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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports
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analytics here on the Wharton podcast
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network. This is Kade Massie hosting
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this week with my longtime
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collaborators, friends, colleagues,
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co-hosts Shane Jensen and Eric Bradlo.
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Audi Winer is celebrating Ali Winer.
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Thanks today. He'll be back. Some
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combination of us four are here almost
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every week of the year and have been for
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11 and a half years. Proud to say 11 and
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a half year podcast. The Wharton podcast
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network is officially a thing now. We've
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been on it for a couple of months and it
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was rolled out publicly this past couple
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of days. And so the Wharton podcast
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network collection of five podcasts at
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the time will grow over time in all
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likelihood. The team at work at Wharton
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Marketing Communications has been
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working very hard on this. We've had a
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lot of great support. We're excited
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about the new platform and we are
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officially off and running. Our format
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today is broadly the same as it usually
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is except we're going to squeeze in an
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extra guest. We'll talk more about that
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at the at the top at the at the bottom
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of this hour I suppose at the midpoint
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of the show. But we're going to open
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with a guest Ana Ragav Ragavan. Ana
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Ragavan from Google is joining us. Ana
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is a sports trends analyst, sports
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trends analyst on the Google trends
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team. Her work is dedicated to
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understanding the evolving interest of
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sports fans through the lens of search
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data. So this is Google doing all kinds
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of interesting things and including fan
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analytics. You guys know that we do
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sports analytics adjacent adjacent shows
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periodically. We jump into things like
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COVID, obviously,
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firefighting. We do we do things like
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this on occasion and fan the fan side of
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sports is a very relevant side of sports
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and we're a business school. So, we
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periodically have these kind of
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conversations as well. We haven't talked
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to Ana before. Ana, thanks for making
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time for us. Welcome to the show.
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>> Yeah, thank you so much for having me.
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And uh I know you guys are Pennsylvania
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based, so I'll just hit you with go
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birds.
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>> Go Birds. There you go. Appreciate that.
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Look, she's she's just catering to us
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already. She understands her fans that
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well. She's coming in from the West
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Coast. She's based in the Bay Area. Ana
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played some D2 basketball growing up and
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then she's been in this business. She
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does a little side work with an NBA
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team, maybe a local NBA team out there
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in the Bay Area, but she's been with
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Google for four years crunching numbers.
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So, give us a sense. give us a sense of
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what you guys do, what your what your
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team look like and what kinds of
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insights you're generating. Maybe just
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give us something concrete that you're
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working on right now to give us an
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example of your work.
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>> Yeah. To just kind of like take a step
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back from there, like my role as a
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fanist, it's exactly what it sounds
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like. I'm an analyst of fans. So, I look
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into the search data of, you know, what
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people are interested in around sports.
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And so as you mentioned earlier, I sit
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on the trends data team which is this
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you know small data analyst team within
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uh the Google search team uh where we
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cover a wide range of topics trends is
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this large publicly available data set
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where people can you know explore
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searches from you know food to
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entertainment to sports which is what I
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specialize in and really lets us keep a
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finger on the pulse of culture. And so,
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you know, right now the culture of
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sports, the big thing going on right now
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is the WNBA playoffs. So, a lot of my
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role revolves around looking at searches
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related to that.
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>> Okay. So, um let let me let me ask you a
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relatively abstract question, guys. Run
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me off of this if it's the wrong way to
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go. What what counts as a what counts as
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a trend? Because you know on the one
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hand what's happening this hour is a
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trend on another hand how search this
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year is different from last year is a
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trend. So one question just to start
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with is like what frequency of trends
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are we concerned about here?
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>> Yeah really depends on what you want to
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look at. So the great thing about trends
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is that you can look at like a
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historical basis. So, our data goes all
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the way back to 2004 to now if you're
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interested in. But, you know, when it
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comes to the W and a lot of those things
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that I do with those partners, it's the
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real- time data we're interested in,
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which goes all the way up to 3 minutes
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ago. So, we can really take a look at
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what people are searching for live, the
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interests and curiosities that um
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trigger them to dig deeper for more
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information.
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>> Okay. So, you you mentioned the WNBA
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playoffs. give us an example of
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something you've seen as we've as we're
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moving into the playoff time of the
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season, a trend in you in the work as
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you dig into it. Like what are people
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just give us one concrete example of
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something that people are paying
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attention to or curious about in search?
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>> Yeah, I mean it's been a huge like
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season this year. WNBA playoffs are
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searched more than ever before. And then
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the awards that come along with this
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postseason are are really uh driving
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people to search more when you know our
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Google Pixel partner Asia Wilson got her
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fourth MVP award the other day. You
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know, people were searching who has the
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most WNBA MVPs and best WNBA players of
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all time because that was just like such
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a, you know, historic feat for her. I
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think she's the first to ever get four.
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Um, and then you have kind of the
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adjacent things that people are
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interested in, like, you know, people
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searching for her were also searching
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for pink wigs. You know, Mark Davis, the
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the AC's owner and Asia's dad showed up
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to to award her that MVP trophy wearing
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the the signature Asia pink wig. So, it
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it's really fun to see kind of where fan
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interests, you know, go based on their
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their favorite players and teams. Eric
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is our resident marketing professor.
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Marketing and statistics I should say
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but sometimes he puts on his marketing
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hat. So Eric wants to jump in here.
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>> That's what I was going to put on the
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hat I was going to put on. Kate so ana
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you mentioned something about the
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partners. So we're also a business
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school and a business show. What's the
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business aspect of this? Like for
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example do you want to like does the NBA
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want to know this information? WNBA want
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to know this information for example
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because then they can go to their
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advertisers and partners and say this is
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how many searches this is how many
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eyeballs or is there not a I'll call it
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a direct form of monetization
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>> I mean our really goal with this
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partnership is to just really increase
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coverage and our commitment to women's
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sports and that was what we did when we
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entered into this in 2021 our goal was
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really to increase the amount of hours
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of women's sports coverage and um just
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make the league more accessible and
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these women's sports leagues more
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accessible. And you know, we've seen
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that since Google entered into the WNBA
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partnership in 2021. Search interest in
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the league has grown over 330.
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>> This implies, by the way, Ana, your
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answer implies that um you're not just a
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passive measurement system that you guys
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are a partner. pursue could you give us
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a sense of what like activities or
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actions Google is taking in partnership
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with let's say the WNBA to increase
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activities?
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Yeah, I mean the the first season that
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we entered into this partnership was the
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W's 25th season. And so, you know, we
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funded to make sure that more of these
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games could be on national television
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and brought, you know, 25 games for the
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25th anniversary to coverage on ESPN to
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help, you know, fans have access to
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those games. and um you know we were one
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of the first WNBA change maker partners
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and we've seen a lot of you know brands
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come in since then
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uh yeah it was really kind of laying the
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foundation and showing that women's
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sports and the W is a business to invest
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in.
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>> Uh how much to the extent are you
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looking at kind of trends within
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individuals over time or just kind of
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trends at the population level over
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time? I could imagine because of some
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media blitz that people might kind of
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like s search like for the WWE or
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whatever example uh more like you know
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in in a particular like you know a week
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after a big media blitz or something
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like that but then you what you really
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would want to capture is more like kind
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of sustained attention on the sport by
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the same individual. So, I don't know
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how much you're measuring like, oh,
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we've got we have a
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a trend is just an influx of of
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searches, you know, at the population
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level or whether you've actually got
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people tracked over, you know, kind of
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individuals essentially tracked over
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time where you're actually able to
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measure their increased engagement in a
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more sustained way.
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>> Yeah, one of the things about trends is
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that we don't track those individuals.
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you know, we're very serious about, you
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know, privacy concerns and making sure
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that there's no personal information.
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So, you know, trends data is is
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aggregated. It's normalized. It's
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anonymized. So, there's no demographic
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information on who these people are that
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are doing these searches. We don't know
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anything about gender, age. The things
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that we know is, you know, where
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searches are coming from. So we can
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break it down by location from the
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country level all the way down to the
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city level. Um but yeah, so it's really
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more of this holistic view of the
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population and you know what fans of
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this league want to know uh as a whole.
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>> Do you track individual players? not the
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demographics of the searchers like do
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you track Paige Bukers or you know uh AJ
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Wilson or other Caitlyn Clark as well as
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overall WNBA types of things.
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>> Yeah, absolutely. Like we can see like
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who are the top searched players that
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people are searching for and you know
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before Allstar like Paige Becker's draft
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outfit was the top searched WNBA outfit
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and you know Caitlyn Clark's the top
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searched player and rookie. uh last year
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when she entered the league. And so
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yeah, we definitely take a look at those
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kind of things within the league. It's
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not just general to the WNBA, but we
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want to kind of dig deeper into okay,
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who are the players people are
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interested in? Who are the teams? What
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are the culturally adjacent things that
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people want to know?
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>> Yeah, I guess uh when we're kind of
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talking about this, I'm sort of thinking
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about my own fandom and how much of it I
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kind of put into search queries as
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opposed to kind of how I exhibit it
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elsewhere. And so I kind of wonder I
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wonder if if if you guys are thinking
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about sort of like the other sort of
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supplementary kind of data sets that you
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have out there essentially on the
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internet. I don't know like Reddit or
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something like that where you could kind
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of like um obviously that's not what you
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guys directly own or but but it would be
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kind of a cool validation or at least
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comparison set to sort of see like how
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how trends are happening you know kind
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of in in sort of like public spaces on
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the internet. you own YouTube but but
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that don't you know don't consist of
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necessarily search terms like I have a
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lot of angst and a lot of opinions about
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the Red Sox
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>> I don't like search you know into Google
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how do you get better pitching because
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you know I know how to do that the Red
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Sox don't they should be the one doing
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the searching perhaps
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>> yeah I mean one of the things that is
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great about Google is like it it might
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not be your end destination but it it is
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part of that journey for digging into
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and finding more information like about
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your questions like maybe you've missed
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the Red Sox game, you know, a couple
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weeks, so you want to go back and look
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at the box scores for their past few
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games. So, you start a Google, you
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search that, maybe you see the
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pitching's going horrible. So, that's
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where, you know, your journey kind of
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takes you to those other platforms to
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get more information. But, you know, I
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think that one of the great things about
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Google and search is like no matter
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where you are in your fandom journey,
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like if you're just like at the
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beginning trying to get into it, get
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more information, learn more, or whether
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you're that super fan, uh really wanting
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that advanced data and and more complex
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uh information like Google's a place
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that you can go to, you know, start or
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in the middle of your journey.
