Chris Dalariva (3:15)

Thank you for having me. I'm looking forward to chatting with the other hip cats here.

Tim Wilson (3:23)

The fact that I am the. Yeah, I'm the oldest one on this mike by a lot. So I think hipcats might even be a little dated for me. So maybe a good place to start would be just by getting a little bit deeper on kind of the what, the why. What were you thinking in the how of the book? What prompted you to even tackle that project in the first place? And then how did you kind of land on Billboard number one songs as a place to do that?

Chris Dalariva (3:56)

Yeah, good question. I don't think if I had some grand scheme this listening journey would have ever started or a book would have ever been written. In fact, I think to write a book you have to be a little bit insane because it, it starts to feel like a fool's errand at multiple part parts during the process. But the reason this started was I was working in consulting when I got out of college, economic consulting. So you know, I was in the data, but I wasn't particularly enamored with my job. I still played in bands, I had released music, but I was just looking for another musical outlet. And I just came up with this idea that I was going to listen to every number one hit. And again, I probably thought this would peter out after 50 songs or something. But I was like, oh, you know, I'll. The Billboard Hot 100 started in 1958. I'll do one song a day. I play along with it on my guitar and you know, it would just be like a nice wind down activity for my day. Eventually I was telling a friend about this probably when I was 20 songs in, and he was like, oh, I want to do that too. So every day I would send him the song, we would chat about it, and that was really it. At a certain point we started ranking the songs, rating the songs out of 10 and it would just be like a fun again, end of the day activity. But because I was already enmeshed in the data world through economic consulting, I started tracking our ratings in a spreadsheet. The spreadsheet grew, yeah, naturally to include.

Val Kroll (5:26)

What else would you do?

Chris Dalariva (5:28)

An absurd amount of information. I noticed some trends and I just started writing about it and I started Sending it around to people and they were like, oh, this is pretty good. You should, you should keep at this. I did. Eventually I realized I sort of have the makings of a book. I'd never published anything before. So ultimately I started trying to get articles published. That's why I started this newsletter. And I eventually parlayed that into eventually getting this book published. It was a weird, circuitous journey. It's all sort of wrapped together, but it just started as a daily way to wind down, I guess.

Tim Wilson (6:06)

And then, then you couldn't wind because didn't have time to do that because you had to work on content. Content production.

Chris Dalariva (6:11)

Yeah, yeah, definitely a personal issue.

Tim Wilson (6:15)

Like it sounds like it, it's. It started as kind of a two column spreadshee. 3. It was year song, however long it was like what? And it grew like, I'm just conceptually thinking that's a bunch of other columns. Every time you added another field. Did you just kind of. You didn't have to go back and re listen to the songs like you. That's this like fundamental question I've been chomping at. The bit to ask you is how the data model. I guess if you think in classic, you know, business speak it's going to be let's design our schema. And this kind of grew more organically. Presumably you had good intuition, but like how did, how did it, how did the width of the data set grow and how did you manage back populating? I guess.

Chris Dalariva (7:02)

Yeah, that's a, that's a good question. Because this process was super organic. And as I was going along and I would think to write about something else, I would have to go back and repopulate it. Some of those things did require re listening to these songs. For example, in chapter five of the book, I write about the evolution of song structure. So I had to tag every single song, the structure this, the structure of every single song. So you know the most common song structure these days in the pop role, just like a verse chorus structure. So as when I realized I wanted to write about that, of course as I kept going forward, I would just, I would fill out the information. But then I was like, all right, I got to go back and fill the other stuff. And for that exercise I had to re listen to everything. But I would do it. At the time I was living with my parents and I would take the bus every morning into New York City for my job. And I would just sit on the bus and listen to these songs and tag them. But other stuff didn't require Most stuff required backfilling. Some of it was easier than others or didn't require listening to all of the songs. Like, another good example is I track a lot of demographic information about who the artist is, where they're from, gender, race. And I do the same for songwriters and producers. Like, looking up that information doesn't require actually listening to the songs. So, I mean, there was a ton of manual work, which I think was actually very. What's the word? Manually filling all this out actually got me so deeply in touch with this data and going back through it a bunch of times. But if I were to set out from the beginning with, you know, 100 different columns again, this book probably would have never happened. It was just the organic nature and the insanity of being like, all right, I'll backfill 800 songs. What else do I have going on?

Val Kroll (9:02)

Riding a bus.

Chris Dalariva (9:04)

Yeah, exactly. Literally. Yeah.

Val Kroll (9:07)

What was one of the first themes that emerged that you were like, oh, this is a little bit of a pattern that's, like, novel or. I hadn't heard talked about before during this time period or from this music. I'm curious.

Chris Dalariva (9:20)

Yeah. The reason I initially was compelled to write something was I noticed there were lots of songs with very grisly lyrical topics. You know, lots of songs about teenagers getting in car wrecks and dying. This is like the late 50s, early 60s. And I'm thinking to myself, like, that's weird. You know, when I turn on the radio, I feel like if you were starting a focus group for how to write a pop song, you would be like, avoid all songs about teen car wrecks. And there's, you know, 10 or 15 songs in a very short period that were all very popular. So I started looking into this, and this was a legitimate. A legitimate trend, so much so that it has a name. They're called teenage tragedy songs. And again, usually it involves two high schoolers in love and one tragically dies. Often it involves a car. And I wanted to sort of pick that apart a little because it was so strange. Yeah. And that was the first weird. That sort of set me off on this journey. The rest of the book isn't so grim, but that was the beginning of. I was like, oh, there's, you know, maybe these songs actually are speaking to something a bit deeper about what's going on in the world around when they come out.

Julie Hoyer (10:32)

So the way the top hit of The Billboard Hot 100 list is, is it like a weekly thing? And did you get runs of a song being number one multiple weeks in a row? So were you like, re listening to a Beyonce song like five times in a row for certain years? You know what I mean? Like, I'm just curious, like.

Chris Dalariva (10:52)

Yeah.

Julie Hoyer (10:52)

How did that feed your data set? Exactly.

Chris Dalariva (10:55)

Yeah. I should have specified that The Billboard Hot 100 is Billboard's pop chart. So the goal of the Hot 100 is the number one song. Billboard is trying to say this is the most popular song in the United States in a given week. So it is weekly data. Most number. It ebbs and flows throughout the years. But most number one songs, I would say, are number one for like, at least three weeks. So usually you get, I say, on average, like 20 number one hits per year. I wouldn't. If a song was number one, like, hey Jude is number one for like nine weeks. I just listen to it one day, you know, and then the next day I go on to the next song.

