmet? Tyrie.
00:43:16
Well, that was the That was the flu.
00:43:18
That was given the moment
00:43:19
>> that was the biggest catch of all time
00:43:21
maybe like like I think like Julio's
00:43:23
catch in the Atlanta Super Bowl where
00:43:25
ended up not actually changing the
00:43:26
outcome but like was a better actual
00:43:29
catch
00:43:30
>> or that OBJ one from way back when he
00:43:32
like I I mean I can think of a lot I can
00:43:34
think of a lot of catches in ter like
00:43:35
that kind of hit the spectacle part of
00:43:37
it but in terms of content
00:43:39
>> what about what about the one at the end
00:43:40
of the Super Bowl was
00:43:42
>> the Edelman one no I I know was it
00:43:44
Steelers Rams or
00:43:46
>> Steelers Cowboys there a toe tap catch
00:43:49
in the uh in the end zone to win the
00:43:52
Super Bowl for it was um Antonio I don't
00:43:55
know it wasn't Antonio Brown it was
00:43:57
somebody
00:43:57
>> but it was the Steelers beating
00:43:58
>> it was
00:44:00
the Seahawks
00:44:02
>> Steelers Cardinals was a great game
00:44:06
>> maybe yeah whatever Kurt Warner
00:44:08
>> we're also not we're not also not The
00:44:10
Cardinals were ahead late and the
00:44:11
Steelers went down and that catch at the
00:44:14
end like 5 seconds left. A touchdown
00:44:16
catch. I don't remember that catch.
00:44:18
Okay,
00:44:18
>> it wasn't as spectacular, but in terms
00:44:20
of consequence, the game-winning catch
00:44:21
by Malcolm Butler and for the Patriots
00:44:23
against the Seattle Seahawks was also
00:44:26
one of the most consequential catches of
00:44:27
all time.
00:44:28
>> Consequential, but not. So, was my 25%
00:44:30
even close?
00:44:31
>> What's the number
00:44:33
around the room?
00:44:34
>> I wanted to chip it a chip. I wanted to
00:44:35
chip it down. I think it's a reasonable
00:44:37
way thing you did, but then I don't want
00:44:38
to give him a pass into the final eight.
00:44:40
And so I want to I want to knock that
00:44:42
down by some
00:44:42
>> I was going to be naive.
00:44:45
>> You didn't ask me for which team. I just
00:44:47
said one team. So you asked me the
00:44:49
maximum probability. You didn't say
00:44:51
which team it was.
00:44:52
>> But you have to apply it to same some
00:44:54
specific.
00:44:54
>> No, no, no. But some team is going to be
00:44:56
in the final four and some team is going
00:44:58
to be the top ranked team. And I think
00:45:00
the top ranked team, whoever that might
00:45:02
be, is going to be
00:45:03
>> It's a big difference.
00:45:04
>> It is a big difference. But but I'm
00:45:05
asking the question, I'm going to give
00:45:07
every team a probability right now. What
00:45:09
is a reasonable maximum probability?
00:45:11
>> That's not what he's asking. That's not
00:45:12
the question he's asking.
00:45:13
>> Well, he did up to that point.
00:45:14
>> No, he's saying at the time of the
00:45:16
playoffs.
00:45:17
>> No, no, no. But ask or I mean I I I
00:45:19
could get I can even say now out of some
00:45:22
team I don't know who it's going to be,
00:45:23
>> but he's not asking. He's saying we're
00:45:28
not interested in that version of the
00:45:29
question.
00:45:29
>> No, it's the same question.
00:45:30
>> No, it's not.
00:45:31
>> I'm sitting here now. some team has a
00:45:35
there's a maximum probability. No, he's
00:45:37
supposed to know what it is.
00:45:38
>> That's not interesting.
00:45:39
>> Yeah,
00:45:39
>> we want we want to know of the specific
00:45:42
teams that we're observing right now can
00:45:44
place bets on or
00:45:45
>> you didn't say which one. You just said
00:45:46
the maximum probability.