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An can you give us an example of the way
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an insight that you've shared with one
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of your partners that has been new for
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them or new for you guys or surprise a
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surprise you may have come across at
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some point in the data that you've
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collected once you start slicing it up.
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um just give us some sense of like one
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level down when you're digging through
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as much as you are and the value you're
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pro you're providing to your partners
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through that through that deeper search
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through that deeper analysis.
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>> Yeah. One of the fun things about trends
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is that we can see kind of those
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adjacent interests that I've I've
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mentioned. And so, you know, this past
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year at WNBA Allstar, we activated this
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space where we showed what fans of
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different WNBA teams are searching for,
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like the different like music genres,
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food types, um, you know, fashion items,
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those kinds of things. And I think that
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one thing that these leagues and
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partners are really interested in is the
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stuff that go that happens, you know,
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off the court, off the field, those
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other cultural touch points. And so, you
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know, we can see that, you know, fans of
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the Liberty search for Chinese cuisine
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the most, whereas fans of,
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you know, Indiana search for barbecue
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the most. I'm just giving like that
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example. I don't remember the data site
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completely off the top of my head, but
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uh I think kind of seeing those
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differentiating elements and what makes
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fandom unique um as well as what are the
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things that people share. We're able to
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see both of those kinds of dichotoies
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within the trends.
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>> How is it that you know the fans are
00:14:22
searching for these things? So, you said
00:14:23
that you know the location, but you're
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not supposed to know like serial
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correlation between searches.
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>> Yeah, we're not following individuals or
00:14:32
a specific like journey, but you know,
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within trends, if you go to the
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trends.google.com website, you can put
00:14:39
in a topic and then you can see those
00:14:41
related searches and related queries. Um
00:14:44
basically like within those related
00:14:46
topics it means that you know people
00:14:48
searching for X also search for Y. So
00:14:51
that topic that you you inputed say you
00:14:54
put in the New York Liberty and in that
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related topics you see you know searches
00:14:59
for Chinese cuisine that means people
00:15:00
searching for Liberty
00:15:02
>> I think is that someone has that data
00:15:06
somewhere.
00:15:06
>> Yeah. Yeah. Uh uh but no no I'm just
00:15:08
saying Google has that data but it's not
00:15:11
like it's you can use it in the sense
00:15:13
that it can give you like you know co-
00:15:16
search types of patterns and stuff but
00:15:18
you nor does anybody at Google is my
00:15:20
understanding have access to that raw
00:15:22
data that would help identify
00:15:24
individuals is what you're saying
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>> correct like a a significant number of
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people would have to be doing this
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search for it to show up in the data
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like it we don't know what any one
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particular individual is doing.
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>> Right. Right. Right. What about um what
00:15:39
about for forecasting in a way? So we
00:15:41
one of the things we like about models
00:15:43
is that we think models often see
00:15:47
truth if you will a little bit before
00:15:49
the rest of the world sees it before
00:15:51
it's plain. This happens sometimes a
00:15:53
good model. Have there been any
00:15:55
instances where something ticks up in
00:15:58
your trends that people didn't know was
00:16:00
a thing yet? like ah this is this
00:16:03
portends favorably or unfavorably for
00:16:05
something in the next few days, weeks,
00:16:07
months.
00:16:09
>> Yeah, we don't we don't like to say that
00:16:11
trends can like predict what's going to
00:16:13
happen, but I think that there's
00:16:14
sometimes that we see things in the data
00:16:16
before, you know, culture really catches
00:16:19
on to it. you know, for example, off the
00:16:22
top of my head is not sports related,
00:16:24
but it's like, you know, people were
00:16:25
searching for Laboos, like, and we saw
00:16:27
that spike happen in the trends before
00:16:29
the the cultural craze really
00:16:32
established. And so, it's not so much
00:16:35
like predictive as it is like we can see
00:16:39
the markers before like it necessarily
00:16:42
becomes uh Yeah.
00:16:44
>> huge.
00:16:45
>> Right. Right. Right. If we were sitting
00:16:47
at a party and I asked you, so you told
00:16:50
me what you did, you know, and I said,
00:16:52
Ana,
00:16:54
what's the one trend happening in the
00:16:58
WNBA right now that you found exciting
00:17:02
or interesting to measure or what's the
00:17:04
big takeaway from what's going on in the
00:17:07
trends of the WNBA right now? What would
00:17:09
you say that is?
00:17:11
>> I think the most exciting thing has been
00:17:13
the the year-over-year growth. Like I
00:17:16
said, like interest this season is at an
00:17:18
all-time high. Last year, we saw it
00:17:20
reach an all-time high and the year
00:17:21
before that. So, it's the league's just
00:17:24
getting bigger and bigger and more
00:17:26
people are are noticing the amazing
00:17:29
talent that's in the league, the amazing
00:17:31
women, and they're wanting to follow
00:17:33
along. They're wanting to learn more and
00:17:35
and dive deeper into them. And I think
00:17:37
that that's just really exciting. And I
00:17:40
think that it's a trend that's going to
00:17:42
continue to grow with, you know, the the
00:17:44
growth of the college game, the amazing
00:17:46
talent that's going to be coming into
00:17:48
the league soon. So, um, it's just a
00:17:51
really, really exciting time for, uh,
00:17:54
the WNBA, and lots of great basketball
00:17:57
ahead.
00:17:58
>> Ana, is there any way for us to see your
00:18:01
work or to tap into these trends if
00:18:03
we're have questions or if other people
00:18:04
want to want to want to sample them?
00:18:07
Yeah, I mean, if you're watching a WNBA
00:18:10
game and the playoffs are going on right
00:18:12
now, um there's actually a couple games
00:18:14
uh tonight and later this week, but you
00:18:17
know, you can see the trends in
00:18:19
broadcast. We have these fan search
00:18:21
segments where, you know, I'm sharing
00:18:23
data with ESPN producers. I'm looking at
00:18:26
what people are searching for live real
00:18:29
time at a game and I'm sharing with
00:18:31
them. So, you know, you can see the work
00:18:34
show up there. We also have a, you know,
00:18:37
daily trends newsletter that people are
00:18:40
able to subscribe to um with a small
00:18:43
sports section in there highlighting
00:18:44
sports trends um every day, a handful.
00:18:48
And I also do a monthly trend sports
00:18:50
newsletter uh highlighting the biggest
00:18:53
things in sports of the month. And then
00:18:55
like I said, you know, if you're just
00:18:57
wanting to go play around with yourself,
00:18:59
trends.google.com google.com is a is a
00:19:01
great tool to go and uh explore see what
00:19:04
people are searching for.
00:19:06
>> Na, if we wanted to get your newsletter,
00:19:07
where do we go?
00:19:09
>> Uh, you can go to trends.google.com and
00:19:14
I believe there's a link to sign up to
00:19:16
that. Um, I know that if you enter your
00:19:18
email at the bottom, it'll subscribe you
00:19:22
to our daily newsletter and within the
00:19:24
daily newsletter, there's a link to sign
00:19:25
up to the uh sports one.
00:19:28
>> Okay. Terrific. All right. Well, listen,
00:19:30
Ana, thank you very much for the time.
00:19:32
Been a delight to talk to you. Wish you
00:19:34
the best with the work that you're
00:19:35
doing.
00:19:36
>> Yeah. Amazing. Thank you so much for
00:19:38
having me. And again, go birds.
00:19:40
>> Go birds. Ana Ragavan, sports trends
00:19:44
analyst on the Google Trends team in the
00:19:48
Bay Area looking at all kinds of issues,
00:19:50
but especially as a partner to the WNBA
00:19:53
on what fans are looking at around the
00:19:54
WNBA.
00:19:56
Thank you, Ana. All right, team. That is
00:19:59
our first interview. We're going to roll
00:20:01
into a second interview momentarily.
00:20:03
We're going to do two here in the first
00:20:05
half of the show. We are going to be
00:20:07
talking to David Dace. David is a vice
00:20:10
president at Goldman Sachs in charge of
00:20:12
their sports finance operation. Come
00:20:14
back and join us briefly after the
00:20:16
break. We're going to roll into a second
00:20:19
interview now. In the first half, we're
00:20:21
gonna we're going to talk to Dave
00:20:22
Dassie. Dave is a VP at Goldman Sachs.
00:20:26
He runs the sports one co-head of the
00:20:27
sports finance unit there and he's going
00:20:31
to be at a conference that we're hosting
00:20:33
here at Wharton later this week. He is
00:20:35
the he's the leadoff session. Dave's the
00:20:37
leadoff session at this at the Wharton
00:20:39
Sports Summit Friday morning and looking
00:20:42
forward to hearing him there. But we
00:20:44
thought it'd be a good chance to talk a
00:20:45
little sports finance with Dave. He was
00:20:48
a Cornell undergrad but a Wharton NBA
00:20:51
and he's been with Goldman for a little
00:20:52
bit. down in Atlanta. And if you've been
00:20:55
paying attention to sports finance, you
00:20:57
know that a lot of interesting players
00:20:59
have gotten into the game in the last
00:21:00
few years. And Goldman Sachs is one of
00:21:02
them. We around Wharton consider sports
00:21:05
investing to be kind of the vanguard of
00:21:07
sports business and one of the most
00:21:09
exciting areas in sports business over
00:21:11
the last 10 years and expected to be
00:21:12
even more so in the future. We needed to
00:21:15
hear more about it. So Dave, thank you
00:21:16
for making the time for it. Appreciate
00:21:18
your um time this afternoon.