Julie Hoyer (11:29)

You weren't a purist that way?

Chris Dalariva (11:31)

No, no. I didn't have to live the past in real time. One day structure was enough.

Tim Wilson (11:36)

That could be one row, I presume, what you had kind of when it was number one. So the data, I guess, what was the unit of analysis? The core of the data was a song, I guess, presumably.

Chris Dalariva (11:47)

Yes, the core was the song. And I mean, I tracked the amount of weeks each song was at number one. And at various times when I was conducting analyses, I'd be like, oh, should I weight it by the number of weeks that the song is at number one? Or should I just be like, all right, if it got to the number, that's an. That's it. We're just going to weight everything equally. So you could approach it differently. But the unit of the data set is at the song level.

Tim Wilson (12:11)

So. So that you started to dip into this and you had one of your. One of your newsletters, kind of talked about some of this. You were picking. You were picking the number one song. But the nature of how that is calculated and what goes into it, like, we think of it as being, this is the number one song because it's labeled the number one song. Everyone understands it. It feels like this very objective, concrete, hard measure. But it in and of itself, the number one song is kind of a proxy. You said it. It's like it is an attempt to say what is the most popular pop song this week. But as soon as it gets converted to gapping that label number one, it's not the number two. And it could have been a coin flip one week, and here we are 40 years, 30, 20 years later, and you're listening to the one song and not the other one. But you had some cases where you even sort of sussed out that maybe that underlying. Was it the 70s or the 80s where it sort of felt like maybe somebody was putting a finger on the scale.

Chris Dalariva (13:20)

Yeah. I mean, you have to realize, like, ultimately Billboard is a trade publication. It's like if you go into. I work at the music industry, if you go into a label, I mean, you still see copies of Billboard magazine laying around. In the same way that. I don't know if you were running a trucking company, I'm sure there's some trucking trade publication that people read. It's just that Billboard's also write. Yeah, Billboard's also writing about celebrities. Right. So it does have that pop culture crossover. But I mean, I don't know. Say you're living in 1970 and you have been tasked with defining what is the most popular song in the United States. I don't know. I mean, how are you going to define it? Okay, we should look at radio. We should look at what people are buying in record stores. How are you going to get that information? At the time, Billboard would just call record stores and be like, hey, what's selling? You know, it's a sample of record stores does seem like a great way to do it, but of course it's imprecise. I mean, maybe the person at the record store gives you bad information accidentally. Maybe they give you bad information on purpose for whatever reason. You know, there are many ways that it could become skewed. And in the 70s, there were. There are various stories about certain songs being put up at number one just for, you know, back there, there's just deals that people were cutting. Like, okay, you give me the number one song this week. And what I write about in chapter five of the book is that you can actually see this in the data. There are some anomalous behavior in how long songs are staying at number one, how, when they lose the top spot, how far are they falling down the charts. So we did a little bit of a fraud investigation there. But it's interesting as. As. As time has gone on, of course, we listen differently these days. And again, it becomes even harder to define what is the most popular song. It's like, okay, we're going to look at streaming. We're going to look at what people are buying on vinyl, what people are downloading digitally. It's a. It's a tough task. It's more accurate now because since in 1991, Billboard switched over to A system called Soundscan, which basically would track when a barcode was scanned at the record store. Like, okay, we're going to log the actual purchase and we're going to get actual playlists from the radio. And today it's even more automated. It's like every streaming service is sending streaming data directly to Billboard. It doesn't mean there's no shenanigans going on, but it's a very hard task. And, you know, I think just the exercise of thinking about it makes you realize how tricky it is to define things. You know, what does it mean for something to be popular? And how do you weight all these things differently? It's. It's hard, but it is.

Tim Wilson (16:07)

You pass over. I mean, even like Nielsen ratings kind of went through.

Chris Dalariva (16:10)

The.

Tim Wilson (16:11)

Went through was better when it was like, we have a sample, representative sample of set top boxes and people are watching TV this way and then they have really struggled to say what is the most popular show. Or I even think with social media, with trying to figure out who is an influencer. And there's the Kelsey Brothers or Taylor Swift. That's an easy. They're way up there. But when you try to get down to some sort of quantifiable measure, you're stuck with trying to pick some sort of proxy. And if that proxy becomes widely adopted, then it's subject to be, you know, manipulated. Oh, it's going to be the number of followers they have on these accounts. Well, then they can. People can go and buy followers. So.

Chris Dalariva (17:01)

No, it's.

Julie Hoyer (17:02)

You said you're not hip.

Tim Wilson (17:04)

Well, yeah. You're dropping the Kelsey Brothers and Taylor Swift. I am captain. Look at that.

Chris Dalariva (17:13)

But they are ultimately proxies. You're right. All this stuff is we're proxying, we're trying to get at something. Is the number one record in a given week dramatically more popular than the number two record? I don't know. Probably not. Is it unpopular? No. I also wouldn't say that like it's getting at popularity. I don't know if you've reached the platonic form of measuring this, but it's a hard problem.

Tim Wilson (17:41)

Does it matter if 1 and 2 along, if 1 and 2 and 3 and 4 and 5 in general have some kind of similar characteristics over time, week to week, based on what you're trying to look at, presumably it would come out in the wash. You're working with some level of uncertainty. But if you're looking at, you know, the size of the backing band or the number of. Number of writers on the song, presumably as you've been trying to look at, what are these trends happening over time? It's like you could have gone and listened to every number three song and probably come up with the exact same or very, very similar results. Right?

Chris Dalariva (18:27)

Yeah. I went through this at the beginning of the book where someone was like. When I was first starting to write this, and I was like, well, our number one songs really a representative sample of what's going on in popular music. Like, shouldn't you be looking at the entire top 10 or the entire top hundred? That would be impossible given that I was listening to the songs and, you know, I. I have to.

Val Kroll (18:46)

The bus ride wasn't that long.

Chris Dalariva (18:48)

No. Well, some days, yeah. Literally, it does. What I came upon is exactly what you were saying, Tim, is that for something to become the most popular song in the United States for even one week, it is typically representative of a larger trend that is going on. And I would see that again and again. Occasionally there is just one random song that shoots the top of the charts. It's completely unconnected from everything else. But most of the time, there are musical movements, and that's how these songs become popular. So they are. If I were to have chosen the number three song, I mean, it doesn't have as nice of a ring to it, but I think the outcome for all of the analyses in the book and my newsletter would largely look the same.

