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Speaker 1: Algorithms are almost like recommendation systems, so it will amplify

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Speaker 1: what's already performed. If you user clicks on AFL men's highlights,

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Speaker 1: the algorithm will give them more men's content. So over time,

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Speaker 1: women's content starts to get squeezed out, not because it's unworthy,

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Speaker 1: but it just hasn't had the same engagement in history.

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Speaker 2: Welcome to the Next. The Next is a monthly podcast

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Speaker 2: from the Female Athlete Project. Each episode, futurist and former

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Speaker 2: athlete Ranna Brown joins Chloe and Bears to explore what's

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Speaker 2: changing in women's sport before it hits the headlines, which

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Speaker 2: I a light on the pockets of the future already here.

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Speaker 2: The signals have changed that are quietly or not so quietly,

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Speaker 2: reshaping women's sport in the present. Not to predict what's coming,

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Speaker 2: but to notice what's already in motion, and then ask,

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Speaker 2: so what now? What what might this mean for women's sport?

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Speaker 2: From here? What kind of women's sports futures do we

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Speaker 2: want to inhabit? And how can we widen the choices

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Speaker 2: available to us today? Rie? Hi, how are you missed?

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Speaker 2: You feel like it's been ages.

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Speaker 1: I know I have a breakover January. It was excellent,

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Speaker 1: the very nineties holiday, no screen, no laptop, you know,

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Speaker 1: outside it was amazing.

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Speaker 2: So no four hundred ninety eight tabs, like you genuinely

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Speaker 2: are good at actually going offline.

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Speaker 3: No, I did my best.

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Speaker 1: I went to my family's place in remote Queensland, so

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Speaker 1: it was tools down and it was well a different

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Speaker 1: kind of tool outdoor kind of tools. So no laptop,

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Speaker 1: no screens. It was amazing. But I'm raring to go.

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Speaker 1: A lot's happened, A lot happens in six weeks.

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Speaker 2: I love that. What are you noticing? What have you

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Speaker 2: been noticing?

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Speaker 1: Well, I actually have a question before I dive into

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Speaker 1: the new tabs that I've opened for twenty twenty six,

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Speaker 1: which is when you think about the most influential decision

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Speaker 1: makers in women's sport, who do you think of who

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Speaker 1: comes to mind immediately?

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Speaker 2: Probably who comes to mind immediately as broadcasters. I just

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Speaker 2: think I don't know if in terms of the public

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Speaker 2: eye they look like the most influential, but just from

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Speaker 2: my experience being involved in some of those discussions around

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Speaker 2: collective bargaining agreements, I think that the broadcasters have huge

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Speaker 2: pull over the sporting leagues around timing of games, around fixturing,

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Speaker 2: all of those things. I think they're a huge player

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Speaker 2: in terms of that influence.

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Speaker 4: Which then have such such a which then has such

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Speaker 4: a huge influence on visibility.

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Speaker 2: Totally.

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Speaker 5: I think yes to broadcasters.

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Speaker 4: I also think we're talking about governance policymakers, people not

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Speaker 4: only in government spaces, but also leaders of those sporting bodies.

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Speaker 4: Christi Coventry is the president of IOC. You know she

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Speaker 4: has a huge role there. I think we're looking now

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Speaker 4: and interestingly, we've got the forty percent females on board

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Speaker 4: rule coming in shortly, so that is an interesting area

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Speaker 4: where I think a lot of sporting organizations are scrambling

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Speaker 4: for some more women on boards, which is a.

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Speaker 5: Positive thing totally. I just do hope that.

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Speaker 4: Those women on boards are experienced in the sport and

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Speaker 4: not just in the business space.

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Speaker 2: That's a discussion for another podcast.

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Speaker 4: The commercial leadership space as well, people making those decisions

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Speaker 4: in regards to brands connections.

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Speaker 2: Absolutely, I think about that all the time. Why. I

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Speaker 2: think you and I talked about that when we had

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Speaker 2: our own podcast interview a couple of years ago. I

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Speaker 2: always think about it. It's the people, how you say

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Speaker 2: it's the people sitting in those rooms. They make decisions

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Speaker 2: based on their story, their experience, and their past history.

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Speaker 2: So I think that's a huge one. The people who

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Speaker 2: have the big dollars who are choosing where that money

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Speaker 2: is invested. I completely agree.

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Speaker 5: And what about athletes, we talk about athlete leaders.

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Speaker 2: I think there's been huge, huge rise in the power

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Speaker 2: of the athlete, that's for sure.

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Speaker 1: Actually, they were pretty good answers, so well done. That

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Speaker 1: was actually very good and very comprehensive, and I would

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Speaker 1: say yes, yes, yes, And so for me, I think

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Speaker 1: we are starting to see some of the biggest calls

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Speaker 1: that might not seem to be that big but actually

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Speaker 1: are very consequential in women's sport being made by algorithms.

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Speaker 1: So I'm talking about who gets seen, which athletes get seen,

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Speaker 1: which games, which clubs, which sports, who can get selected,

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Speaker 1: who gets paid, which athletes get paid, but also algorithms

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Speaker 1: deciding who gets protected. So which athletes get protected and

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Speaker 1: what does that look like? The difference I think is

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Speaker 1: that almost nobody can see inside of these decisions.

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Speaker 4: Yeah, so for someone gen X are here, a genuine

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Speaker 4: gen X, tell me I my understanding of the algorithm

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Speaker 4: and AI pretty narrow. Tell me what's the difference, what

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Speaker 4: are they both and what is the difference and how

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Speaker 4: do they interact with each other.

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Speaker 1: It's actually a tricky question, I would say, because actually

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Speaker 1: not many people know the neat answer to that. To

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Speaker 1: be really honest, in a way, I tend to describe

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Speaker 1: and understand AI as a broad label for a bunch

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Speaker 1: of computer systems that can do tasks you that we

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Speaker 1: usually associate with human intelligence, like looking at patterns, understanding language,

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Speaker 1: you know, creating images and texts. Algorithms are the instructions

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Speaker 1: that computers follow, so like the recipe, you know, and

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Speaker 1: who decides what goes in and what ingredients are in

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Speaker 1: is often by the people that create the algorithms.