00:21:21
>> Thank you. Terrific to be here. and uh
00:21:22
look forward to the discussion and
00:21:24
getting back to uh getting back to
00:21:25
Wharton on Friday.
00:21:26
>> Yes. Uh we we will see you there on
00:21:28
Friday. Glad you're going to make make
00:21:30
it up and um looking forward to the
00:21:32
session. But let's talk a little bit
00:21:33
about sports finance here. Give us a
00:21:36
sense just kind of big picture. Why is
00:21:38
why did Goldman get into sports finance?
00:21:40
How over what period of time did this
00:21:41
happen? And what are the big drivers
00:21:43
that bring you guys into the market?
00:21:45
>> Yeah, I'd say, you know, we've been in
00:21:47
the sports business for 20 plus years.
00:21:50
historically had been more opportunistic
00:21:52
around selling a team or financing a
00:21:54
building, new stadium, new arena. And
00:21:56
with the explosion in sports valuations,
00:21:59
which you've chronicled on many of your
00:22:01
many of your podcasts, along with the
00:22:03
opening up of the market to
00:22:05
institutional capital, again, small
00:22:08
steps, but a move towards
00:22:10
democratization to some extent of, you
00:22:12
know, capital coming into the sports
00:22:14
ecosystem. We felt that we really
00:22:16
started thinking about it more as an
00:22:17
asset class like we would any other
00:22:18
industry. And so it's clearly at least a
00:22:20
trillion dollar asset class. If you take
00:22:21
the entire uh ecosystem around sports,
00:22:24
we think it's, you know, probably closer
00:22:25
to two trillion. And as we think about
00:22:28
the business and we wanted to become
00:22:30
much more systematic in terms of how we
00:22:31
thought about it, where we wanted to
00:22:33
spend time, where there were
00:22:34
opportunities, and where there may
00:22:35
potentially be value, where we could,
00:22:37
you know, potentially help clients um,
00:22:39
you know, achieve their goals,
00:22:40
objectives. And so we set up the
00:22:42
business. We're about a year and a half
00:22:43
in or so. We think about it in three
00:22:46
buckets. Leagues and teams are one area.
00:22:48
Sports adjacencies are another. So,
00:22:51
think about putting, you know, all the
00:22:52
leagues and the teams in the inner
00:22:54
circle. And then ticketing, gaming, data
00:22:56
analytics, uh apparel companies, um
00:22:59
medtec companies, etc. And we're
00:23:01
probably tracking about 150 to 200
00:23:03
companies in that sports adjacent
00:23:05
market. And those are, you know,
00:23:06
companies which need debt, they need
00:23:07
equity, they do M&A, they buy, they
00:23:09
sell, those types of things. And then
00:23:10
the third bucket is infrastructure. So
00:23:12
stadiums and arenas. My co-head, Greg
00:23:14
Kerry, he's been doing this for 30 plus
00:23:16
years. Um, and the financing of those
00:23:18
has gotten incrementally more um, well,
00:23:21
the dollars have gotten much bigger.
00:23:23
It's gotten more complicated because a
00:23:25
lot of just building an arena doesn't
00:23:27
really pencil from an economic
00:23:28
perspective. You need to in essence
00:23:30
build build a city. You need mixeduse
00:23:32
development. You need entertainment
00:23:33
zones, you need hotels, you need
00:23:35
apartments, you need office, etc. Um and
00:23:38
that combined with the institutional
00:23:39
capital coming in which we'll talk about
00:23:41
you know quite a bit I'm sure um each
00:23:43
one of those buckets has different
00:23:44
return characteristics each one of them
00:23:46
has different cost of capital
00:23:47
characteristics um but there are broad
00:23:49
uh allocations of institutional capital
00:23:51
that really wants to move into it. So
00:23:53
that was the impetus and we're about 18
00:23:55
months in and really excited about what
00:23:56
we're seeing and even more excited uh
00:23:58
about where we're going in the future.
00:24:00
That's a very helpful lay of the land
00:24:03
for us because the first the inner hub
00:24:05
you described sports and teams is the
00:24:07
highest most highly visible part for for
00:24:10
example you know the Celtics just sold
00:24:12
understand you guys were a part of that
00:24:14
doesn't get more high-profile than that
00:24:16
but then you named these other two tiers
00:24:17
and we know just enough about this space
00:24:19
to know these are big tiers can you give
00:24:20
us some sense of the breakdown you said
00:24:23
in general this is about a1 to2 trillion
00:24:26
dollar asset class roughly and only only
00:24:29
roughly But how should we think about
00:24:31
the sports and teams part of that versus
00:24:33
the adjacency part of that versus the
00:24:35
infrastructure?
00:24:37
>> Yeah, I mean I think I think it's pretty
00:24:39
interesting. I mean the first two again
00:24:40
it depends a little bit how you think
00:24:42
about big streaming companies, right? So
00:24:43
if you took big media companies or an
00:24:45
Amazon etc right that would blow out and
00:24:47
so my comments are kind of net of those
00:24:50
of that type of commentary.
00:24:52
>> Yep. But look, I mean, clearly the
00:24:54
teams, I mean, you've seen where, you
00:24:55
know, the average NBA valuation now. I
00:24:57
mean, you've seen the Celtic 66, you
00:24:59
know, the Lakers at 10. Um, the
00:25:01
Trailblazers are out there, you know,
00:25:02
rumored to be around, you know, 4
00:25:03
billion or so. Um, from that
00:25:05
perspective, um, NFL teams are clearly
00:25:08
in that six plus, six to 10 zone. You've
00:25:10
seen minority stakes trade or, you know,
00:25:11
rumored to be trading close to that $10
00:25:13
billion type zone. So, um, there's a lot
00:25:16
of focus on the teams certainly, uh, in
00:25:19
the leagues. We worked for Liberty Media
00:25:21
a year ago buying a company called Dorna
00:25:23
which is Moto GP which is the motorbike
00:25:24
equivalent of Formula 1, right? Um Soder
00:25:27
Lakes taking Endeavor um private or has
00:25:29
taken it private, right? And so you
00:25:31
think about sports agencies um and
00:25:33
everything around the sports ecosystem
00:25:34
and what companies like Endeavor do and
00:25:36
so there's a lot DraftKings, FanDuel, um
00:25:39
you know all the Tik Toky platforms. I
00:25:41
mean there's a lot of goodsized market
00:25:42
cap that's in that in that middle bucket
00:25:44
from an adjacency perspective. And then
00:25:46
the third bucket, the infrastructure is,
00:25:48
you know, fundamentally, you know, it's
00:25:50
kind of a a beast of its own to some
00:25:52
extent, right? It's just different. It's
00:25:54
in terms of um investment capital.
00:25:57
>> I think Eric wants to jump in here. We
00:25:59
should say Eric is our vice dean of Wia
00:26:02
AI works with you because Dave has
00:26:05
recently joined the Wii AI board and so
00:26:08
these guys have a prior relationship.
00:26:11
Eric, why don't you jump in?
00:26:12
>> That is absolutely true. So Dave, just
00:26:14
to clarify for our listeners here, um
00:26:17
what you're not saying is that Goldman
00:26:19
Sachs takes an equity position and is
00:26:22
buying and selling teams. You guys are
00:26:24
on the financing and debt part of
00:26:26
things. So for example, Eric Bradler
00:26:29
wants to buy the Boston Celtics and
00:26:31
needs $8 billion. He doesn't have $8
00:26:33
billion just sitting around necessarily.
00:26:35
So he comes to you and Goldman Sachs and
00:26:37
you guys help put together the financing
00:26:39
for it as opposed to Goldman Sachs is
00:26:43
going to start buying teams or be
00:26:45
partial shareholders of teams. Is that
00:26:47
correct?
00:26:47
>> That's right, Eric. And that's I mean
00:26:49
that's a great example. So Bill Chisum
00:26:51
who is the new owner of the Boston
00:26:52
Celtics Wharton.
00:26:57
Uh so we worked with Bill um he you know
00:26:59
ultimately paid six billion plus for the
00:27:01
Boston Celtics and it's
00:27:04
multi-dimensional so we can help raise
00:27:06
traditional more debt oriented type
00:27:08
capital. Um we talk about 1GS at Goldman
00:27:11
Sachs so one Goldman Sachs it's
00:27:12
multi-divisional and cross-divisional.
00:27:14
We have in our uh wealth management
00:27:16
business our asset wealth management
00:27:18
business uh a family office coverage
00:27:20
group called Apex. They are now covering
00:27:22
over 700 multi-billion dollar family
00:27:24
offices around the globe. Not
00:27:26
surprisingly, there's a lot of interest
00:27:28
in in those types of family offices for
00:27:30
sports assets. U so we will partner with
00:27:33
them. We will partner with our private
00:27:34
wealth management group um to help
00:27:36
identify um individuals who have an
00:27:39
interest from an equity perspective in
00:27:41
terms of co-investing uh along with the
00:27:43
um you the governor and the NBA which um
00:27:45
in this case is is Bill Chisum. We also
00:27:48
the NBA's opened up and the other
00:27:50
leagues have opened up ability for you
00:27:51
know more institutional capital to come
00:27:53
in. in the NBA it's a 30% cap. And so in
00:27:57
effect in that we talked to multiple
00:27:59
institutions that were out there about
00:28:01
potentially providing capital um into
00:28:04
the capital stack in terms of uh
00:28:05
financing the Celtics. And so that's
00:28:07
what we do. So it's it's more akin to um
00:28:09
investment banking advisory type work as
00:28:11
opposed to principal type work.