00:45:48
>> We don't have to. We're going to we're
00:45:49
going to we didn't he's not asking about
00:45:51
Ohio. He's saying if you take all the
00:45:52
teams out there and and calculate
00:45:54
whatever system as of today, what are
00:45:57
their probability of winning the college
00:45:58
football championship? What is the
00:45:59
maximum of those numbers? He's not
00:46:01
asking what Ohio's number is or what
00:46:03
Oklahoma's number is. He said when we
00:46:05
once we rank them, find the max.
00:46:07
>> Yeah. I mean, my naive much more naive
00:46:09
version of that that answer would be
00:46:11
like, you know, cap like
00:46:13
>> you know, the the best team's going to
00:46:14
make it to the to the final eight and
00:46:16
then flat from there.
00:46:18
>> Okay.
00:46:18
>> So, that would that would give you like
00:46:19
what like 8% or something like that or
00:46:21
>> Well, which team has the highest
00:46:23
probability of getting a buy at the
00:46:25
final eight? I'm giving a very naive my
00:46:28
He's given him buys there, but then his
00:46:30
coin flips. So, it's going to end up
00:46:31
some somewhere between these two guys is
00:46:32
my is my guess.
00:46:33
>> So, my my my guess is there be there's a
00:46:36
there's one or two teams that are almost
00:46:38
certainly to get a buy uh right now.
00:46:40
>> Well, if you believe that, then you got
00:46:42
to go towards my number.
00:46:43
>> That's right. It's it's close to your
00:46:45
number, right?
00:46:46
>> Okay. So,
00:46:47
>> but it's going to be a little less
00:46:48
because there isn't any team that has
00:46:49
that and that has a little smaller
00:46:51
factor and you got to multiply that. So,
00:46:52
you're going with 25. I'm gonna probably
00:46:54
go with their 18.
00:46:55
>> Yeah, I'm with odd because I don't I'm
00:46:56
not giving anybody a pass to the final
00:46:57
eight right now. Um I'm going to go with
00:46:59
odd. go 20 and the top one's 29 which I
00:47:02
think is absurdly high.
00:47:03
>> Yeah.
00:47:04
>> Oh, by the gambling metrics
00:47:05
>> by by the athletic sim
00:47:07
>> sim and that's not the only that's not
00:47:10
the only sim.
00:47:10
>> And what does the gambling I bet you the
00:47:11
gambling metrics would go would be what
00:47:13
does Vegas do?
00:47:14
>> Great question. I don't have that in
00:47:15
front of me right now. So we can we can
00:47:16
find it but I don't know.
00:47:17
>> So the question is
00:47:18
>> I bet it's lower than 29%. So the
00:47:20
question is what's Ohio State
00:47:22
>> better than plus 300? They have to be
00:47:23
more than plus three. Plus 300 plus 25%
00:47:26
on well there's a vig in that. So we
00:47:28
take but it's not that high. Why don't
00:47:29
you look?
00:47:30
>> Okay, we'll get it. In the meantime, let
00:47:32
me say give you one other bit on college
00:47:33
football just in terms of updating
00:47:35
everybody. Um that that people think
00:47:37
people started talking about like this
00:47:38
week is the split on the playoff bids
00:47:42
between the Big 10 and the and the SEC,
00:47:45
>> right?
00:47:45
>> So there, think of it as being basically
00:47:48
there are four teams that were going to
00:47:49
be in from some other conference. Notre
00:47:52
Dame's going to get in as long as they
00:47:53
win out. Um, there's probably a group of
00:47:56
five, maybe. Well, there's should be the
00:47:58
conference winners from the from the ACC
00:48:00
and the Big 12 and the top rated group
00:48:02
of five. Right.
00:48:03
>> By the way, you want a fun wrinkle? The
00:48:05
ACC is so bloody awful this year.
00:48:07
>> Yeah, they're awful.