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Speaker 3: Does that make sense?

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Speaker 1: So they're kind of rules. Algorithms are the rules. AI

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Speaker 1: is a new suite of technology that we associate with

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Speaker 1: human task So maybe that's like the voiceovers, the clones,

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Speaker 1: the video imaging and whatnot, But algorithms are the rules

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Speaker 1: behind it, the instructions.

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Speaker 2: Yes, that does make sense.

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Speaker 1: Okay, someone will tell me otherwise online, I'm sure, but

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Speaker 1: it's a general starting point.

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Speaker 2: Yeah, I'm sure they'll let you know, don't you worry.

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Speaker 1: Point here being that I think algorithms are started to

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Speaker 1: become an invisible infrastructure that's actually making decisions around sport.

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Speaker 1: So all the people that you've mentioned, people being the

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Speaker 1: operative word, I think, are absolutely influential, But so are

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Speaker 1: the algorithms and the technologies that we use.

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Speaker 2: You touched on some of the key themes if we

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Speaker 2: start with that first one around who gets seen? Is

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Speaker 2: it humans deciding what's newsworthy now? Or is it moving

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Speaker 2: even more so to the machines deciding who's newsworthy.

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Speaker 3: I'd say it's a bit of both.

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Speaker 1: I'm so intrigued about how technology quietly, I talk about

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Speaker 1: this idea of just algorithmic drift, and all of these

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Speaker 1: things happen and happened so quietly we tend to talk

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Speaker 1: about it. Then all of a sudden it's here. So

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Speaker 1: we're seeing a few changes. So one of them is

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Speaker 1: this idea that the production room is becoming very machine like.

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Speaker 1: So more sports are using technology to create and distribute

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Speaker 1: highlight reels in seconds, so that's entirely automated. You know,

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Speaker 1: the editor and the commentator is becoming a machine. In

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Speaker 1: I think it was twenty twenty four ESPN started to

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Speaker 1: spin out AI generated recaps. But the thing that was

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Speaker 1: interesting is that we're focusing on underserved sports that their

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Speaker 1: journals couldn't get to, including the NWSL.

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Speaker 3: So the kind of saying we can.

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Speaker 1: Do all of these recaps for sports that you may

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Speaker 1: not have a broadcast, a team, or a journal there.

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Speaker 1: So the NWSL has partnered with them. They've rolled out

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Speaker 1: aipower highlights. You can watch those reels like five seconds

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Speaker 1: after the game finishes, so really completely removes all of

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Speaker 1: the human elements of that. I think they said there's

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Speaker 1: one highlight reel created every three point one seconds and

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Speaker 1: no human editors being involved.

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Speaker 5: That's incredible.

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Speaker 2: Where's the quality control in that? Like they're so that's

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Speaker 2: a huge volume of highlights.

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Speaker 1: Well that's the question, isn't it. I mean you probably

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Speaker 1: have been watching highlights. If I watch I always give

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Speaker 1: the example of watching the WNBA highlights. I've been doing

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Speaker 1: that for a while. In fact, I'd say most people

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Speaker 1: watch sport now via highlights, and the shift here is

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Speaker 1: that they're probably, if not will increasingly be done through

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Speaker 1: digital technologies, right down to the commentator being digital. So ESPN,

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Speaker 1: I think they released their Sports Center for you. This

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Speaker 1: was last year and they were cloning the voices of

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Speaker 1: their own commentators and anchors. So you can get personalized

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Speaker 1: highlight packages, you know, as two different fans are two

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Speaker 1: different clubs. So Bears you might get a personalized package

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Speaker 1: with a gen X voice, and Chloe you might get

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Speaker 1: a personalized package based on your specific you know, fans

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Speaker 1: and athletes that you follow, and you can actually choose

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Speaker 1: a different kind of anchor. So this is getting really sophisticated,

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Speaker 1: really targeted. But I think the shift here too is

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Speaker 1: it's happening massively at scale. Now.

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Speaker 2: My question that comes to mind on that is like

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Speaker 2: as well about probably protecting the rights of the commentators

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Speaker 2: and the experts as well, like what what are their

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Speaker 2: deals looking like that they're signing for their for their

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Speaker 2: voices just to be cloned and just like off you

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Speaker 2: go out into the ether.

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Speaker 1: One hundred percent, I think that I think this is

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Speaker 1: what I'm saying. We're drifting into these futures that ask

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Speaker 1: enormous questions and we're already on route, like this is

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Speaker 1: already in motion. Imagine your voice, yeah, exactly, being recorded

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Speaker 1: once and then it being able to roll out to

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Speaker 1: like one hundred and fifty million highlights across the globe.

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Speaker 4: It's a bit like it's a bit like almost the

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Speaker 4: future has to exist to be able to then police it.

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Speaker 4: For in a certain aspects, it's we don't really know

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Speaker 4: what's going to happen, and then all of a sudden

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Speaker 4: it's happening and we're like, oh gosh, we should put

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Speaker 4: some guardrails around that.

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Speaker 1: Totally, that is bears exactly what this podcast is.

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Speaker 2: Oh I knew. I saw the way we reacted with

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Speaker 2: her hands and shaked her head. I was like, you've

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Speaker 2: noiled that. You're just try to suck up to the

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Speaker 2: boss here, aren't you? Look are you?

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Speaker 1: That is the neatest description that would have taken me

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Speaker 1: fifty pages to draft. To say, the whole premise of

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Speaker 1: the podcast is to make exactly make these implicit changes

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Speaker 1: explicit because they're here. I always say that, you know,

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Speaker 1: William Gibson, futures already here. It's just unevenly distributed. We're

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Speaker 1: already tripping over things on the way to the future.

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Speaker 1: We don't even have to speculate, but we need to

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Speaker 1: bring them to light and talk about them and say, well,

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Speaker 1: what do we want and what you know it's in motion,

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Speaker 1: but do we want it going in that direction? Do

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Speaker 1: we want to pull things up? A good example though,

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Speaker 1: is I think a couple of things there is that

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Speaker 1: major broadcasters casters are now building these things, not just

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Speaker 1: social platforms. So that's really interesting. But whoever owns that

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Speaker 1: highlight pipeline. Although you know the highlight reels and the algorithms,

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Speaker 1: remember the rules that shape what goes into those reels.