00:28:13
>> Let me ask a related question. um you
00:28:16
would think the value of a sports
00:28:17
franchise which is certainly related to
00:28:20
one's ability to borrow it might relate
00:28:22
to the performance of that sports
00:28:24
franchise. So when you guys think about
00:28:27
getting involved or when people are
00:28:28
thinking about investing, do you have
00:28:30
this is now I'm putting on my Wharton AI
00:28:32
and analytics hat. Does somebody at
00:28:34
Goldman have to build a forecasting
00:28:36
model? Let's just use Bill Chisum and
00:28:38
the Celtics. Like here's kind of a
00:28:40
projected revenue model for the Celtics.
00:28:42
But you could argue if the Celtics are 9
00:28:44
and 73, that's going to be a different
00:28:47
revenue model than if they're 60 and 22.
00:28:50
So does that play a role at all? Does
00:28:52
onfield performance, how does that
00:28:54
relate to what anybody lending money
00:28:56
would care about, which is projected
00:28:58
cash flow?
00:28:59
>> Um, it it does, but I'd say it's kind of
00:29:01
through a cycle, right? I mean, I I
00:29:03
think there are performance over time.
00:29:05
if you were 9 and 73 in perpetuity, I
00:29:07
think that would be one thing uh year
00:29:09
after year. But um and we're really
00:29:12
seeing this and I think you're starting
00:29:13
to see this emerge in terms of some of
00:29:15
these valuation differences in these
00:29:16
leagues that there are franchises and
00:29:19
brands which resonate have this deep
00:29:21
connection with fan base. It's
00:29:23
multigenerational. It's live sports. As
00:29:25
you know, media businesses are having a
00:29:27
really really hard time aggregating any
00:29:29
kind of audience together um absent
00:29:32
sports. And so, um, we're really
00:29:33
starting to see this this focus on
00:29:35
brand, um, and ultimately, and we can
00:29:37
talk about direct to consumer and where
00:29:39
that may be going, but ultimately what's
00:29:40
the addressable market potentially of of
00:29:43
that brand and where could it go? Um,
00:29:46
we're really, really bullish in, uh,
00:29:48
from that perspective. But I think
00:29:49
different teams, different clubs with
00:29:51
different legacies and different cities,
00:29:52
different fan bases, different passion
00:29:54
levels will have, you know, score
00:29:56
differently on all those types of
00:29:58
metrics. And the ability to, you know,
00:30:00
get to some of that breakout type
00:30:02
growth, I think will align more be more
00:30:04
highly correlated with those those
00:30:06
bigger names, those bigger brands, uh,
00:30:08
who have those opportunities.
00:30:10
>> I mean, Brad Lo's a Yankees fan and it's
00:30:12
been a while since they've won the World
00:30:14
Series, but somehow somehow there still
00:30:16
hasn't been that long.
00:30:18
Cowboys. Cowboys are like the perfect
00:30:20
example of like impervious.
00:30:22
>> You know, my motto, uh Dave talked about
00:30:23
this. This first thing he said,
00:30:25
addressable market. When you have a big
00:30:26
N, even a small P times a big N still
00:30:29
yields a big number. And so, you know,
00:30:32
the Boston Celtics are a big brand.
00:30:34
There's a big addressable market. The
00:30:35
same is true for New York Yankees. Same
00:30:37
is true for his home city of Atlanta.
00:30:39
There's a lot of people there. So, it's
00:30:41
a large addressable market. You just got
00:30:42
to figure out how to monetize a larger
00:30:45
fraction of it. So Dave, we we often
00:30:47
think about sports finance. We think
00:30:49
about it from the investor side and you
00:30:51
mentioned at the top the democratization
00:30:53
of owning some of these assets. That's
00:30:55
been the real the one of the real
00:30:56
drivers in the last 10 years. But it
00:30:59
seems like there's also some benefit, if
00:31:01
you will, to the club side. And I've
00:31:03
heard you guys talk about new revenue
00:31:05
streams that organizations might be able
00:31:07
to take advantage of. Can you give us
00:31:08
some sense of how other than the owners?
00:31:11
I know this is a good way to monetize um
00:31:14
a previously pretty difficult asset to
00:31:17
monetize.
00:31:18
What ways do organizations benefit and
00:31:20
what are these streams that they're
00:31:22
going to see that they didn't see
00:31:23
before?
00:31:24
>> Well, I think and you've seen this in
00:31:25
the professional leagues and you're
00:31:26
really starting to see in college sports
00:31:27
now too. Um a professionalization of
00:31:29
management that these are these are real
00:31:32
businesses with multiple dimensions to
00:31:34
them. um you know, product development,
00:31:37
marketing, engagement, um food and
00:31:40
beverage, certainly what you do inside
00:31:42
the physical infrastructure. Um and so I
00:31:46
think you're going to continue to see
00:31:47
more and more of that. Place where I'm
00:31:48
really bullish, we touched on a little
00:31:50
bit before is just direct to consumer.
00:31:52
And we're just at the the real
00:31:55
beginnings of this in terms of teams and
00:31:58
who do they connect with, knowing who
00:31:59
their fans are, who's coming in, what
00:32:02
their family structure is like. And so
00:32:04
you think about best-in-class consumer
00:32:05
marketing and a new direct to consumer,
00:32:08
you know, digital world and the ability
00:32:11
to build really deep, enduring
00:32:13
relationships that are incredibly
00:32:14
sticky, right? There's passion behind
00:32:16
this. Often times it's
00:32:17
multigenerational. Often times it's you
00:32:19
two or three generations. And so to
00:32:21
build that kind of engagement with a
00:32:23
brand is really, really powerful. And so
00:32:26
I think you're going to start hearing
00:32:27
concepts about lifetime value of a fan,
00:32:30
right? Concepts you've seen and heard in
00:32:31
other types of businesses. um or
00:32:33
lifetime business, you know, lifetime
00:32:35
value about a a family. Uh I think
00:32:37
you're going to start seeing more and
00:32:38
more metrics like that.
00:32:41
>> What is Goldman's role in in pushing
00:32:45
them organizations along in that way? So
00:32:47
you hear this, you hear this, I'm kind
00:32:49
of on the call side, which is my great
00:32:50
passion. You hear people worried about
00:32:53
private equity because if they're in
00:32:55
they're going to want to say, but
00:32:57
there's an advisory professionalization
00:32:59
aspect of it that is more benign than
00:33:01
that. I'm just curious when you know
00:33:03
Eric was saying well y'all finance deals
00:33:05
and you'll put things together but we
00:33:07
hadn't heard much about the what role
00:33:09
you would play to promote these things.
00:33:10
>> That was gonna be exactly my question
00:33:12
which was I assume this was exact I love
00:33:15
the way Kade phrased it. What I was
00:33:16
going to say is I assume you just don't
00:33:18
lend money or act as a broker and sort
00:33:19
of lend them and then walk away and say
00:33:21
good luck guys. You know this is the
00:33:23
advisory part. That's why I'm excited to
00:33:24
hear this.
00:33:25
>> Well, you know we look we would you know
00:33:26
a team sailor any business sale or an
00:33:28
IPO um right? Right? I mean, we led the
00:33:30
StubHub IPO last week, right? So, that
00:33:32
would be in the sports adjacent, you
00:33:33
know, area arena as well, just given the
00:33:35
dominance of sports ticketing that's
00:33:36
running through, right? Running through
00:33:38
these ticketing platforms. But, uh,
00:33:40
anytime we take on an assignment or try
00:33:41
and sell something, we you it's in
00:33:43
essence like an IPO. You try to come up
00:33:44
with the best story you can, you curate
00:33:46
it, you work on it. What are the what
00:33:48
are the key selling points and how do
00:33:50
you think about it? So, Eric touched on
00:33:51
building a financial model. So, yes, we
00:33:53
do build financial models. We do go out,
00:33:55
we think about what upside cases, right,
00:33:57
could be. We've worked with European
00:33:59
soccer teams that have over 100 million
00:34:01
fans in their database, right? And they
00:34:02
will tell us, gosh, we probably know
00:34:04
what maybe one or two percent of those
00:34:05
fans, who they are, and what they're
00:34:07
doing. And so, you think about in a
00:34:09
software context where you have kind of
00:34:11
five cohorts, if you could just move 50
00:34:13
basis points of fans from cohort two to
00:34:15
one and three to two and four to three,
00:34:17
you could grow for another 25, 30, 40,
00:34:20
50 years. And some of these brands, and
00:34:22
you've touched on a couple in
00:34:24
basketball, be the Celtics, be the
00:34:25
Lakers. These are not only, you know,
00:34:27
regional national brands. These are
00:34:28
truly potentially global brands, right?
00:34:31
And so that 100 million for a European
00:34:33
soccer team, many of them think that
00:34:34
should be 200 or 250 or 300. And so you
00:34:38
start to think about this relationship
00:34:39
and what you could do and promotion and
00:34:42
uh getting people into the stadium,
00:34:44
meeting players, it just it's so I'm not
00:34:46
sure it's any different fundamental
00:34:48
ideas, but I think your ability to do it
00:34:49
in a virtual digital world um is just
00:34:52
greatly enhanced. And so the depth of
00:34:54
that relationship and the engagement of
00:34:55
that relationship is just much more
00:34:57
valuable.
00:34:59
>> Wonderful. All right. Well, we're on
00:35:00
tight time with you, so we're gonna have
00:35:01
to let you go soon, but maybe one last
00:35:03
question. Give us some sense, you have a
00:35:05
great vantage point on this. Give us
00:35:07
some sense of where you think sports
00:35:08
will go or how sports will be different
00:35:10
five or 10 years from now because of
00:35:12
this change because it's now an asset
00:35:14
class. What do you see different five or
00:35:17
10 years from? Uh well, I'd say first on
00:35:18
the institutional capital side or the
00:35:20
capital side. I think if you went back
00:35:22
20 years in private equity, there really
00:35:23
wasn't a secondary market for if a firm
00:35:26
wanted to sell 20% of fund three, there
00:35:29
really wasn't a process for doing that.
