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Speaker 1: This is Wins and Losses with Clay Trevis, play talks

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Speaker 1: with the most entertaining people in sports, entertainment and business.

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Speaker 1: Now here's Clay Trevis. Welcome in Wins and Lost his

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Speaker 1: podcast where we talk about hopefully really entertaining things in

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Speaker 1: the world of sports, media, politics, business, you name it,

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Speaker 1: with hopefully entertaining people. And I think you're really gonna

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Speaker 1: enjoy this guy. Mike mulve Hill. Uh, he is, I

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Speaker 1: believe at Michael Mulvehill on Twitter or at mulbi Hill.

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Speaker 1: We'll figure it out here in a sec. But anyway,

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Speaker 1: I'll be tweeting out his Twitter handle. I would encourage

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Speaker 1: you to follow him because I actually first started noticing

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Speaker 1: Mike on Twitter years and years ago as he was

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Speaker 1: publishing some of the data that he was seeing in

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Speaker 1: the world of sports. And if you are interested in

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Speaker 1: the business of sports or media, you're gonna absolutely love

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Speaker 1: everything about this conversation. So I want to go ahead

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Speaker 1: and welcome in Mike Mulvihill. Mike, what is your official

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Speaker 1: title at this point in time? I am the head

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Speaker 1: of Strategy and Analytics for Fox Sports. So that is

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Speaker 1: an really interesting and awesome job title and as we

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Speaker 1: unpack exactly what you do, I think people are going

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Speaker 1: to see why that is. Uh, But I don't know.

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Speaker 1: It occurred to me. I mean, I've known you for

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Speaker 1: years now, but it occurred to me that I don't

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Speaker 1: really know that much about how you ended up getting

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Speaker 1: the job that you got. So I believe I'm correct

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Speaker 1: that you grew up in the Pittsburgh area. And at

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Speaker 1: what point did you recognize where did you go to

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Speaker 1: school and kind of what was the first job that

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Speaker 1: kind of got your foot in the door in the

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Speaker 1: sports industry. That's a great question, And first of all,

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Speaker 1: thanks for inviting me to be on the podcast. Really

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Speaker 1: excited to get into a little bit of my background

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Speaker 1: and the things that we talked about every day, uh

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Speaker 1: here at this job. UM. In my case, you know,

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Speaker 1: I started working in non commercial radio when I was fifteen.

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Speaker 1: I did grow up in Pittsburgh, as you mentioned, uh,

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Speaker 1: and I started working at the University of Pittsburgh's student

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Speaker 1: radio station, UM prior to my junior year in high school.

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Speaker 1: It was a situation where a lot of those kids

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Speaker 1: went home for the summer. They had a hard time

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Speaker 1: keeping the station on the air, and so they needed

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Speaker 1: younger kids who would volunteer UM to just become DJs

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Speaker 1: and read the news and read sports clips and do

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Speaker 1: whatever they needed UM to keep the station going twenty

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Speaker 1: four hours. And so I got into this environment where

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Speaker 1: I was working in radio every day, and I really

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Speaker 1: got hooked UM immediately. It was just one of those

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Speaker 1: things where sometimes as a young person, you walk into

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Speaker 1: a professional setting and you just know instantaneously that it's

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Speaker 1: where you want to be for a long time. And

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Speaker 1: that was the way I felt, And so at a

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Speaker 1: really young age, I felt like I wanted to work

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Speaker 1: in broadcasting and media in some capacity. Went to the

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Speaker 1: University of Missouri, where they traditionally have great media programs.

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Speaker 1: I worked at the college radio station there. I ran

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Speaker 1: the college radio station for a time. UM. I actually

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Speaker 1: got very lucky. While I was at MISSOO, I put

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Speaker 1: together a concert that was a reunion UM of a

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Speaker 1: nineteen seventies band called Big Star with Alex Chilton, who

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Speaker 1: sang the Letter and was in the Box Stops in

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Speaker 1: the sixties. Relatively obscure band, but a band that had

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Speaker 1: a lot of influence, and that reunion show generated a

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Speaker 1: fair amount of attention. UM just a really lucky thing,

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Speaker 1: right place, right time, and coming out of that show,

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Speaker 1: I was able to get an internship with Fox in

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Speaker 1: Los Angeles. So prior to my last year at Miszoo,

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Speaker 1: UM I came out here and I interned in what's

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Speaker 1: called a current programming department, thinking that what I wanted

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Speaker 1: to do was work on episodic TV and work on

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Speaker 1: sitcoms and dramas and you know, have the kind of

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Speaker 1: job where you're involved in the creative process and developing

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Speaker 1: series television, and that's what I really firmly believed I

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Speaker 1: wanted to do. UM went back to Miszoo for my

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Speaker 1: last year, graduated, came to New York, graduated into kind

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Speaker 1: of a tough job market UM, and was only able

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Speaker 1: to get a job in New York working in media

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Speaker 1: research and market research, which is dealing with the ratings

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Speaker 1: and audience measurement and trying to understand, um, what people

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Speaker 1: are watching and why. Really not a creative job at all,

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Speaker 1: And to complete surprise, UM, I kind of got hooked

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Speaker 1: on that too. I mean, I found that I really

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Speaker 1: enjoyed working with the data. I enjoyed identifying situations where

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Speaker 1: the perception of the business was at odds with the

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Speaker 1: reality of the data and trying to figure out what

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Speaker 1: drove that gap in perception and what the truth really was. Uh.

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Speaker 1: And I also found that I wanted to get out

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Speaker 1: of entertainment and get into sports. You know, this was

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Speaker 1: at a time where Fox had just completed its first

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Speaker 1: season of the NFL, we had just acquired the rights

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Speaker 1: to Major League Baseball. UM, and it was pretty clear

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Speaker 1: to me that the people, at least in this company

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Speaker 1: who were working in sports were having a lot more

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Speaker 1: fun than the people who were working in entertainment. And

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Speaker 1: you know, this is the mid nineties. At that time,

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Speaker 1: there were no sports marketing degree programs. You know, people

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Speaker 1: who were working in sports were generally coming from communications

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Speaker 1: or journalism majors. And if you just had a passion

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Speaker 1: for sports and you could write a little bit, and

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Speaker 1: you were willing to just grind for a long time,

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Speaker 1: there's a certain process of natural selection in this business

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Speaker 1: where it doesn't really matter where you went to school

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Speaker 1: or what your last name is. You know, if you

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Speaker 1: get your foot in the door and you're just willing

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Speaker 1: to keep grinding and keep working. UM, the people who

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Speaker 1: wanted the most, I think, tend to find a way forward,

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Speaker 1: and that's what happened to me. It's really so much

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Speaker 1: interesting stuff in there. And so I want to go

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Speaker 1: back to you going in at fifteen years old and

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Speaker 1: working at a college radio station. Were you a big

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Speaker 1: radio guy, like listening to sports on radio? Did that

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Speaker 1: partly drive you? Was it music that you were listening

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Speaker 1: to on the radio? What was it that initially took

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Speaker 1: you two I think you said the University of Pittsburgh

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Speaker 1: to be able to work there during that summer? What

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Speaker 1: was the impetus? Uh, It was a lot of what

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Speaker 1: you just mentioned. It was also that I didn't want

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Speaker 1: to have a regular job, you know, one of these

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Speaker 1: things where you're a teenager and your dad or my

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Speaker 1: dad was pressuring me to go, you know, work at

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Speaker 1: a fast food restaurant or work at a Baston Robbins

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Speaker 1: and and actually make a little bring a little bit

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Speaker 1: of money into the house. Um, and that didn't seem

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Speaker 1: that interesting to me. And so this opportunity you came

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Speaker 1: up to work on a volunteer basis in radio, I

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Speaker 1: thought it sounded a lot more interesting, a lot more fun.

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Speaker 1: I was able to sell my family on the idea

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Speaker 1: that it might lead to something bigger and better, which

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Speaker 1: it did. Uh. And I did I love the music. Um,

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Speaker 1: I love that the station had a daily news and

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Speaker 1: sports show that I could be a part of. You know,

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Speaker 1: Pittsburgh obviously has a really strong sports culture and a

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Speaker 1: strong sports media culture. So I grew up kind of

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Speaker 1: wanting to be part of that in some way and

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Speaker 1: not really understanding how I ever could be. Uh. And

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Speaker 1: as soon as I walked into that station, I think

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Speaker 1: it a white bulb went off and I felt like

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Speaker 1: there actually was a path for me to do this

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Speaker 1: or do something like it for a living. I think

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Speaker 1: for a lot of us who work in media, there's

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Speaker 1: a moment where you sort of realized that people actually

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Speaker 1: get paid to do this, and it's a little bit overwhelming,

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Speaker 1: Like you can't believe that this is actually a career option,

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Speaker 1: And as soon as you understand it, you don't want

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Speaker 1: to do anything else. It is really fascinating. So, and

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Speaker 1: did you grow up a fan of the Pirates, of

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Speaker 1: the Penguins, of the st Laers, all those teams evenly,

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Speaker 1: or how would you say you assess your sports fandom

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Speaker 1: as a kid. I grew up as a fan of

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Speaker 1: the Pirates and Steelers, and of the University of Pittsburgh

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Speaker 1: football and basketball. I mean I grew up at a

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Speaker 1: time where the Pirates had some of the most charismatic

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Speaker 1: teams of our lifetime. Is that we are family Pirates

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Speaker 1: team that won the World Series in nineteen seventy nine.

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Speaker 1: I grew up around the Steel Curtain Steelers and the

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Speaker 1: pitt Panthers teams that had Dan Marino. UM So that

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Speaker 1: was a really vibrant sports culture at that time. UM

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Speaker 1: it is a pretty favorable place to grow up where

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Speaker 1: your your teams are having a lot of success and

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Speaker 1: it encourages you to sort of stay with your sports fandom.

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Speaker 1: But Pittsburgh in the seventies, it's a great sports town

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Speaker 1: town now, but at that time, it was this coming

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Speaker 1: together of really successful teams, really compelling charismatic athletes, a

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Speaker 1: local economy that at that time was really thriving. I mean,

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Speaker 1: Pittsburgh was one of the great working class in middle

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Speaker 1: class cities in the country, which I think Foster is. UM,

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Speaker 1: a great sports culture, and all of those things came

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Speaker 1: together to make me a fan for life. Let me uh,

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Speaker 1: let me go to when you go to Missouri, were

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Speaker 1: you did you have that like a cultural shock at

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Speaker 1: all going from Pittsburgh to Columbia, Missouri. Or did you

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Speaker 1: immediately fit in when you went away to college. Uh? No,

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Speaker 1: I had a cultural shock, and I wanted to have

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Speaker 1: a cultural shock. You know, when I grew up in Pittsburgh,

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Speaker 1: I grew up very much in the center city. You know,

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Speaker 1: it was an urban environment, um, which I appreciated a lot.

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Speaker 1: I loved having a city childhood. But when it came

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Speaker 1: time to go to school, UM, I felt like it

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Speaker 1: was my one opportunity to have a really different experience

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Speaker 1: and live in a small town, live in the Midwest.

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Speaker 1: You know, Missoo is one of those kind of classically

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Speaker 1: all American big school experiences. Uh. And it was really

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Speaker 1: really appealing to me. So it was a culture shock,

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Speaker 1: but it was a culture shock that I sought out.

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Speaker 1: I wanted it. Be sure to catch live editions about

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Speaker 1: kicked the coverage with Clay Travis week days at six

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Speaker 1: am Eastern, three am Pacific. We're talking to Michael mul Hill.

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Speaker 1: This is the Wins and Losses podcast on Clay Travis. Okay,

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Speaker 1: so you get that first job and you start to realize,

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Speaker 1: you know what, I kind of like diving into these

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Speaker 1: numbers and sometimes finding counterintuitive lessons from the data that

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Speaker 1: might not necessarily be common sense, might not certainly be

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Speaker 1: conventional wisdom. Do you remember the first time you discovered

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Speaker 1: something that surprised you and that other people were impressed

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Speaker 1: you knew in that job. Yeah, and I don't think

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Speaker 1: it was anything earth shattering or I don't know if

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Speaker 1: it's a story that would really make a great impression

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Speaker 1: on people. But there were a couple of instances early

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Speaker 1: on UM where the data was telling me something other

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Speaker 1: than what the perception of the industry was. You know.

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Speaker 1: One of the perceptions at that time was that John Madden,

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Speaker 1: who was the most prominent NFL broadcaster, probably the greatest

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Speaker 1: to ever do it. I think a lot of people

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Speaker 1: would agree, And there was this perception that part of

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Speaker 1: the value of having John Madden as you're on air

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Speaker 1: talent was that he could keep viewers tuned into a

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Speaker 1: blowout game because they would continue to watch just because

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Speaker 1: John was so entertaining, and he was that entertaining. The

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Speaker 1: data didn't back it up. Uh, And it was interesting

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Speaker 1: to me that very senior executives, people who had infinitely

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Speaker 1: more experience, and we're making a lot more money, UM

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Speaker 1: and having a lot more success had a perception that

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Speaker 1: was not in any way backed up by the numbers.

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Speaker 1: I don't there wasn't really anything for me to do

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Speaker 1: with that data, but it was interesting just to recognize

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Speaker 1: that there was a disconnect there. Um. It was interesting

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Speaker 1: to notice that as the Bates Baseball rights holder, we

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Speaker 1: would sometimes get excited about a pitching matchup that we had,

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Speaker 1: and what you would come to find was that the

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Speaker 1: ratings didn't pop based on a great pitching matchup any

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Speaker 1: more than they would for kind of a generic pitching matchup.

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Speaker 1: I came to learn years later that um Bill James

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Speaker 1: had actually done a very similar analysis that was based

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Speaker 1: on attendance rather than TV ratings, and came to the

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Speaker 1: same conclusion. The pitching matchup didn't drive attendance at all. Again,

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Speaker 1: just another case where you feel like, Gee, the whole

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Speaker 1: business believes one thing, and I'm looking at numbers that

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Speaker 1: tell me something else. So who's wrong here? And I

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Speaker 1: think when you start to identify, um those kinds of circumstances,

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Speaker 1: you can get hooked on it, and it can be

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Speaker 1: a little bit exciting to feel like I've got a

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Speaker 1: data set here that's telling me something that maybe a

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Speaker 1: lot of other people haven't figured out yet. And I

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Speaker 1: still kind of get a charge out of coming up

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Speaker 1: with some kind of insight or or nugget of information

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Speaker 1: that gives me a window of understanding that maybe some

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Speaker 1: other people haven't figured out yet. How do you find

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Speaker 1: that people respond when their conventional wisdom is challenged, Because

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Speaker 1: sometimes when you've got the data to back you up,

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Speaker 1: people can feel like you're attacking them. When you're saying, Hey,

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Speaker 1: you know that thing that you believe, which is the

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Speaker 1: pitching matchups, drive ratings, or John Madden is so good

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Speaker 1: at his job that people could keep listening to him,

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Speaker 1: it doesn't actually stand up when the data is actually analyzed.

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Speaker 1: How have you found people responding to things like that? Yeah,

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Speaker 1: I think one of the most fortunate things that has

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Speaker 1: happened to me in my career, and certainly that happened

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Speaker 1: to me at a young age, was that I got

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Speaker 1: to work at Fox Sports at a time when it

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Speaker 1: was run by a guy named David Hill, who was

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Speaker 1: the founding president of Fox Sports and had previously been

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Speaker 1: an executive in the UK and Australia. And David is

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Speaker 1: one of the most creative people to ever work in

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Speaker 1: this business. He certainly has one of the most active

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Speaker 1: and most restless minds and imaginations that I've ever been around.

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Speaker 1: Uh And at that time, where David was at the

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Speaker 1: top of this business and the top of this company,

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Speaker 1: and I was nobody, as a twenty four year old

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Speaker 1: research anils, she couldn't possibly matter less. Um he was

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Speaker 1: willing to hear those things out, and he was really

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Speaker 1: encouraging of the hearing data and facts and ratings information

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Speaker 1: that maybe ran counter to what he already thought. And

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Speaker 1: I think if I had had a different boss at

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Speaker 1: that time, I might not have been longed for this business.

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Speaker 1: But David was really encouraging and Clay, as you know

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Speaker 1: from you know, all the work that you've done with

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Speaker 1: us at Fox, this building is littered with people who

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Speaker 1: had experiences like that with David Hill, people who you know,

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Speaker 1: probably would not be in this business today had they

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Speaker 1: not gotten the encouragement that they got from David at

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Speaker 1: a young age. So that was really pivotal to me.

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Speaker 1: And it really shows like what a difference um the

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Speaker 1: right boss and an open minded boss at a young

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Speaker 1: age can can make, and also what culture is at

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Speaker 1: a job right where you can be at twenty four

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Speaker 1: year old and if you've got information that you think

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Speaker 1: is helpful, people in positions of power will listen to it.

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Speaker 1: And I think that's a kind of the story of

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Speaker 1: Fox in general. There are a lot of people like

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Speaker 1: you who came in as twenty two year olds or

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Speaker 1: even younger and started working there. I mean, you look

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Speaker 1: at Brad Zager, you can look at Eric Shanks. I

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Speaker 1: mean guys who just got into business at an insanely

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Speaker 1: early age and just grind it at some of the

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Speaker 1: lowest jobs imaginable until they rose up like you did.

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Speaker 1: And like those guys have two pretty high positions at

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Speaker 1: the company. I mean, I think it's Eric Shanks used

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Speaker 1: to do who is now runs Fox Sports. Used to

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Speaker 1: be like the p A, the production assistant who would

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Speaker 1: pick up Terry Bradshaw and some of the young guys

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Speaker 1: at the very earliest days of of Fox Sports and

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Speaker 1: drive them around. Yeah. I think that's exactly right. And

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Speaker 1: when I talk about this business have a certain survival

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Speaker 1: of the fittest element, that's really what I mean. You

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Speaker 1: know that there were guys like Shanks and Zeger, Jacob Allman,

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Speaker 1: hopefully me um who started at a really junior level,

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Speaker 1: and we're able to just keep grinding and make it

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Speaker 1: to the jobs that we have today, partly on desire

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Speaker 1: and determination, but also because we were part of a

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Speaker 1: culture that UH encourage young people with that attitude to thrive.