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Speaker 1: That shapes attention and commercial value, and it also shapes

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Speaker 1: a sense of meaning, like what gets captured. There's so

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Speaker 1: many times where I've yelled at this at my screen,

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Speaker 1: you know, a close game, and the high right reel

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Speaker 1: has cut out some of the most important parts of that.

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Speaker 1: So meaning is very different to you know, an algorithm

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Speaker 1: saying well these rules, therefore the game, the highlight reel

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Speaker 1: stops here.

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Speaker 2: On that meaning gap. I feel like that Alex Morgan

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Speaker 2: example is so interesting. So you talked about the AI recaps,

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Speaker 2: so ESPN Robin Howd in twenty twenty four they wrote

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Speaker 2: a two hundred and fifteen word recap of Alex Morgan's

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Speaker 2: final professional match, and it did not get a single

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Speaker 2: mention of Alex Morgan. She's like one of the greatest

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Speaker 2: footballers of all time, right for the US two time

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Speaker 2: World Cup win, Islmpic gold medalist, and then they had

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Speaker 2: to come They came back the next day to correct it.

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Speaker 5: Yep.

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Speaker 1: And for me, this is a new kind of gap

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Speaker 1: that's emerging. It's not necessarily about the accuracy. So these

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Speaker 1: sophisticated technologies can definitely tell the difference of a goal

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Speaker 1: and a corner, and you know when there's momentum in

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Speaker 1: a game and when there isn't. But it cannot overlay meaning,

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Speaker 1: So the way in which you experienced sport in many

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Speaker 1: ways can become like a thinner kind of attention.

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Speaker 2: And how does that then play out if we look

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Speaker 2: at I guess it can identify a goal in these

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Speaker 2: very traditional historic data points, How does that then impact

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Speaker 2: how they are Rhythm feeds us more men's sport than

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Speaker 2: women's sport.

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Speaker 1: Because yeah, exactly, because algorithms are almost like recommendation systems,

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Speaker 1: So it will amplify what already performs or what's already

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Speaker 1: historically there.

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Speaker 3: So imagine going into a library.

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Speaker 1: And it's just all men's sport rather than like three

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Speaker 1: books that are in there. But most books coming out

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Speaker 1: of there will be about men's sport, right. I think

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Speaker 1: vic Uni did a brandiant piece of research I think

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Speaker 1: it was twenty four to twenty five, and they were saying,

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Speaker 1: if you use it clicks on AFL men's highlights, the

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Speaker 1: algorithm will give them more men's content. So over time,

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Speaker 1: women's content starts to get squeezed out, not because it's unworthy,

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Speaker 1: but it just hasn't had the same engagement in history.

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Speaker 1: Even though at the same time, you know, we're seeing

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Speaker 1: stories about women's sport rising and people wanting to watch

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Speaker 1: more women's sport, we're seeing the algorithm starting to make

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Speaker 1: these decisions.

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Speaker 2: One of the stats that I actually quite love in

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Speaker 2: this space from TikTok was that the women in sports

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Speaker 2: posts grew two four hundred percent in twenty twenty four,

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Speaker 2: So what was that one point two million post? But

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Speaker 2: a twenty twenty five study found this growth is driven

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Speaker 2: by creators forcing it in, not the algorithm surfacing it.

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Speaker 2: So it's pretty much you have to fight against the machine.

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Speaker 2: I feel like.

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Speaker 5: That's yeah, man, we've got to make sure there's more

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Speaker 5: books in.

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Speaker 3: The library one hundred percent.

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Speaker 5: People who are.

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Speaker 4: Going in and secretly pushing books in the shelves, like

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Speaker 4: when your books get released and I put them on

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Speaker 4: the top shelf, on the bottom, self.

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Speaker 2: Put my books on the top shelf. I see you,

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Speaker 2: thank you.

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Speaker 4: I was in an airport once and that Chloe's book

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Speaker 4: was down on the bottom shelf, and I may have

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Speaker 4: just moved it up to the top shelf.

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Speaker 1: It is that, but digital. It is one hundred percent that,

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Speaker 1: but digital.

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Speaker 5: Digital book shuffling.

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Speaker 2: I love that. That's a great analogy. I'm here for

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Speaker 2: that guy.

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Speaker 1: I need to. I mean, just over the holidays, my

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Speaker 1: algorithm shifted from all you can imagine the algorithm of

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Speaker 1: my work account on Instagram very you know, futures related

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Speaker 1: and because I just force fed my YouTube so much

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Speaker 1: golf technique tips and DIY construction in the backyard and landscaping.

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Speaker 1: Now that's mostly what I get. But because I was

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Speaker 1: so intensely doing that for like six weeks, I did

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Speaker 1: start to readjust the algorithm, which I kind of need

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Speaker 1: to readjust back.

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Speaker 2: I was scrolling and I saw like I got a sponsored.

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Speaker 2: I'd come up for this like magnetic thing that you

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Speaker 2: can I don't know, put on an aeroplane or hooked

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Speaker 2: to your laptop, so your phone can be next to

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Speaker 2: your laptop, and I was like, I took a screenshot.

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Speaker 2: I was like, I'm not clicking on your link. Before

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Speaker 2: I'd even scrolled down to the next video, like the

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Speaker 2: next video was already half up, and it was again

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Speaker 2: it was now a travel adapter, and then you go

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Speaker 2: again and now I'm just suddenly getting trouble stuff just

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Speaker 2: because I have it on this video for three seconds.

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Speaker 2: It's insane how how powerful it is.

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Speaker 5: Is it the hovering or does it know you're screenshotting?

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Speaker 2: I don't know. I was wondering that about the screenshot part,

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Speaker 2: but I definitely hovered for longer than I usually would.

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Speaker 5: Yeah, you didn't just fly past it.

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Speaker 1: Correct, The algorithm knows. The algorithm as in the rules

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Speaker 1: and the instructions will say there was like a six

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Speaker 1: six second hover.

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Speaker 2: Oh okay, there you go.