00:35:30
Today, the private market structure set
00:35:32
up. There's a real process for doing
00:35:34
that. I think sports will move in that
00:35:35
direction. I think it'll be slower the
00:35:37
governance of the leagues, the
00:35:38
requirements around approval rights and
00:35:40
everything else. But I think ultimately
00:35:42
for these valuations to continue and
00:35:43
increase, and we do think they will
00:35:45
continue to increase, you need more
00:35:47
pockets of capital that are going to
00:35:48
have the ability to come in. And so,
00:35:50
right, insurance companies, sovereign
00:35:51
wealth funds, other pockets of capital
00:35:53
that may not have had the opportunity to
00:35:55
start to come in. I think you will see
00:35:58
more flexibility among the leagues. Not
00:36:00
saying when exactly how, but I think
00:36:03
ultimately you're going to have you're
00:36:04
going to want the ability for all
00:36:06
different pockets of capital to come in
00:36:08
uh and have that opportunity. And I
00:36:09
think you will see more uh from a
00:36:11
secondary um secondary market
00:36:13
perspective. I also think you're going
00:36:14
to see we talked about it direct to
00:36:16
consumer but these brands these big
00:36:17
brands you know there's probably 20 25
00:36:20
of them around the world in different
00:36:22
sports but I think they are going to go
00:36:25
fullon and I think this direct to
00:36:27
consumer element is going to I think the
00:36:29
winners those who sit in one of those
00:36:31
seats I think are going to get an
00:36:33
incredible amount of economics out of
00:36:36
that. It won't dilute what the core
00:36:39
right what teams are doing in you know
00:36:41
good markets because you in essence will
00:36:42
be part of the league you'll still be
00:36:44
getting the league economics and
00:36:45
everything else but what we thought of
00:36:47
historically is everybody's kind of
00:36:49
getting you know similar type economics
00:36:51
from the league um you know in terms of
00:36:53
media rights and everything else I think
00:36:54
these bigger brands are going to be able
00:36:56
to um you know in essence find new
00:36:59
revenue streams through a direct to
00:37:00
consumer model which I think will have
00:37:01
implications and from a governance
00:37:03
perspective and I think college you know
00:37:05
college is going through a lot right
00:37:07
now. Um, you know, ultimately ultimately
00:37:11
it's going to get figured out, but it's
00:37:12
it's it's kind of messy and there's
00:37:14
there's a lot of tricky factors around
00:37:16
it. But, um, you know, I think I think
00:37:18
it ultimately gets resolved in a way
00:37:19
that um, Olympic sports do great,
00:37:22
women's sports do great. Um, but it's
00:37:25
it's complicated because you just don't
00:37:27
have the you just don't have the
00:37:28
governance around it to try to make
00:37:30
decisions unlike what you've seen in the
00:37:31
North American sports leagues, which has
00:37:33
really driven a lot of the value. The
00:37:34
governance system uh and the decision-m
00:37:37
system that sits in place in those
00:37:39
leagues has really driven a lot of
00:37:40
value. S
00:37:41
>> super interesting and and provocative
00:37:43
there. We could just do another hour
00:37:45
right on that. I was going to ask
00:37:46
because as you said, you know, this B
00:37:48
TOC for the top 30 or 40 brands across
00:37:51
the world in sports, those guys are
00:37:54
going to really see an acceleration. I I
00:37:56
was curious, does that include the top
00:37:58
handful of brands in college sports? And
00:38:00
will they participate? Will they be be
00:38:02
able to participate in the same way? And
00:38:03
you know, like you said, depends on
00:38:04
governance depends on governance, but
00:38:06
it's but it's right there presumably.
00:38:07
It's it's there for those in the same
00:38:09
way that it would be for professional.
00:38:11
>> I think it potentially is. I mean, if
00:38:12
you went back, at least when I was
00:38:13
growing up, you know, college football
00:38:14
is still a very regional thing, right?
00:38:16
And now most of the top 20, 30 brand,
00:38:19
they're they're national brands, right?
00:38:20
Or they're they're big brands. Kids are
00:38:22
coming from all over the country to go
00:38:24
to these schools. So, I think that
00:38:25
opportunity exists and I think there
00:38:27
will be elements of what happens in
00:38:28
college that replicate some of what
00:38:30
we've seen in the in the professional
00:38:32
leagues.
00:38:34
>> Wonderful. All right, Dave, we'll let
00:38:36
you go. Greatly appreciate the time with
00:38:38
you. I know you're doing a few other
00:38:40
things, so it's it's we really
00:38:43
appreciate you making time for us, but
00:38:44
we'll look forward to seeing you on
00:38:45
campus on Friday.
00:38:46
>> Absolutely. Thank you all. Enjoyed it.
00:38:48
>> Thank you.
00:38:49
>> Absolutely. Dave Dassy, he's he is head
00:38:52
of the Southeast region at at G Goldman
00:38:54
Sachs and the co-head of their sports
00:38:56
unit. They've been doing sports
00:38:57
investment for in financing for a long
00:39:00
time, it sounds like, but they're
00:39:01
officially have this group only in the
00:39:03
last few years. A number of big players
00:39:06
have moved into the sports finance
00:39:07
world. All right, guys. That's been the
00:39:10
first half of the show. Welcome back.
00:39:12
Welcome back to Wharton Moneyball.
00:39:14
Rolling into the open segments part of
00:39:17
the show. We're going to go for 15 or 20
00:39:19
minutes now after having talked to a
00:39:22
couple of guests, new sports business
00:39:24
guests. We don't usually dabble that
00:39:25
much in that direction, but man oh man,
00:39:27
I thought both were quite interesting.
00:39:29
We could have talked to sports finance
00:39:31
for another couple of hours. It's
00:39:33
definitely going to be a topic. It's a
00:39:34
It's grown around Wharton and it's only
00:39:36
going to grow more, not just Wharton,
00:39:38
other places as well. But why don't we
00:39:39
talk Enfield Sports a little bit and in
00:39:42
the time we have we're we're going to be
00:39:44
here. This is Cade with Eric and Shane.
00:39:46
Audi is out this week, but why don't we
00:39:48
just do a little round robin or three
00:39:51
two or three what caught your eyes? And
00:39:54
there's lots of different things we
00:39:55
could talk about. I'm curious what
00:39:56
you're most interested in talking about.
00:39:58
Why don't we start with Shane? What
00:39:59
caught your eye recently, Shane?
00:40:03
Well, what caught my eye today is that
00:40:05
for 20 starting 2026, we're finally
00:40:07
going to have some semblance of
00:40:10
automated balls and strikes, robo umps.
00:40:13
It's, you know, it's a little challenge
00:40:15
system. It's all inter, you know, I
00:40:17
mean, honestly, they could clearly roll
00:40:19
it out. You know, they could roll it out
00:40:21
completely and just call balls and
00:40:23
strikes with the robot. But now at least
00:40:24
the players or the the players directly
00:40:28
involved in the play can ch we'll have a
00:40:30
challenge system by which we can uh get
00:40:32
uh a smaller you know a larger set of uh
00:40:36
calls rights.
00:40:36
>> If a ball touches the strike zone is
00:40:39
that a strike?
00:40:40
>> Yes. Yep. Yeah. if you if watched it in
00:40:43
spring training basically if if it if a
00:40:46
sliver of that ball hits the strike zone
00:40:48
I mean again at that resolution I don't
00:40:50
know exact you know but like uh if a
00:40:52
sliver of the ball hits a strike zone
00:40:53
>> let me ask you a question then since I
00:40:55
would assume that the challenges can be
00:40:59
seen like right now just like they say
00:41:01
listen you know they put their
00:41:02
headphones on they say challenge that
00:41:04
call at first base
00:41:05
>> it's only the batter the pitcher and the
00:41:06
catcher that can call challenge
00:41:09
>> but the can I get a signal from the
00:41:11
dugout.
00:41:12
>> Nope.
00:41:12
>> It's unbelievable.
00:41:13
>> Oh,
00:41:14
>> that's that sounds like such a mess.
00:41:15
That's remarkable.
00:41:16
>> I say you have to you have to I mean
00:41:18
it's still a g Yeah. I mean, again,
00:41:20
>> it's so funny. It's kind of like the IRS
00:41:22
being like, "Oh, well, you got you got
00:41:24
to file your tax and tell them much how
00:41:26
you know, but we actually secretly know
00:41:27
because we can send you to jail if you
00:41:28
don't file them correctly." It's like
00:41:30
they know what the correct balls of
00:41:31
strike. We still need to have the
00:41:34
umpire. Tell me we're not going to have
00:41:35
a a Houston assoc situation where I'm in
00:41:37
the dung and I'm like,
00:41:39
you know, where I start banging on
00:41:41
something.
00:41:42
>> Well, yeah. I mean, the currently
00:41:43
there's that system. If a bad call is
00:41:46
made, the the manager goes, "Hey, what
00:41:48
the heck?" You know, and and yells,
00:41:50
>> right?
00:41:50
>> And and the batter could, you know, I I
00:41:53
mean, that type of stuff or or the fans
00:41:55
reacting. You can't I mean I mean
00:41:57
there's a lot of people with eyes on the
00:41:58
strike zone that can signal to the
00:42:00
pitcher and batter. But I mean the
00:42:02
batter himself probably has the best
00:42:03
idea of all that it was a bad call.
00:42:06
>> I don't
00:42:06
>> or the pitcher.