00:48:07
>> So, the the the the playoff format isn't
00:48:11
>> the champions of the best four
00:48:12
conferences and then the best fifth one
00:48:14
from one of the group of fives. The
00:48:16
playoff format is the highest rated top
00:48:18
five conference champions. And so it's
00:48:21
possible if the ACC champ is so bad that
00:48:24
there could be two group of five champs
00:48:27
that are higher rated and the ACC could
00:48:30
theoretically miss the playoffs
00:48:31
altogether and two group of five
00:48:32
champions would be it. Would that not be
00:48:34
spectacular? You want chaos, Eric?
00:48:35
>> That would be great. I'm all right with
00:48:37
that.
00:48:38
>> Two group of fives.
00:48:39
>> You want to know the answer?
00:48:39
>> Yeah.
00:48:40
>> Below 29%.
00:48:41
>> No. Vegas. Well, the Vegas odds are
00:48:43
slightly above it, but when you subtract
00:48:44
the Vegas, it's 29%.
00:48:46
>> Oh my goodness. So Austin
00:48:48
something
00:48:49
>> uh plus two plus 200. It's about right.
00:48:52
>> I want to short theid. I want to short
00:48:53
the Buckeyes winning.
00:48:55
>> And it's the Buckeyes by the way.
00:48:56
>> Yeah, it is.
00:48:56
>> Yeah. Yeah. Yeah.
00:48:57
>> I I Okay. I I would short that. I'll
00:48:59
take I'll take I'll I'll give those
00:49:01
odds. I'll I'll
00:49:03
>> way that's a good bet.
00:49:04
>> So 185 is what is the worst bet you can
00:49:07
get. Now 225 is currently the best you
00:49:09
can find right now. And that's 225 is
00:49:12
right around uh 31%. Subtract the VIG,
00:49:14
you're right around 29%. Interesting.
00:49:16
Okay. Well, I can believe that they have
00:49:18
a lot of attention. I don't think that's
00:49:19
smart money.
00:49:20
>> Let me ask you a different question. I
00:49:21
mean, related though, it's the same
00:49:22
topic. So, I gave an answer of 25%. And
00:49:26
then you guys, it's fine. I I understand
00:49:28
the logic behind that number is even too
00:49:31
high. But like I always find it
00:49:34
interesting when people try to move you
00:49:36
off like you wanted to go lower and the
00:49:38
answer is actually higher. So, what's
00:49:40
then wrong in your logic? Because that
00:49:42
sim, it's not even just the betting
00:49:44
odds. This is a sim. So why are you guys
00:49:48
all with reasonable arguments saying 18
00:49:50
19%?
00:49:51
>> I don't think they have enough 29%.
00:49:53
>> Yeah. And I think the market is too sure
00:49:55
the I mean
00:49:56
>> market will bet up the favorites.
00:49:58
>> Yeah. We we know that
00:49:59
>> I wasn't worried about the market bet
00:50:00
but the sim
00:50:02
>> there's not enough uncertainty in the
00:50:03
sim and I see that from other 25 to 18
00:50:08
that degree.
00:50:08
>> That's the problem with sims don't have
00:50:10
enough uncertainty. No, no, no. I I give
00:50:11
you you look you look at more than just
00:50:13
that number and you just they're too
00:50:15
sure they know what teams are going to
00:50:16
make it. Look at the probabilities. They
00:50:18
like they're certain they know like
00:50:20
>> I don't know 10 of the 12 playoff teams
00:50:23
and there's just no way with four rounds
00:50:25
four games ahead left in the schedule
00:50:27
that they can be that sure. You just see
00:50:28
that there's too much uncertainty, too
00:50:30
little uncertainty in the
00:50:31
>> Ohio State by I think you would agree
00:50:32
with this. For Ohio State not to be a
00:50:34
top four, they would probably have to
00:50:37
lose two games. No, they could. They It
00:50:39
depends on what else happens, but if
00:50:41
they don't look great in the Big 10
00:50:42
final against Indiana, that could
00:50:44
happen.