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Speaker 3: Yeah, whatever you're worried about with all of those things,

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Speaker 3: assume yes.

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Speaker 2: Oh good. But back to this women's sports chat. One

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Speaker 2: of the I find my current stats a lot of

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Speaker 2: the time, and I just I love them and I

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Speaker 2: talk about them a lot. One of my favorite status

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Speaker 2: has come out of the women's Rugby World Cup. They

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Speaker 2: had one point one billion social engagements on their own

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Speaker 2: channels in twenty twenty five during that tournament. But the

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Speaker 2: one I love is that two thirds of the new

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Speaker 2: followers on Instagram and TikTok across that Women's Rugby World

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Speaker 2: Cup were female followers, And I just love I guess

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Speaker 2: those data pieces that are really challenging that idea that

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Speaker 2: women don't watch women's sport, which I see in our

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Speaker 2: comment section all the time.

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Speaker 1: I think there's actually been some studies on that now

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Speaker 1: which I find really interesting. Ey did a study in

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Speaker 1: the UK, I think, and it was saying sixty six

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Speaker 1: percent of gen Z adult females engaged for sport versus

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Speaker 1: seventy nine percent of gen Z males, So that's a

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Speaker 1: gender gap narrowing in younger cohorts. And I think the

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Speaker 1: point I found really interesting is that they arrive via

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Speaker 1: social media, so it really is flipping the script. Retention

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Speaker 1: in sport is very different, and what it means to

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Speaker 1: engage in sport has a new kind of idea. You know,

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Speaker 1: the old metrics used to be, well, if you're not

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Speaker 1: playing in club sport, you're not engaging in sport. Women

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Speaker 1: aren't playing sport, But it's a much broader sense of

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Speaker 1: what does that actually mean now? Similar in the US,

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Speaker 1: they've started to do some studies with younger people and saying, actually,

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Speaker 1: that growth rate for gen zed women engaging in women's

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Speaker 1: sport and gen zed men, but particularly gen Z women

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Speaker 1: is coming and VI social media. So who makes the

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Speaker 1: decisions about how they arrive? What we get to see

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Speaker 1: really matters because that front door is now digital.

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Speaker 2: How does that look when we talk about I guess

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Speaker 2: the control and power element. We've seen women's bodies policed

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Speaker 2: by humans for many generations, many decades, But how does

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Speaker 2: that translate to this algorithm discussion?

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Speaker 1: In many ways, I think like the algorithm is indicative

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Speaker 1: of our culture because we design a set of rules

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Speaker 1: that we then encode into a system. So the rules

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Speaker 1: in platforms can become cultural rules.

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Speaker 3: It's both ways.

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Speaker 1: It's like we have a culture, we set rules, they

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Speaker 1: get entrined into platforms and vice versa. But we know

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Speaker 1: that we've seen examples of like there was an example

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Speaker 1: of a sports where women's sportswear brand that kept getting

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Speaker 1: better and on TikTok because they featured athletes in sports

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Speaker 1: clothes that were considered inappropriate.

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Speaker 3: The same with gymnasts divers.

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Speaker 1: I read a piece. I think she was a diver.

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Speaker 1: She was mostly on TikTok, and she decided to go

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Speaker 1: to only fans because her content kept getting flagged as

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Speaker 1: either nudity or suicidal. So the algorithm was saying, no,

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Speaker 1: you can't, you can't do that. At the same time,

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Speaker 1: there was an investigation from algorithm Watch saying that Instagram's

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Speaker 1: algorithm found it was fifty four percent more likely to

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Speaker 1: surface posts of women's in underwear or bikinis.

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Speaker 2: WHOA.

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Speaker 4: So the system's delivering it, but then saying it's wrong.

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Speaker 4: And to your point in regards to policing and controlling this,

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Speaker 4: it's interesting what they're choosing to control and police. You

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Speaker 4: love diving into our comments section at times.

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Speaker 2: If I'd say it's a love hate relationship.

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Speaker 3: Do you read all the on your posts?

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Speaker 5: She does. She's a control for it.

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Speaker 2: I definitely wouldn't say I read all of them. But

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Speaker 2: I feel like I absolutely have a responsibility to moderate

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Speaker 2: the comments sections because I think otherwise it's really it's

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Speaker 2: really dangerous if you don't. So I definitely don't read

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Speaker 2: all the comments, but I try my very best to

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Speaker 2: stay across it because I think it's important and you can.

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Speaker 4: Kind of feel which posts and which issues are going

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Speaker 4: to encourage.

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Speaker 5: Oh yeah, the ridiculous behaviors.

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Speaker 4: And so to that point, you're in there trying to

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Speaker 4: weed out those ridiculous behaviors and people, why isn't the

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Speaker 4: algorithm doing that for us? They're happy to have a

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Speaker 4: crack at someone diving in a bikini, but they're not

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Speaker 4: happy to have a crack at Brad. Who's Chad? Dad's

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Speaker 4: who are having a crack at women in general?

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Speaker 2: Yeah, and using vile language and doing it over and

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Speaker 2: over again, multiple different comments on the same post, and

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Speaker 2: I'm sure multiple different comments on different pages. The fact

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Speaker 2: that that seems to be too gray an area for them,

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Speaker 2: but they can call a female diver nudity and suicidal,

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Speaker 2: to me is just like, come on, but who sets

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Speaker 2: the rules?

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Speaker 4: Right?

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Speaker 1: So the AI has learned, the algorithms have learned what's

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Speaker 1: appropriate from a data set where women's bodies was often

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Speaker 1: categorized as sexual content. So we we as in society

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Speaker 1: in the dominant you know, people designing these algorithms set

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Speaker 1: the rules even though it's contradictory. It's like we've got

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Speaker 1: women's sport content at the same time that's getting suppressed

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Speaker 1: at the same time that you know, you're fifty four

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Speaker 1: percent more likely to surface posts of women in underwear

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Speaker 1: and bikinis and that will go through the roof. They're

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Speaker 1: literally creators of sports apparel saying every time we put

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Speaker 1: a athlete in certain like bikinis or whatever, the posts

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Speaker 1: will go up. And yet at the very same time

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Speaker 1: we've experienced the complete opposite. But that is very reflective

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Speaker 1: of where we are in society, which I think is interesting.