Julie Hoyer (19:41)

I wonder if your fraud one would have looked the same or not, though, because I doubt people are like, you know, if they're trying to put their finger on the scale, are they really gonna be happy with number three? They're like, no, give me number one. You know what I mean? Like, I do wonder if some of those, like, drastic anomalies would exist if you had done, you know, top three instead of.

Chris Dalariva (19:59)

That's a good point. I should have done that as a comparison point, but I didn't.

Julie Hoyer (20:06)

Get a list of things to do.

Tim Wilson (20:08)

I bet they would care more about whether they're 10 or 11 than they would care about whether they're 4 or they're 5. Right. I mean, I would. I could see you just a little.

Julie Hoyer (20:17)

In the top 10.

Tim Wilson (20:18)

People say Billboard top 10, right?

Chris Dalariva (20:20)

Yeah, they're top four. Top 40 has been the thing for decades where it's like a radio format. So if you were at number 40, there were lots of shows over the years that have counted down the top 40 songs every week. So if you were 41, that's really bad for you. So that's. There's probably weird, really Weird behavior around there, too. Especially back when radio was very important to how music work.

Val Kroll (20:44)

Top nine at nine. I'm just, like, revisiting all these, like, memories of, like, the radio, like, counting things down. But I remember being, like, on Friday nights, like, I'm from Chicago, so B96. I would always record it. So I'm like, this is it. This is the top nine. Like, they know. They figured it out. Like, we gotta record it.

Podcast Host (21:00)

This is.

Val Kroll (21:01)

This is the playlist everyone's gonna be listening to while they're, what, playing hopscotch? I don't even know how old I was, but I was pretty obsessed with that. I do recall something else that you.

Tim Wilson (21:10)

Said was that you started listening and the generating of the data was just you and your friend with kind of a rating and a ranking. But then you already kind of alluded to, well, there's other stuff that you realized was kind of other metadata about the song, but you went to other sources that wasn't from a. I'm listening to it and creating the data. Which. Do you remember when the first time you're like, oh, I could add 12 columns to this data set by going and pulling data from somewhere. The whole supplementing that's kind of intriguing as well, that your data set grew from other data sets.

Chris Dalariva (21:58)

Yeah, there are a couple sources that I started tapping pretty early in the process. Of course, the big thing is, who was involved in making this song the top. Beyond the performer, the top things are songwriters and producers. Songwriters have always fascinated me. So there are some songwriter databases run by BMI and ascap, which are two of the big, biggest music publishers in the United States. And they track. You know, they get songwriters paid. So I would go there to get songwriter information. Spotify also generates a ton of information about the audio files on their platform. You know, things like bm, bpm. But they also gauge loudness and energy and what they call dance ability and acousticness. They've shut down. They've sadly shut down a lot of these APIs, because I think people were probably using them to train LLMs. Luckily, I had gotten all the information before that.

Val Kroll (22:56)

You got your time. Yeah.

Tim Wilson (22:58)

Were there. Were there any. Were there any APIs that you were like, this is like, they, like, really. Or this data was very, very. So easily accessible. That was, like, really pleasant. And on the flip side, was there somewhere like that, you know, they have the data, but they've done. It's an absolute nightmare to pull it at any sort of scale.

Chris Dalariva (23:17)

Spotify, I mean, still, the Spotify API is really easy to use. It's. They've just changed what you can actually get access to. So that was always, always a pleasure. Spotify. Thank you. Because I would have playlists of these songs on Spotify, and it's very easy to just take a playlist of songs and ask the API to spit back certain metadata with a Python script. So that was great. Lyrics are really easy to get around the web. Those are always a pleasure to grab. And I've done a lot of stuff over the years on my newsletter and in the book using data from Wikipedia. And Wikipedia people have built, like ap. There's an API, and then people have built nice little wrappers for it, and it works pretty well. So I never. There are music data sources that you have to pay for. I never really had to rely on them for the stuff that I was doing because I feel like a lot of it was sort of wacky and, you know, based on historical data where no one's monetizing label or songwriter credits from hits in the 1960s. If you want to. If you. If I want information on, like, streaming trends, that stuff. Like streaming trends right now, that stuff you really have to pay for from. There's a couple sources out there, but for the book, I didn't really rely on stuff like that.

Julie Hoyer (24:40)

How did you decide when you were done making this data set? Or is it like your baby and you're like, I can't stop. I'm just going to. I'm already writing the next book, adding it.

Chris Dalariva (24:48)

Yeah, well, I sort of joke about that at the end of the book. I'm like, well, I had to end the book. But it's not like music doesn't end. There is the day this comes out, it's already going to be dated. There's going to be other number one songs. The data set grew until I finished the last chapter. And I was like, all right, the book is done. It grew. I'm saying it grew lengthwise in terms of songs, but also width wise. You know, I would. If I was writing the last section, the last chapter, and I was like, you know, there would be this really cool analysis that I could run and I didn't have the data. I mean, I would. You have to have a little bit of. You have to be a little nuts to, I think, do something like this and be. Not feel angry that you're gonna have to go back and fill in a ton of information. So it just grew as I. Yeah, it grew as I needed it to grow and not really much beyond that.

Julie Hoyer (25:38)

Did you have a lot of it automated by the end. Like, you would listen, you'd have maybe 20 columns you fill out. I don't know, maybe that's like really small and you're like, I wish. Or you're like, no, that's crazy. 20. It's too many. But did you have a lot of it automated and was it pretty easy to just keep adding to.

Chris Dalariva (25:53)

Actually, like, very, very little of it was automated. But the reason it never really became automated was because I was only listening to one song a day. So I was like, you're just listening and, you know, I'm typing information as I'm listening. I really usually only think about automating things if I'm taking, you know, grabbing tons and tons of data at once. But it, it was very tractable for me to be like, all right, today I'm going to sit down and listen to, I don't know, Hot Stuff by Donna Summer. You know, I listen. By the time you listen through twice, you probably filled out all the information you're looking for.

Tim Wilson (26:29)

Oh, that was actually. So was that kind of the typical. Was it sort of two listens or was it like, how many times did you have to listen to feel good about?

Chris Dalariva (26:38)

I would tell myself I would always listen twice whether you know some of the songs, even if I wasn't alive at the time. I mean, you're intimately familiar with, but still, I would try to listen twice with fresh ears to see if anything struck me. I mean, at this point, I've heard many of these songs too many times, but twice, some of them twice is all you need and hopefully never hear again.

Val Kroll (27:03)

Okay, that's some of the stuff I'm super interested in. I have, I do have a data related question, but is there any song that you like, heard that you like, wasn't one of your favorites or not even on your radar, but now it's like a go to playlist item or inverse. You're like, if I have to listen to the song one more time.