00:42:07
>> Well, but two things here. I I reacted
00:42:11
first the same thing y'all reacted to
00:42:12
which is man I mean you've only got two
00:42:15
challenges all game and you're going to
00:42:16
and the players
00:42:17
>> No, no, no. Two two unsuccess you're
00:42:19
limited to two unsuccessful challenges.
00:42:22
If you have a successful challenge, you
00:42:23
can keep going.
00:42:24
>> Okay. Well, still that's a very precious
00:42:26
it's a precious commodity to be in. Oh,
00:42:28
yeah. Everybody's got discretion and
00:42:30
nobody has control. That's a heck of a
00:42:32
system.
00:42:33
>> No, that's that's why it's only an
00:42:34
intermediate state because they're got
00:42:36
there's going to be like six challenges
00:42:38
they wanted to make every game and like
00:42:40
so like a season or two from now we'll
00:42:41
just go unlimited challenges and i.e.
00:42:44
the system where it gets called
00:42:45
correctly,
00:42:46
>> right? So this this is just baby steps
00:42:48
in because the number of calls this will
00:42:51
affect is very small per game. I mean,
00:42:53
there are 200 200 pitches a game.
00:42:56
>> I think it's an interesting prediction
00:42:57
exercise how many successful challenges
00:42:59
you'll get per game.
00:43:00
>> Two unsuccessful. What's the number? I
00:43:02
get two challenges or I can I get as
00:43:04
many as two unsuccessful challenges.
00:43:06
>> Well, you get uh uh you get you get two
00:43:08
unsuccessful challenges. You have as
00:43:10
many challenges as 20. If I think I'm
00:43:12
right and if you think you're right
00:43:15
>> on all 20, then go for it. You'll get 20
00:43:17
right. But if you're wrong for more than
00:43:19
two or two or more, you've burned
00:43:20
through all your team's challenges for
00:43:22
the game.
00:43:22
>> And if you're Yeah. So you're wor Eric,
00:43:25
you're shaking your head because you
00:43:26
know Jazz Chisome is going to burn
00:43:27
through the Yankees challenges in his
00:43:29
first at bat. Cuz I've never seen a guy
00:43:31
argue more balls and strikes where he's
00:43:33
actually wrong.
00:43:33
>> There's no way I'm going to let my
00:43:37
batters without some wink wink signal or
00:43:40
something challenge squat. No way.
00:43:44
Batters are wrong all the time.
00:43:46
>> They could be.
00:43:47
You know, there's no way I'm going to
00:43:48
let a batter challenge without me giving
00:43:50
them the signal.
00:43:51
>> Or I certainly think there'll probably
00:43:52
be trust systems open up where like
00:43:55
certain batters will quickly get like a
00:43:56
red light from their magic being like
00:43:57
you don't use our challenges. You're not
00:44:01
good at it. Yeah. No, I mean I think
00:44:02
that type of stuff will happen. But
00:44:04
again, it's and again it's like this
00:44:05
weird imperfect intermediary state, but
00:44:07
it's better than before where we had no
00:44:10
no recourse other than yelling at the
00:44:12
ump and getting thrown out. I'm I'm just
00:44:15
going to celebrate it as surely an
00:44:17
intermediate step on the way to a more
00:44:19
comprehensive system. That's surely the
00:44:21
the what what this really means. Okay,
00:44:23
Eric, what caught your eye?
00:44:25
>> So, I didn't know the answer to this
00:44:28
until I looked it up. So, obviously I'm
00:44:30
a Bucks fan. I didn't There was It was a
00:44:33
point in the game right at the end and I
00:44:36
didn't know what the right thing for the
00:44:38
Bucks to do was just to just to tell you
00:44:40
all the situation. The Bucks are up by
00:44:42
six points. The score is 26 to 20. Okay,
00:44:48
a minute 50 left.
00:44:50
The other team has no timeouts.
00:44:53
Okay, it's fourth and one.
00:44:55
>> Well, the other t the other team is the
00:44:57
Jets, isn't it?
00:44:58
>> Correct. Okay,
00:44:59
>> the Bucks are playing the Jets. The
00:45:00
Bucks are up 26-20.
00:45:04
Under two minutes left. Okay, they're up
00:45:07
six. It's fourth and one from the 18.
00:45:12
Now, let's play this out.
00:45:13
>> Your own 18 or the opponent's 18?
00:45:15
>> The opponent's 18.
00:45:17
>> Wait, wait, wait.
00:45:18
>> Wait, wait. You go for it. You make it.
00:45:21
You win the game.
00:45:23
>> You kick the field goal, you go up nine,
00:45:26
you win the game.
00:45:27
>> Yeah.
00:45:28
>> So, the question is, which do you do?
00:45:31
And I was sitting there live wondering,
00:45:34
I really don't know. Like, I could make
00:45:36
it. Now, by the way, what happened? And
00:45:38
I'm sure all of you know the Bucks
00:45:40
decide to kick the field goal. It was
00:45:42
blocked and returned to the house.
00:45:45
The Bucks went down 2726. Now, of
00:45:47
course, thank God we have Baker, the
00:45:49
touchdown maker, and so he drove us down
00:45:51
the field and we kicked a field goal and
00:45:52
won the game. But it was really an
00:45:56
intriguing question. Did I know the
00:45:58
numbers? Do you guys have any because
00:46:00
they they
00:46:00
>> Okay, it does seem like we were getting
00:46:02
like a lot of blocked kicks this year
00:46:04
just
00:46:06
I I assume you're historical. I I am
00:46:07
curious. I mean, fourth and one if
00:46:09
you've got a good quarterback sneak is
00:46:11
got to be like 90% or something like
00:46:13
that, right?
00:46:13
>> Yeah. So, just let me know it was almost
00:46:16
it was almost indifferent. The by going
00:46:18
for it, the Bucks had a 96% win
00:46:21
probability. By not going for it and
00:46:23
kicking the field goal, they had a 94%
00:46:26
win probability. But I think this is one
00:46:28
of those things where you have to say
00:46:30
maybe it's not an expected value play,
00:46:32
it's an uncertainty play. Like if you go
00:46:35
for it and don't get it, let's remember
00:46:37
you're up six. They still have to drive
00:46:39
80 yards.
00:46:39
>> I would want I kind of want the relative
00:46:41
probabilities of the next play for my
00:46:43
decision. What you gave me is the win
00:46:45
probabilities and that's probably what
00:46:46
you had on hand.
00:46:47
>> That's what I had. That's what I had.
00:46:49
But you can think about it like
00:46:51
>> and by the way, another thing to say is
00:46:53
but see this is why you have to go two
00:46:54
steps ahead because you say, "Well, the
00:46:56
kick could get blocked and return to the
00:46:58
house." Okay, it did. But then you still
00:47:01
have the ball only down one point with a
00:47:03
minute something left. So you can win
00:47:04
the game then too
00:47:08
>> which is what happened. But even not
00:47:09
being a you know outcome oriented
00:47:11
person, let's think about the
00:47:12
probability. It was one of those
00:47:14
situations where I literally had no good
00:47:18
mathematical intuition about which of
00:47:21
the two was better. And by the way, I
00:47:23
think minor tweaks of it like, all
00:47:25
right, suppose I told you the Jets had
00:47:28
one timeout, or suppose I told you there
00:47:31
was 2 minutes and 3 seconds left, or
00:47:34
suppose I told you it was fourth and
00:47:36
two. It's almost like the derivative of
00:47:39
the probability with respect to some
00:47:41
little perturbation could actually be
00:47:44
quite huge. And so I have to I mean, and
00:47:46
by the way, this is why it's painful to
00:47:48
know a little bit of statistics. I
00:47:50
should have just been enjoying the game
00:47:52
and I'm sitting there thinking, "Oh my
00:47:53
god, we're gonna have to talk about this
00:47:55
on the air and I gotta think about these
00:47:56
perturbations and what if it's," you
00:47:58
know, it's hard to enjoy a game when
00:47:59
you're like when you're when the Jets
00:48:01
are still in it in the fourth quarter.
00:48:03
You're team's not doing well if you're
00:48:05
if you're a separate issue, but I have
00:48:07
to That's what I was thinking live at
00:48:09
the time.
00:48:09
>> Okay, but this isn't my what caught my
00:48:11
eye, but just a quick riff on that. I
00:48:13
discovered something last night. I've
00:48:14
been very slow to get to the Manning
00:48:16
cast, and it's not for any reason. I'm
00:48:18
I'm pro those guys, but last night the
00:48:20
Ravens were on and I just went full
00:48:23
Manning cast the whole time. And usually
00:48:25
when I watch the Ravens, I'm like a
00:48:27
great big anxiety bucket. I'm a
00:48:29
nine-year-old boy. It's not impressive
00:48:32
in any form or fashion. And somehow
00:48:34
watching those guys distracted me enough
00:48:36
that I just was not stressed as I
00:48:38
watched the game. Of course, I quit
00:48:39
watching it at halftime, which was a
00:48:40
very wise, mature decision. But I'm
00:48:43
telling you, the maniccast for those of
00:48:44
you who get worked up watching your
00:48:46
team, if you got a maniccast
00:48:48
alternative, it's a wonderful
00:48:49
distraction. Okay. Well, cut my eye. Um,
00:48:51
there's a fellow out there named Andrew
00:48:53
Persal who builds a college football
00:48:56
metrics consensus. He he and he's and
00:48:58
he's made it all publicly available and
00:48:59
he's done phenomenal stuff on Tableau.
00:49:01
So, you can get up and you can get all
00:49:03
kinds of figures and numbers and graphs
00:49:04
and all and he's it's a real service and
00:49:07
he's got a bunch of models in there and
00:49:08
he's tracking model performance, all
00:49:09
kinds of things. So, first of all's CFB
00:49:12
metrics consensus is worth looking into.