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Speaker 2: Moving on to I guess a major theme Number two

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Speaker 2: ree when we look at who gets picked, how is

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Speaker 2: that control exhibited by the algorithm?

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Speaker 1: Similar theme of the shift from human based judgment in

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Speaker 1: small numbers. You know, there's only so many people that

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Speaker 1: you can coach and see to machine based judgment at scale.

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Speaker 1: So we definitely have now seen these technologies enter the

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Speaker 1: coaching box. There was a head coach credited and criticized

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Speaker 1: for using chach pit as a tactical spark as part

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Speaker 1: of her game day preparations and tactics.

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Speaker 2: She got slammed. I saw her getting absolutely slammed. But

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Speaker 2: if you look at the results second bottom to fourth

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Speaker 2: and making the playoffs like can you really hold it

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Speaker 2: against it?

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Speaker 1: I don't think so, I mean, can you This is

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Speaker 1: part of the challenge of it, isn't it. It's like,

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Speaker 1: I guess the shift here in a way is that

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Speaker 1: she wasn't replaced. She was augmenting her capability exactly.

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Speaker 4: She obviously identified that she had a potential weakness in

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Speaker 4: that tactical space and was using tools that were available

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Speaker 4: to her to better herself.

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Speaker 2: Or was she great and she was just like I'm

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Speaker 2: going to try this and it worked.

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Speaker 1: I mean, this stuff is already in here and now

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Speaker 1: and in motion. I would say what's shifting is it's

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Speaker 1: getting much more sophisticated and integrated rather than just being

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Speaker 1: like a novel thing. On the side, we're definitely seeing

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Speaker 1: very specific personalized use of AI bops in training. That's

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Speaker 1: in cycling, that's quite common. Takes all of your physiological

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Speaker 1: data and you get very specific microcessions. I'm seeing a

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Speaker 1: real shift in the sophistication of the training technology. So

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Speaker 1: you know, we know already that we collect a lot

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Speaker 1: of information data. Athletes are like hyper quantified these days.

442
00:24:04,800 --> 00:24:07,520
Speaker 1: But I've started to read some studies. There was a

443
00:24:07,560 --> 00:24:12,119
Speaker 1: study around the WSL looking across eleven teams, you know,

444
00:24:12,160 --> 00:24:15,959
Speaker 1: four hundred plus injuries, and was claiming that their machine learning,

445
00:24:16,040 --> 00:24:22,000
Speaker 1: their technology forecast injury risks one to seven days prior

446
00:24:22,040 --> 00:24:24,920
Speaker 1: to the injury seventy two percent of the time.

447
00:24:26,040 --> 00:24:29,000
Speaker 2: I would have loved that. So why you were looking

448
00:24:29,000 --> 00:24:29,479
Speaker 2: at me like that?

449
00:24:29,520 --> 00:24:33,919
Speaker 4: You would never have played oh jokes, she could be

450
00:24:33,960 --> 00:24:34,960
Speaker 4: injured the next seven days.

451
00:24:34,960 --> 00:24:35,959
Speaker 5: Better rest her?

452
00:24:36,400 --> 00:24:38,760
Speaker 4: No but sorry, in all honesty, that is and that's

453
00:24:38,760 --> 00:24:39,640
Speaker 4: an incredible tool.

454
00:24:40,320 --> 00:24:43,400
Speaker 2: It is an incredible tool. And I think the load

455
00:24:43,440 --> 00:24:46,200
Speaker 2: management piece, like for you guys have both obviously been

456
00:24:46,200 --> 00:24:48,879
Speaker 2: in high performance environments for a lot of your sporting careers,

457
00:24:49,920 --> 00:24:52,120
Speaker 2: the I think the load performance for people who don't

458
00:24:52,160 --> 00:24:56,640
Speaker 2: have an insight into what it looks like, it's incredibly responsible.

459
00:24:56,680 --> 00:24:58,240
Speaker 2: I would say for a lot of the soft tissue

460
00:24:58,240 --> 00:25:01,480
Speaker 2: injuries that occur. And I had a couple of soft

461
00:25:01,520 --> 00:25:04,280
Speaker 2: tissue injuries throughout my career where it had happened, and

462
00:25:04,359 --> 00:25:07,119
Speaker 2: I would feel a real sense of frustration like, Okay,

463
00:25:07,720 --> 00:25:09,639
Speaker 2: where did this go wrong? How do we get my

464
00:25:09,680 --> 00:25:12,280
Speaker 2: load management wrong? This? This shouldn't have happened, you know, Like

465
00:25:12,320 --> 00:25:16,080
Speaker 2: I had injuries in my career where my teammate and

466
00:25:16,119 --> 00:25:18,560
Speaker 2: I tackled the same person, so her head hit my cheekbone.

467
00:25:18,600 --> 00:25:19,480
Speaker 2: That's unavoidable.

468
00:25:19,560 --> 00:25:22,560
Speaker 4: But I know I can remember, you know, specifically a

469
00:25:22,560 --> 00:25:24,240
Speaker 4: couple of your injuries where you knew it was going

470
00:25:24,320 --> 00:25:27,679
Speaker 4: to happen. You felt tight, and you felt that your

471
00:25:27,720 --> 00:25:31,359
Speaker 4: body wasn't in the in the shape it should have

472
00:25:31,359 --> 00:25:33,800
Speaker 4: been at that time, but you pushed on again. It's

473
00:25:33,840 --> 00:25:36,400
Speaker 4: that kind of responsibility of continuing to play.

474
00:25:36,440 --> 00:25:37,160
Speaker 5: It's your job.

475
00:25:37,440 --> 00:25:40,320
Speaker 4: You know, you're trying to secure your spot on the team.

476
00:25:40,400 --> 00:25:44,000
Speaker 4: So if there's something that like an AI system that says,

477
00:25:44,040 --> 00:25:46,640
Speaker 4: actually your percent, you send me two percent a chance

478
00:25:46,640 --> 00:25:49,720
Speaker 4: of getting injured here, the load management becomes an easier conversation.