Chris Dalariva (27:20)

Yeah, you have to know what they are. There's another nice thing I think about using number one hits as a sample is that if you were to look at some list of music of the 1960s, it would probably be packed with things that are now considered classics. But in every decade, in every era, there is good stuff released and there is horrible stuff released. The horrible stuff just gets, you know, swept away and we never listen to it again. But when you listen to just number one hits again, it's just what was popular in one single week. You get the Good, the Bad and the Ugly. And I certainly saw all that through my journey. I mean, there's one. There's a song in their early 70s called want ads by a group called Honeycomb that's completely forgotten and has by however you measure it. I mean, this song has no cultural footprint and it's absolutely awesome. And I always am telling people about it. And there's. There's also stuff that has no cultural footprint anymore that is horrible. I mean, there are tons of novelty songs from over the years. There's a song called Disco duck from the 70s, which was like a. A joke about disco.

Julie Hoyer (28:26)

It's really been like, unfamiliar.

Tim Wilson (28:28)

No, that hit me when I was. I was at a certain age. Yeah, it was.

Val Kroll (28:33)

It would be like my Macarena.

Chris Dalariva (28:36)

The Macarena is a. Another great example of. I mean, dance crazes have also people been dancing for thousands of years. So you can imagine that every, every generation has their handful of dance songs. Some of them seem silly now. I mean, I'm not going to throw on the Macarena, you know, on a Saturday afternoon, but it's a. It's. It's representative of something that was going on, something larger in the cultural atmosphere at that time.

Val Kroll (29:05)

That's funny. So I'm curious about. And you wrote about this in, in one of your posts about the one Hit albums or one Hit Wonder albums. I'm not going to say that correctly, but in the context of this exercise, I'm curious if there was something that was a little bit more qualitative that you were rating that as you went through it. It made you think, oh, I need to reassess my definition. Or even when we were talking earlier about like, teenage tragedies, that I was just thinking, even though, like, on the face like, that feels, you know, like yes or no, but, you know, when the girl meets the boy from the wrong side of the tracks, like, you know, maybe, yes, it fit in it does it not. Like, I'm curious about how some of that stuff was scored or how you even document it for yourself. What some of those definitions were to be consistent across all your listens.

Chris Dalariva (29:54)

Tim mentioned this earlier. Is when we think about popular culture, it's like there's, there's a fuzziness to it. I mean, you know, we're not talking about chemistry class or physical compounds. As much as I would want to be able to categorize certain things perfectly. You have to, you have to understand the fact that some of this stuff is always going to be fuzzy. No Matter what. Some of it won't. You know, I could I track how long the song is. I don't think there's going to be ton of a debate about, you know, how many seconds Benny and the jets by Elton John goes on for. But I. That's something I would. I grapple with constantly before I was. This is, I think, a perfect example and I write about this in the book is this topic is a little bit like Inside Baseball. Ish. But the structure of popular songs is when we. Most people think of a popular song these days, they think of your typical verse, chorus, song. I mean, most stuff you hear on the radio now to talk about Chapel Roan is going to be you have your verses and you have your big repeated chorus that goes on and on throughout the song. And I wanted to see if that was the most common way popular songs were structured over the decades. The short answer was it was not. There were different song structures that were popular before that. The most common structure at the time is sometimes called like the Great American Song Structure or AABA and something use something like over the Rainbow by Judy Garland. Of this structure. There's not really a chorus in that song. Every verse opens with a refrain and then there's a B section which is like the Someday I'll wish upon a star. I don't want to get too much into the weeds there.

Val Kroll (31:37)

But.

Chris Dalariva (31:37)

But the point is that even I wish there were just like five song structures that people perfectly adhered to and I could just be like, all right, this is song structure three on this song. But that's not how artists work. You know, I. There would be songs I'm like, this is very, very close to this one structure. It's a little bit different, but I'm still just going to call it that because that's what it's close to. You do have to wrestle with stuff like that throughout the process. And when I write my newsletter, I often try to show the process that I'm going through because often I think the thought process is just as interesting as the final result because you have to wrestle with this fuzziness.

Tim Wilson (32:17)

Well, and there's a big part of that, like when you. I think it was like the one was One Hit Wonders one where there were kind of a series of pieces that all seemed like you were like. This seemed straightforward. It was straightforward and it was absolutely did not pass the sniff test. Nobody is going to believe that the Grateful Dead is. I can't remember what the examples were, but you were like according. So I have to Then change the way I do the analysis. So that was kind of a step removed from the data collection, right?

Chris Dalariva (32:49)

Yeah, I believe you're talking about. I did one about two hit wonders. A co worker of mine asked me, we were joking at work about the band 38 special that they have two. They have these two songs that we would both always hear in the supermarket. And he was like, oh, I wonder who the greatest two hit wonder of all time is. And I was like, I think I'm the guy to find out for you.

Julie Hoyer (33:13)

You're good luck.

Chris Dalariva (33:17)

This seems like a really simple question, like, all right, let's just find every band that had two top 40 hits and nothing else. You can easily download the entire history of the Billboard Hot 100 going back 65 years. And then you. And then I was like, and then I'll sort those artists by their current popularity on Spotify. Also data that's easy to get. And then the number one two hit wonder, the most popular two hit wonder was Pink Floyd. And I was like, that cannot be. How is that possible? This is one of the greatest selling bands of all time. But Pink Floyd didn't really release a lot of singles in the US they were mostly known for their albums. And Pink Floyd truly only had in the US two top 40 singles. Money in another brick in the wall. And there's a lot of other weird bands that end up on this list like the Killers or the Cure, you know, bands that are tremendously successful. And I was like, that can't be right. So then what I had to do, I was like, all right, you have to have two top 40 singles and you can never have had a top 10 album. And then suddenly you end up with a list that that more passes the smell test. You get a group like Aha. Who is most famous for their huge hit take on Me. But they had another top 40 hit in the U.S. they're more popular in Europe. Crowded House for our 2000s kids. Cascada had two hits, Evacuate the Dance Floor and Every Time We Touch. And when I was looking at this list, I was like, I was like this. These feel like legitimate two hit wonders, but you have to grapple with like, you have an idea in your head about a lot of this stuff and then you follow it through and it doesn't make any sense. And you realize that your intuitive definition is not accurate enough or does not reflect reality in the way that you think it does. And that applies in the work I do in the book in my newsletter, but I think in data analytics Generally that applies. Like you have to figure out how you want to define things, but then.