00:49:15
But I just grabbed his current top 20
00:49:18
and by his I mean the the ensemble that
00:49:21
he's built of these various metrics and
00:49:24
and he has a helpful column in this
00:49:26
thing which lists the teams are ranked
00:49:29
by consensus metric consensus the you
00:49:32
know the power rankings and then he's
00:49:33
got a column that says what about the
00:49:35
average poll and so you can kind of
00:49:37
instantly see some teams that stand out
00:49:39
and this is catching my eye because
00:49:42
Alabama by the consensus of power
00:49:44
rankings according According to Pible,
00:49:46
Alabama's number three in the country,
00:49:48
but remember they lost that opening
00:49:49
weekend game against Florida State and
00:49:51
people have been kind of they've been a
00:49:53
little bit off the radar. It matters
00:49:55
because this week, week five of college
00:49:58
football season has some huge games and
00:50:01
one of them is Alabama at Georgia. And
00:50:04
the Crimson Tide is a three-point
00:50:06
underdog. They go into Athens. It's on
00:50:09
the road, but this consensus says that
00:50:12
Alabama is actually a point a point and
00:50:14
a half better on a neutral field than
00:50:16
Georgia. And you know, homefield is
00:50:19
probably worth maybe two these days. And
00:50:21
so there's a little bit of an edge there
00:50:22
if you want. But more than the, you
00:50:24
know, details of the of the edge is just
00:50:27
that Alabama's looking pretty underrated
00:50:30
by the lay people, by the polls in
00:50:32
particular, coming in at 14, the average
00:50:34
poll rating, when in fact they're way,
00:50:36
way up there in the power ranks.
00:50:39
>> Well, I think you also point out, kids,
00:50:40
good point. If I told you, forget about
00:50:42
the columns that you This is a great
00:50:45
thing that you posted. Suppose I told
00:50:47
you the number 14 team by the polls was
00:50:52
playing the number four team by the
00:50:53
polls, which is what this metrics
00:50:55
consensus says. And by the way, I'm also
00:50:57
going to tell you it's the home field is
00:51:00
the number four team. What most people
00:51:03
historically, if that's the only
00:51:04
information I gave you, I'm not going to
00:51:06
tell you it's Alabama. I'm not going to
00:51:07
tell you it's Georgia. I'm just going to
00:51:08
tell you the rank position and I'm going
00:51:10
to tell you who's whose stadium it's at.
00:51:12
I think most people would say in most
00:51:14
years four would be favored at home over
00:51:17
14 by a lot more than three points.
00:51:20
>> Yeah, you might expect seven or 10 or
00:51:22
something like that.
00:51:23
>> Yeah, that's my point. And so this is to
00:51:26
me this is a big discrepancy between the
00:51:31
polls and their implied point
00:51:34
differential and the power rankings and
00:51:37
their implied differential. So this that
00:51:39
to me is fascinating. Well, there's
00:51:41
another good one um this weekend as
00:51:43
well. So, there there's three big games,
00:51:45
two monster games. So, the Alabama
00:51:46
Georgia, the other is um Oregon going
00:51:49
into Penn State, which is let huge for
00:51:52
the Big 10. It's a white out game over
00:51:54
in State College. And then the third big
00:51:56
there's there's it's this is probably
00:51:58
the third biggest is
00:52:00
LSU and Miss. And again, LSU is way up
00:52:04
in the polls. Miss is a bit of a
00:52:06
sleeper. And the power rankings don't
00:52:08
see it that way. the power rankings seem
00:52:10
miss stronger than LSU. And so it's the
00:52:13
both of those SEC games are going to
00:52:15
kind be kind of prove it games because
00:52:17
the the team that has had a harder time
00:52:20
so far this season or hasn't been quite
00:52:22
as impressive are actually seen as
00:52:24
stronger by the by the consensus power
00:52:26
rankings.
00:52:28
>> So just to be clear this site Andrew
00:52:31
Perl he doesn't build his own models per
00:52:33
se. He builds consensus or he combines
00:52:37
he's doing a meta analysis. He's
00:52:38
combining other analyses.
00:52:40
>> It's in a lot of us ensemble that's
00:52:42
something that people should do.
00:52:43
Ensembles are are great. You don't know
00:52:45
which one's best. You can track
00:52:46
performance, but in general just get a
00:52:48
bunch a bunch of good ones. But he's
00:52:50
going beyond that. First of all, he's
00:52:51
doing like this public service by making
00:52:53
all these data available. He's cutting
00:52:54
it in all kinds of ways. You can link to
00:52:56
it. It's it's a real service. But but
00:52:59
for us, it's just a useful way to look
00:53:01
at consensus. All right. I think we have
00:53:03
time for
00:53:03
>> I know I want to ask one question. Is
00:53:05
Massie Peab buddy one of them? It's not
00:53:06
because we're not we don't update our
00:53:08
rankings fast enough. So these things,
00:53:11
one of the nice things about what Peral
00:53:12
does, he's getting he's getting these
00:53:13
updates out, you know, by Monday. I got
00:53:15
I think I grabbed these data yesterday
00:53:17
and you can run sims off them or
00:53:19
whatever you want to do. Um okay, why
00:53:21
don't we do a quick kind of a lightning
00:53:23
round? What caught your eye for? Give
00:53:25
you guys another bite at the apple.
00:53:26
Shame.
00:53:29
>> Uh I guess I'll talk about how the
00:53:31
Atlanta Falcons didn't cross the 30 yard
00:53:34
line.
00:53:34
>> Oh no. Was it that bad?
00:53:36
>> Yeah, they literally did not have a
00:53:38
possession. That's got to be And I I I
00:53:40
I'm not good enough.
00:53:41
>> You mean their own 30? You mean the
00:53:42
other team's 30?
00:53:44
>> I think it might have been. No, they
00:53:45
can't.
00:53:47
But they basically never No red zone
00:53:49
possessions. No, no close to red zone
00:53:51
positions. And that's got to be I kind
00:53:53
of feel like that's a stat. I mean, I
00:53:55
just sort of saw that stat out there. Um
00:53:57
I'd like to know the null distribution
00:53:59
on that. I kind of wonder what the how
00:54:01
often that happens, how rare that is.
00:54:04
It's got to be pretty unique
00:54:06
>> 100%. And that props to the Carolina
00:54:09
Panthers. Props to Eric Eager, our
00:54:12
friend who is in the front office there.
00:54:14
That was a big win for the Panthers.
00:54:16
First of the year, but just a smack. I
00:54:18
think they said I think I saw it might
00:54:19
have been the biggest margin of victory
00:54:21
in Panthers football history. I mean,
00:54:24
>> that's amazing because they've actually
00:54:26
I mean, it's not like they've you know,
00:54:27
they've had some incredibly good teams
00:54:28
over the years as well, but that's that
00:54:30
that's very very impressive.
00:54:31
>> I hope I didn't get that wrong. I might
00:54:32
have gotten that wrong. It might be the
00:54:33
biggest in their in their rivalry, but
00:54:35
it's may maybe that's what it is. But
00:54:37
anyway, it was a 30 nothing win. Big
00:54:39
notable 30 nothing win. Okay, Eric
00:54:42
Bradlo, your second. What caught your
00:54:43
eye?
00:54:46
Is Jaylen Herz an elite quarterback yet?
00:54:48
Can we all agree to that? The guy has
00:54:51
won
00:54:53
17 straight games. Remember the last
00:54:57
game he lost, he was injured in the
00:55:00
first quarter and then that he played a
00:55:03
full game. He's won 17 straight games.
00:55:06
And so all I know is the guy wins over
00:55:11
that period of time. His completion
00:55:13
percentage is 70.4%
00:55:16
and he only has two interceptions in
00:55:18
this 17game winning streak. So, at some
00:55:22
point, we have to give Jaylen Herz. That
00:55:26
caught my eye. This guy just keeps
00:55:28
winning and winning and winning and
00:55:31
winning. And at some point, we have to
00:55:33
say he's not a game manager. He's not,
00:55:36
you know, when they were down 26-7 and
00:55:39
he lit up the Rams in the second half,
00:55:41
all of a sudden he's not a game manager.
00:55:43
Now, if you're telling me, is the system
00:55:45
the Eagles built meant for him to pass
00:55:48
for 400 yards and four touchdowns? No.
00:55:52
But you know what? I I'm just impressed.
00:55:55
I That's all that
00:55:56
>> Well, yeah. I mean, I understand. I
00:55:59
mean, I love I love the age-old debate.
00:56:00
Is are are wins a QB stat? I mean,
00:56:03
because that's, you know, the stat I saw
00:56:06
was that Jaylen Herz is 10 and0 against
00:56:10
teams with other tier one quarterbacks.
00:56:13
correct
00:56:13
>> from since like the start of the 2023
00:56:15
season and it was quoted as Jaylen Herz
00:56:17
is 10 and0 not that the Eagles are 10
00:56:19
and0 it's I I just want to kind of point
00:56:21
out it is in fact a team store score uh
00:56:24
team sport and I think the mark against
00:56:27
Jaylen if you want to because the word
00:56:28
elite
00:56:30
I I think implies
00:56:33
even good win not necessarily
00:56:36
like good good what Mahomes is doing now
00:56:40
where he's still actually performing
00:56:41
fairly well even agree to the following.
00:56:44
I think we agree to the following.
00:56:46
>> You could take Jaylen Herz and put him
00:56:48
on another team
00:56:50
>> and under a different system, it's not
00:56:53
obvious that he would add a lot to the
00:56:56
expected number of wins of that team.
00:56:59
And you and by your definition or mine,
00:57:01
we might say that's not elite. Like put
00:57:03
Tom Brady at his peak on any team and
00:57:06
that team's doing better than
00:57:08
expectation. I'm not sure that that
00:57:11
would be true of Jaylen Herz, but I will
00:57:13
say you could Carl he's won the Super
00:57:16
Bowl and he he's on the undefeated
00:57:19
season right now.