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Speaker 1: And I think if there's anything that defines the culture

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Speaker 1: of this company now, it's that those people you just

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Speaker 1: mentioned who have risen to pretty senior positions want to

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Speaker 1: keep that dynamic in place. You know, we want this

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Speaker 1: to be a place where a young person at twenty

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Speaker 1: three or twenty four can walk into the office of

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Speaker 1: somebody who's a president or an e v P and

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Speaker 1: have an idea or a way of thinking that's different, uh,

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Speaker 1: and really be heard and make an impact. That's the

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Speaker 1: way it should work. First time I saw your name

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Speaker 1: and started to pay attention to your work was you

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Speaker 1: wrote what I would say is a counter into intuitive narrative,

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Speaker 1: diving into the numbers behind Major League Baseball and explod

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Speaker 1: meaning why the baseball business was more sound than a

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Speaker 1: lot of people thought. And let me just kind of

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Speaker 1: for a background for people out there in general. And

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Speaker 1: I'm curious if you're gonna buy into this hypothesis I'm

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Speaker 1: gonna lay out and then I'll let you kind of

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Speaker 1: dive into it. In general, I think that the data

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Speaker 1: you presented shows that Major League Baseball is incredibly popular

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Speaker 1: on a local level. In fact, if you compare Major

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Speaker 1: League Baseball teams, for instance, to NBA franchises in the

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Speaker 1: same market, the Major League Baseball team is buying large,

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Speaker 1: wildly popular. Where baseball struggles on a national level is

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Speaker 1: pretty much every NFL team. If you're a sports fan,

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Speaker 1: you know a couple of guys that play on that

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Speaker 1: team and maybe a couple of their stories. Certainly in

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Speaker 1: the NBA there are six or seven or eight names

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Speaker 1: that are so big they kind of dominate. On the

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Speaker 1: national level. Baseball is regionally stronger than almost any sport,

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Speaker 1: but nationally does not dominate in the same way, and

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Speaker 1: so we don't talk about it as much as we

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Speaker 1: probably should. Is that roughly accurate as the data, like

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Speaker 1: kind of spoke to you, and would you say about baseball?

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Speaker 1: Maybe that would be counterintuitive to people out there listening

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Speaker 1: right now. So that's a lot to respond to. I mean,

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Speaker 1: I think that the way that we tend to evaluate

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Speaker 1: television programming and media content is things that are strong

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Speaker 1: nationally and that air once a week, UM tend to

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Speaker 1: be viewed a little bit more favorably because that's the

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Speaker 1: model of television programming that we all grew up with.

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Speaker 1: You know, we all grew up with a hit television

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Speaker 1: show being something that aired once a week in prime time,

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Speaker 1: whether that was a sitcom or a drama or Monday

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Speaker 1: night football. And so it's a way of thinking about

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Speaker 1: content that everybody is really comfortable with. And that way

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Speaker 1: of thinking about content very much favors the NFL model

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Speaker 1: and somewhat less so favors the college football model and

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Speaker 1: the NBA model, which, as you say, even though they

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Speaker 1: play eighty two games a year, that is very very

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Speaker 1: much a leak that's driven by five to ten superstar

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Speaker 1: personalities that can drive at national aiding baseball because it

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Speaker 1: is so UM, It's popularity is so locally driven, and

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Speaker 1: it's it's so market to market doesn't really UM is

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Speaker 1: not really advantaged by a way of thinking about content

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Speaker 1: that's all about national and weekly programming. So you have

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Speaker 1: to adjust your thinking a little bit and evaluate baseball

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Speaker 1: in terms of the local viewership that it generates a

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Speaker 1: hundred and sixty two times a year, and when you

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Speaker 1: convert those local ratings into minutes of consumption in a

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Speaker 1: market like New York, Chicago, Los Angeles, wherever, what you

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Speaker 1: come to realize is that people in those cities spend

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Speaker 1: more of their time watching Major League Baseball than they

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Speaker 1: spend watching literally anything else. Um. And if you can

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Speaker 1: get people to kind of reframe their thinking so that

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Speaker 1: you're thinking more locally and more five or six days

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Speaker 1: a week, which in television terms would be more like

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Speaker 1: a syndication model, rather than thinking in terms of national

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Speaker 1: and once a week, I think it does make sense

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Speaker 1: to pe and we have been able to persuade a

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Speaker 1: lot of people who covered this business for a living,

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Speaker 1: who by advertising, who invest in content, um, that there's

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Speaker 1: a much greater value to the baseball business than maybe

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Speaker 1: was previously understood. I actually think that, And it's not

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Speaker 1: just me making that argument. I mean it's being made

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Speaker 1: by people at the Commissioner's office every day, people who

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Speaker 1: work for the regional sports networks. But I think we've

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Speaker 1: had a lot of success in helping people reframe their

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Speaker 1: thinking around baseball and why it's a lot more successful

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Speaker 1: as a television property than maybe was understood as recently

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Speaker 1: as five years ago. Fox Sports Radio has the best

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Speaker 1: sports talk lineup in the nation. Catch all of our

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Speaker 1: shows at Fox sports Radio dot com and within the

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Speaker 1: I Heart Radio app search f s R to listen live.

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Speaker 1: I want to go into the decision that you made

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Speaker 1: and uh and and certainly that Fox has made to

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Speaker 1: build a college football pregame show that airs on Fox,

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Speaker 1: and to follow it up with what you guys are

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Speaker 1: calling Big Noon Saturday, a big game called by Joel klatt,

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Speaker 1: Us Johnson, Jenny Taft. How do you look at the

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Speaker 1: data and decide we're gonna go after noon in college football?

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Speaker 1: And then what goes into that? How do you have

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Speaker 1: the hypothesis, what data do you like, what pricks that

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Speaker 1: interest initially? And then how does something like that end

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Speaker 1: up happening. Sure, so let's start by just kind of

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Speaker 1: laying out the landscape of college football. I mean that's

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Speaker 1: a sport where the Disney Networks, ABC, ESPN and ESPN

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Speaker 1: two UM really have a dominant position and have for

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Speaker 1: many many years. You know, they're involved in all five

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Speaker 1: of the power conferences. UM they've had a powerful presence

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Speaker 1: in primetime now for probably ten years or more UM,

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Speaker 1: and so we're coming into the collegiate space, or we

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Speaker 1: came into the collegiate space, I guess it was six

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Speaker 1: seasons ago, UM facing a really powerful competitor. And then

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Speaker 1: in addition to those Disney networks, CBS obviously has their

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Speaker 1: relationship with the SEC, which is really powerful and generally

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Speaker 1: is in that late afternoon window. And NBC has six

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Speaker 1: or seven or Dame games a year. So it's a

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Speaker 1: marketplace that's very crowded, and your competitors are extremely well established.

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Speaker 1: And so even though our conference relationships are excellent, the

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Speaker 1: portfolio of content that we have is terrific. UM, it's

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Speaker 1: hard to break through. And I think what we were

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Speaker 1: finding was that when we would put our best games

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Speaker 1: on in prime time, UM, we were just walking into

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Speaker 1: a very crowded, competitive marketplace. And you would hear from fans,

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Speaker 1: you would hear on social media, I would hear it

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Speaker 1: from you personally that the most frustrating thing as a

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Speaker 1: college football fan would be to show up at three

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Speaker 1: thirty on a Saturday afternoon and there were five good

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Speaker 1: games on five different networks, whereas just a few hours

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Speaker 1: earlier there was nothing to watch. UM. That's that seems

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Speaker 1: like a market inefficiency, right, That seems like something that

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Speaker 1: we can exploit. Simultaneous to that, our Big Ten contract

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Speaker 1: UM prohibits us from playing from playing as many games

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Speaker 1: in prime time as we might otherwise late in the season,

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Speaker 1: you know, the weather gets cold, the calendar turns to November.

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Speaker 1: Some of those Big ten campuses, they don't want to

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Speaker 1: host games under the lights, and so we're required to

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Speaker 1: play big games, including Ohio State Michigan early in the day.

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Speaker 1: And what we found was that we were having a

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Speaker 1: lot of success with those games that we were required

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Speaker 1: to play earlier in the day. UM. Sometimes what you

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Speaker 1: are required to do turns out to have been a

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Speaker 1: pretty good idea all along. And so I felt and

435
00:21:25,920 --> 00:21:28,440
Speaker 1: we felt as a company that we were seeing enough

436
00:21:28,480 --> 00:21:30,800
Speaker 1: success with the games that we were required to play

437
00:21:30,880 --> 00:21:33,560
Speaker 1: outside of prime time that it probably made sense to

438
00:21:33,560 --> 00:21:36,359
Speaker 1: go even further with that strategy and see if we

439
00:21:36,359 --> 00:21:40,880
Speaker 1: could just orient our collegiate brand around the early part

440
00:21:40,880 --> 00:21:44,240
Speaker 1: of the day, which meant going out and upgrading our

441
00:21:44,320 --> 00:21:47,520
Speaker 1: studio show, creating the pregame show that's now called Big

442
00:21:47,520 --> 00:21:51,440
Speaker 1: Noon Kickoff UM, and having that lead into our best

443
00:21:51,480 --> 00:21:54,920
Speaker 1: game of the day, which typically this season, The New

444
00:21:54,920 --> 00:21:56,720
Speaker 1: and Eastern game is our best game of the day.

445
00:21:56,760 --> 00:21:59,160
Speaker 1: You know, I had felt for some time that every

446
00:21:59,160 --> 00:22:02,520
Speaker 1: other network and Alton College football had an identity. You know,

447
00:22:02,880 --> 00:22:05,920
Speaker 1: cbs is identity is the best game from the best conference.

448
00:22:05,920 --> 00:22:07,920
Speaker 1: You know, we can debate whether the SEC is still

449
00:22:07,920 --> 00:22:09,600
Speaker 1: the best conference. I know you believe that it is,

450
00:22:09,840 --> 00:22:12,520
Speaker 1: but that's a very clear identity to put forward to people.

451
00:22:12,920 --> 00:22:16,480
Speaker 1: NBC's identity is all the iconography and history that comes

452
00:22:16,480 --> 00:22:18,960
Speaker 1: with Notre Dame football. That's a very clear identity. And

453
00:22:19,000 --> 00:22:21,359
Speaker 1: the Disney identity is just volume. I mean, they're almost

454
00:22:21,359 --> 00:22:24,399
Speaker 1: like a public utility of college athletics. You can just

455
00:22:24,440 --> 00:22:26,280
Speaker 1: turn them on any time and know that you're going

456
00:22:26,320 --> 00:22:29,720
Speaker 1: to get something relevant and something watchable. Well, what was

457
00:22:29,800 --> 00:22:31,959
Speaker 1: our identity? You know, we didn't really know what it was.

458
00:22:32,400 --> 00:22:34,760
Speaker 1: And now I think we've been able to establish this

459
00:22:34,840 --> 00:22:37,800
Speaker 1: narrative that our identity is that the first place you

460
00:22:37,800 --> 00:22:39,679
Speaker 1: should go when you get up on a college football

461
00:22:39,720 --> 00:22:42,240
Speaker 1: Saturday is Fox. We want to be the first must

462
00:22:42,240 --> 00:22:44,680
Speaker 1: see game of the day. Uh. And I think it's working,

463
00:22:44,720 --> 00:22:46,080
Speaker 1: you know, to kind of get into the ratings a

464
00:22:46,119 --> 00:22:49,480
Speaker 1: little bit. We're five weeks into the season. Um Our

465
00:22:49,560 --> 00:22:54,400
Speaker 1: college football in general is up thirty. Our college football

466
00:22:54,440 --> 00:22:57,760
Speaker 1: games at noon Eastern are up se and this is

467
00:22:57,760 --> 00:23:00,960
Speaker 1: at a point in the season where the board generally

468
00:23:01,119 --> 00:23:04,080
Speaker 1: across every network is flat with where it was a

469
00:23:04,160 --> 00:23:06,439
Speaker 1: year ago. So the sport in general is kind of

470
00:23:06,480 --> 00:23:08,960
Speaker 1: going sideways. And we've been able to take a big

471
00:23:08,960 --> 00:23:12,040
Speaker 1: step forward just because we've rethought the way that we

472
00:23:12,160 --> 00:23:14,440
Speaker 1: used those assets and we saw an opportunity to take

473
00:23:14,440 --> 00:23:16,000
Speaker 1: advantage of the early part of the day, and I

474
00:23:16,040 --> 00:23:18,520
Speaker 1: think it's working out really well. How do you pick games?

475
00:23:18,680 --> 00:23:22,439
Speaker 1: That's a question I think that people ask all the time, Uh,

476
00:23:22,520 --> 00:23:25,800
Speaker 1: the scheduling of you. You mentioned that Fox has got

477
00:23:25,840 --> 00:23:28,440
Speaker 1: the Big twelve, the Pac twelve, the Big ten. Uh.

478
00:23:28,480 --> 00:23:31,919
Speaker 1: Certainly ESPN has got a lot, NBC Notre Dame, and

479
00:23:32,359 --> 00:23:35,639
Speaker 1: SEC is on CBS. How do you uh, in a

480
00:23:35,680 --> 00:23:38,920
Speaker 1: given week? How does the draft process work when you're

481
00:23:39,359 --> 00:23:42,240
Speaker 1: not when it's easy if you have every game right

482
00:23:42,320 --> 00:23:46,000
Speaker 1: like so, uh, if you're like that, For instance, the ESPN,

483
00:23:46,040 --> 00:23:48,919
Speaker 1: I believe, has virtually every A C C game, So

484
00:23:49,000 --> 00:23:51,040
Speaker 1: they know that they've got every A C C game,

485
00:23:51,040 --> 00:23:52,959
Speaker 1: they can schedule and work about where those games are

486
00:23:52,960 --> 00:23:56,560
Speaker 1: gonna air. But when you're dealing with multiple schools in

487
00:23:56,680 --> 00:23:59,640
Speaker 1: situations like that, how do you how do you make decisions?

488
00:23:59,880 --> 00:24:02,480
Speaker 1: And how far out are you picking games? Like? How

489
00:24:02,480 --> 00:24:06,120
Speaker 1: does that work? So it's a multi step process, and

490
00:24:06,200 --> 00:24:09,359
Speaker 1: the first step happens in the spring where we and

491
00:24:09,680 --> 00:24:13,280
Speaker 1: Disney we'll sit down and have a draft in which

492
00:24:13,359 --> 00:24:17,399
Speaker 1: we um pick windows. Were not actually picking games, but

493
00:24:17,480 --> 00:24:20,359
Speaker 1: we're picking what the selection order will be in each

494
00:24:20,359 --> 00:24:22,959
Speaker 1: week of the season as the year goes on. So

495
00:24:23,080 --> 00:24:26,880
Speaker 1: to kind of put that in more relatable terms, when

496
00:24:26,880 --> 00:24:29,440
Speaker 1: we do our Big Ten draft, we have the first

497
00:24:29,480 --> 00:24:32,240
Speaker 1: pick in the Big Ten, and we don't say we're

498
00:24:32,280 --> 00:24:35,879
Speaker 1: taking Ohio State Michigan, but we say we'll take the

499
00:24:35,960 --> 00:24:42,000
Speaker 1: number one selection on November and that the massive likelihood

500
00:24:42,000 --> 00:24:43,919
Speaker 1: is that you will later take Ohio State Michigan. But

501
00:24:43,960 --> 00:24:47,639
Speaker 1: what you're actually selecting is the right to select that

502
00:24:47,680 --> 00:24:49,520
Speaker 1: match up later in the year. So we have the

503
00:24:49,600 --> 00:24:52,680
Speaker 1: number one pick, We'll take November thirty, they'll come back

504
00:24:52,800 --> 00:24:56,600
Speaker 1: and take October. Then we come back and take November,

505
00:24:57,520 --> 00:25:00,240
Speaker 1: and you go through this process of just divid hiding

506
00:25:00,359 --> 00:25:02,280
Speaker 1: up the dates so by the time you get through

507
00:25:02,320 --> 00:25:06,280
Speaker 1: that draft, you know who has picks one through seven

508
00:25:07,160 --> 00:25:10,640
Speaker 1: on every week of the season. Then as you get

509
00:25:10,640 --> 00:25:13,879
Speaker 1: closer to the season, you actually fill out those boxes

510
00:25:14,119 --> 00:25:16,800
Speaker 1: with the matchups themselves. And what that allows you to

511
00:25:16,840 --> 00:25:20,240
Speaker 1: do is it gives you the flexibility to react to

512
00:25:20,359 --> 00:25:23,240
Speaker 1: events on the field, UM as they happen. You know,

513
00:25:23,280 --> 00:25:28,600
Speaker 1: certain teams overperform expectations, other teams obviously underperform expectations, And

514
00:25:28,640 --> 00:25:32,000
Speaker 1: instead of being locked into a game that you fought

515
00:25:32,200 --> 00:25:35,040
Speaker 1: was going to be strong in March or April, you

516
00:25:35,080 --> 00:25:37,560
Speaker 1: can react to that as it happens. And that process

517
00:25:37,640 --> 00:25:40,359
Speaker 1: is managed by a guy on our team named Derek

518
00:25:40,400 --> 00:25:43,320
Speaker 1: Crocker UH and a small team that he has that

519
00:25:43,400 --> 00:25:47,440
Speaker 1: focuses exclusively on collegiate sports, and they spend all their

520
00:25:47,480 --> 00:25:50,800
Speaker 1: time just gaining out these draft scenarios and what will

521
00:25:50,840 --> 00:25:53,240
Speaker 1: happen if we take this game number one, and then

522
00:25:53,480 --> 00:25:55,679
Speaker 1: ESPN takes this one number two, what's left for us

523
00:25:55,720 --> 00:25:59,240
Speaker 1: after that. It's a really fascinating process. If you're a

524
00:25:59,240 --> 00:26:02,879
Speaker 1: passionate fan of college football, um, it's one of the

525
00:26:02,920 --> 00:26:05,440
Speaker 1: most fun jobs you could possibly have. I always say

526
00:26:05,440 --> 00:26:08,959
Speaker 1: that the college football draft process is, um the world's

527
00:26:09,000 --> 00:26:13,359
Speaker 1: greatest fantasy football It should be, right, well, it's the

528
00:26:13,440 --> 00:26:15,399
Speaker 1: it's a great fantasy football draft. It just happens to

529
00:26:15,440 --> 00:26:18,560
Speaker 1: cost five million dollars to play instead of fifty bucks.