Tim Wilson (35:16)

You wind up down the path of telling your business partner that yeah, you just add the business equivalent of what are the top two hit wonders. And when you come back and say, well, that actually didn't make sense. I have to give you enough other little context of where I had to correct for that does seem like that's a regular challenge of the analyst.

Julie Hoyer (35:41)

I was going to say this is such a good example of I remember being a newer analyst and like I think music is so personal to people and everyone can connect to it easily. So like the type of things you're talking about Chris, and running into, I think it's more obvious to people like, oh well, you have to consider XYZ or did you think to ask yourself this type of question? But when you are a newer analyst and you get into data that you're not as familiar with or don't have as much context around it, I think you fall into the trap of taking it at face value and not asking some of those probing questions of like how is it collected? How are they determining certain aspects or characteristics of the data. It's just interesting that music really shows the squishiness of the data around it. And when you're trying to answer a question, how you really have to think through and refine the definitions and your assumptions. Because actually I was going to ask too in your it's funny that we all read your like one hit Wonder articles. I was reading the one album Wonder article and you had a quote in there saying one of the most important things you need to learn when working with data is when the data is insufficient to solve your issue. And I was just curious like you highlight it for the one album Wonder topic, but were there other topics you've run into where you're like, yeah, the data just can't really answer this question that I had about music or pop culture.

Chris Dalariva (37:14)

I run into this frequently where I want to write about something or someone suggests something and I just can't get the information. The longest running topic I have related to this, much to the anger of one friend that suggested it to me, is he's convinced that if you are a punk, popular punk musicians are much more likely to have had had their parents get divorced than the average person in society.

Val Kroll (37:42)

So you need that trauma.

Chris Dalariva (37:45)

I was yeah, I was like, well I mean this again, this is theoretically shouldn't be hard to figure out, but it's just like first you need a list of Punk musicians, which you could get somewhere. I mean I use Wikipedia a lot and there's always like. There's tons of lists on Wikipedia, like list of punk musicians from Australia or something. But then, you know, pairing that with who people's parents are and if they, if their marriage survived, it's almost impo. It's almost impossible. So yeah, every time.

Val Kroll (38:18)

Like ancestry.

Chris Dalariva (38:20)

Yeah.

Tim Wilson (38:21)

Genealogy data. Yeah.

Julie Hoyer (38:22)

Maybe if she makes you your data set then you could do this. You know, his labor of love following up on how the marriages went.

Chris Dalariva (38:31)

I do get upset about that topic because I thought it was. I mean it's a grim. But it's sort of, sort of a funny topic. And I sort of believe. I believe his theory too. But you would just need. Not only would you need that, but I would also. I would want to have comparisons for other genres than comparisons to like the general population. It's never gonna. It's never gonna happen. But you have to know when to. When to say I can't write about this because I don't have the information. Like data can't address. That's something that is measurable, but you can't get the information. Then there's other stuff. It's like, it's probably not measurable in the right way. And you know, a Python code.

Tim Wilson (39:14)

That is another one that kind of parallels to the more qualified leads instead of hit singles would be that your interest. The topic of interest is around punk musicians and their parents. But you can't really draw conclusions without seeing if that is great. Somebody thinks, well if you had all of that data and then you're like yeah, but even all that data that wouldn't tell you if that is how that compares. Is that. Yeah. So all of a sudden the data set that's needed has to expand to some degree.

Chris Dalariva (39:57)

This is a problem I have frequently because I do some stuff where I just. There's a pre existing data set and I crunch the numbers. But I tend to a lot of the stuff I do, I have to build a data set which leads to I think some interesting writing. But you know, it's a pain to do so that case would. It's never going to happen. But we can dream.

Julie Hoyer (40:19)

Do you ever time box yourself on analyses or like. Yeah, I guess publisher.

Tim Wilson (40:25)

Publisher probably did for the right.

Julie Hoyer (40:29)

For the book. You definitely did. But like in general when you get like. I guess two, two thoughts here. One, how do you source some of your like hypotheses going into an analysis and then once you get into it, are you like, okay, I'M going to give myself X amount of time or X amount of effort. Or you just kind of go until you hit an obstacle that fizzle out, can't overcome. Yeah, like.

Chris Dalariva (40:52)

I'm. So I publish every Thursday, pretty religiously. And maybe the only thing I've learned on the Internet over the last decade across social media platforms is that you should just put stuff out, even if it's not perfect or finished in the way that you want, because you never know what people are going to connect with. And I've had stuff where I'm like, ah, this is okay, it's not perfect. And then it ends up doing really well. And then you have the thing you've worked on for two and a half months and you put out, no one cares. So I usually, I just usually spend a couple days on this stuff. Unless it's something I'm like, I know this is going to take more time. I'm going to publish it in a couple weeks. But it's usually in the matter of a week. I'm going to sit down. I have a list of topics that I. Whenever I think of something, I'll be like, all right, let's try to do this. If it's not going anywhere, move on to something else. But I know that I have to put something out on a Thursday, so I'm very schedule oriented. So I'm always working under the clock. Funny if the book was less like that because I couldn't get a publisher for this book for so many years that by the time I found a publisher, nearly all of it was written. I had to edit it, but I wasn't on a time clock as much. Then with the weekly newsletter, which I'm in complete control of.

Julie Hoyer (42:08)

That's nice.

Val Kroll (42:09)

So then your list of topics are those like the hypotheses, like Julie was saying. And how do you come up with that list? Is it like comments or questions from. From prior weeks in publishing or how.

Chris Dalariva (42:20)

Do you come up with that? Yeah, I mean, at this point, people do ask me a lot of questions that if I think I can get information on, I'm willing to take a hack at. But other stuff is just, I don't. I. I spend a lot of time thinking about music. I work, my day job is in music. I play in bands. Over the years, you know, I've worked on this book, I read about music. So stuff just comes up over time and I just write it down. And then when I sit down one week, I just see which, which one seems interesting, what inspires you so do.

Val Kroll (42:55)

You tell your band mates, like, I have all this data, I know all the ingredients, the secret sauce for the perfect song. We just have to follow the AB classic pattern. Gotta have some heartbreak in there.

Chris Dalariva (43:09)

Sadly, no. I actually joke about this at the end of the book because this is a frequent question is like, have you discovered the secret? And I'm like, well, if I discovered the secret, I would have wrote the song instead of the book because probably more lucrative and would take much less time. So yeah, I mean, there are the people I play music with joke that if they make an observation about music to me, they may see it in a newsletter. But no, there's no, there's no real secret sauce. I mean, the only thing I could tell you is that things that are pop, when something's very popular, usually there are many other things that are popular that are similar ish to it. So if you want to write a really popular song, you should be working within the constraints or the structures that are popular at a given moment.