00:57:20
>> Yeah. And I I think it's for me it's
00:57:22
more but kind of like for eliteness or
00:57:23
or or you know I mean I and I I I
00:57:25
actually really like Jaylen Herz. I I'm
00:57:27
I'm not actually really making the
00:57:28
argument he's not elite, but I think for
00:57:30
me it's about the floor as opposed to
00:57:32
ceiling. like he like, you know, I think
00:57:35
what speaks against him is when that
00:57:37
team fell apart a couple years ago and
00:57:39
like, you know, things really fell apart
00:57:40
on on everything. I mean, Jaylen was
00:57:43
kind of part of that story.
00:57:45
>> Yep.
00:57:45
>> But not all of that story, but I I guess
00:57:48
it's sort of like I still need to kind
00:57:49
of see I guess him perform in in uh in
00:57:54
kind of like with an off. It would be
00:57:56
extra impressive to me if they get a I
00:57:58
I'm not hoping for this, but if they
00:58:00
were to get like a couple key injuries
00:58:01
where he's having to do, you know, kind
00:58:04
of win still continue to win games, keep
00:58:06
his team in games like with like, you
00:58:09
know, kind of like with what Mahomes is
00:58:11
dealing with right now in in Kansas City
00:58:12
with his like receiver injuries and
00:58:14
stuff like that or or something like
00:58:15
that. And I I wouldn't wish that on the
00:58:17
Eagles or Herds to have to go through
00:58:19
that, but that would be more compelling
00:58:20
to me.
00:58:21
>> Well, how about the following? We have
00:58:22
to agree we got to give a lot of credit
00:58:24
to the Eagles front office and coaching
00:58:26
staff for not only building
00:58:28
complimentary players around him but for
00:58:30
also building a system where he can be
00:58:33
extraordinarily
00:58:34
>> that's where I'm a little bit out on
00:58:36
Shane's criteria because I mean that
00:58:40
they've built a robustness I think in
00:58:42
that in that system and complimentary as
00:58:44
Eric says but moreover I I I would add
00:58:47
something I'm I'm curious now is there
00:58:50
can we get some can we do something and
00:58:52
modeling to get at
00:58:54
high leverage situations in football. I
00:58:56
mean, it seemed like the other day, the
00:58:59
other day, I they were down 26 to seven,
00:59:02
I think, at one point, and they this was
00:59:04
this amazing comeback. And when they
00:59:06
were on the way back,
00:59:07
>> I didn't watch at all, but every time I
00:59:10
saw a high leverage play that Herz was
00:59:12
quarterbacking, they converted like like
00:59:14
it might have been 100%. And look, small
00:59:18
sample, I get it. And this hard to I get
00:59:20
it. I get it. I get it. Shouldn't we be
00:59:21
able to say something about quarterbacks
00:59:24
who show up in those situations if it's
00:59:26
true? If it's true. It sure does look
00:59:28
like it might be true.
00:59:28
>> No, I I I think that's I I would love to
00:59:31
see that kind of analysis. I I think it
00:59:33
would have to be compleimemented
00:59:35
analysis by how I would also like to see
00:59:38
for different quarterbacks how often
00:59:40
they get themselves into high leverage
00:59:41
situations. I mean, you know, Jaylen
00:59:43
Herz was having to complete a 26 to 7 or
00:59:45
20 to7 comeback.
00:59:46
>> Good. That's very fair.
00:59:48
>> Very fair. Right. So,
00:59:50
>> you know, it's it's kind it's kind of
00:59:51
like certain, you know, who who you
00:59:53
know, the quarterbacks with like that
00:59:54
these big streaks of like comeback
00:59:56
victories or something like that. It's
00:59:57
like, well,
00:59:59
>> you know, I guess that means you were
01:00:00
playing good at the end of the game.
01:00:01
>> That's it flipped. It flipped it flips
01:00:03
the thing that they were saying about
01:00:04
Harbaugh last week about he's lost more
01:00:06
double digit leads than anybody but Andy
01:00:08
Reid. It's like, but you look at the
01:00:09
guys on that list and it's a bunch of
01:00:11
guys who have had their teams up by
01:00:12
double digits. You have it's that's the
01:00:14
flip of that. Okay, so we we're going to
01:00:16
run out of time, guys. My last caught
01:00:18
your eye, of course, is the Ryder Cup.
01:00:22
We only get this thing every other year.
01:00:23
We only get it in the States every four
01:00:25
years, and it's this weekend. It's only
01:00:27
three days, guys. It's a quick thing
01:00:29
that happens. It's been building up for
01:00:31
literally two years.
01:00:33
The thing that I'm seeing in the markets
01:00:36
fits with what I thought we were going
01:00:38
to see, which is that the that the
01:00:40
states, the US is a pretty big favorite.
01:00:43
people when you look at when you just
01:00:44
run down the names on the two teams
01:00:47
all US fans have so much PTSD they're
01:00:50
like oh we're gonna get killed oh we're
01:00:51
gonna get killed but that's just not the
01:00:53
case if you look at the data that the
01:00:56
the US team looks stronger and all the
01:00:58
markets suggested like the US is a
01:01:00
favorite in the betting markets the US
01:01:02
is favored by the by the by the model at
01:01:05
that at data golf the US is for example
01:01:08
58% 59% on poly market to win which
01:01:12
match is the data go model. Exactly.
01:01:15
It's it's it's now look anything can
01:01:17
happen
01:01:18
>> compared to the last is is that kind of
01:01:19
lopsided compared to the last few times
01:01:22
through you don't need number. I just
01:01:23
kind of generally that that seems like a
01:01:25
pretty big edge if it's like really at
01:01:27
like the 5860 range or whatever.
01:01:29
>> Yeah, I agree that looks like a big
01:01:30
edge. I don't have those numbers from
01:01:32
previous matches but I it feels to me
01:01:34
like the US team is probably stronger.
01:01:36
Look, let's look at data golf's numbers.
01:01:37
So data golf runs, you know, overall
01:01:40
rank they they give a a strokes versus
01:01:43
average and it's and it's modeled to be
01:01:45
predictive and it seems a lot better
01:01:47
than the world golf rankings. And so we
01:01:49
can look at those and just run down the
01:01:51
team. And what's true is that the
01:01:53
Europeans have a topheavy team. So
01:01:56
Sheffler is clearly the best players,
01:01:58
best of the 24 players on the two teams.
01:02:01
The next three guys come from the top of
01:02:04
the European team. John Rom, Rory Maroy,
01:02:06
Tommy Fleetwood. Absolutely. They would
01:02:09
be guys two through four on the US team,
01:02:12
but also the bottom five guys on the
01:02:15
list come from the European team, and
01:02:17
that's almost half the team. So, yep,
01:02:20
>> it's we're going to see. I mean, it may
01:02:21
be that the Friday, Saturday plays
01:02:24
tight, but it should it should the
01:02:27
numbers say Sunday would favor the
01:02:28
Sunday is the 12 match plays. Sunday
01:02:31
will favor the the US team. Okay, I
01:02:34
think that's it. Anything else, fellas?
01:02:38
>> All right,
01:02:39
>> big weekend in sports.
01:02:40
>> Big weekend in sports is good across.
01:02:42
Oh, we didn't even talk about baseball.
01:02:43
How I mean, look, I I I looked at the
01:02:45
baseball standings over breakfast this
01:02:47
morning. You guys have this kind of
01:02:48
impact on me. You should be proud of
01:02:50
yourselves.
01:02:51
>> And the AL playoff race is fascinating.
01:02:54
I think you should be looking at
01:02:55
baseball I think you should be looking
01:02:56
at baseball scores all week long to see
01:02:59
guys divisions are still in play.
01:03:02
Certainly all the wild cards
01:03:03
>> the Mets are doing quite possibly a
01:03:05
historic collapse here. So uh so that's
01:03:07
kind of
01:03:07
>> might drop all the way out after having
01:03:09
like a 14game lead or something that's
01:03:11
maybe never has it happened in the
01:03:12
division era.
01:03:13
>> I don't think it's happened in like you
01:03:14
know certainly the current sort of
01:03:17
>> I don't I think almost everything is
01:03:20
still in play meaning who are the top
01:03:22
seeds in each division who are the wild
01:03:24
card teams on the side
01:03:25
>> who are going to win the
01:03:26
>> on the on the on the side. No, no, no,
01:03:30
no, no. I mean, it's the the top seed's
01:03:32
going to be either the Brewers or the
01:03:34
Phillies. We don't know yet which one.
01:03:36
The Phillies are only
01:03:37
>> the top two seed. I I mean, but it's
01:03:38
it's kind of like, you know, uh,
01:03:40
>> who cares?
01:03:41
>> I guess maybe that that little one two
01:03:43
ordering is minutely important.
01:03:45
>> Yeah, we don't know who the Wildard
01:03:47
teams are going to be in the NL or the
01:03:48
AL.
01:03:49
>> Yeah, the the NL. We still have the West
01:03:53
race between the Dodgers and the
01:03:54
Padres's as well. But anyway, good fun.
01:03:56
And I I mean, this is close to peak
01:03:58
sports. This is close to peak 7 10day
01:04:01
window of the year and the writer cup
01:04:04
really kind of elevates it. Um, okay
01:04:07
guys, why don't we stop it there then
01:04:09
full show we'll wrap it then. Thank you
01:04:11
guys for listening on behalf of Shane
01:04:13
Jensen and Eric Bradler who have been in
01:04:15
here for the whole time for Audi Winer
01:04:17
and Absentia. Dion Simpkins who makes
01:04:19
the whole thing go. D Patel the boss
01:04:21
lady. Marissa Raina our producer. Thank
01:04:23
you guys for listening. Come back and
01:04:25
join us next time between now and then.
01:04:28
Enjoy your sports.
01:04:30
[Music]