479
00:25:49,840 --> 00:25:53,040
Speaker 4: It's not just the athlete going to an SNC coach

480
00:25:53,119 --> 00:25:55,840
Speaker 4: or a physio and saying, you know, my calf is tight.

481
00:25:56,800 --> 00:25:58,360
Speaker 5: There's actually numbers behind it.

482
00:25:58,520 --> 00:26:01,840
Speaker 2: I love that. I like the objective in these situations

483
00:26:01,840 --> 00:26:04,040
Speaker 2: and ideally taking pressure off the athlete.

484
00:26:04,280 --> 00:26:07,800
Speaker 1: I'm just a total laddite, So for me, I think

485
00:26:07,840 --> 00:26:12,240
Speaker 1: that I mean the subtle shift here too is objectively Yes,

486
00:26:12,320 --> 00:26:15,800
Speaker 1: I totally agree, but the whole point of the conversation

487
00:26:15,880 --> 00:26:20,879
Speaker 1: today is that it starts to shift decision making to platforms,

488
00:26:20,920 --> 00:26:25,240
Speaker 1: tech pack platforms and the vendors. Right, so they've got

489
00:26:25,280 --> 00:26:27,919
Speaker 1: a algorithm, a set of rules that have said if this,

490
00:26:28,160 --> 00:26:32,000
Speaker 1: then that, so they then become the decision makers. A

491
00:26:32,040 --> 00:26:37,399
Speaker 1: lot of those algorithms were based on men's data sets.

492
00:26:38,640 --> 00:26:41,040
Speaker 1: But what is exciting is there is we're starting to

493
00:26:41,080 --> 00:26:44,520
Speaker 1: see women's specific analytics emerging as a whole new product category.

494
00:26:44,560 --> 00:26:47,040
Speaker 1: So that's really helpful. But does it make sense? Like

495
00:26:47,080 --> 00:26:49,600
Speaker 1: it shifts the power of who gets to decide then

496
00:26:49,680 --> 00:26:53,000
Speaker 1: around you? It's like, well, sorry that the algo says

497
00:26:53,080 --> 00:26:53,520
Speaker 1: you're done.

498
00:26:54,400 --> 00:26:57,440
Speaker 2: Well, yeah, I would like to think that you've got

499
00:26:57,560 --> 00:27:00,080
Speaker 2: a really great high performance team around you that is

500
00:27:00,240 --> 00:27:04,840
Speaker 2: using multiple sources of information. I think it's dangerous if

501
00:27:05,160 --> 00:27:09,840
Speaker 2: someone's using that as their gospel. But yeah, using that

502
00:27:10,040 --> 00:27:14,639
Speaker 2: as one piece of information to complement an assessment by

503
00:27:14,640 --> 00:27:17,280
Speaker 2: a physiotherapist, a potential MRI, Like, there's a whole range

504
00:27:17,280 --> 00:27:19,119
Speaker 2: of things I think that can go alongside that.

505
00:27:19,680 --> 00:27:23,840
Speaker 4: And to your point where you know, moving forward in

506
00:27:23,880 --> 00:27:27,159
Speaker 4: the future, when when the data is coming from a

507
00:27:27,200 --> 00:27:32,359
Speaker 4: broader base of female specific you know, stats and data

508
00:27:32,400 --> 00:27:35,920
Speaker 4: that that is important. You're right, probably right now, given

509
00:27:35,960 --> 00:27:38,960
Speaker 4: it's I'm sure very much based on a male athlete,

510
00:27:39,520 --> 00:27:41,600
Speaker 4: it's potentially not as accurate as we would like it

511
00:27:41,600 --> 00:27:41,800
Speaker 4: to be.

512
00:27:42,840 --> 00:27:45,879
Speaker 1: Yeah, absolutely, I mean I think it's playing out in

513
00:27:46,720 --> 00:27:50,240
Speaker 1: you know, the anticipation of injury that's already starting to

514
00:27:50,480 --> 00:27:53,960
Speaker 1: play out with seeing a play out in informing coaching

515
00:27:54,040 --> 00:27:58,600
Speaker 1: decisions tactically. The interesting stuff I kind of liked researching

516
00:27:58,640 --> 00:28:01,160
Speaker 1: this because I found a bit eging is that when

517
00:28:01,240 --> 00:28:03,960
Speaker 1: we're starting to see the use of these technologies as

518
00:28:03,960 --> 00:28:08,920
Speaker 1: a way to talent, id to productize that. So if

519
00:28:08,960 --> 00:28:13,119
Speaker 1: I think about my own sporting background, I grew up

520
00:28:13,160 --> 00:28:15,400
Speaker 1: in the bush. It was like seven hundred kilometers from

521
00:28:15,600 --> 00:28:19,120
Speaker 1: any other girl that played women's cricket. I didn't even

522
00:28:19,119 --> 00:28:21,639
Speaker 1: know they played women's cricket. And the only reason I

523
00:28:21,680 --> 00:28:25,159
Speaker 1: think I ended up in the system, the high performance system,

524
00:28:25,320 --> 00:28:29,320
Speaker 1: was just by chance of my sister bumping into someone

525
00:28:29,840 --> 00:28:33,639
Speaker 1: you know from cricket that said by actually my sister plays.

526
00:28:34,240 --> 00:28:36,960
Speaker 1: But we're now seeing the use of really cool tools

527
00:28:37,119 --> 00:28:41,120
Speaker 1: of AI scouting apps. There was a really great signal

528
00:28:41,240 --> 00:28:44,160
Speaker 1: of a young girl from Jersey, so a tiny island,

529
00:28:44,720 --> 00:28:47,880
Speaker 1: it didn't have any professional football, there was no scouting pathways.