00:50:46
>> But you said it was the top ranked
00:50:47
teams.
00:50:48
>> Yeah.
00:50:48
>> You're saying they could fall to number
00:50:50
five even with just one loss?
00:50:52
>> Well, it's a fair question. Could that
00:50:54
happen? What what has to happen is that
00:50:57
three SEC teams have to jump them
00:51:00
because it's not they're not going to
00:51:01
come from I mean in if tech Texas Tech
00:51:04
goes in and blows somebody out in the
00:51:05
Big 12 final are they going to jump Ohio
00:51:07
State? So it would be have to be Texas
00:51:09
Tech, Alabama, and Georgia potentially,
00:51:11
but they're probably playing each other.
00:51:13
And so two, yes.
00:51:15
>> Or what if there's a greatl looking SEC
00:51:17
team that just misses the SEC final
00:51:20
because they are lose some tiebreaker.
00:51:23
>> Say this could happen. And so you you
00:51:26
have two strong SEC teams in the SEC
00:51:29
final. You have a third one floating
00:51:30
around that lost a tiebreaker and
00:51:32
they're just as good as the other ones.
00:51:33
Maybe they even like them better. The
00:51:34
the markets like them better. And then
00:51:36
you have a really strong tech. So I've
00:51:38
given you now four possible teams that
00:51:41
could
00:51:41
>> I just don't see them passing Ohio State
00:51:44
with only one loss. I just don't see it.
00:51:47
>> It's possible. It's I I it's got to be
00:51:49
something. But you've talked me into
00:51:50
believing that it it's higher
00:51:51
probability than
00:51:52
>> and just to kind of calibrate in in in
00:51:54
the Houseion days of like when Alabama
00:51:57
was like 10 points above the next
00:51:59
highest team. What would be the max like
00:52:01
Alabama you you would ever given ma talk
00:52:04
if we were in one of those seasons
00:52:06
what's what's the m what's the
00:52:07
probability you would have assigned the
00:52:09
max in one of those season like would
00:52:11
you for what winning the championship
00:52:13
>> yeah the same one you're arguing is way
00:52:15
too high at Ohio State at 29
00:52:17
>> at this point in the season yeah
00:52:19
>> but we have a very different playoff
00:52:20
system now
00:52:21
>> right
00:52:21
>> yeah I guess I'm trying to say like you
00:52:23
know pretend like the we had the same
00:52:25
kind of team separation we we had back
00:52:27
when Alabama was so dominant Oh, I see.
00:52:30
But current structure
00:52:31
>> current structure, but you know, Alabama
00:52:34
is
00:52:36
it would be interesting to see what the
00:52:37
upper bound essentially is on that
00:52:39
probability logic. We'll give him a
00:52:41
pass. We'll give him a pass into the
00:52:43
final four
00:52:44
>> and then we're just going to do
00:52:45
something more strong with the Eric's.
00:52:48
Eric started out saying one over eight
00:52:50
and then he went double that for the
00:52:52
better teams. Correct. And we would just
00:52:55
it would be less than because their
00:52:56
their lead was about 10 points higher
00:52:58
than the next best team.
00:53:00
>> No, that's too much.
00:53:00
>> Is it too much? No, it was pretty high.
00:53:02
Was it 33?
00:53:04
>> This is the kind of intuition I'm
00:53:05
getting at. The fact that you instantly
00:53:06
reacted to that being too much. So if
00:53:08
you use my rule of thumb, which is not
00:53:11
accurate for college,
00:53:12
>> seven was big. Like I would say five or
00:53:14
six was a typical
00:53:14
>> each point multiply by two and a half.
00:53:16
>> 17 and a half. So 67 to 2/3
00:53:21
over the second best team. and a lot
00:53:22
more over
00:53:23
>> over the next one. So I see. So you'd
00:53:25
say something like 75% over the second
00:53:28
best team, maybe 80.