Tim Wilson (43:58)

So I mean, we're, I mean, given on that front though. Did you, did you pick up on any sort of dimensions, like very, very clear kind of inflection points that something shifted? I mean, we intuitively think 60s pop sounds different from 70s pop sounds different from early 80s to late 80s. But is it always kind of a gradual continuum or were there ones where you said, no, the invention of this technique or something shifted everything.

Chris Dalariva (44:29)

One of the key themes throughout the book is that musical innovation, this is my theory, not my personal theory, but what I write about a lot. Musical innovation is often down downstream of technological change. So things are gradual, but there are certainly moments where something is invented or something changes and it fundamentally shifts the way things sound. You were talking about the 80s and it's actually, it is interesting because even people who only listen to music very passively can usually listen to something and have a general idea. When it's from the creation of drum machines in the late 1970s, which became ubiquitous throughout the 1980s, is many, many popular songs are created with drum machines. That's sort of an inflection point where the sound of popular music changes. In the early 90s, hip hop becomes very popular. And that's a fundamental shift you can see in the data on multiple fronts. Because for the first half of the 20th century, your most popular songs were usually focused around melody and harmony, you know, something you could sing along to in the shower. And then by the late 90s, early 2000s, popular music becomes much more focused on lyricism and rhythm. Not to say that there's no melodic qualities of hip hop, but you're not gonna hum Lose Yourself by Eminem in the shower. I mean, just. It's not.

Val Kroll (46:04)

Speak for yourself, Chris.

Tim Wilson (46:05)

That's not.

Chris Dalariva (46:08)

But you can see stuff like this. And I write about it. It's like words per minute in songs. I mean, it dramatically increases in the 90s because hip hop is a much wordier genre than the things that come before it. And one of my other favorite examples, I always talk about where in the first half of the 20th century and I measure this, there's a larger portion of songs without vocals. So instrumental portions of songs after, like, 1960. I mean, if there's a. If there's a front person in a group, it's almost always the singer. And we basically take this for granted, that the singer is the star. But that wasn't the case for decades upon decades. If you look at all these old big bands, the band leader is usually like the. The tuba player or the clarinet player. And part of the reason you see this shift is just because microphone technology improves. Previously, if you were. Again, imagine a world without microphones. If there's an orchestra playing, how are you going to hear a voice over it? You're going to have to sing at the top of your lungs, and even then, you probably need a choir of people to be heard. But in, like, the 30s and the 40s, microphones get a lot better. And suddenly you can be Frank Sinatra, and you can sing with this very soft croon, and it can be heard over everything with clarity. That technology fundamentally changes, you know, who's the star in groups and which vocal styles can even be performed. Again, if you can't pick up a quiet voice, then you don't get Bing Crosby or Frank Sinatra. It's just a different singing style. So long and short is. Yes, you. There are these inflection points, and it's not. I don't think it's deterministic. But technology really does shape how we hear songs and what songs are made. And you see that in musical data.

Julie Hoyer (47:59)

I have to admit, when you said musical technology, because I'm obviously not a musician, my brain went to the whammy bar. That's, like, all I could think about. I was like, what's musical technology?

Chris Dalariva (48:11)

That is like guitar pedals. No, that is totally. That's even like the electric. The creation of the electric guitar. Like, you don't get rock and roll if someone. Someone. Leo Fender or whoever, doesn't figure out how to plug in a guitar and Distort it and whatever.

Julie Hoyer (48:30)

Yeah, Tim, before you cut us off.

Tim Wilson (48:33)

No, no, no. I just. I gotta shout out the Roland TR808 drum machine, which the 99% invisible did an entire podcast episode about. And I also not really a musician, so I didn't. But when you said the drum machine technology, like they, they walk through an exhaustive explanation similar to what Chris. I just. There's some listener who's listening to other podcasts and saying, but what about the 99pi episode?

Chris Dalariva (49:00)

I have heard that episode. That's a great. I mean, that whole podcast is great.

Julie Hoyer (49:04)

Yeah, that's awesome. Because I was gonna say, I wanna ask one more question. I'm curious.

Tim Wilson (49:09)

I'll allow it.

Julie Hoyer (49:10)

Do you? Why, thank you. I. I want to know because again, when I was reading through some of your newsletters, you mentioned that, like, you go through this process, you know, sometimes you have to iterate. Iterate on definitions. And I love that you, like, show your readers that process as you go through. But it made me think, are there any analyses, as time has gone on, that you've gone back and wanted to redo, or are there any that, like you did in the past, that you're like, oh, that was such a bear, I hated it, or that was my favorite. I kind of wanted to get like a hit list of your top 10 analyses or top five, you know, your.

Val Kroll (49:46)

Top charts, your top five charts, whatever.

Julie Hoyer (49:49)

Category you want to pick.

Chris Dalariva (49:51)

Yeah, there are a couple that come to mind. Every Christmas season, I try to do an addition on predicting new Christmas classics, usually from some data approach. And I. And I try to. I've tried to use like Spotify playlist data. I've tried to use looking up songs. What are people looking up on Wikipedia? Like I said, I love Wikipedia.

Julie Hoyer (50:13)

I never would have thought to use Wikipedia as a data source, by the way. But you talking about it like, I'm like, oh, wow, it's sounds like a plethora of information.

Chris Dalariva (50:22)

It is. I mean, I use it. I use it in the book and I use it in the newsletter from time to time. But yeah, I go back to that Christmas one a lot. Not because I think that the earlier ones were bad, but I don't think it's constantly changing. And the ways I've tried are not exhausted. I'm sure there are other ways to pick up on which Christmas songs are becoming more popular. There's others.

Julie Hoyer (50:47)

Have you predicted some correctly?

Chris Dalariva (50:50)

I. I think that the two. I don't think this is a surprise. The two modern classics are on their Way to classic Dumb are Santa Tell Me by Ariana Grande and Underneath the Tree by Kelly Clarkson seem to, from the last decade seem to be getting more play. There are some things I write where I start taking an analytical approach and I'm like, this isn't working. And I just end up. I don't always write. Sometimes I just write about like the music industry or some trend. Not really. From a quantitative perspective, one that comes to mind was I wanted to write about the evolution of music and supermarkets. Just how, how has that changed over the years? You know, what you hear when you're in the produce section. And I couldn't, I couldn't get historical data on it. I was talking to some people who like companies that they pipe in the music to stores and I ended up not getting it. But I just. And then wrote a sort of a history of music and supermarkets, which was sort of interesting. But if I could get that data.