530
00:26:19,240 --> 00:26:21,080
Speaker 1: But then as we go through the year, you know,

531
00:26:21,119 --> 00:26:24,359
Speaker 1: we'll get into situations where I think November sixteenth of

532
00:26:24,359 --> 00:26:29,080
Speaker 1: this season is a great example. Michigan State Michigan is there, Wisconsin,

533
00:26:29,160 --> 00:26:33,200
Speaker 1: Nebraska is there. If you had been asked in April,

534
00:26:33,320 --> 00:26:36,000
Speaker 1: you would have said Michigan State Michigan is probably the

535
00:26:36,080 --> 00:26:40,240
Speaker 1: number one game on that day. Because Wisconsin just blew

536
00:26:40,320 --> 00:26:43,480
Speaker 1: Michigan out, that might change your thinking. Wisconsin might still

537
00:26:43,520 --> 00:26:45,159
Speaker 1: be alive for a spot in the playoff by the

538
00:26:45,160 --> 00:26:47,560
Speaker 1: time we get to November six and so, because we're

539
00:26:47,600 --> 00:26:50,920
Speaker 1: not locked into a matchup, but we've selected the right

540
00:26:51,480 --> 00:26:54,159
Speaker 1: to make a certain selection on that date, you know

541
00:26:54,240 --> 00:26:57,000
Speaker 1: we can adjust and adapt as we see results come

542
00:26:57,000 --> 00:27:00,600
Speaker 1: in throughout the year. Is the selection process us occurring

543
00:27:00,680 --> 00:27:02,920
Speaker 1: via phone, like everybody's sitting around the table with a

544
00:27:02,960 --> 00:27:06,639
Speaker 1: speaker phone or are they in the same room? Um?

545
00:27:07,000 --> 00:27:09,280
Speaker 1: One year we did it in the same room, and

546
00:27:09,320 --> 00:27:12,200
Speaker 1: we found that it was actually more efficient to just

547
00:27:12,320 --> 00:27:15,320
Speaker 1: do it on the phone because it can be a

548
00:27:15,359 --> 00:27:18,080
Speaker 1: slow process. That's the reason it wouldn't make for good

549
00:27:18,119 --> 00:27:21,240
Speaker 1: television is that there can be long periods where you're

550
00:27:21,280 --> 00:27:24,440
Speaker 1: just thinking about what your next move is and where

551
00:27:24,480 --> 00:27:27,160
Speaker 1: you want to go next in the schedule. In our case,

552
00:27:27,200 --> 00:27:30,760
Speaker 1: we're drafting our three conferences simultaneously, So you might make

553
00:27:31,280 --> 00:27:33,399
Speaker 1: UM a couple of Big twelve selections and then a

554
00:27:33,440 --> 00:27:36,400
Speaker 1: couple of Pack twelve selections, and then four Big Ten selections,

555
00:27:36,760 --> 00:27:39,680
Speaker 1: and they're they're all intertwined, you know, each conference affects

556
00:27:39,720 --> 00:27:43,879
Speaker 1: the others UH, and so that can be a really slow, UM,

557
00:27:44,119 --> 00:27:47,800
Speaker 1: deliberate uh process to go through, and it just doesn't

558
00:27:47,800 --> 00:27:49,240
Speaker 1: make that much sense for us to all be in

559
00:27:49,280 --> 00:27:51,320
Speaker 1: the same room, like we've found that it's just easier

560
00:27:51,359 --> 00:27:54,000
Speaker 1: to do it via teleconference. But we do have a

561
00:27:54,000 --> 00:27:56,119
Speaker 1: lot of fun with it. It's an opportunity for us

562
00:27:56,119 --> 00:27:59,280
Speaker 1: to sit with our talent and get their opinions. UM.

563
00:27:59,359 --> 00:28:02,119
Speaker 1: They all seem find the process really interesting. You know.

564
00:28:02,160 --> 00:28:04,600
Speaker 1: This is obviously our first season working with urban Meyer,

565
00:28:05,200 --> 00:28:07,800
Speaker 1: and he provided a lot of insight for us into

566
00:28:08,160 --> 00:28:10,520
Speaker 1: each of the Big ten schools. And it was really

567
00:28:10,560 --> 00:28:13,720
Speaker 1: a conversation that we had with urban Um that led

568
00:28:13,800 --> 00:28:17,439
Speaker 1: us to select the Army Michigan game on September seventh

569
00:28:17,960 --> 00:28:21,080
Speaker 1: over the Cincinnati, Ohio State game. You know, all things

570
00:28:21,119 --> 00:28:24,600
Speaker 1: being equal, Ohio State is probably a little bit better

571
00:28:24,720 --> 00:28:27,520
Speaker 1: ratings draw than Michigan is nationally. I mean, they're both

572
00:28:27,520 --> 00:28:30,919
Speaker 1: elite brands, but Ohio State is probably a little stronger.

573
00:28:31,320 --> 00:28:33,520
Speaker 1: But in that case, because of the conversation we had

574
00:28:33,560 --> 00:28:36,679
Speaker 1: had with Urban in which he really stressed the difficulty

575
00:28:36,720 --> 00:28:39,760
Speaker 1: of preparing for that triple option offense, we went ahead

576
00:28:39,800 --> 00:28:42,120
Speaker 1: and took the Army Michigan game. And you know what

577
00:28:42,200 --> 00:28:45,280
Speaker 1: happened next. I think Ohio State beat Cincinnati by forty

578
00:28:45,360 --> 00:28:47,480
Speaker 1: and the Army Michigan game went to overtime. Turned out

579
00:28:47,480 --> 00:28:48,880
Speaker 1: to be one of the most interesting games of the

580
00:28:48,920 --> 00:28:51,240
Speaker 1: season so far. So you know, that's the case where

581
00:28:51,240 --> 00:28:54,040
Speaker 1: you really benefit from getting your talent involved in the process.

582
00:28:54,640 --> 00:28:56,320
Speaker 1: How do you much do you see brands? You were

583
00:28:56,320 --> 00:28:59,280
Speaker 1: talking about some of the counterintuitive wisdom that you might

584
00:28:59,280 --> 00:29:01,760
Speaker 1: have picked up early on with John Madden and UH

585
00:29:01,800 --> 00:29:05,960
Speaker 1: and with baseball pitching. How much do brands themselves matter?

586
00:29:06,080 --> 00:29:08,800
Speaker 1: So you you mentioned like Ohio State in Michigan, the

587
00:29:08,880 --> 00:29:11,600
Speaker 1: audience is primarily coming to watch Ohio State playing a

588
00:29:11,600 --> 00:29:14,600
Speaker 1: game against Cincinnati. The audience is primarily coming UH to

589
00:29:14,640 --> 00:29:17,400
Speaker 1: watch Michigan playing a game against army. What are the

590
00:29:17,440 --> 00:29:20,680
Speaker 1: best brands in college football just in terms of delivering

591
00:29:21,200 --> 00:29:24,320
Speaker 1: audience and also in the NFL, like just teams the

592
00:29:24,360 --> 00:29:27,560
Speaker 1: irrespective of who they're playing. How much do teams move

593
00:29:27,640 --> 00:29:32,520
Speaker 1: the needle? Um brands matter enormously. I think you almost

594
00:29:32,560 --> 00:29:37,040
Speaker 1: cannot overstate the importance of brands because not everybody is

595
00:29:37,080 --> 00:29:40,280
Speaker 1: following the league or following the college game as closely

596
00:29:40,480 --> 00:29:43,000
Speaker 1: as you or I or people who would be listening

597
00:29:43,000 --> 00:29:45,600
Speaker 1: to this podcast. You know, the majority of the audience

598
00:29:45,920 --> 00:29:49,600
Speaker 1: are people who are following it in a very casual way, uh,

599
00:29:49,600 --> 00:29:53,520
Speaker 1: and they're not clued into teams that are overperforming expectations.

600
00:29:53,560 --> 00:29:55,280
Speaker 1: What they know is that when they think of the

601
00:29:55,320 --> 00:29:57,880
Speaker 1: Big Ten, they think of Ohio State and Michigan first.

602
00:29:57,920 --> 00:30:00,600
Speaker 1: When they think of the SEC, they think about Bama first,

603
00:30:00,600 --> 00:30:03,160
Speaker 1: and maybe Georgia and l s U after that, or

604
00:30:03,200 --> 00:30:08,720
Speaker 1: in other years it's been Florida. Um Brands are incredibly important,

605
00:30:08,720 --> 00:30:11,680
Speaker 1: and I think we have a constant debate about the

606
00:30:11,800 --> 00:30:16,800
Speaker 1: value of excuse me, powerful brands who are maybe underperforming

607
00:30:16,800 --> 00:30:21,600
Speaker 1: on the field versus less powerful brands that are actually

608
00:30:21,600 --> 00:30:24,840
Speaker 1: winning games. Excuse me, there are winning games and delivering

609
00:30:24,840 --> 00:30:27,840
Speaker 1: on the field real life example that we just faced

610
00:30:28,200 --> 00:30:31,959
Speaker 1: in the PAC twelve. Um, we had USC and Washington

611
00:30:32,560 --> 00:30:36,600
Speaker 1: and on the same day we had Washington State and Utah. Now,

612
00:30:36,720 --> 00:30:39,400
Speaker 1: in terms of performance on the field, the better football

613
00:30:39,480 --> 00:30:43,320
Speaker 1: game was likely to be Washington State and Utah. The

614
00:30:43,360 --> 00:30:48,000
Speaker 1: better brands inarguably our USC and Washington. And we had

615
00:30:48,040 --> 00:30:50,480
Speaker 1: a lot of back and forth about that, and ultimately

616
00:30:50,480 --> 00:30:53,680
Speaker 1: we decided to go ahead and put the USC Washington

617
00:30:53,760 --> 00:30:57,040
Speaker 1: game on the broadcast network and put the Washington State

618
00:30:57,120 --> 00:31:00,360
Speaker 1: Utah game on FS one, thinking that particular or case

619
00:31:00,400 --> 00:31:03,320
Speaker 1: that was the right decision. Um. But it's a constant

620
00:31:03,320 --> 00:31:07,040
Speaker 1: debate because there are cases where the weaker brand is

621
00:31:07,080 --> 00:31:09,800
Speaker 1: so much stronger on the field, um, that it does

622
00:31:09,920 --> 00:31:13,040
Speaker 1: carry more weight than potentially a stronger brand. You Know,

623
00:31:13,080 --> 00:31:16,080
Speaker 1: in the NFL, I don't think it would be any surprise, um,

624
00:31:16,120 --> 00:31:19,280
Speaker 1: that the Cowboys are the most powerful brand nationally. And

625
00:31:19,320 --> 00:31:23,920
Speaker 1: then there's a second tier that I think concludes the Packers,

626
00:31:24,760 --> 00:31:28,640
Speaker 1: the Bears, the Pittsburgh Steelers, and by now you'd have

627
00:31:28,680 --> 00:31:30,560
Speaker 1: to say the Patriots. I mean, there was a time,

628
00:31:30,680 --> 00:31:32,520
Speaker 1: you know, in our life as fans, where in the

629
00:31:32,560 --> 00:31:34,960
Speaker 1: Patriots were actually one of the weakest brands in the league.

630
00:31:35,280 --> 00:31:38,240
Speaker 1: But they've now been so good for so long. Um.

631
00:31:38,240 --> 00:31:40,000
Speaker 1: But I think you'd have to consider them as being

632
00:31:40,080 --> 00:31:43,320
Speaker 1: solidly on that second tier of NFL brands, at least

633
00:31:43,320 --> 00:31:45,960
Speaker 1: for as long as Brady and Belichick are are with

634
00:31:46,040 --> 00:31:48,880
Speaker 1: the organization. In an errow, when there is almost an

635
00:31:48,880 --> 00:31:53,440
Speaker 1: infinite number of entertainment options, are brands becoming more valuable

636
00:31:53,520 --> 00:32:00,440
Speaker 1: because they cut through the noise? Probably yes, Um, I

637
00:32:00,480 --> 00:32:03,840
Speaker 1: think that's probably fair. And look, a brand can be

638
00:32:04,080 --> 00:32:07,600
Speaker 1: a school, um, A brand can be a team, and

639
00:32:07,760 --> 00:32:09,840
Speaker 1: a brand can be an individual like in the case

640
00:32:09,880 --> 00:32:11,760
Speaker 1: of the n b A, I think they are driven

641
00:32:11,800 --> 00:32:17,240
Speaker 1: more by individual stars than college football or NFL football.

642
00:32:17,840 --> 00:32:21,560
Speaker 1: But those individual stars are brands, just the same as

643
00:32:21,920 --> 00:32:24,320
Speaker 1: the star on the side of the Dallas Cowboys helmet

644
00:32:24,520 --> 00:32:27,920
Speaker 1: is a brand. UM. I do think that, as you say,

645
00:32:27,960 --> 00:32:32,240
Speaker 1: we're in a world of virtually infinite choice, virtually infinite

646
00:32:32,280 --> 00:32:35,880
Speaker 1: flexibility in terms of how, when, and where you can

647
00:32:35,920 --> 00:32:40,880
Speaker 1: consume your media content. I think you hear anecdotally all

648
00:32:40,920 --> 00:32:44,560
Speaker 1: the time that people feel almost overwhelmed by the degree

649
00:32:44,560 --> 00:32:47,440
Speaker 1: of choice that's out there, and so it's useful to

650
00:32:47,520 --> 00:32:50,760
Speaker 1: be able to simplify that process for people. And I

651
00:32:50,800 --> 00:32:55,280
Speaker 1: think what's really simple is Packers Cowboys at on a

652
00:32:55,320 --> 00:32:57,720
Speaker 1: Sunday afternoon. You know you don't have to be paying

653
00:32:57,760 --> 00:33:00,680
Speaker 1: that close of attention to understand that that's kind of

654
00:33:00,720 --> 00:33:03,920
Speaker 1: a cool watchable matchup. So I tend to agree with

655
00:33:03,960 --> 00:33:05,840
Speaker 1: the premise. I think brand has become more important all

656
00:33:05,840 --> 00:33:08,440
Speaker 1: the time. Be sure to catch live editions about Kick

657
00:33:08,480 --> 00:33:11,640
Speaker 1: the Coverage with Clay Travis week days at six am Eastern,

658
00:33:11,720 --> 00:33:15,760
Speaker 1: three am Pacific. We're talking to Michael mulvihill. I'm Clay Travis.

659
00:33:15,760 --> 00:33:18,600
Speaker 1: This is the Wins and Losses podcast. In that world

660
00:33:18,600 --> 00:33:22,360
Speaker 1: of infinite options, Read Hastings recently said that he thinks

661
00:33:22,520 --> 00:33:26,720
Speaker 1: live television is basically going to become news and sports.

662
00:33:27,200 --> 00:33:31,160
Speaker 1: Do you think that thesis is correct? I mean, I

663
00:33:31,160 --> 00:33:33,959
Speaker 1: think it becomes more correct every year. And that's a

664
00:33:33,960 --> 00:33:37,480
Speaker 1: way of thinking that we um talk about within Fox

665
00:33:37,520 --> 00:33:41,440
Speaker 1: Sports a lot. That the world of video content is

666
00:33:41,480 --> 00:33:45,920
Speaker 1: separating into live and on demand, and the on demand

667
00:33:45,960 --> 00:33:51,520
Speaker 1: marketplace is increasingly controlled by companies like Reads and companies

668
00:33:51,560 --> 00:33:55,240
Speaker 1: that allow consumers to choose the content that they want,

669
00:33:55,360 --> 00:33:57,520
Speaker 1: any time that they wanted. I mean, that's an environment

670
00:33:57,560 --> 00:34:01,640
Speaker 1: that's all about um consumer and hourman and flexibility and

671
00:34:01,640 --> 00:34:04,280
Speaker 1: then on our side, we're part of a marketplace of

672
00:34:04,320 --> 00:34:07,520
Speaker 1: content that demands to be viewed in real time, and

673
00:34:07,560 --> 00:34:12,160
Speaker 1: I think that is primarily or almost entirely UM premium

674
00:34:12,200 --> 00:34:16,400
Speaker 1: sports and twenty four hour news. So I tend to

675
00:34:16,440 --> 00:34:19,120
Speaker 1: agree with that. I think it becomes more challenging all

676
00:34:19,120 --> 00:34:23,480
Speaker 1: the time, UM to do entertainment content in an environment

677
00:34:23,560 --> 00:34:26,600
Speaker 1: that is not on demand. There's still a business there.

678
00:34:26,960 --> 00:34:29,080
Speaker 1: You know, there are still tens of millions of people

679
00:34:29,600 --> 00:34:34,640
Speaker 1: UM watching entertainment programming on traditional television every night, so

680
00:34:34,800 --> 00:34:37,040
Speaker 1: it hasn't disappeared, and I don't think it's going to

681
00:34:37,080 --> 00:34:39,640
Speaker 1: truly disappear anytime soon. But I think it's a reality

682
00:34:39,760 --> 00:34:44,320
Speaker 1: that the business bifurkates more into that on demand world

683
00:34:44,360 --> 00:34:47,920
Speaker 1: and the live world UM literally every quarter, and it's

684
00:34:47,960 --> 00:34:50,360
Speaker 1: to the benefit of companies like Netflix, and it's to

685
00:34:50,400 --> 00:34:55,600
Speaker 1: the benefit of brands like Fox Sports, ESPN, Fox News, CNN,

686
00:34:56,040 --> 00:34:58,160
Speaker 1: brands that are all about content that you have to

687
00:34:58,200 --> 00:35:00,840
Speaker 1: see as it happens, which, by away, if you're watching

688
00:35:00,840 --> 00:35:03,480
Speaker 1: the content as it happens, that means you're not avoiding

689
00:35:03,480 --> 00:35:07,080
Speaker 1: the advertising. And from strictly a business point of view, UM,

690
00:35:07,120 --> 00:35:09,239
Speaker 1: I think that's one of the most compelling arguments that

691
00:35:09,320 --> 00:35:12,399
Speaker 1: we have for the future of our content. How much

692
00:35:12,440 --> 00:35:16,800
Speaker 1: do you see people making decisions between news and sports?