00:53:29
>> Got in the in the extreme years.
00:53:31
>> Yeah, in the extreme years.
00:53:33
>> Yeah, that's what I'm trying to get at
00:53:34
is kind of almost the upper bound on
00:53:36
what you'd ever want to give the max or
00:53:37
you know, type of thing.
00:53:39
>> How it depends on what you think of one
00:53:41
versus four. What's one versus four?
00:53:43
>> Yeah, I mean they catch up pretty quick
00:53:45
those they start sacking up pretty
00:53:46
quick. So there's not as much obviously
00:53:48
not as much of a spread as you go. So,
00:53:50
but so I'm gonna answer Shane's directly
00:53:52
by saying I don't know 35%.
00:53:55
>> Okay.
00:53:56
>> That's again why I think 29 is way too
00:53:58
high.
00:53:58
>> Yeah.
00:53:59
>> But Eric's talking me out.
00:54:00
>> So Ohio State is twice as dominant in
00:54:02
your mind as Alabama was back in the day
00:54:05
>> because you you you had him down more
00:54:06
like 17% or something.
00:54:07
>> You mean Alabama's twice as dominant?
00:54:09
>> Yeah. D Yeah. Half as dominant. Yeah.
00:54:11
>> Yeah. that the this the talent that was
00:54:12
stacked up by the even Ohio State back
00:54:15
in the day, but especially Alabama and
00:54:16
Georgia for those years. It was just I
00:54:18
mean everybody talks about it's just it
00:54:20
was just
00:54:20
>> And by the way, do the final four teams
00:54:22
when when let's let's just pretend that
00:54:24
five, six, seven, eight all beat. Do one
00:54:26
through four play on their home field?
00:54:29
>> No. Um the first round is in the home
00:54:32
stadiums.
00:54:32
>> So 5 through eight and 9 through 12 is
00:54:34
home, but the next one are the bowl are
00:54:36
bowl games.
00:54:36
>> Yeah. Yeah. And that's a some people
00:54:38
think that's a knock on the system that
00:54:39
they don't they take away a home game
00:54:40
from the teams that got the buy and
00:54:42
they'd like another round of home
00:54:44
fields.
00:54:44
>> I think I would too.
00:54:45
>> I I think it's reasonable.
00:54:46
>> Is it because of money? Did they lose
00:54:47
the home game or
00:54:49
>> I think I mean I I feel like Exactly.
00:54:52
Exactly. It's maybe an intermediary
00:54:54
state to kind of like keep the bowl
00:54:55
legacy thing.
00:54:56
>> They may lose it eventually. Um people
00:54:58
people loved the home field playoff
00:55:00
games last year. People were euphoric
00:55:02
about them. It's like a really big deal
00:55:04
these late season playoff games at a
00:55:06
home state. I know friends who went in
00:55:08
Austin and it's just a whole different
00:55:09
deal
00:55:10
>> and I mean and you heard in the media as
00:55:12
well just real quickly and then we'll do
00:55:14
maybe a real quick round.
00:55:15
>> Okay.
00:55:15
>> Um the questions whether the Big 10 can
00:55:18
get a fourth team in and hold off a
00:55:20
fifth SEC team or whether SEC can pull
00:55:23
in this fifth team and hold off
00:55:25
>> the four big there's Indiana Ohio State
00:55:27
>> Oregon looking pretty good that win they
00:55:29
had a last minute win right after um yes
00:55:31
Indiana's last minute win two
00:55:33
back-to-back unbelievable Big 10 games.
00:55:34
So people are thinking Oregon's pretty
00:55:36
good, but then they're like SC is still
00:55:38
kind of in the mix. If Michigan made a
00:55:40
run, they're kind of in the mix. And
00:55:41
then in Texas, in SEC, there's a few
00:55:44
teams buying for a fifth spot. Texas,
00:55:46
for example, Oklahoma, for example. All
00:55:48
right. Why don't we do in the last four
00:55:49
minutes, can you do it a really quick?