Julie Hoyer (52:00)

Get that data, yeah.

Chris Dalariva (52:03)

So there, there's, there's a lot of stuff like that where I can't get the data and I'm at least like, well, there's something interesting here that I can write about and not quantify in the way that I was hoping to. But yeah, there's all, there's sort of a laundry list of topics like that.

Tim Wilson (52:20)

Well, man, I feel like we could ask a million more questions. Plus, it's just fun to have somebody who's got a near encyclopedic reference point for examples of X, Y and Z on the music front. But now we're going to actually take a quick break with our friend Michael Kaminsky from recast the MMM and GeoLift platform, helping teams forecast accurately and make better decisions. Michael, sharing bite sized marketing science lessons over the coming months to help you measure smarter. So Michael, over to you.

Michael Kaminsky (52:56)

In our last measurement Bite, we talked about the fundamental problem of causal inference and how our inability to control for unobserved differences across individuals makes observational causal inference either extremely difficult or just impossible. The beauty of randomization is that in an experimental setting, if we're able to truly randomize across individuals, we don't have to do any statistical adjustments at all. That randomization takes care of adjusting for observed confounders is of enormous utility. But that it takes care of adjusting for unobserved confounders is truly miraculous. And the reason why is beautifully simple. With large enough sample sizes, if we can truly randomize treatment, all of those individual differences will simply average out between our treatment and control group and we can empirically show this, either with a mathematical proof or via simulation in code. The magic is that since this works for everything we can observe, we know it will also work for everything we can't observe. This is the magic of randomization, and it's why a randomized experimental design is always the first stop of every researcher. It just works. It not only handles the hard work of statistically adjusting for observed confounders, but without any extra effort, it works the miracle of adjusting for your unobserved confounders as well.

Tim Wilson (54:07)

All right, so if you enjoyed that mini lesson, Michael and the team at Recast have put together a library of marketing science content specifically for analytics Power Hour listeners. For everything from building MMMs in house to communicating uncertainty to your board, head over to www.getrecast.comaph that's www.getrecast.com aph. So now the last thing we do on the show is go around the horn and share a last call. Something that you've read or seen or heard or thought about might be of interest to our users. And Chris, you're kind of just a fountain of last calls on this whole episode. But do you have a last call, something you haven't shared that you'd like to share?

Chris Dalariva (54:54)

Yeah, there's this very, very popular music youtuber named Rick Beato. Also, this has nothing to do with analytics, but for some reason I think people may enjoy this. Rick Beato's very I think he's probably the most subscribed to person on YouTube that just talks about music and he does a lot of great stuff. But he's been in a war with Universal Music recently because they've been filing copyright claims on his videos because he usually has like 10 second snippets of songs and he's been posting about them, trying to take down these videos and there hasn't been a resolution yet, but there's been a couple videos and then other people in the music YouTube space have been posting backing him up, saying this should be fair use. So I, I wrote about this a little, but I've been watching with bated breath to try to figure out what's going on here. Like he's definitely providing traffic to Cat Music in the Universal catalog, so it doesn't seem like something they should really care about given that the ad revenue they would generate from his videos would, you know, be a rounding error on their balance sheet. So I'm just curious to see where this goes. But I just checked right before this. I just checked. I don't know when this is going to come out. But there was no update up to this moment. But when this comes out, maybe you could see if Rick Beato has resolved his issues.

Tim Wilson (56:19)

Well, and you did. We'll link to the newsletter that you did about that so people can refer. That was kind of a fascinating one about, like, what the options are when you push music, right? Because the reason you can find that stuff on YouTube is because the person who uploads it can say, I want to leave it up, but any ad revenue goes to the rights holder for the music. Is that right?

Chris Dalariva (56:45)

Yeah, yeah, that's generally right. If I use 30 seconds of a chapel roan song, her people can claim the revenue from the video and they can say, all right, you can leave it up, but we're going to collect the money. But usually 10 seconds and it's criticism, you know, that should be in my rudimentary understanding of fair use. It should be fair use. But here we are.

Tim Wilson (57:08)

Here we are. Nice. Val, what about you? What's your last call?

Val Kroll (57:13)

All right, so this one is actually related to the topic. One thing that I always find so interesting when it comes to music and thinking about quantification is genre. And however many genres you think there are of music, there's like a hundred more than whatever that number is. And there's this website called Every Noise at Once. Have you heard of this one, Chris, by chance?

Chris Dalariva (57:38)

Oh, I am. I am intimately familiar with Every noise.

Val Kroll (57:45)

So this site has. It's a visual kind of display of every genre that exists, and you can click it and it will play, like, examples of those songs, or you can, like search for an artist to figure out exactly where they fall. So in the spirit of quantification of genres, it's kind of a fun thing to play around to figure out what is the difference between techno, rave, hard industrial techno and Belgian techno. Well, turns out a lot. A lot is different. So anyways, it's kind of just a fun thing if you're looking to burn 10 minutes or however deep in your hole you get. Maybe an hour in my case.

Tim Wilson (58:21)

Just thinking if you homeschooled Abby, how that would somehow be part of the core curriculum for when she turns 8.

Val Kroll (58:29)

You bet. You bet.

Julie Hoyer (58:31)

I love that.

Chris Dalariva (58:32)

Nice.

Tim Wilson (58:33)

What about you, Julie? What's your last call?

Julie Hoyer (58:36)

Well, I wish I was on trend and had something about music. It'd be much more fun. But actually this one does loop back to you, Chris. I was reading your newsletter and you had made a shout out for BI Bytes and you had linked to an article by them about time series data. And so I was reading through that and I actually thought it was a really good article. It's called Same Data, Different Questions Transforming Time Series Data for Better Insights. And I love that it was because we talk about time series data a lot, especially in the industry that we work in. It's just something you run up against. And so I love anything that helps, like break down better ways to think about looking at time series data or how to analyze it. And so I love that it was a list of like four to five different visuals that are really helpful when you're trying to break down and understand is there any significant change in this time series data. So I thought that was awesome. So thanks for leading me to that.

Chris Dalariva (59:33)

Oh, no problem. Yeah. There.

Julie Hoyer (59:35)

Tim, what about you?

Tim Wilson (59:37)

Yeah, I missed that. So now I've got something to go read.

Julie Hoyer (59:41)

I love that I found something Tim didn't find. That's weird.