693
00:35:16,880 --> 00:35:19,279
Speaker 1: In other words, you talked about like a casual fan

694
00:35:19,360 --> 00:35:21,840
Speaker 1: out there might not know what's going on in particular

695
00:35:21,880 --> 00:35:24,800
Speaker 1: with Ohio State or Michigan or the Dallas Cowboys in

696
00:35:24,840 --> 00:35:27,319
Speaker 1: a given year, but they know those brands and they're

697
00:35:27,360 --> 00:35:30,120
Speaker 1: more likely to watch those brands because of, you know,

698
00:35:30,160 --> 00:35:32,719
Speaker 1: associational relationships. I guess that would be in their head

699
00:35:33,000 --> 00:35:35,120
Speaker 1: that I know this is a big game because it

700
00:35:35,160 --> 00:35:37,600
Speaker 1: involves so and so or at least it's something that

701
00:35:37,600 --> 00:35:40,200
Speaker 1: they feel familiar with, which makes them want to watch

702
00:35:40,680 --> 00:35:43,239
Speaker 1: when there are big news events. Are there very many

703
00:35:43,239 --> 00:35:46,040
Speaker 1: people who sit down in front of their television and decide, Hey,

704
00:35:46,080 --> 00:35:48,440
Speaker 1: instead of watching an NFL game today, I'm gonna go

705
00:35:48,480 --> 00:35:51,640
Speaker 1: flip over to MSNBC or CNN or Fox News and

706
00:35:51,640 --> 00:35:53,839
Speaker 1: watch the latest news of the day. Do you see

707
00:35:53,840 --> 00:35:56,920
Speaker 1: a lot of those people existing? Yes? I think that

708
00:35:57,080 --> 00:36:00,960
Speaker 1: absolutely happens every day. Um, you know, you'll remember that

709
00:36:01,000 --> 00:36:06,520
Speaker 1: in NFL viewership was actually down by pretty noticeable percentages,

710
00:36:06,560 --> 00:36:09,920
Speaker 1: by high single digit percentages, still the most powerful content

711
00:36:10,000 --> 00:36:12,680
Speaker 1: in TV, but the league was on kind of a

712
00:36:12,719 --> 00:36:15,000
Speaker 1: declining trend in those seasons, and I think the number

713
00:36:15,080 --> 00:36:19,839
Speaker 1: one reason why was because the election of sucked up

714
00:36:19,920 --> 00:36:23,200
Speaker 1: so much oxygen and took up so much public interest

715
00:36:23,239 --> 00:36:26,799
Speaker 1: that it affected everything else in the television environment. And

716
00:36:26,800 --> 00:36:31,960
Speaker 1: then obviously the result of the election was so surprising,

717
00:36:32,480 --> 00:36:35,840
Speaker 1: and the first year of the Trump presidency um was

718
00:36:35,920 --> 00:36:38,880
Speaker 1: so eventful. I think news interest at that time was,

719
00:36:39,480 --> 00:36:42,759
Speaker 1: you know, probably the highest that it's ever been in peacetime, right, Like,

720
00:36:42,800 --> 00:36:45,840
Speaker 1: that's kind of an interesting hypothesis. I guess that for

721
00:36:45,960 --> 00:36:48,440
Speaker 1: news interest to be any higher than it was from

722
00:36:48,480 --> 00:36:52,440
Speaker 1: the election up through the say, eighteen months that followed,

723
00:36:52,719 --> 00:36:55,239
Speaker 1: I don't know that you can have a faster um

724
00:36:55,360 --> 00:36:58,400
Speaker 1: news cycle and still be in a peacetime scenario. So

725
00:36:58,440 --> 00:37:01,879
Speaker 1: I think that definitely impact to sports viewing during those

726
00:37:01,920 --> 00:37:05,480
Speaker 1: two seasons. More recently, you know, we just had our

727
00:37:05,480 --> 00:37:07,840
Speaker 1: first Thursday night football game of the season last week,

728
00:37:08,400 --> 00:37:10,560
Speaker 1: UM Eagles went to Green Bay. Games turned out to

729
00:37:10,600 --> 00:37:15,040
Speaker 1: be terrific. We were up over the previous year. The

730
00:37:15,080 --> 00:37:18,360
Speaker 1: reason we were up wasn't only because the game was

731
00:37:18,400 --> 00:37:22,279
Speaker 1: a quality game between pretty compelling brands. It was also

732
00:37:22,360 --> 00:37:25,959
Speaker 1: because last year's Thursday night football game on the same

733
00:37:26,000 --> 00:37:30,040
Speaker 1: week was the day of the Brett Kavanaugh hearing, um

734
00:37:30,080 --> 00:37:33,600
Speaker 1: which went on for hours and obviously galvanized the attention

735
00:37:33,600 --> 00:37:38,080
Speaker 1: of the country and resulted in millions more people watching

736
00:37:38,120 --> 00:37:41,399
Speaker 1: cable news instead of watching our Thursday night football game.

737
00:37:41,680 --> 00:37:43,720
Speaker 1: You take that big news story out of the mix,

738
00:37:44,000 --> 00:37:46,600
Speaker 1: those people migrate back to football all of a sudden,

739
00:37:46,600 --> 00:37:51,280
Speaker 1: we're up. So I think the two are are absolutely correlated,

740
00:37:51,360 --> 00:37:54,439
Speaker 1: um every week. So you get nervous about that thinking

741
00:37:54,440 --> 00:37:58,000
Speaker 1: about Obviously you have a big investment in sports programming

742
00:37:58,080 --> 00:38:02,000
Speaker 1: and you can't control what happens. But it seems like,

743
00:38:02,040 --> 00:38:04,799
Speaker 1: as we're talking now, a little over a year out,

744
00:38:05,200 --> 00:38:10,719
Speaker 1: that would probably challenge potentially for the craziness, the zany

745
00:38:10,760 --> 00:38:13,640
Speaker 1: nous of it, and that might pull eyeballs away from

746
00:38:13,719 --> 00:38:17,279
Speaker 1: whatever big sporting events are going on. Yeah, and look,

747
00:38:17,280 --> 00:38:20,360
Speaker 1: I don't want to pretend to be a political prognosticator,

748
00:38:20,440 --> 00:38:23,000
Speaker 1: but the news cycle is moving so fast right now

749
00:38:23,040 --> 00:38:26,160
Speaker 1: that it's it's hard to speculate with any confidence on

750
00:38:26,200 --> 00:38:29,439
Speaker 1: what the the campaign might look like a year from now.

751
00:38:29,480 --> 00:38:33,240
Speaker 1: I mean, we could be looking at um unexpected candidates,

752
00:38:33,320 --> 00:38:36,600
Speaker 1: unexpected nominees, you know, who knows where we could be.

753
00:38:37,239 --> 00:38:40,319
Speaker 1: But I think the premise that you're coming from is

754
00:38:40,360 --> 00:38:44,040
Speaker 1: the correct one that we should expect um the election

755
00:38:44,120 --> 00:38:48,680
Speaker 1: cycle to have an impact on all other content, including

756
00:38:48,719 --> 00:38:51,920
Speaker 1: but not limited to sports. Now, the better news for

757
00:38:52,040 --> 00:38:56,640
Speaker 1: us is that a at this company, we're also partnered

758
00:38:56,680 --> 00:38:59,319
Speaker 1: with the leading brand in twenty four hour news, and

759
00:38:59,400 --> 00:39:02,920
Speaker 1: so even if there's a certain risk factor there for sports,

760
00:39:02,920 --> 00:39:07,120
Speaker 1: there's also a tremendous upside for our news business. And

761
00:39:07,280 --> 00:39:11,719
Speaker 1: I think even if there's a potential impact to viewership,

762
00:39:12,239 --> 00:39:15,080
Speaker 1: the amount of money that we expect to see spend

763
00:39:15,560 --> 00:39:20,040
Speaker 1: UH in political advertising next year is so significant that

764
00:39:20,120 --> 00:39:22,480
Speaker 1: we may be headed for a situation is isn't just Fox,

765
00:39:22,520 --> 00:39:24,920
Speaker 1: I mean, this is everybody who's associated with premium sports.

766
00:39:25,239 --> 00:39:28,279
Speaker 1: We might be headed for an election cycle that is

767
00:39:29,000 --> 00:39:32,080
Speaker 1: not necessarily good for viewership but good for revenue. Like

768
00:39:32,120 --> 00:39:34,279
Speaker 1: it wouldn't surprise me if that's the way it played out,

769
00:39:35,080 --> 00:39:36,520
Speaker 1: do you think at all? I mean, I'm kind of

770
00:39:36,520 --> 00:39:41,200
Speaker 1: fascinated by this, just I think when everything is so politicized,

771
00:39:41,239 --> 00:39:43,520
Speaker 1: I haven't made paid a lot of attention, but I'll say,

772
00:39:43,520 --> 00:39:47,520
Speaker 1: for example, in the mid terms last year, I noticed

773
00:39:47,520 --> 00:39:50,240
Speaker 1: on the SEC network that a ton of the Senate

774
00:39:50,360 --> 00:39:53,160
Speaker 1: candidates were buying games? You know, so, in other words,

775
00:39:53,320 --> 00:39:56,600
Speaker 1: yet a competitive senate race in Missouri. Uh, the Senate

776
00:39:56,640 --> 00:40:00,600
Speaker 1: candidates in Missouri, we're buying ads during those games. Uh?

777
00:40:01,200 --> 00:40:03,160
Speaker 1: Is that a big I don't even know. I can't

778
00:40:03,239 --> 00:40:06,960
Speaker 1: think necessarily. Is it typical that candidates spend a lot

779
00:40:06,960 --> 00:40:10,640
Speaker 1: of money on ads during NFL and college football games?

780
00:40:10,800 --> 00:40:13,120
Speaker 1: Or is that a sign of just there being so

781
00:40:13,200 --> 00:40:15,399
Speaker 1: much money that it's kind of something that they buy

782
00:40:15,480 --> 00:40:17,239
Speaker 1: on top of everything else. Do you do you know

783
00:40:17,280 --> 00:40:20,240
Speaker 1: what I'm asking, like, does it index highly for sports

784
00:40:20,239 --> 00:40:24,080
Speaker 1: to get bought for by political candidates, um on the

785
00:40:24,200 --> 00:40:27,959
Speaker 1: national level? Not necessarily, Like, there wouldn't be that much

786
00:40:28,440 --> 00:40:30,920
Speaker 1: opportunity for a candidate in a mid term and a

787
00:40:30,960 --> 00:40:35,640
Speaker 1: congressional race to buy the NFL or buy college football nationally.

788
00:40:35,640 --> 00:40:37,960
Speaker 1: There's just no need for them to reach a national audience.

789
00:40:38,080 --> 00:40:41,240
Speaker 1: What does exist is a great opportunity to reach people

790
00:40:41,400 --> 00:40:45,520
Speaker 1: via sports on your regional sports networks, where you're buying

791
00:40:45,960 --> 00:40:50,520
Speaker 1: Major League Baseball, the NBA or the NHL. I think

792
00:40:50,680 --> 00:40:54,319
Speaker 1: that is actually an underutilized asset. And I think I

793
00:40:54,360 --> 00:40:56,520
Speaker 1: can now say that with a little bit of objectivity

794
00:40:56,560 --> 00:40:58,920
Speaker 1: because Fox no longer owns those r s n S,

795
00:40:59,320 --> 00:41:01,799
Speaker 1: so I hope I'm not being just a homer. I

796
00:41:01,840 --> 00:41:05,239
Speaker 1: think there's some objectivity in saying that the audiences that

797
00:41:05,280 --> 00:41:09,759
Speaker 1: are delivered by pro sports on m r s ns

798
00:41:09,880 --> 00:41:14,359
Speaker 1: all over the country, those audiences correlate really strongly with

799
00:41:14,480 --> 00:41:17,919
Speaker 1: likely voters. Right like, those sports audiences skew a little

800
00:41:17,920 --> 00:41:20,840
Speaker 1: bit older, they tend to be a little bit more affluent,

801
00:41:21,160 --> 00:41:25,280
Speaker 1: better educated. UM. In general, those sports audiences are about

802
00:41:25,320 --> 00:41:28,960
Speaker 1: two thirds male, one third female, which might correlate slightly

803
00:41:29,000 --> 00:41:31,600
Speaker 1: better with UM likely voters, But I don't know that

804
00:41:31,600 --> 00:41:34,120
Speaker 1: that makes a huge impact. But you know, if you're

805
00:41:34,120 --> 00:41:37,799
Speaker 1: thinking in terms of your likeliest voters being people who

806
00:41:37,920 --> 00:41:41,640
Speaker 1: are a little older, a little more affluent, and a

807
00:41:41,640 --> 00:41:44,080
Speaker 1: little better educated, and I think that's a pretty well

808
00:41:44,120 --> 00:41:47,640
Speaker 1: documented fact, there's no better place to be than in

809
00:41:48,239 --> 00:41:51,200
Speaker 1: the local MLB team or the local NBA team, And

810
00:41:51,239 --> 00:41:54,399
Speaker 1: I think some of the smarter campaigns have figured that out, UM,

811
00:41:54,400 --> 00:41:56,000
Speaker 1: But I feel like there's still a lot of runway

812
00:41:56,040 --> 00:41:58,560
Speaker 1: there that the campaigns could take much better advantage of

813
00:41:58,560 --> 00:42:00,959
Speaker 1: the rs n S. One of the questions that gets

814
00:42:00,960 --> 00:42:03,720
Speaker 1: asked all the time is who has the best fans

815
00:42:03,719 --> 00:42:06,960
Speaker 1: in America? What cities care about sports. The most you

816
00:42:07,080 --> 00:42:10,040
Speaker 1: look into the data, and the data kind of tells

817
00:42:10,120 --> 00:42:13,200
Speaker 1: you some interesting stories. Right. For instance, I am very

818
00:42:13,280 --> 00:42:15,799
Speaker 1: confident in saying, based on the data that I've seen,

819
00:42:16,280 --> 00:42:21,040
Speaker 1: no city in America loves college football more than Birmingham, Alabama.

820
00:42:21,120 --> 00:42:24,400
Speaker 1: Right Like Birmingham over index is off the charts for

821
00:42:24,520 --> 00:42:27,759
Speaker 1: virtually every college football game in America. I think from

822
00:42:27,800 --> 00:42:31,480
Speaker 1: your data, the NFL is more popular. I believe it's

823
00:42:31,480 --> 00:42:34,239
Speaker 1: true in New Orleans than any city in America. What

824
00:42:34,440 --> 00:42:36,239
Speaker 1: am I correct in those two in your mind? And

825
00:42:36,360 --> 00:42:38,920
Speaker 1: what other city data have you looked at and just

826
00:42:38,960 --> 00:42:42,640
Speaker 1: found to be fascinating? Um? I would agree with you

827
00:42:42,680 --> 00:42:46,439
Speaker 1: that Birmingham is probably your strongest college football market. And yeah,

828
00:42:46,440 --> 00:42:50,319
Speaker 1: I believe New Orleans is the strongest NFL market and

829
00:42:50,400 --> 00:42:54,120
Speaker 1: the strongest overall football market. Where if you're looking at

830
00:42:54,520 --> 00:42:57,320
Speaker 1: the viewing that's being done on both Saturdays and Sundays,

831
00:42:57,360 --> 00:42:59,680
Speaker 1: I don't think there's a market that shows up for

832
00:42:59,760 --> 00:43:02,440
Speaker 1: both the college game and the pro game pro game

833
00:43:02,880 --> 00:43:04,759
Speaker 1: UM to the extent that New Orleans does, I mean

834
00:43:04,760 --> 00:43:07,440
Speaker 1: they'll do close to a fifty rating. And what that

835
00:43:07,480 --> 00:43:11,279
Speaker 1: means is that close to of all the homes in

836
00:43:11,360 --> 00:43:14,200
Speaker 1: a market are tuned into the game, they'll do close

837
00:43:14,239 --> 00:43:16,560
Speaker 1: to a fifty rating for the Saints, and then they'll

838
00:43:16,560 --> 00:43:18,480
Speaker 1: go out and do a twenty or twenty five for

839
00:43:18,640 --> 00:43:22,560
Speaker 1: non Saints games, and they'll also do for an LSU

840
00:43:22,640 --> 00:43:25,239
Speaker 1: game or an Alabama game on Saturday. I don't think

841
00:43:25,239 --> 00:43:28,520
Speaker 1: there's another city in the country, UM that has that

842
00:43:28,600 --> 00:43:33,319
Speaker 1: kind of all weekend consumption of football. And look, there

843
00:43:33,320 --> 00:43:35,040
Speaker 1: are a lot of cities in this country that are

844
00:43:35,480 --> 00:43:38,920
Speaker 1: passionate about their NFL team or passionate about their local university.

845
00:43:38,960 --> 00:43:42,960
Speaker 1: But I really do believe that in the post Katrina world,

846
00:43:43,400 --> 00:43:46,200
Speaker 1: the relationship between the Saints and the city of New

847
00:43:46,320 --> 00:43:50,160
Speaker 1: Orleans UM is unique and it has helped to drive

848
00:43:50,840 --> 00:43:54,560
Speaker 1: UM football interest and Saints interest to a level that

849
00:43:55,040 --> 00:43:56,880
Speaker 1: no other city can match. I just think that what

850
00:43:57,000 --> 00:44:00,319
Speaker 1: that team meant to the city after Katrina, and what

851
00:44:00,400 --> 00:44:02,520
Speaker 1: it meant to them in their Super Bowl season a

852
00:44:02,560 --> 00:44:06,040
Speaker 1: couple of years later, has really carried through and created

853
00:44:06,160 --> 00:44:10,080
Speaker 1: a unique circumstance. What does the Super Bowl data tell you?