00:55:51
What caught your eye? Just
00:55:52
>> Okay, I'll just go quickly. Um, there's
00:55:54
been lots of stories out there that the
00:55:56
Sixers are actually better without Joel
00:55:59
Embiid. There's been similar stories
00:56:01
that the Lakers are intentionally
00:56:02
sitting LeBron even though he's got a
00:56:04
sciatic issue. So the question is, is it
00:56:06
possible now at this point in the career
00:56:08
that the Sixers Lakers are the are a
00:56:10
better team without it? Now I say in the
00:56:13
last 10 seconds I have for my one minute
00:56:16
Embiid's plus minus on the court is
00:56:18
horrific. The team is negative with him
00:56:21
on the court right now. So, I'm just
00:56:23
wondering, is it possible that at the
00:56:25
end of careers, even superstar players
00:56:27
who demand the ball, if you just used
00:56:29
him as a part-time player, but coaches
00:56:31
won't do it? So, that caught my eye.
00:56:33
>> Interesting, Adi.
00:56:35
>> Well, I have to say what caught my eye
00:56:37
was the gambling in Major League
00:56:39
Baseball.
00:56:39
>> Oh, yeah. Right.
00:56:40
>> I mean, the idea that these
00:56:42
multi-million dollar players would risk
00:56:44
their careers
00:56:45
>> like 12,000
00:56:46
>> for just tens of thousands of dollars.
00:56:48
>> Baseball or basketball? Baseball? No,
00:56:50
I'm talking about the baseball. They
00:56:51
were throwing the baseball announced
00:56:52
that they're no longer going to that
00:56:54
they don't want the the gambling
00:56:56
companies to and the gambling companies
00:56:58
agreed not to allow bets in game on the
00:57:00
outcome of a pitch.
00:57:02
>> Um because individual players found it
00:57:04
too easy and too attractive to collude
00:57:07
with a gambler to throw a strike or to
00:57:09
throw a ball when when certain part and
00:57:12
and there were there were parlayies.
00:57:14
eight. One of the guys had bet eight
00:57:16
successive um balls and one of the
00:57:20
accused pitchers, I can't remember which
00:57:22
of the two, throwing it in the dirt and
00:57:24
then finally he throws the eighth one
00:57:26
again in the dirt. Pitcher hit her swung
00:57:29
at it.
00:57:32
>> That's great.
00:57:34
>> That's hilarious.
00:57:35
>> Amazing.
00:57:37
>> That's why I call it gambling.
00:57:38
>> Yeah. No, that's that's downright
00:57:40
charming right there.
00:57:41
>> And this is I mean this is tragic. I
00:57:43
mean, because it seems, you have to
00:57:45
argue that the reason why the
00:57:47
protection, the integrity of the game is
00:57:49
protected based on the fact that they're
00:57:50
already rich and to scale up to a to
00:57:54
size that would make a money that
00:57:55
matters to them would be noticeable.
00:57:58
>> You know, these little $50,000 bets here
00:58:00
and there are okay, people shouldn't you
00:58:02
can pull them off without noticing, but
00:58:04
they shouldn't care about them. Look,
00:58:06
there's a general thing here about
00:58:07
gambling, getting away from these
00:58:09
in-game individual plays like the and so
00:58:12
it's possible that we get away from that
00:58:13
alto together. Shane,
00:58:15
>> well, I'll just mention I I just saw the
00:58:17
uh it's probably going to they're
00:58:19
probably going to get back on top, but
00:58:20
the Oilers are playing terribly right
00:58:22
now. And I'll just point out that they
00:58:23
just lost 91 to the Avalanche a couple
00:58:26
nights ago. And the kind of unique thing
00:58:28
about this particular game, 9-1, they
00:58:30
lost one.
00:58:30
>> Four guys scored two goals.