Tim Wilson (59:46)

So I will march yeah, this wasn't the I didn't know if one of us was going to do the Cassie Kostrikov article that we were having an exchange about, but we'll save that. Okay, well, I will march even farther away from music with a past guest Current I'm a super fan of Ben Stancil, but in one of his recent newsletters he linked to something he wrote back when he was still at Mode back in 2020 called a dispassionate examination of the empirical evidence regarding positional punctuation in SQL. So this was basically for many listeners or SQL writers. So the old debate around the leading or the trailing comma as to what makes more sense. And he basically concluded that yes, objectively, putting a leading comma at the start of the line is the better way to go. But the trailing comma is just kind of that's kind of still what people do and it doesn't look weird. So it's funny because he's a hilarious writer. But I did want to go ahead and quote the way that he wrapped it up said so the next time you find yourself questioning where commas go in SQL query select statements, the answer is simple. While leaders lead with leading commas and trailing commas are leading signs of failing lines and the tail aligns no matter the database breed. We're not agreed that it's best to concede to lead because the more we scale our query needing, the more we follow the trail to trailing from leading because it's people who do the reading and that was written pre LLMs hitting the mainstream.

Chris Dalariva (61:24)

But it's nicely Done.

Tim Wilson (61:27)

Yeah. So. All right. And with that, thank you, Chris, again, for coming on. This was. I had high expectations for how much fun this would be and it exceeded those dramatically. So thanks for coming on.

Chris Dalariva (61:45)

Oh, it was a pleasure. Even though I do use trailing commas in my SQL queries.

Tim Wilson (61:56)

As do I. Yeah, it's a funny. I mean, it's actually kind of an empirical analysis. So he does. He pulls from different places. So it actually maybe there is a little bit of a link to similarly trying to answer a question that maybe doesn't have deep consequence, but he dug into it anyway, so thanks for coming on. That was a great discussion. We'll have links to your newsletter and the book and. Yeah, well, I was just thinking about. That was like, am I going to link to every single song or album that was referenced? And I will tell you, no, probably not.

Val Kroll (62:35)

It's not as thorough as Chris's data set.

Tim Wilson (62:40)

Oh, that. Have you considered or have you. Are you going to ever publish or share the data set itself?

Chris Dalariva (62:47)

Oh, it's out there. I would love if you shared. It's just in a Google sheet. Anyone can use it.

Tim Wilson (62:55)

Okay. We will definitely add that for now. I wish I'd taken a look at that before.

Val Kroll (63:02)

I know.

Chris Dalariva (63:02)

I'm like, damn.

Tim Wilson (63:03)

Yeah, so many more questions. Okay, so for that next book, we'll have you back.

Chris Dalariva (63:09)

So.

Tim Wilson (63:11)

So everyone out there, thanks for listening as always. We would appreciate. Love to have a review or a rating or both on the platform that you listen on, should that be a possibility. We do kind of ebbs and flows on our end, the volume of requests for podcast stickers that we get. But if you go to our website, there's a simple link in the global nav and we'd be happy to send you a sticker or three or four for free. Just fill in a little form. I am hosting this, so I'll go ahead and say if you haven't bought a copy of analytics the Right Way, a Business Leader's Guide to Putting Data to Productive Use by yours truly and Joe Sutherland. The holidays are coming and nothing is the perfect stocking stuffer like a book on analytics. We'd love to hear from you. Feel free. Reach out to us on LinkedIn. You can reach out to the. The podcast page. You can reach out to any of us as individuals can reach out on the measure Slack. You can just send us an email at contactnalyticshourio. So regardless of what, what era, what decade you are stuck in listening to and thinking about, and now you're gonna Go back and re. Listen to and analyze and whatever songs you're not, you're. You're listening to and getting distracted by. By when you should be looking at the data. For Julie, for Val, I'm Tim. Keep analyzing.

Podcast Host (64:38)

Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions. On Twitter @analyticshour, on the web at analyticshour IO, our LinkedIn group and the Measure Chat Slack group. Music for the podcast by Josh Crowhurst.

Tim Wilson (64:56)

Those smart guys wanted to fit in.

Chris Dalariva (64:58)

So they made up a term called analytics. Analytics don't work.

Podcast Host (65:02)

Do the analytics say go for it no matter who's going for it. So if you and I were on the field, the analytics say go for it. It's the stupidest, laziest, lamest thing I've ever heard.

Tim Wilson (65:13)

For reasoning in competition, you should tell that story to. Although he probably has everybody tells him their like quirky Billboard stories. Now that.

Val Kroll (65:23)

I was telling. Do people tell you all the time?

Chris Dalariva (65:27)

No. No. I'm curious what this is going to be.

Val Kroll (65:30)

You're like, it's one of two directions. No, I'll try to be brief. When I was in 6th grade, I went to CD Warehouse with my allowance and bought the Billboard top 10 CDs from like 1960 to 1969 and just became obsessed. And so on Friday days for music class, we could bring. Everyone could bring a cd. We wrote the names on the board, teacher would draw a number, you got to play your song. And the week that the new Spice Girls album came out, I brought Billboard 1965 and played Eve of Destruction, which is like a very heavy topic protest song that's about four and a half minutes long. And it wasn't the favorite of the people in my class. Surprisingly, to my surprise, this is not what everyone was dying to hear, but.

Chris Dalariva (66:17)

I was obsessed with it. That's awesome.

Tim Wilson (66:20)

And you said the bullying was already happening or that's what. Yep.

Chris Dalariva (66:25)

Okay.

Tim Wilson (66:26)

That's good.

Val Kroll (66:28)

I couldn't figure out what I was doing wrong. You know.

Tim Wilson (66:34)

I mean, if you made that choice, it feels like maybe there were other choices being made that were.

Val Kroll (66:41)

I feel like my teacher should have been like, okay, all right. Maybe two minutes.

Julie Hoyer (66:47)

Yeah. Oh, boy.

Tim Wilson (66:51)

All right. Okay with that. I really wanted to have. Have that recorded. I've heard that now three times. Never on a.

Chris Dalariva (67:01)

It's still funny.

Tim Wilson (67:02)

Yeah, it's good.

Val Kroll (67:04)

Maybe I'll give you a couple bars at the end in my best Barry voice. Or what's his name? Barry.

Tim Wilson (67:10)

It's gonna be your last call. There's this hot song.

Val Kroll (67:12)

Yeah, you might not have heard it. It's a tick tock sound, but yeah.

Tim Wilson (67:27)

Rock swag and disco duck.