854
00:44:10,160 --> 00:44:12,480
Speaker 1: Fox has got the Super Bowl this year, it's in Miami.

855
00:44:13,239 --> 00:44:15,960
Speaker 1: I'm fascinated by what you can learn from the data

856
00:44:16,000 --> 00:44:18,240
Speaker 1: for the Super Bowl, for instance, I'll start with this question.

857
00:44:18,560 --> 00:44:22,799
Speaker 1: How representative of the American audience in general is the

858
00:44:22,840 --> 00:44:25,200
Speaker 1: Super Bowl audience? In other words, does it pretty much

859
00:44:25,200 --> 00:44:28,960
Speaker 1: perfectly mirror America in general, or what audience is skew

860
00:44:29,040 --> 00:44:31,560
Speaker 1: more popular with the Super Bowl than maybe a represented

861
00:44:31,600 --> 00:44:34,719
Speaker 1: in the country. Yeah, that's a really great question. I mean,

862
00:44:34,960 --> 00:44:38,560
Speaker 1: television consumption is always going to skew older than the

863
00:44:38,640 --> 00:44:42,239
Speaker 1: general population. So there's a limit to how much a

864
00:44:42,280 --> 00:44:46,160
Speaker 1: television audience can truly reflect the American population. It's always

865
00:44:46,160 --> 00:44:48,440
Speaker 1: going to be a little bit older. But if you

866
00:44:48,520 --> 00:44:53,279
Speaker 1: accept that premise, the super Bowl probably comes closest or

867
00:44:53,360 --> 00:44:57,560
Speaker 1: comes closer to to reflecting the cross section of the

868
00:44:57,560 --> 00:45:01,600
Speaker 1: American population than anything else it's out there. Um, the

869
00:45:01,640 --> 00:45:04,680
Speaker 1: audience is more female than it is for a regular

870
00:45:04,680 --> 00:45:08,239
Speaker 1: season game. It's significantly more younger than it is for

871
00:45:08,280 --> 00:45:11,920
Speaker 1: a regular season game. Um, the halftime entertainment tends to

872
00:45:11,960 --> 00:45:16,839
Speaker 1: bring in audiences and demographics that are not weekend, week

873
00:45:16,840 --> 00:45:19,360
Speaker 1: out football viewers. That helps it make that helps to

874
00:45:19,360 --> 00:45:21,920
Speaker 1: make the Super Bowl a little bit more representative of

875
00:45:22,000 --> 00:45:25,319
Speaker 1: the entire country. I mean, I really think that, And

876
00:45:25,320 --> 00:45:27,640
Speaker 1: and maybe I'm being a little bit lofty about it

877
00:45:27,640 --> 00:45:29,759
Speaker 1: because I've been close to it for a long time,

878
00:45:29,800 --> 00:45:31,319
Speaker 1: and it's an event that means a lot to me.

879
00:45:31,440 --> 00:45:35,880
Speaker 1: But I think the Super Bowl is more reflective of

880
00:45:35,920 --> 00:45:39,319
Speaker 1: the character and the fabric of this country than any

881
00:45:39,360 --> 00:45:43,080
Speaker 1: other day. Forget about any other television event. Super Bowl

882
00:45:43,160 --> 00:45:47,799
Speaker 1: Sunday reflects where we are as a country more accurately

883
00:45:47,920 --> 00:45:51,600
Speaker 1: than the Fourth of July, more accurately than Thanksgiving, more

884
00:45:51,640 --> 00:45:53,879
Speaker 1: accurately than any holiday that you want to name. It's

885
00:45:53,880 --> 00:45:56,960
Speaker 1: the truest representation of who we are. And that's why

886
00:45:56,960 --> 00:45:58,759
Speaker 1: it's so exciting for us to be able to be

887
00:45:58,840 --> 00:46:01,319
Speaker 1: the people that get to get to present it once

888
00:46:01,360 --> 00:46:04,759
Speaker 1: every couple of years. So when the Super Bowl airs,

889
00:46:04,800 --> 00:46:07,799
Speaker 1: how much do the teams matter that are playing? And

890
00:46:07,840 --> 00:46:10,080
Speaker 1: I'm not telling you to be a fan of the teams,

891
00:46:10,160 --> 00:46:13,919
Speaker 1: But if I told you right now Fox's Super Bowl

892
00:46:14,120 --> 00:46:18,160
Speaker 1: is going to have the Cowboys and the Patriots. Is

893
00:46:18,160 --> 00:46:21,799
Speaker 1: that the best possible draw from a purely team perspective.

894
00:46:22,000 --> 00:46:24,440
Speaker 1: And how much the teams matter in terms of audience,

895
00:46:25,160 --> 00:46:27,520
Speaker 1: They matter a little. They don't matter as much as

896
00:46:27,560 --> 00:46:29,960
Speaker 1: they matter in the World Series or as much as

897
00:46:30,000 --> 00:46:32,319
Speaker 1: they matter in the NBA Finals, where I think you

898
00:46:32,320 --> 00:46:36,360
Speaker 1: can have a huge degree of variability between one matchup

899
00:46:36,400 --> 00:46:40,040
Speaker 1: and another um, but I wouldn't say that they don't

900
00:46:40,080 --> 00:46:42,440
Speaker 1: matter at all. No matter who gets to the Super Bowl,

901
00:46:42,800 --> 00:46:45,839
Speaker 1: it's going to be, by an enormous margin, the most

902
00:46:45,920 --> 00:46:49,200
Speaker 1: watched television program of the year. I mean, by fifty

903
00:46:49,239 --> 00:46:51,719
Speaker 1: million people, it'll be the most watched television program of

904
00:46:51,760 --> 00:46:54,600
Speaker 1: the year. And that's true even if the matchup is

905
00:46:55,040 --> 00:46:59,800
Speaker 1: Jaguars and Panthers, right, But there is some variability where

906
00:47:00,320 --> 00:47:03,399
Speaker 1: you know, having the Cowboys in the game might get

907
00:47:03,440 --> 00:47:08,759
Speaker 1: you an extra five Having the Cowboys against the Steelers,

908
00:47:08,920 --> 00:47:11,440
Speaker 1: or you know, another prominent a f C brand might

909
00:47:11,440 --> 00:47:14,120
Speaker 1: get you a couple of extra percent on top of that.

910
00:47:14,320 --> 00:47:17,279
Speaker 1: So I do think there are matchups that are better

911
00:47:17,320 --> 00:47:21,479
Speaker 1: than others, but there really is no matchup that would

912
00:47:21,560 --> 00:47:23,600
Speaker 1: scare you or make you think that the bottom is

913
00:47:23,640 --> 00:47:25,799
Speaker 1: going to fall out of the Super Bowl. I think

914
00:47:25,840 --> 00:47:29,399
Speaker 1: this year there's an interesting debate to be had, and

915
00:47:29,440 --> 00:47:33,680
Speaker 1: I'm not sure that I have a conclusive opinion on it. Um.

916
00:47:33,719 --> 00:47:36,920
Speaker 1: The Patriots are probably the strongest brand on the a

917
00:47:37,040 --> 00:47:40,920
Speaker 1: f C side, but because they've become so familiar on

918
00:47:41,000 --> 00:47:45,560
Speaker 1: Super Bowl Sunday, Uh, it's possible that having Pat Mahomes

919
00:47:45,560 --> 00:47:47,239
Speaker 1: and the Chiefs in the game might be better for

920
00:47:47,360 --> 00:47:51,560
Speaker 1: viewership than having another year of Brady and Belichick. Again,

921
00:47:51,600 --> 00:47:53,600
Speaker 1: I think it's debatable. You really could argue it from

922
00:47:53,640 --> 00:47:58,640
Speaker 1: either side, UM, but I would say any combination of Cowboys, Packers,

923
00:47:58,680 --> 00:48:02,400
Speaker 1: Bears on the NFC side, Patriots or Chiefs on the

924
00:48:02,400 --> 00:48:06,320
Speaker 1: a f C side UM would get you really into

925
00:48:06,320 --> 00:48:08,759
Speaker 1: a stratosphere and get you into place where you could

926
00:48:08,760 --> 00:48:12,040
Speaker 1: threaten viewership records. I think this will surprise people. You

927
00:48:12,080 --> 00:48:15,000
Speaker 1: mentioned the halftime show. Sometimes the halftime show is the

928
00:48:15,080 --> 00:48:18,520
Speaker 1: highest rated part of the Super Bowl, and I think

929
00:48:18,520 --> 00:48:21,719
Speaker 1: that data reflects that that sometimes is the case that

930
00:48:21,920 --> 00:48:24,400
Speaker 1: stuns me or stunned me when I saw it as

931
00:48:24,440 --> 00:48:29,399
Speaker 1: a sports fan. Did that surprise you when you saw it? Yeah,

932
00:48:29,440 --> 00:48:31,440
Speaker 1: And that's a relatively recent thing. I mean, I think

933
00:48:31,440 --> 00:48:33,840
Speaker 1: it's only in the last five to ten years that

934
00:48:33,960 --> 00:48:37,800
Speaker 1: the halftime acts um have been booked with an eye toward,

935
00:48:38,800 --> 00:48:41,320
Speaker 1: you know, an audience that maybe isn't watching the NFL

936
00:48:41,400 --> 00:48:43,280
Speaker 1: every week. You know, we went through a period where

937
00:48:43,719 --> 00:48:49,240
Speaker 1: the halftime acts were pretty straightforward guitar guitar rock act,

938
00:48:49,440 --> 00:48:52,799
Speaker 1: starting with the YouTube show in New Orleans and now

939
00:48:52,800 --> 00:48:55,279
Speaker 1: almost twenty years ago. And then there was this run

940
00:48:55,360 --> 00:49:00,160
Speaker 1: of YouTube, Springsteen, Tom Petty, the who I mean, I'm

941
00:49:00,160 --> 00:49:02,759
Speaker 1: I'm painting with a very broadbrush here, but those are

942
00:49:02,800 --> 00:49:06,040
Speaker 1: acts that are probably resonant to an audience that tends

943
00:49:06,080 --> 00:49:10,080
Speaker 1: to watch the NFL every week. And then we've moved

944
00:49:10,120 --> 00:49:14,200
Speaker 1: since then into this period where the acts have become Beyonce,

945
00:49:14,760 --> 00:49:18,799
Speaker 1: Katie Perry, Bruno Mars, and we find that there is

946
00:49:18,840 --> 00:49:22,520
Speaker 1: an audience that will show up at halftime, watch the

947
00:49:22,560 --> 00:49:25,439
Speaker 1: halftime show, and then hopefully stick around for the second half,

948
00:49:25,760 --> 00:49:27,879
Speaker 1: and it can create a dynamic where the highest rated

949
00:49:27,920 --> 00:49:30,600
Speaker 1: part of the game, uh is the halftime show. So yeah,

950
00:49:30,640 --> 00:49:33,239
Speaker 1: that's that's a pretty recent change. And in theory for

951
00:49:33,280 --> 00:49:36,920
Speaker 1: Fox's Super Bowl this year with Jennifer Lopez and Shakira, like,

952
00:49:37,360 --> 00:49:39,239
Speaker 1: I don't think and I could be wrong, I don't

953
00:49:39,239 --> 00:49:42,880
Speaker 1: think that your average hardcore Jennifer Lopez fan also sits

954
00:49:42,880 --> 00:49:46,080
Speaker 1: and watches eight hours of NFL every day. So in theory,

955
00:49:46,120 --> 00:49:48,880
Speaker 1: that kind of selection also brings in the potential for

956
00:49:48,920 --> 00:49:51,719
Speaker 1: a larger audience that might not otherwise be consuming the

957
00:49:51,760 --> 00:49:55,319
Speaker 1: Super Bowl. That's a that's a decent thesis I would imagine, right, Yeah,

958
00:49:55,320 --> 00:49:57,120
Speaker 1: I think that's exactly right. I think that's what we

959
00:49:57,160 --> 00:50:01,160
Speaker 1: hope happens. Okay, So leaving behind mind the super Bowl

960
00:50:01,200 --> 00:50:03,439
Speaker 1: for a second, Uh, there are a lot of things

961
00:50:03,440 --> 00:50:06,680
Speaker 1: going on in the world of entertainment, sports, media, business,

962
00:50:06,719 --> 00:50:11,080
Speaker 1: everything else. Gambling is starting to grow. You've got all

963
00:50:11,120 --> 00:50:14,080
Speaker 1: of this different proliferation, as we've mentioned before, of different

964
00:50:14,120 --> 00:50:17,600
Speaker 1: channels of different different aspects of entertainment options that are

965
00:50:17,640 --> 00:50:21,239
Speaker 1: out there. If you had a billion dollars, Let's say

966
00:50:21,239 --> 00:50:23,640
Speaker 1: you had a billion dollars right now and you had

967
00:50:23,719 --> 00:50:26,960
Speaker 1: to invest, you had to invest it in an aspect

968
00:50:27,120 --> 00:50:29,560
Speaker 1: of sports. And I always like to act like think

969
00:50:29,600 --> 00:50:31,640
Speaker 1: about questions like these, because, for instance, if we've been

970
00:50:31,640 --> 00:50:34,600
Speaker 1: having this conversation fifty years ago and we've been talking

971
00:50:34,600 --> 00:50:37,319
Speaker 1: about the three most popular sports in the world right

972
00:50:37,320 --> 00:50:40,000
Speaker 1: then would have been baseball, horse racing, and boxing, right

973
00:50:40,320 --> 00:50:41,759
Speaker 1: And you could have been like, I'm gonna I know

974
00:50:41,800 --> 00:50:43,960
Speaker 1: you still love uh, I know you still love horse

975
00:50:44,040 --> 00:50:47,440
Speaker 1: racing and everything else. But if you had a billion dollars,

976
00:50:47,800 --> 00:50:50,799
Speaker 1: pretend we're having this conversation fifty years from now, what

977
00:50:50,880 --> 00:50:53,360
Speaker 1: would you have wanted to put that billion into that

978
00:50:53,480 --> 00:50:56,680
Speaker 1: you think would be much more popular fifty years from now,

979
00:50:56,840 --> 00:50:59,160
Speaker 1: or at least as popular or grow at a great rate.

980
00:50:59,239 --> 00:51:01,480
Speaker 1: Where are we head it? If you had to project,

981
00:51:02,480 --> 00:51:04,479
Speaker 1: I'm going to give you a really boring answer, which

982
00:51:04,480 --> 00:51:06,840
Speaker 1: is that I would put that money into an NFL team,

983
00:51:07,160 --> 00:51:09,759
Speaker 1: recognizing that the irony in that answer is that you

984
00:51:09,800 --> 00:51:11,880
Speaker 1: can't even buy an NFL team for a billion dollars,

985
00:51:11,920 --> 00:51:15,719
Speaker 1: So you'd probably be looking at buying bullish though on

986
00:51:15,760 --> 00:51:17,560
Speaker 1: the NFL, because some people would say, oh, I'm not

987
00:51:17,600 --> 00:51:19,759
Speaker 1: bullish on the NFL. You would put a billion into

988
00:51:19,760 --> 00:51:23,840
Speaker 1: the NFL right now, I would put a billion into owning,

989
00:51:24,160 --> 00:51:27,640
Speaker 1: you know, a third to half of an NFL franchise.

990
00:51:27,719 --> 00:51:29,600
Speaker 1: I think that if you had that kind of money

991
00:51:29,640 --> 00:51:33,000
Speaker 1: to invest, UM, the part of the business where that

992
00:51:33,080 --> 00:51:35,719
Speaker 1: you would want to be on is team ownership. You know,

993
00:51:35,760 --> 00:51:39,160
Speaker 1: there's obviously a lot of volatility and uncertainty in the

994
00:51:39,239 --> 00:51:42,239
Speaker 1: media side of the business. UM. I like where we are,

995
00:51:42,440 --> 00:51:45,480
Speaker 1: given that our brand is built on premium live content.

996
00:51:45,520 --> 00:51:47,640
Speaker 1: I think it leaves us, It leaves Fox really well

997
00:51:47,640 --> 00:51:51,160
Speaker 1: positioned for the next say ten years. But I think

998
00:51:51,160 --> 00:51:55,080
Speaker 1: the safest bet is to be on the team ownership

999
00:51:55,120 --> 00:51:59,800
Speaker 1: side and be providing the content to that changing and

1000
00:52:00,040 --> 00:52:03,720
Speaker 1: uncertain media environment. And in the case of the NFL.

1001
00:52:04,040 --> 00:52:07,240
Speaker 1: I think that, Look, this is a very well established

1002
00:52:07,280 --> 00:52:10,920
Speaker 1: trend that every year, the viewership and therefore the value

1003
00:52:11,080 --> 00:52:15,000
Speaker 1: of the NFL separates itself further and further from everything

1004
00:52:15,000 --> 00:52:19,760
Speaker 1: else that's out there on TV. So it's not necessarily

1005
00:52:19,800 --> 00:52:22,520
Speaker 1: a very creative answer. It would probably be more interesting

1006
00:52:22,560 --> 00:52:26,040
Speaker 1: to say that you would invest in a gambling company,

1007
00:52:26,160 --> 00:52:28,080
Speaker 1: or in E sports, or in some of the things

1008
00:52:28,080 --> 00:52:32,000
Speaker 1: that are um more rapidly emerging and maybe are a

1009
00:52:32,000 --> 00:52:35,160
Speaker 1: little sexier to people. But I think the correct answer

1010
00:52:35,719 --> 00:52:39,359
Speaker 1: is an NFL team. Fox Sports Radio has the best

1011
00:52:39,400 --> 00:52:42,239
Speaker 1: sports talk lineup in the nation. Catch all of our

1012
00:52:42,280 --> 00:52:45,759
Speaker 1: shows at Fox Sports Radio dot com and within the

1013
00:52:45,800 --> 00:52:48,919
Speaker 1: I Heart Radio app search f s R to listen live.