00:58:32
>> Four guys go. Not only that, the
00:58:33
Avalanche went 0 for seven on the power
00:58:35
play despite scoring nine goals.
00:58:39
>> Is that amaz I don't know. I don't know
00:58:41
if you know the headline that four guys
00:58:43
had scored two goals which is quite rare
00:58:45
apparently in hockey.
00:58:46
>> Absurd. I mean how many minutes do they
00:58:48
have a regular
00:58:50
wonder like how did it even
00:58:52
>> Why is that that rare? If if the other
00:58:53
team's if I'm really down I'm going to
00:58:55
start taking it out on the other team.
00:58:56
Once I'm down 617 I'm going to start
00:58:58
hacking the other team. Just Yeah, why
00:59:00
not?
00:59:00
>> Jeez. Wow.
00:59:01
>> I'm already going to lose the game.
00:59:02
Yeah, but I mean if you're already
00:59:03
giving up goals at that rate, why how
00:59:04
are you suddenly like not doing it on
00:59:06
power? Like Yeah, it's
00:59:07
>> just remind me what what fraction of
00:59:09
goals are on power play.
00:59:10
>> Oh, like maybe a third or something like
00:59:12
that.
00:59:12
>> Is there any argument changes quickly
00:59:14
for 10 more seconds on your topic that
00:59:16
you're ahead by so much that you just
00:59:18
kill time on the power play? In other
00:59:20
words, if you're up seven to one, what
00:59:21
do you need to
00:59:22
>> Yeah. No, I mean, it's true. You don't
00:59:24
kind of get a sense of the load
00:59:25
management that probably went in the
00:59:27
second half of this game. Like I mean
00:59:29
certainly they took out the Edmonton
00:59:30
took out their goalender after like four
00:59:32
or five you that that we can assume but
00:59:34
like otherwise the load management of
00:59:36
what the Avalanche were doing and what
00:59:37
the Oilers were doing in the second half
00:59:38
that's that would merit further study.
00:59:40
>> Why would they take out their goalender?
00:59:41
>> Well well I mean well he let in four
00:59:43
goals and like 13 shots like that. When
00:59:46
you look at a score like 91 you're like
00:59:47
well there was definitely a goalender
00:59:49
replacement probably somewhere in there
00:59:50
but how much they took out their other
00:59:53
we need to curve probability of scoring
00:59:54
on the power play versus goal
00:59:55
differential. And what we're going to
00:59:57
see is maybe in the far right tail teams
00:59:59
don't even try to score.
01:00:01
>> They're just resting, hanging out.
01:00:03
>> But but let's not
01:00:04
>> because the other opponent can't score
01:00:05
on you pretty much.
01:00:05
>> Don't bury the lead. The Oilers suck
01:00:07
apparently
01:00:08
>> for now. For now, let's enjoy it for
01:00:09
now. You know, come play. Yeah, I mean
01:00:12
the Flames are even more.
01:00:13
>> They're my adopted NHL team.
01:00:15
>> They're going to build momentum.
01:00:16
>> What happened to Canada? I thought they
01:00:17
used to be able to play uh to hockey.
01:00:18
>> They would they focus on baseball now.
01:00:22
>> And they got so close.
01:00:24
>> They got so close.
01:00:27
Why don't we leave it there? That's a
01:00:29
perfect way to end. Why don't we wrap it
01:00:30
up for the whole team here? This has
01:00:32
been Kate Massie, Shane Jensen, Audi
01:00:34
Winer, and Eric Bradlo coming to you in
01:00:36
person in the Wharton studios. Thank you
01:00:37
for being here. Big shout out thanks to
01:00:39
D Patel and the whole team. It does take
01:00:41
a whole team and we appreciate it. Dion
01:00:43
Sipkins in particular in the house back
01:00:45
where he belongs with us over the last
01:00:47
11 plus years. Thank you guys for
01:00:49
listening. Come back and join us next
01:00:50
time. Between now and then, enjoy your
01:00:52
sports.