530
00:28:48,320 --> 00:28:52,120
Speaker 1: She downloaded I think it was Chelsea's AI scouting app

531
00:28:52,960 --> 00:28:57,120
Speaker 1: and filmed drills at home, uploaded the video. The AI

532
00:28:57,240 --> 00:28:59,680
Speaker 1: reviews the skill because it makes a judgment, remember it

533
00:28:59,720 --> 00:29:05,880
Speaker 1: shift judgment now onto the algorithm the rules and sent

534
00:29:05,920 --> 00:29:07,920
Speaker 1: that to the recruitment team at Chelsea and she ended

535
00:29:08,000 --> 00:29:12,560
Speaker 1: up playing in their underage squads. Impossible without the app

536
00:29:12,600 --> 00:29:15,280
Speaker 1: that exists. I know that they use. I think it

537
00:29:15,320 --> 00:29:18,920
Speaker 1: was in the Women's Premier League cricket in India. They

538
00:29:18,920 --> 00:29:22,120
Speaker 1: also use AI to scout women's cricket players and evaluate

539
00:29:22,160 --> 00:29:25,360
Speaker 1: the skill. So this can be done at absolute scale,

540
00:29:25,840 --> 00:29:26,920
Speaker 1: like enormous scale.

541
00:29:28,120 --> 00:29:30,960
Speaker 2: I would love to know the detail behind it. Like

542
00:29:31,000 --> 00:29:33,240
Speaker 2: I am not a footballer, so I don't know enough

543
00:29:33,640 --> 00:29:38,680
Speaker 2: around that, but I'm like, where, where does it? How

544
00:29:38,720 --> 00:29:41,120
Speaker 2: objective is it? Like how objectively can you look at

545
00:29:41,120 --> 00:29:43,680
Speaker 2: a player and decide you should be playing for Chelsea?

546
00:29:43,800 --> 00:29:45,640
Speaker 5: What are the skill sets it's ticking off on?

547
00:29:45,880 --> 00:29:48,240
Speaker 2: Yeah? Like what are the data points? It says I

548
00:29:48,240 --> 00:29:51,160
Speaker 2: don't know you kicked this ball off one touch or

549
00:29:51,200 --> 00:29:53,200
Speaker 2: like yeah, I'm very curious about that part.

550
00:29:53,320 --> 00:29:56,680
Speaker 4: On the flip side of that, when you are trying

551
00:29:56,720 --> 00:29:59,320
Speaker 4: to make your way into elite sport and sometimes you

552
00:29:59,360 --> 00:30:03,560
Speaker 4: come up against to coach that is potentially in charge

553
00:30:03,600 --> 00:30:07,360
Speaker 4: of your local representative program that doesn't like you for

554
00:30:07,440 --> 00:30:14,440
Speaker 4: whatever reason. Does this AI tool remove that personal relationship

555
00:30:15,080 --> 00:30:17,600
Speaker 4: where you can be assessed purely on skill.

556
00:30:17,920 --> 00:30:20,520
Speaker 2: I do like that because I think the politics, particularly

557
00:30:20,520 --> 00:30:22,560
Speaker 2: I mean even at community sport and then all the

558
00:30:22,560 --> 00:30:25,120
Speaker 2: way up to the elite level, politics are always going

559
00:30:25,200 --> 00:30:27,400
Speaker 2: to be there. But I feel like, have we for

560
00:30:27,480 --> 00:30:30,920
Speaker 2: most of this episode been talking about how AI isn't

561
00:30:31,000 --> 00:30:33,160
Speaker 2: completely objective, but neither are humans?

562
00:30:33,160 --> 00:30:35,400
Speaker 1: And this is the tension. This is a broader tension

563
00:30:35,440 --> 00:30:38,840
Speaker 1: in society. It's like, how do we consider this is

564
00:30:39,480 --> 00:30:42,959
Speaker 1: such a broad dilemma in workplaces? In this is what

565
00:30:42,960 --> 00:30:45,920
Speaker 1: I'm saying, it's now happening in sport. It's we've got

566
00:30:45,960 --> 00:30:48,240
Speaker 1: a new decision maker, We've got new inputs, they.

567
00:30:48,120 --> 00:30:50,280
Speaker 3: Have new bias. What does that mean?

568
00:30:50,400 --> 00:30:55,080
Speaker 1: From a scouting perspective, that means talent pathways could be

569
00:30:55,160 --> 00:30:59,200
Speaker 1: about using an app and then the rules of who

570
00:30:59,200 --> 00:31:01,720
Speaker 1: get selected. I think back of you know, think of

571
00:31:02,080 --> 00:31:06,280
Speaker 1: this law LORI not law, but not law, but the

572
00:31:06,320 --> 00:31:09,640
Speaker 1: cultural law of some sports of saying, oh no, you know,

573
00:31:09,840 --> 00:31:12,120
Speaker 1: twenty years ago you had to be this high, this

574
00:31:13,240 --> 00:31:16,560
Speaker 1: wide to be an AFL men's player, and they didn't

575
00:31:16,560 --> 00:31:19,080
Speaker 1: pick anyone that was little. And clearly I have a

576
00:31:19,080 --> 00:31:23,880
Speaker 1: real personal bias around this because we is the kind

577
00:31:23,920 --> 00:31:26,280
Speaker 1: of call it the sports factory. You know, everyone started

578
00:31:26,280 --> 00:31:30,040
Speaker 1: to look the same because we had the same instructions

579
00:31:30,120 --> 00:31:32,960
Speaker 1: about what makes a good player. Well, these types of

580
00:31:32,960 --> 00:31:38,120
Speaker 1: technologies take instructions and make the same you know, it's

581
00:31:38,120 --> 00:31:41,920
Speaker 1: the same process of determining then who gets picked and why.

582
00:31:42,680 --> 00:31:43,360
Speaker 5: And just to.

583
00:31:45,120 --> 00:31:47,400
Speaker 4: Make it a little bit more to your point of

584
00:31:48,000 --> 00:31:51,080
Speaker 4: this app actually opening it opening sport up to more people,

585
00:31:51,200 --> 00:31:55,560
Speaker 4: or opening elite pathways up to more people having experience

586
00:31:56,040 --> 00:31:59,160
Speaker 4: in rugby in particular, where you watch young kids from

587
00:31:59,160 --> 00:32:01,520
Speaker 4: the bush have to move move to the city to

588
00:32:01,720 --> 00:32:05,280
Speaker 4: continue on their pathway. A lot of time in male sport,

589
00:32:05,400 --> 00:32:08,400
Speaker 4: those boys end up boarding at a GPS school, you know,

590
00:32:08,560 --> 00:32:10,440
Speaker 4: at an expensive school. And I know this happens in

591
00:32:10,480 --> 00:32:14,200
Speaker 4: all sport, and that's open to them. In the women's

592
00:32:14,280 --> 00:32:17,120
Speaker 4: or the girls' space that is not as prevalent. It's

593
00:32:17,240 --> 00:32:20,880
Speaker 4: becoming more prevalent. But again I just feel like this

594
00:32:21,320 --> 00:32:27,520
Speaker 4: in this particular instance, AI is allowing pathways and exposure

595
00:32:28,120 --> 00:32:30,560
Speaker 4: to more people, which I don't think is a bad.