1014
00:52:50,120 --> 00:52:52,720
Speaker 1: We're talking to Mike Mulvihill. I'm Clay Travis. Wins and Loss.

1015
00:52:52,800 --> 00:52:55,319
Speaker 1: Is your first reaction when they when you heard that

1016
00:52:55,400 --> 00:53:00,120
Speaker 1: Fox might be interested in the w W E was what? Oh?

1017
00:53:00,160 --> 00:53:03,480
Speaker 1: I love it. I think it's incredibly exciting. Um. I

1018
00:53:03,560 --> 00:53:07,960
Speaker 1: love that we are getting into a sports entertainment business. UM.

1019
00:53:08,000 --> 00:53:10,880
Speaker 1: I think it's great for us in prime time on

1020
00:53:10,960 --> 00:53:14,040
Speaker 1: the broadcast network. Uh. And I think we're getting into

1021
00:53:14,080 --> 00:53:19,160
Speaker 1: business with an executive in Vince McMahon and an organization

1022
00:53:19,680 --> 00:53:25,520
Speaker 1: that understands showmanship and understands television as well or better

1023
00:53:25,560 --> 00:53:29,279
Speaker 1: than any other organization in sports or entertainment. So I

1024
00:53:29,320 --> 00:53:32,200
Speaker 1: think it's really exciting and I can't wait to go

1025
00:53:32,280 --> 00:53:34,560
Speaker 1: to Staples tomorrow and see our first show. I mean,

1026
00:53:34,600 --> 00:53:36,319
Speaker 1: it's gonna be pretty cool. That whole Cogan the Rock

1027
00:53:36,360 --> 00:53:39,400
Speaker 1: are there, right, Yeah, it's gonna be amazing. I mean,

1028
00:53:39,440 --> 00:53:41,920
Speaker 1: I think it's gonna be for a casual observer of

1029
00:53:42,080 --> 00:53:44,120
Speaker 1: w w E, it's going to be practically everybody that

1030
00:53:44,160 --> 00:53:46,719
Speaker 1: you've ever been familiar with since songs like going to

1031
00:53:46,719 --> 00:53:50,400
Speaker 1: the Academy Awards of Wrestling. Uh okay, let's go to

1032
00:53:50,600 --> 00:53:53,640
Speaker 1: you mentioned brands and how valuable they are in terms

1033
00:53:53,719 --> 00:53:57,200
Speaker 1: of producing audiences in the world of sports. How much

1034
00:53:57,280 --> 00:54:01,880
Speaker 1: are individual opinion maker brands valuable? In other words, Colin

1035
00:54:01,920 --> 00:54:04,960
Speaker 1: Cowherd is now at Fox. You've got Skip Bailiss at Fox.

1036
00:54:05,000 --> 00:54:07,799
Speaker 1: Both of those guys came over from ESPN and have

1037
00:54:07,880 --> 00:54:12,200
Speaker 1: brought pretty substantial audiences for themselves that are quite a

1038
00:54:12,239 --> 00:54:15,319
Speaker 1: bit more than we're watching other programming on At the

1039
00:54:15,360 --> 00:54:18,160
Speaker 1: same time, is the value of those brands in the

1040
00:54:18,200 --> 00:54:22,280
Speaker 1: opinion business going up? In your mind relative to again,

1041
00:54:22,320 --> 00:54:24,920
Speaker 1: with the same issue of noise that's out there, it's

1042
00:54:24,960 --> 00:54:27,280
Speaker 1: hard to cut through. Do you think that is also

1043
00:54:27,360 --> 00:54:29,800
Speaker 1: true in the opinion space, And by the way, it

1044
00:54:29,840 --> 00:54:32,720
Speaker 1: could probably be true in the opinion space of sports,

1045
00:54:32,840 --> 00:54:35,040
Speaker 1: and also certainly would be true in the opinion space

1046
00:54:35,120 --> 00:54:38,120
Speaker 1: of news, where it's hard to cut through and create

1047
00:54:38,280 --> 00:54:41,359
Speaker 1: kind of an audience. And once you have, is their

1048
00:54:41,480 --> 00:54:44,000
Speaker 1: value there over and above maybe than what you would

1049
00:54:44,000 --> 00:54:47,280
Speaker 1: have anticipated. Sure, I don't think we would be investing

1050
00:54:47,280 --> 00:54:49,360
Speaker 1: in the talent that you just mentioned if we didn't

1051
00:54:49,360 --> 00:54:55,640
Speaker 1: believe that UM opinion driven programming was valuable and likely

1052
00:54:55,719 --> 00:54:58,720
Speaker 1: to increase in value. I mean, this is a business

1053
00:54:58,800 --> 00:55:01,319
Speaker 1: that is all is going to be driven first and

1054
00:55:01,400 --> 00:55:03,920
Speaker 1: last by the events, right, and we can never lose

1055
00:55:03,960 --> 00:55:06,640
Speaker 1: sight of that. Like, what really drives this business is

1056
00:55:07,160 --> 00:55:09,680
Speaker 1: having the rights to the games UM, and when we

1057
00:55:09,719 --> 00:55:12,879
Speaker 1: have an exclusive right to a game, it's the only

1058
00:55:12,920 --> 00:55:15,319
Speaker 1: thing we can deliver to an audience that nobody else

1059
00:55:15,320 --> 00:55:17,440
Speaker 1: can deliver, and so we always have to have that

1060
00:55:17,520 --> 00:55:20,960
Speaker 1: as our our top priority. But having said that, you know,

1061
00:55:21,040 --> 00:55:25,360
Speaker 1: it's a reality that on Fox Sports one, on ESPN,

1062
00:55:25,440 --> 00:55:28,840
Speaker 1: on ESPN two, we've got eight thousand seven hundred sixty

1063
00:55:28,840 --> 00:55:32,279
Speaker 1: hours of programming to fill every year. And that's airtime

1064
00:55:32,400 --> 00:55:36,239
Speaker 1: that our distributors are paying for, us for the consumers

1065
00:55:36,239 --> 00:55:40,480
Speaker 1: are paying for through their monthly cable or satellite bill. UM.

1066
00:55:40,520 --> 00:55:42,680
Speaker 1: And it's got to be filled with something compelling. And

1067
00:55:42,760 --> 00:55:45,839
Speaker 1: so when you have um what we sometimes call an

1068
00:55:45,840 --> 00:55:50,640
Speaker 1: opinionist who can fill a high volume of hours with

1069
00:55:50,800 --> 00:55:54,160
Speaker 1: something that is entertaining and watchable, I mean, I think

1070
00:55:54,160 --> 00:55:56,640
Speaker 1: there's incredible value there. I mean, I know, you're doing

1071
00:55:57,000 --> 00:55:59,640
Speaker 1: a radio show every day, you're doing Lock It In

1072
00:55:59,680 --> 00:56:03,760
Speaker 1: with Us on FS one, You're doing this podcast, you're writing.

1073
00:56:04,160 --> 00:56:07,120
Speaker 1: You know, you you kind of position yourself as an

1074
00:56:07,120 --> 00:56:10,719
Speaker 1: individual who is also a content factory. And I think

1075
00:56:10,760 --> 00:56:14,520
Speaker 1: once we get away from the live games, a lot

1076
00:56:14,600 --> 00:56:17,200
Speaker 1: of the other programming becomes a volume play. You know,

1077
00:56:17,239 --> 00:56:19,840
Speaker 1: you really are looking for people who can be a

1078
00:56:19,880 --> 00:56:23,400
Speaker 1: content factory, who have the ability to sit in a

1079
00:56:23,440 --> 00:56:27,040
Speaker 1: studio and provide watchable content for three or four hours

1080
00:56:27,040 --> 00:56:29,480
Speaker 1: a day, which will get you to maybe fift dred

1081
00:56:29,560 --> 00:56:31,799
Speaker 1: or two thousand hours a year of that time that

1082
00:56:32,400 --> 00:56:35,040
Speaker 1: you need to account for. I think those people have

1083
00:56:35,320 --> 00:56:39,640
Speaker 1: unbelievable value and it is a really rare and special

1084
00:56:39,680 --> 00:56:42,160
Speaker 1: skill set. I know that. You know, it's easy to

1085
00:56:42,160 --> 00:56:44,960
Speaker 1: be cynical about some of that programming. It's easy to

1086
00:56:44,960 --> 00:56:47,840
Speaker 1: be sarcastic about it, um, but it is a unique

1087
00:56:47,840 --> 00:56:50,600
Speaker 1: ability to be able to sit in a studio and

1088
00:56:50,640 --> 00:56:55,680
Speaker 1: talk about sports competently and entertainingly for three hours every day.

1089
00:56:55,719 --> 00:56:56,960
Speaker 1: Not a lot of people can do it, and they

1090
00:56:57,000 --> 00:57:00,000
Speaker 1: have great value. Yeah, it is interesting when you mentioned

1091
00:57:00,160 --> 00:57:03,200
Speaker 1: because it's it's almost like a picture who's going to

1092
00:57:03,280 --> 00:57:05,640
Speaker 1: give you a huge number of innings? Right, Like this

1093
00:57:05,640 --> 00:57:07,520
Speaker 1: guy is gonna give me two hundred innings because what

1094
00:57:07,560 --> 00:57:10,239
Speaker 1: did you say? Eight thousand, seven hundred and sixty hours

1095
00:57:10,239 --> 00:57:12,200
Speaker 1: of programming. I mean when you think about a guy

1096
00:57:12,239 --> 00:57:15,560
Speaker 1: like Cowherd, who is doing three hours of live radio,

1097
00:57:15,640 --> 00:57:17,800
Speaker 1: I'm doing three hours of radio two. But three hours

1098
00:57:17,800 --> 00:57:21,360
Speaker 1: of radio on television that's compelling in rates? Well, I

1099
00:57:21,360 --> 00:57:26,160
Speaker 1: mean that's an awful lot of just minute audience hours, right, Yeah,

1100
00:57:26,160 --> 00:57:28,320
Speaker 1: it absolutely is. I mean that's also the reason why

1101
00:57:28,560 --> 00:57:30,920
Speaker 1: I believe The Today Show and Good Morning America are

1102
00:57:30,920 --> 00:57:34,280
Speaker 1: the highest revenue generating shows on television. They may not

1103
00:57:34,400 --> 00:57:37,600
Speaker 1: be as highly rated on an average minute basis as

1104
00:57:37,640 --> 00:57:41,280
Speaker 1: something in prime time or as an NFL game, but

1105
00:57:41,480 --> 00:57:44,760
Speaker 1: they're on the fifteen hours a week, twenty hours a

1106
00:57:44,760 --> 00:57:48,680
Speaker 1: week every week, and they're just churning out an enormous

1107
00:57:48,760 --> 00:57:53,000
Speaker 1: volume of sellable content. And just the same way that

1108
00:57:53,400 --> 00:57:55,160
Speaker 1: it's rare to be able to do that on the

1109
00:57:55,160 --> 00:57:57,400
Speaker 1: sports side for three hours a day, it's rare to

1110
00:57:57,400 --> 00:57:59,440
Speaker 1: be able to fill those three hours on a network

1111
00:57:59,480 --> 00:58:01,840
Speaker 1: morning show every day. And that's that's why those people

1112
00:58:01,880 --> 00:58:04,439
Speaker 1: are worth. With their worth, the data that you look

1113
00:58:04,480 --> 00:58:08,720
Speaker 1: at has probably never been more available in terms of

1114
00:58:08,760 --> 00:58:12,000
Speaker 1: your ability to actually get it. But sometimes it's got

1115
00:58:12,000 --> 00:58:14,520
Speaker 1: to feel like you're trying to drink from a fire

1116
00:58:14,560 --> 00:58:17,560
Speaker 1: hose in terms of all of that data that's coming

1117
00:58:17,600 --> 00:58:21,000
Speaker 1: at you. How do you distinguish between the signal and

1118
00:58:21,080 --> 00:58:23,400
Speaker 1: a noise, like something that is just creating a lot

1119
00:58:23,440 --> 00:58:27,440
Speaker 1: of noise versus something that actually matters. Yeah, that's that's

1120
00:58:27,480 --> 00:58:30,000
Speaker 1: a great question. And I think that you're right. I mean,

1121
00:58:30,000 --> 00:58:34,120
Speaker 1: we are overwhelmed by data. Um, I think sometimes there's

1122
00:58:34,160 --> 00:58:37,000
Speaker 1: a a perception. It's not just a perception, it's a

1123
00:58:37,000 --> 00:58:41,800
Speaker 1: reality that television is at a data disadvantage compared to

1124
00:58:41,840 --> 00:58:46,120
Speaker 1: a tech company like Google or Amazon. And yet even

1125
00:58:46,160 --> 00:58:49,360
Speaker 1: if we are at a data disadvantage, We're still getting

1126
00:58:49,760 --> 00:58:53,040
Speaker 1: far more data every single day than we could ever

1127
00:58:53,200 --> 00:58:56,600
Speaker 1: really process and make use of. And so what you

1128
00:58:56,640 --> 00:59:00,240
Speaker 1: really need are people who can separate the signal from

1129
00:59:00,240 --> 00:59:02,600
Speaker 1: the noise and take all that data and sort of

1130
00:59:02,600 --> 00:59:08,000
Speaker 1: massage it into a narrative that makes sense to content partners,

1131
00:59:08,040 --> 00:59:11,560
Speaker 1: to advertisers, to press to all the constituencies that we

1132
00:59:11,680 --> 00:59:14,640
Speaker 1: deal with. I mean, I work in an analytics department,

1133
00:59:15,160 --> 00:59:17,520
Speaker 1: and I think there's an assumption that the people who

1134
00:59:17,560 --> 00:59:22,000
Speaker 1: work for me should be uh, mathematic geniuses, and it's

1135
00:59:22,040 --> 00:59:25,760
Speaker 1: actually not necessary. It's helpful to have an aptitude for

1136
00:59:25,840 --> 00:59:29,320
Speaker 1: the numbers, obviously, but what you really need our storytellers,

1137
00:59:29,440 --> 00:59:31,120
Speaker 1: you know, what you really need are people who can

1138
00:59:31,120 --> 00:59:33,720
Speaker 1: sip through all that data and come up with a

1139
00:59:33,720 --> 00:59:37,760
Speaker 1: handful of data points that you can string together into

1140
00:59:37,800 --> 00:59:42,440
Speaker 1: a narrative about a sport, or about our company, or

1141
00:59:42,480 --> 00:59:46,400
Speaker 1: about where this business is going, that resonates and makes

1142
00:59:46,400 --> 00:59:50,160
Speaker 1: intuitive sense to people. UM. And I think that's surprising

1143
00:59:50,440 --> 00:59:52,720
Speaker 1: when I say that, you know pretty frequently, and I

1144
00:59:52,720 --> 00:59:55,480
Speaker 1: think it's sometimes surprising to people UM to hear me

1145
00:59:55,600 --> 00:59:58,880
Speaker 1: say that I don't necessarily need numbers people. I need

1146
00:59:58,960 --> 01:00:02,800
Speaker 1: people who can put data points together into a story

1147
01:00:02,960 --> 01:00:07,160
Speaker 1: that that adds up to something meaningful as part of

1148
01:00:07,160 --> 01:00:09,439
Speaker 1: all that noise. And I'm fascinated by that too, because

1149
01:00:09,480 --> 01:00:11,800
Speaker 1: you have to. It's almost like you have to test hypotheses, right,

1150
01:00:11,840 --> 01:00:14,360
Speaker 1: I mean, how often do you look at something say hey,

1151
01:00:14,440 --> 01:00:16,680
Speaker 1: I wonder if this might be what's going on, and

1152
01:00:16,760 --> 01:00:18,520
Speaker 1: kind of test it for a couple of weeks, right

1153
01:00:18,680 --> 01:00:21,240
Speaker 1: or longer to try to figure out whether the data

1154
01:00:21,400 --> 01:00:24,720
Speaker 1: is just noisy and it's not necessarily reflecting, or causation

1155
01:00:24,840 --> 01:00:28,680
Speaker 1: is an issue and everything else. Because stories are constantly evolving,

1156
01:00:28,760 --> 01:00:31,680
Speaker 1: your story to explain the data has to evolve in

1157
01:00:31,680 --> 01:00:36,080
Speaker 1: some ways to right. Yeah, And we do experiment sometimes,

1158
01:00:36,080 --> 01:00:39,840
Speaker 1: whether that's experimentation with where a show airs or um,

1159
01:00:40,000 --> 01:00:41,680
Speaker 1: you know, in the case of the NFL, where we

1160
01:00:41,720 --> 01:00:45,280
Speaker 1: regionalize games and we're assigning a game to two hundred

1161
01:00:45,320 --> 01:00:48,280
Speaker 1: different markets all over the country, we might say, well,

1162
01:00:48,360 --> 01:00:50,360
Speaker 1: let's just see what happens if we put this team

1163
01:00:50,400 --> 01:00:53,200
Speaker 1: into this market up against this competition. Let's see what

1164
01:00:53,240 --> 01:00:56,280
Speaker 1: we find out. And I've been working for Fox Now

1165
01:00:56,320 --> 01:00:59,120
Speaker 1: for over twenty years, as you know, hard to believe,

1166
01:00:59,160 --> 01:01:01,720
Speaker 1: as that is, uh, And I feel like I'm constantly

1167
01:01:01,800 --> 01:01:04,760
Speaker 1: learning new ways of thinking about programming, and the only

1168
01:01:04,800 --> 01:01:07,560
Speaker 1: way to learn those things is to try things on

1169
01:01:07,600 --> 01:01:10,760
Speaker 1: the air that you haven't tried before. When you look

1170
01:01:10,880 --> 01:01:15,640
Speaker 1: at all the noise of social media beneficial or negative

1171
01:01:15,800 --> 01:01:20,600
Speaker 1: overall to the business of what you do, because you

1172
01:01:20,640 --> 01:01:23,560
Speaker 1: may have a hypothesis and the narrative can be accurate,

1173
01:01:23,560 --> 01:01:25,560
Speaker 1: it can be inaccurate. You can see, uh, you know,

1174
01:01:25,840 --> 01:01:28,760
Speaker 1: if it can lead to the spread of accurate, inaccurate information.

1175
01:01:29,000 --> 01:01:31,760
Speaker 1: Do you pay attention at all to social media in

1176
01:01:31,880 --> 01:01:34,000
Speaker 1: terms of looking at the data or do you have

1177
01:01:34,040 --> 01:01:37,200
Speaker 1: so much data that you don't need more opinion? UM?

1178
01:01:37,240 --> 01:01:40,320
Speaker 1: I don't pay much attention to social media in terms

1179
01:01:40,560 --> 01:01:44,200
Speaker 1: of audience feedback. I mean, I feel like the most

1180
01:01:44,320 --> 01:01:48,520
Speaker 1: useful insights that I can glean I'm typically getting from

1181
01:01:48,640 --> 01:01:53,360
Speaker 1: the Nielsen data set. UM. I do scroll through Twitter obsessively.

1182
01:01:53,440 --> 01:01:55,720
Speaker 1: I'm on it way too much, so I can't claim

1183
01:01:55,720 --> 01:01:58,280
Speaker 1: to not pay attention to it. UM. But I tend

1184
01:01:58,360 --> 01:02:02,280
Speaker 1: to value the insights the Nielsen ratings over the insights

1185
01:02:02,280 --> 01:02:06,720
Speaker 1: that you're getting anecdotally. UM from Twitter. Now, there's another

1186
01:02:06,760 --> 01:02:10,120
Speaker 1: way of talking about social media, which is, rather than

1187
01:02:10,240 --> 01:02:12,960
Speaker 1: using it as a gauge of public opinion. You know,

1188
01:02:13,080 --> 01:02:16,080
Speaker 1: I feel like I've been able to use it um

1189
01:02:16,120 --> 01:02:19,200
Speaker 1: as a way to get the data out there into

1190
01:02:19,240 --> 01:02:25,320
Speaker 1: this never ending conversation that's happening on Twitter, specifically about

1191
01:02:25,400 --> 01:02:28,000
Speaker 1: our business and about sports. I mean, I think Twitter

1192
01:02:28,080 --> 01:02:35,120
Speaker 1: has become absolutely fascinating as an endless incubator of ideas

1193
01:02:35,200 --> 01:02:37,760
Speaker 1: and a place to just pressure test ideas that you

1194
01:02:37,840 --> 01:02:41,400
Speaker 1: have about where our business is going, what's working, what's

1195
01:02:41,440 --> 01:02:44,760
Speaker 1: not working. It's interesting sometimes to float a data point

1196
01:02:44,760 --> 01:02:48,280
Speaker 1: out via Twitter and just see what sticks, what do

1197
01:02:48,360 --> 01:02:50,640
Speaker 1: people respond to, or what do people push back on

1198
01:02:50,720 --> 01:02:54,440
Speaker 1: and reject um. I think it's become a really fascinating

1199
01:02:54,520 --> 01:03:00,840
Speaker 1: laboratory for ideas that then can harden into can mentional wisdom,

1200
01:03:00,920 --> 01:03:04,120
Speaker 1: and that's a really new dynamic. I think that you know,

1201
01:03:04,200 --> 01:03:06,480
Speaker 1: you're even more active on Twitter than I am. I'm

1202
01:03:06,520 --> 01:03:09,720
Speaker 1: sure you've observed that um, probably more frequently than I do.

1203
01:03:10,120 --> 01:03:12,880
Speaker 1: But it's fascinating to see how an idea can be

1204
01:03:13,640 --> 01:03:17,320
Speaker 1: debated and pressure tested on social media and then make

1205
01:03:17,360 --> 01:03:20,080
Speaker 1: its way into more traditional media, and then once it

1206
01:03:20,120 --> 01:03:22,520
Speaker 1: does make its way into more traditional media, it becomes

1207
01:03:22,560 --> 01:03:26,400
Speaker 1: conventional wisdom. I'm fascinated by it, and I don't ever

1208
01:03:26,520 --> 01:03:30,680
Speaker 1: know how representative any kind of story is for instance,

1209
01:03:30,680 --> 01:03:33,840
Speaker 1: in my audience, we know that, at least according to data,

1210
01:03:33,960 --> 01:03:36,720
Speaker 1: like Twitter is gonna skew young, It's gonna skew Liberal,

1211
01:03:36,880 --> 01:03:39,800
Speaker 1: It's gonna skew you know, probably costal more than it's

1212
01:03:39,840 --> 01:03:41,880
Speaker 1: middle part of the country. But what I love is

1213
01:03:41,920 --> 01:03:44,480
Speaker 1: just using it as a resource, even with those flaws.

1214
01:03:44,760 --> 01:03:46,919
Speaker 1: When I'm up in the morning doing my radio show

1215
01:03:46,960 --> 01:03:49,640
Speaker 1: and we come up with an interesting poll question, I

1216
01:03:49,720 --> 01:03:52,880
Speaker 1: love just to see the results, right, Like if people

1217
01:03:52,880 --> 01:03:56,080
Speaker 1: are going to vote on something, I just genuinely love

1218
01:03:56,160 --> 01:03:59,680
Speaker 1: seeing what they're going to say. And also love thinking

1219
01:03:59,760 --> 01:04:01,880
Speaker 1: when in I'm gonna expecting to get an answer one

1220
01:04:01,880 --> 01:04:03,920
Speaker 1: way and it goes the other way, right, like whether

1221
01:04:04,040 --> 01:04:07,640
Speaker 1: my needle is accurate in terms of what my anticipation is. Now,

1222
01:04:08,120 --> 01:04:10,040
Speaker 1: I'm curious here. I've only got a couple more questions

1223
01:04:10,040 --> 01:04:11,880
Speaker 1: for you, because I know how busy you are. Fox

1224
01:04:11,920 --> 01:04:15,040
Speaker 1: Sports Radio has the best sports talk lineup in the nation.

1225
01:04:15,320 --> 01:04:18,280
Speaker 1: Catch all of our shows at Fox Sports Radio dot

1226
01:04:18,280 --> 01:04:21,320
Speaker 1: com and within the I Heart Radio app search f

1227
01:04:21,640 --> 01:04:25,160
Speaker 1: s R to listen live. We're talking to Mike Multihill.

1228
01:04:25,200 --> 01:04:28,360
Speaker 1: I'm Clay Travis. This is Wins and Losses podcast. I

1229
01:04:28,440 --> 01:04:31,560
Speaker 1: have a theory, thesis, hypothesis, whatever you want to call it,

1230
01:04:32,000 --> 01:04:34,480
Speaker 1: that we're in the middle of a major paradigm shift

1231
01:04:34,520 --> 01:04:37,520
Speaker 1: in the world of sports media in particular, and I

1232
01:04:37,520 --> 01:04:39,880
Speaker 1: think we've had several of these over I would say

1233
01:04:39,920 --> 01:04:42,680
Speaker 1: the last forty years. The first was cable. I think

1234
01:04:42,680 --> 01:04:45,160
Speaker 1: cable changed everything in terms of the way that you

1235
01:04:45,240 --> 01:04:49,840
Speaker 1: consume sports. I think a secondary major impact was fantasy football,

1236
01:04:49,960 --> 01:04:53,520
Speaker 1: which I think drove football in general two different heights

1237
01:04:53,560 --> 01:04:56,360
Speaker 1: of popularity than it had ever seen before. I think

1238
01:04:56,360 --> 01:04:58,960
Speaker 1: the third major paradigm shift that we're going to see

1239
01:04:59,160 --> 01:05:02,240
Speaker 1: is gambling. Would you buy into those being the three

1240
01:05:02,400 --> 01:05:05,080
Speaker 1: kind of seminal events of the last thirty five to

1241
01:05:05,200 --> 01:05:09,040
Speaker 1: forty years of the sports industry? Would you add any others?

1242
01:05:09,400 --> 01:05:11,560
Speaker 1: And do you think that gambling is going to be

1243
01:05:11,600 --> 01:05:14,560
Speaker 1: as transformative as I believe it will in terms of

1244
01:05:14,600 --> 01:05:17,920
Speaker 1: the way sports are covered. Let's go through those three again.

1245
01:05:18,000 --> 01:05:21,160
Speaker 1: One was cable TV right now, the last one was gambling,

1246
01:05:21,200 --> 01:05:23,120
Speaker 1: and then what was the one in between? I think

1247
01:05:23,120 --> 01:05:26,640
Speaker 1: fantasy football, but fantasy sports in general really change. I

1248
01:05:26,920 --> 01:05:28,960
Speaker 1: think I think, in my opinion, this is my thesis.

1249
01:05:29,040 --> 01:05:31,040
Speaker 1: You may have data that says I'm an idiot for this.

1250
01:05:31,560 --> 01:05:34,920
Speaker 1: I think that the reason why the NFL suddenly skyrocketed,

1251
01:05:35,200 --> 01:05:37,520
Speaker 1: and I believe the data reflects this in the mid

1252
01:05:37,560 --> 01:05:40,400
Speaker 1: to late nineties and continued on its upward trajectory. So

1253
01:05:40,520 --> 01:05:45,440
Speaker 1: rapidly was fantasy football became so popular that people liked

1254
01:05:45,480 --> 01:05:48,560
Speaker 1: the NFL already, but suddenly you had a reason to

1255
01:05:48,640 --> 01:05:51,520
Speaker 1: watch every game with a steak in that game, right,

1256
01:05:51,560 --> 01:05:53,320
Speaker 1: I'm playing against somebody. I want to see how this

1257
01:05:53,400 --> 01:05:56,080
Speaker 1: running back does I need a touchdown to win my week.

1258
01:05:56,320 --> 01:05:58,520
Speaker 1: I'm in a high stakes fantasy football league that I'm

1259
01:05:58,520 --> 01:06:01,040
Speaker 1: embarrassed how much we're all putting into this thing. And

1260
01:06:01,280 --> 01:06:05,520
Speaker 1: I was like hitting refresh maniacally during Monday Night Football

1261
01:06:05,560 --> 01:06:06,960
Speaker 1: to see whether or not I was going to beat

1262
01:06:07,000 --> 01:06:08,960
Speaker 1: the guy that I was playing against. Right, And by

1263
01:06:08,960 --> 01:06:10,960
Speaker 1: the way, Ferman and Sal from our show lock it

1264
01:06:11,040 --> 01:06:13,440
Speaker 1: in or in that same league, and we spend a

1265
01:06:13,480 --> 01:06:15,480
Speaker 1: decent amount of time talking about how that league is going,

1266
01:06:15,480 --> 01:06:17,560
Speaker 1: because there's a lot of money at stake, right, And

1267
01:06:17,760 --> 01:06:20,960
Speaker 1: I I already am watching NFL games obsessively, but it

1268
01:06:21,000 --> 01:06:22,720
Speaker 1: makes me care more. And it made me care more

1269
01:06:22,760 --> 01:06:25,520
Speaker 1: back in nine and ninety six when suddenly I could

1270
01:06:25,560 --> 01:06:27,560
Speaker 1: start to play it on the internet with my buddies,

1271
01:06:27,800 --> 01:06:30,240
Speaker 1: and I think that's a big driver for NFL. And

1272
01:06:30,240 --> 01:06:33,000
Speaker 1: I think gambling is gonna be similar because it gives

1273
01:06:33,040 --> 01:06:36,680
Speaker 1: you a steak, and anytime you can create a interest

1274
01:06:36,960 --> 01:06:40,920
Speaker 1: or a incentive or a connection with a viewer, I

1275
01:06:41,000 --> 01:06:43,880
Speaker 1: think it drives up interest, whether it's fifty bucks, twenty bucks,

1276
01:06:44,000 --> 01:06:46,280
Speaker 1: or the potential to win a hundred thousand dollars in

1277
01:06:46,320 --> 01:06:49,520
Speaker 1: a twenty five team parlay. I think that all matters

1278
01:06:49,520 --> 01:06:53,040
Speaker 1: in a big way, very beneficial for sports. So I

1279
01:06:53,120 --> 01:06:56,240
Speaker 1: would agree with each of the three that you mentioned,

1280
01:06:56,280 --> 01:06:58,960
Speaker 1: And then if we were going to talk about potentially

1281
01:06:59,000 --> 01:07:00,760
Speaker 1: a fourth, I think you would have to look at

1282
01:07:00,800 --> 01:07:06,560
Speaker 1: the evolution of facility construction and how much more advanced

1283
01:07:06,800 --> 01:07:09,360
Speaker 1: fee the venues and the buildings that we go to

1284
01:07:09,520 --> 01:07:13,080
Speaker 1: to watch sports are today compared to where they were

1285
01:07:13,080 --> 01:07:15,520
Speaker 1: thirty or forty years ago. I just got done reading

1286
01:07:15,800 --> 01:07:19,000
Speaker 1: an awesome book called Ballpark Um by a guy who's

1287
01:07:19,000 --> 01:07:21,960
Speaker 1: a Poetzer prize winning architecture critic, and so he's talking

1288
01:07:21,960 --> 01:07:27,040
Speaker 1: about ballpark construction through the lens of um architecture and

1289
01:07:27,160 --> 01:07:30,040
Speaker 1: architecture criticism and really thinking about it in a in

1290
01:07:30,120 --> 01:07:32,720
Speaker 1: a thoughtful way, And there was this great anecdote in

1291
01:07:32,720 --> 01:07:36,240
Speaker 1: that book where UM George will was putting forward the

1292
01:07:36,280 --> 01:07:39,600
Speaker 1: idea that the three most significant things that have happened

1293
01:07:39,600 --> 01:07:45,400
Speaker 1: to baseball in the post war decades are integration, free agency,

1294
01:07:45,880 --> 01:07:48,920
Speaker 1: and the construction of Camden Yards. Like that's a really

1295
01:07:48,960 --> 01:07:51,560
Speaker 1: interesting idea, and it speaks to three things that are

1296
01:07:51,600 --> 01:07:54,520
Speaker 1: so fundamental to the business of baseball. It's all about

1297
01:07:54,960 --> 01:07:58,880
Speaker 1: who's allowed to play, who are you allowed to play for?

1298
01:07:59,600 --> 01:08:02,000
Speaker 1: And where are we going to play the games? Right?

1299
01:08:02,120 --> 01:08:04,720
Speaker 1: And I think that the building of Camden Yards and

1300
01:08:04,840 --> 01:08:08,880
Speaker 1: the generation of ballparks that it lead to UM has

1301
01:08:08,960 --> 01:08:12,600
Speaker 1: fundamentally changed the way that we experience baseball. It's made

1302
01:08:12,640 --> 01:08:15,760
Speaker 1: the experience of going to a game UM I think

1303
01:08:15,840 --> 01:08:18,719
Speaker 1: more palatable for somebody who wants to bring their family,

1304
01:08:19,240 --> 01:08:21,280
Speaker 1: or wants to bring their kids, or maybe go on

1305
01:08:21,320 --> 01:08:24,080
Speaker 1: a date to a game. UM. The experience is just

1306
01:08:24,120 --> 01:08:27,639
Speaker 1: so much more comfortable and entertainment driven. With that obviously

1307
01:08:27,720 --> 01:08:30,840
Speaker 1: comes a higher price point, and maybe some of those

1308
01:08:30,840 --> 01:08:35,080
Speaker 1: people who were passionate fans and regular attendees UM at

1309
01:08:35,120 --> 01:08:37,680
Speaker 1: facilities thirty or forty years ago now can't afford to

1310
01:08:37,680 --> 01:08:40,519
Speaker 1: go to a game. That changes the nature of the event.

1311
01:08:40,920 --> 01:08:43,439
Speaker 1: But I think it's not just baseball clearly. You know,

1312
01:08:43,520 --> 01:08:45,800
Speaker 1: you go to NFL stadiums and it's mind boggering to

1313
01:08:45,920 --> 01:08:48,680
Speaker 1: me the amenities that we have UM in the more

1314
01:08:48,720 --> 01:08:51,840
Speaker 1: recent construction. The same is true with NBA arenas, and

1315
01:08:51,880 --> 01:08:54,280
Speaker 1: so I think facility construction would also have to be

1316
01:08:54,320 --> 01:08:57,720
Speaker 1: part of that conversation about things that have been transformational

1317
01:08:58,080 --> 01:09:00,439
Speaker 1: in sports. But that's kind of a tangent to get

1318
01:09:00,439 --> 01:09:03,639
Speaker 1: back to your point about gambling. I mean, I couldn't

1319
01:09:03,680 --> 01:09:06,880
Speaker 1: agree more. Um, having the job that I have, I

1320
01:09:07,040 --> 01:09:10,640
Speaker 1: felt for years that the two most impactful things that

1321
01:09:10,720 --> 01:09:14,920
Speaker 1: could happen to sports television would be for out of

1322
01:09:14,960 --> 01:09:18,240
Speaker 1: home television viewing to be included in the ratings. Well,

1323
01:09:18,280 --> 01:09:20,160
Speaker 1: that's now going to happen, you know, and your people

1324
01:09:20,200 --> 01:09:22,040
Speaker 1: out there who like that's when you go to a

1325
01:09:22,080 --> 01:09:26,519
Speaker 1: bar or you're you know, like out in a public venue, right, correct,

1326
01:09:26,640 --> 01:09:29,600
Speaker 1: So all the viewing of sports that you do in

1327
01:09:29,680 --> 01:09:32,519
Speaker 1: a bar, in a hotel room, maybe there's a TV

1328
01:09:32,600 --> 01:09:35,080
Speaker 1: set in your place of business, or you're in an airport,

1329
01:09:35,680 --> 01:09:38,000
Speaker 1: all of that viewing will now be able to be

1330
01:09:38,080 --> 01:09:41,880
Speaker 1: captured by Nielsen and counted in the viewership metrics that

1331
01:09:41,920 --> 01:09:44,559
Speaker 1: we sell to our advertisers, So that's a seismic change.

1332
01:09:44,880 --> 01:09:46,880
Speaker 1: So that's one of what I felt like could be

1333
01:09:46,920 --> 01:09:49,479
Speaker 1: the two biggest changes to our business. And the other, obviously,

1334
01:09:49,960 --> 01:09:52,599
Speaker 1: is the legalization of gambling, and I think we're just

1335
01:09:52,680 --> 01:09:55,760
Speaker 1: at the beginning of that transformation. There are so many

1336
01:09:55,960 --> 01:10:00,040
Speaker 1: more states still to um figure out what they're a

1337
01:10:00,120 --> 01:10:02,440
Speaker 1: way forward is going to be in terms of legalization.

1338
01:10:03,040 --> 01:10:06,400
Speaker 1: But we've developed our participation in the gaming space in

1339
01:10:06,479 --> 01:10:09,519
Speaker 1: the expectation that five to ten years from now you

1340
01:10:09,600 --> 01:10:13,880
Speaker 1: might have thirty ish states representing roughly two thirds of

1341
01:10:13,920 --> 01:10:17,719
Speaker 1: the country that will have some form of legal sports wagering.

1342
01:10:17,800 --> 01:10:21,040
Speaker 1: And when that comes, it's going to make the television

1343
01:10:21,080 --> 01:10:25,080
Speaker 1: product so much more engaging and so much more compelling

1344
01:10:25,160 --> 01:10:28,080
Speaker 1: to somebody who has a small wager on the game.

1345
01:10:28,120 --> 01:10:29,920
Speaker 1: And we're not talking about people who are going to

1346
01:10:30,439 --> 01:10:33,040
Speaker 1: quit their jobs and become professional sports gamblers, but we're

1347
01:10:33,040 --> 01:10:38,200
Speaker 1: talking about people who are passionate Philadelphia Eagles fans, and

1348
01:10:38,280 --> 01:10:40,840
Speaker 1: they may find that their enjoyment of the game is

1349
01:10:40,880 --> 01:10:43,760
Speaker 1: amplified just a little bit by having ten or twenty

1350
01:10:43,840 --> 01:10:46,360
Speaker 1: dollars on the outcome of this Sunday's Eagles game, and

1351
01:10:46,400 --> 01:10:49,280
Speaker 1: I think that's the kind of audience where the opportunity

1352
01:10:49,320 --> 01:10:52,280
Speaker 1: really lies. How important is it for your job for

1353
01:10:52,360 --> 01:10:55,919
Speaker 1: you to be intellectually curious about things other than sports.

1354
01:10:58,880 --> 01:11:01,080
Speaker 1: I would like to think that it is important, you know.

1355
01:11:01,120 --> 01:11:04,440
Speaker 1: I'd like to think that you're able to glean insights

1356
01:11:04,479 --> 01:11:08,479
Speaker 1: from other interests that might be relevant to, uh, the

1357
01:11:08,600 --> 01:11:11,000
Speaker 1: job that we do here every day. Um. I just

1358
01:11:11,040 --> 01:11:14,400
Speaker 1: got done reading a really interesting book, um called How

1359
01:11:14,520 --> 01:11:17,880
Speaker 1: Music Works by David Byrne, who fronted Talking Heads for

1360
01:11:17,920 --> 01:11:20,320
Speaker 1: a lot of years, and so much of what he

1361
01:11:20,439 --> 01:11:24,479
Speaker 1: had to say about the nature of performance and the

1362
01:11:24,600 --> 01:11:27,840
Speaker 1: nature of a mass audience coming together to have a

1363
01:11:27,880 --> 01:11:30,960
Speaker 1: shared experience in a club or in a concert hall

1364
01:11:31,800 --> 01:11:35,439
Speaker 1: was so interesting and so applicable to the nature of

1365
01:11:35,479 --> 01:11:38,120
Speaker 1: live sports and the nature of the events that we televise,

1366
01:11:38,320 --> 01:11:40,880
Speaker 1: you know, every day. So you want to believe that

1367
01:11:41,200 --> 01:11:43,720
Speaker 1: if you have some curiosity about things outside of the

1368
01:11:43,720 --> 01:11:46,920
Speaker 1: world of sports, that curiosity will lead you to places

1369
01:11:46,920 --> 01:11:49,519
Speaker 1: that you can then bring back to this job. Um.

1370
01:11:49,520 --> 01:11:51,920
Speaker 1: That that's the way I try to approach it anyway. Yeah,

1371
01:11:51,920 --> 01:11:55,240
Speaker 1: I always tell people that the best way to I

1372
01:11:55,280 --> 01:11:58,760
Speaker 1: think have creative ideas is to experience a variety of

1373
01:11:58,760 --> 01:12:01,400
Speaker 1: creative disciplines. So it doesn't mean that you have to

1374
01:12:01,439 --> 01:12:03,600
Speaker 1: be an expert in something else, but you have to

1375
01:12:03,600 --> 01:12:07,400
Speaker 1: be intellectually curious enough to think outside of whatever realm

1376
01:12:07,439 --> 01:12:10,080
Speaker 1: you're involved in. To me, to allow you to make

1377
01:12:10,120 --> 01:12:12,519
Speaker 1: connections that are bigger than whatever you're involved in. Does

1378
01:12:12,560 --> 01:12:15,600
Speaker 1: that make sense? Like there is? In my opinion. I

1379
01:12:16,160 --> 01:12:18,479
Speaker 1: tell people all the time, if there's a high school

1380
01:12:18,520 --> 01:12:20,760
Speaker 1: kid listening right now to our conversation and he wants

1381
01:12:20,760 --> 01:12:22,240
Speaker 1: one day to have the job that you have or

1382
01:12:22,280 --> 01:12:24,360
Speaker 1: the job that I have, I say, you gotta read

1383
01:12:24,400 --> 01:12:26,679
Speaker 1: as much as you can. And it doesn't necessarily need

1384
01:12:26,720 --> 01:12:30,240
Speaker 1: to be everything about the world of sports, because I

1385
01:12:30,280 --> 01:12:32,800
Speaker 1: think everybody has been out to a dinner party or

1386
01:12:32,880 --> 01:12:34,760
Speaker 1: you've got a friend and a lot of times he's

1387
01:12:34,760 --> 01:12:37,240
Speaker 1: a guy who all he can talk about is sports,

1388
01:12:37,320 --> 01:12:39,519
Speaker 1: and I'll just see, like my wives like eyes roll,

1389
01:12:40,160 --> 01:12:41,800
Speaker 1: you know, because you're out a dinner and it's like

1390
01:12:42,080 --> 01:12:44,880
Speaker 1: it's a really nitty gritty sports conversation that you're in

1391
01:12:44,960 --> 01:12:47,120
Speaker 1: the middle of, you know, like you're analyzing four star

1392
01:12:47,200 --> 01:12:50,200
Speaker 1: linebackers from uh, the state of Georgia compared to the

1393
01:12:50,200 --> 01:12:53,040
Speaker 1: state of Alabama, and there's a small subset of people

1394
01:12:53,040 --> 01:12:55,080
Speaker 1: that care about that, but it's probably not everybody who's

1395
01:12:55,080 --> 01:12:57,880
Speaker 1: sitting around a dinner table, right, And so there's a

1396
01:12:58,160 --> 01:13:00,680
Speaker 1: there's an ability, I think, if you're a generalist in

1397
01:13:00,760 --> 01:13:02,439
Speaker 1: some sense of the word, to be able to be

1398
01:13:02,479 --> 01:13:05,280
Speaker 1: interested in a lot of different things, to recognize maybe

1399
01:13:05,320 --> 01:13:07,479
Speaker 1: connections in the world of sports and beyond that others

1400
01:13:07,479 --> 01:13:10,800
Speaker 1: wouldn't see if they're obsessed with one particular thing. Hey,

1401
01:13:10,880 --> 01:13:13,120
Speaker 1: that's a great point. I think it's really relevant to

1402
01:13:13,240 --> 01:13:15,400
Speaker 1: my job and to the people that I work with

1403
01:13:15,439 --> 01:13:18,160
Speaker 1: because we are a data analytics department, and it's very

1404
01:13:18,200 --> 01:13:21,400
Speaker 1: easily it's very easy to fall into a trap of

1405
01:13:21,439 --> 01:13:23,760
Speaker 1: making it all about the data. You know, you have

1406
01:13:23,840 --> 01:13:27,559
Speaker 1: to remember that the data is only relevant in that

1407
01:13:27,720 --> 01:13:30,679
Speaker 1: it's a language that we can use to talk about

1408
01:13:30,720 --> 01:13:33,439
Speaker 1: things that are really difficult to quantify. You know. The

1409
01:13:33,520 --> 01:13:37,040
Speaker 1: things that really make this business work are the feeling

1410
01:13:37,080 --> 01:13:39,280
Speaker 1: that you get when you have a shared experience at

1411
01:13:39,320 --> 01:13:42,080
Speaker 1: a game with somebody that matters to you, or the

1412
01:13:42,120 --> 01:13:44,680
Speaker 1: feeling that you get when your favorite team wins a championship.

1413
01:13:45,160 --> 01:13:47,800
Speaker 1: You can't put a number to that, um but the

1414
01:13:47,920 --> 01:13:50,759
Speaker 1: ratings and the attendance metrics and the revenue metrics allow

1415
01:13:50,880 --> 01:13:54,679
Speaker 1: us to sort of approximate what those things mean to people.

1416
01:13:55,040 --> 01:13:59,160
Speaker 1: If you ever become so narrow minded and shortsighted as

1417
01:13:59,200 --> 01:14:02,320
Speaker 1: to make it all about the numbers and you lose

1418
01:14:02,400 --> 01:14:06,160
Speaker 1: sight of the emotions and the experiences that those numbers represent,

1419
01:14:06,600 --> 01:14:09,400
Speaker 1: you're done, You're lost. You You always have to remember

1420
01:14:09,520 --> 01:14:13,040
Speaker 1: that it's about the experiences. It's about the feelings that

1421
01:14:13,120 --> 01:14:16,519
Speaker 1: sports generate, and the ratings are just a shorthand for

1422
01:14:16,600 --> 01:14:19,599
Speaker 1: us to talk about it. We watched the Alabama Clemson

1423
01:14:19,640 --> 01:14:22,479
Speaker 1: game together. Speaking of the environment of stadiums, you were

1424
01:14:22,479 --> 01:14:25,400
Speaker 1: talking about reading about the geography of stadiums in a

1425
01:14:25,560 --> 01:14:27,680
Speaker 1: suite that allowed you to get your d n A

1426
01:14:27,800 --> 01:14:31,240
Speaker 1: tested now San Francisco, remember that, like how we do

1427
01:14:31,400 --> 01:14:33,960
Speaker 1: that was now wasn't open while we were there. But

1428
01:14:34,040 --> 01:14:36,160
Speaker 1: I was like, I can't even believe that something like

1429
01:14:36,200 --> 01:14:38,760
Speaker 1: this exists. And I know you do it and I

1430
01:14:38,800 --> 01:14:40,759
Speaker 1: do it too, and I think it's instructive and helpful

1431
01:14:40,800 --> 01:14:42,600
Speaker 1: for a lot of people out there. Every now and

1432
01:14:42,640 --> 01:14:45,280
Speaker 1: then it's important to just pinch yourself and make yourself

1433
01:14:45,360 --> 01:14:47,160
Speaker 1: realize that this is what you do for a living,

1434
01:14:47,160 --> 01:14:49,439
Speaker 1: because I don't I don't think it matters what your

1435
01:14:49,520 --> 01:14:52,040
Speaker 1: job is. At some point in time, you can get

1436
01:14:52,080 --> 01:14:56,120
Speaker 1: so immersed in that job that you forget how excited

1437
01:14:56,160 --> 01:14:58,080
Speaker 1: you would have been to have that job. If you've

1438
01:14:58,080 --> 01:15:00,960
Speaker 1: been talking to yourself twenty years ago, or fifteen years

1439
01:15:01,000 --> 01:15:04,960
Speaker 1: ago or thirty years ago, how important is that to you? Uh,

1440
01:15:05,000 --> 01:15:07,519
Speaker 1: to be able to almost uh what I like to

1441
01:15:07,520 --> 01:15:09,960
Speaker 1: say is uh the illusion of the first time right

1442
01:15:10,040 --> 01:15:12,320
Speaker 1: actors and actresses, It's all about being able to come

1443
01:15:12,320 --> 01:15:14,200
Speaker 1: out on the stage even if they've done the same

1444
01:15:14,240 --> 01:15:17,360
Speaker 1: play a hundred times, and sell you on the idea

1445
01:15:17,400 --> 01:15:19,840
Speaker 1: that they're doing it for the first time, to present

1446
01:15:19,920 --> 01:15:22,240
Speaker 1: something fresh. Do you think about things like that in

1447
01:15:22,240 --> 01:15:26,000
Speaker 1: in in your endeavor? Yeah, I think about it constantly

1448
01:15:26,040 --> 01:15:29,000
Speaker 1: and really literally, just before we take this podcast, I

1449
01:15:29,040 --> 01:15:31,639
Speaker 1: was walking back from a session that we did here

1450
01:15:31,640 --> 01:15:34,680
Speaker 1: on the Fox lot where our president Eric Shanks was

1451
01:15:34,760 --> 01:15:38,080
Speaker 1: interviewing Vince McMahon in advance of our w w E

1452
01:15:38,200 --> 01:15:40,840
Speaker 1: premiere tomorrow night, and I was walking back over here

1453
01:15:40,880 --> 01:15:43,520
Speaker 1: with Charlie Dixon got who you know, well, he's responsible

1454
01:15:43,560 --> 01:15:46,360
Speaker 1: for all our studio programming on this one, and we

1455
01:15:46,360 --> 01:15:50,759
Speaker 1: were having exactly this conversation that it's amazing that Vince

1456
01:15:50,840 --> 01:15:54,240
Speaker 1: is able to retain his sense of showmanship and his

1457
01:15:54,320 --> 01:15:56,559
Speaker 1: sense of what will please an audience. And it got

1458
01:15:56,680 --> 01:15:59,720
Speaker 1: us talking about what excites us as people who have

1459
01:15:59,760 --> 01:16:01,680
Speaker 1: been this business for a number of years now, and

1460
01:16:01,720 --> 01:16:04,439
Speaker 1: how excited we still get when we launch a new

1461
01:16:04,479 --> 01:16:06,400
Speaker 1: property or a new show, or we get to go

1462
01:16:06,479 --> 01:16:10,840
Speaker 1: to a Super Bowl. And you never lose that astonishment

1463
01:16:10,880 --> 01:16:13,320
Speaker 1: that somebody actually pays you to do this. I mean,

1464
01:16:13,360 --> 01:16:16,280
Speaker 1: it's just incredible. And I said to him just on

1465
01:16:16,360 --> 01:16:19,240
Speaker 1: the way over here. If you ever lose that sense

1466
01:16:19,439 --> 01:16:22,320
Speaker 1: that you get up on Super Bowl Sunday and you

1467
01:16:22,400 --> 01:16:24,360
Speaker 1: can't believe that you actually get to do this for

1468
01:16:24,400 --> 01:16:26,840
Speaker 1: a living, you have to quit. You have to move

1469
01:16:26,880 --> 01:16:29,200
Speaker 1: on and just go do something else, because you've lost it.

1470
01:16:29,439 --> 01:16:32,200
Speaker 1: When you become one of those people who for whom

1471
01:16:32,240 --> 01:16:35,360
Speaker 1: it is only about this game did an eight rating

1472
01:16:35,439 --> 01:16:38,400
Speaker 1: this year and a nine rating last year, and you've

1473
01:16:38,439 --> 01:16:41,200
Speaker 1: lost your connection to the event itself and the sense

1474
01:16:41,240 --> 01:16:45,000
Speaker 1: of wonder and excitement and happiness that it brings to

1475
01:16:45,040 --> 01:16:47,719
Speaker 1: the tens of millions of people who care about this stuff,

1476
01:16:48,240 --> 01:16:50,240
Speaker 1: then you're of no use to this company. You have

1477
01:16:50,360 --> 01:16:53,800
Speaker 1: to maintain your connection to that outstanding stuff. How can

1478
01:16:53,840 --> 01:16:56,080
Speaker 1: people find you on social media and see what you're

1479
01:16:56,160 --> 01:16:58,759
Speaker 1: the information that you're putting out there on a regular basis.

1480
01:16:59,560 --> 01:17:02,840
Speaker 1: I'm on Twitter. I'm on at eighteen hours a day.

1481
01:17:03,400 --> 01:17:06,920
Speaker 1: My handle is at Mulvihill seventy nine. The seventy nine

1482
01:17:07,000 --> 01:17:09,160
Speaker 1: is a reference to the seventy nine Pittsburgh Pirates team

1483
01:17:09,200 --> 01:17:12,120
Speaker 1: that won the World Series. And UH, I love having

1484
01:17:12,120 --> 01:17:14,479
Speaker 1: new followers. I love interacting people with people who have

1485
01:17:14,520 --> 01:17:17,320
Speaker 1: any curiosity about this business. So come check us out,

1486
01:17:17,880 --> 01:17:21,479
Speaker 1: uh and UM and I appreciate that at Mulvihill seventy nine.

1487
01:17:21,840 --> 01:17:23,559
Speaker 1: Go make sure you check them out because you're gonna

1488
01:17:23,560 --> 01:17:26,040
Speaker 1: get a lot of great information there. I know you're busy, guy.

1489
01:17:26,080 --> 01:17:29,280
Speaker 1: I appreciate the time today and look forward to two

1490
01:17:29,280 --> 01:17:30,920
Speaker 1: people being able to experience and see what you do

1491
01:17:30,960 --> 01:17:34,599
Speaker 1: for a living. Thanks, man, really enjoyed it. It's Michael Mulvihill.

1492
01:17:34,640 --> 01:17:36,400
Speaker 1: I'm Clay Travis. You've been listening to the Winds and

1493
01:17:36,439 --> 01:17:39,080
Speaker 1: Losses podcast. If you enjoyed this conversation, there's a lot

1494
01:17:39,120 --> 01:17:42,160
Speaker 1: more real enjoy as well. Go listen and subscribe. Appreciate

1495
01:17:42,240 --> 01:17:44,960
Speaker 1: you all. Fox Sports Radio has the best sports talk

1496
01:17:45,040 --> 01:17:47,760
Speaker 1: lineup in the nation. Catch all of our shows at

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