596
00:32:30,360 --> 00:32:32,680
Speaker 1: Thing if you get access to those training bots. So

597
00:32:32,720 --> 00:32:35,560
Speaker 1: I think, like a good example Bears is growing up

598
00:32:35,600 --> 00:32:39,160
Speaker 1: in the bush. Again, it's a tool for democratization in

599
00:32:39,200 --> 00:32:43,239
Speaker 1: a way if I had access to AI coaching. So

600
00:32:43,320 --> 00:32:47,680
Speaker 1: I played State Netbull believe it or not, because must

601
00:32:47,680 --> 00:32:49,040
Speaker 1: have been a same a center and.

602
00:32:49,000 --> 00:32:49,760
Speaker 3: A wing attack.

603
00:32:51,880 --> 00:32:54,960
Speaker 1: And I will never forget that we didn't have a coach.

604
00:32:55,200 --> 00:32:57,280
Speaker 1: Literally the coach was just a parent and this is

605
00:32:57,320 --> 00:33:00,800
Speaker 1: in a state championship who was willing to take us

606
00:33:00,800 --> 00:33:04,720
Speaker 1: on a fifteen hour bus trip When we arrived, the

607
00:33:04,760 --> 00:33:08,840
Speaker 1: Metropolitan team had Vicki Wilson, and if listeners don't know,

608
00:33:08,880 --> 00:33:12,760
Speaker 1: go on google her. She is an absolute legend icon

609
00:33:12,960 --> 00:33:13,640
Speaker 1: of the game.

610
00:33:13,680 --> 00:33:15,680
Speaker 3: I met her in person once. She's amazing.

611
00:33:16,240 --> 00:33:19,760
Speaker 1: But the disparity of access to the skill of that

612
00:33:20,000 --> 00:33:22,400
Speaker 1: was like we literally a parent had no idea what

613
00:33:22,440 --> 00:33:24,200
Speaker 1: they're doing, and then we turn up and we're playing

614
00:33:24,200 --> 00:33:26,760
Speaker 1: against kids coach by Vicki Wilson. So yeah, I would

615
00:33:26,760 --> 00:33:29,320
Speaker 1: have loved that Bote.

616
00:33:29,360 --> 00:33:31,800
Speaker 2: If we move on to the theme three around who

617
00:33:31,840 --> 00:33:35,800
Speaker 2: gets paid, are we starting to see the athlete's values

618
00:33:35,800 --> 00:33:39,360
Speaker 2: are kind of predetermined almost by the algorithm before they're

619
00:33:39,400 --> 00:33:42,160
Speaker 2: even walking into these rooms and into these stadiums. It

620
00:33:42,200 --> 00:33:42,520
Speaker 2: can be.

621
00:33:42,640 --> 00:33:46,240
Speaker 1: It's kind of it's happening both ways in a sense.

622
00:33:46,320 --> 00:33:49,640
Speaker 1: So increasingly part of the job of being an athlete

623
00:33:49,840 --> 00:33:53,280
Speaker 1: is that you need to be online, and that's both

624
00:33:53,320 --> 00:33:55,520
Speaker 1: a blessing and a curse in many ways because it

625
00:33:55,560 --> 00:34:00,800
Speaker 1: opens up new economic opportunities, but also then get targeted,

626
00:34:00,920 --> 00:34:04,320
Speaker 1: which we'll talk about a couple of interesting things. I

627
00:34:04,400 --> 00:34:07,040
Speaker 1: think that well, firstly, we know that women are outperforming

628
00:34:07,080 --> 00:34:11,640
Speaker 1: men online, women athletes are. So there's a lot of

629
00:34:11,760 --> 00:34:15,080
Speaker 1: data around the college stuff saying that posts from the

630
00:34:15,840 --> 00:34:19,640
Speaker 1: women athletes engage followers nearly eight times the rate of

631
00:34:19,680 --> 00:34:23,239
Speaker 1: men's posts, but the pay gap still exists. So I

632
00:34:23,280 --> 00:34:26,560
Speaker 1: read an article from the Washington Post saying that it

633
00:34:26,600 --> 00:34:30,640
Speaker 1: looked across twelve schools saying that men out earned women

634
00:34:30,800 --> 00:34:33,200
Speaker 1: in college and was like ninety two million dollars to

635
00:34:33,280 --> 00:34:39,680
Speaker 1: nineteen million. So, yeah, women outperforming men online, which I

636
00:34:39,719 --> 00:34:43,000
Speaker 1: think is very interesting, but men still earning more from

637
00:34:43,040 --> 00:34:44,520
Speaker 1: that process and.

638
00:34:44,480 --> 00:34:47,200
Speaker 4: The rise of the digital agent. How about this, what

639
00:34:47,239 --> 00:34:47,800
Speaker 4: do you reckon?

640
00:34:49,320 --> 00:34:52,920
Speaker 5: I'm looking at Chloe.

641
00:34:53,360 --> 00:34:55,440
Speaker 4: I think this is a really interesting one, and I

642
00:34:55,480 --> 00:34:58,839
Speaker 4: think it kind of speaks back to the athlete having

643
00:34:58,840 --> 00:35:04,200
Speaker 4: more power these conversations, in negotiations, in knowing their worth

644
00:35:04,239 --> 00:35:05,719
Speaker 4: in a way, I think it's probably one of the

645
00:35:05,760 --> 00:35:06,239
Speaker 4: biggest things.

646
00:35:06,239 --> 00:35:09,160
Speaker 5: When you talk to athletes, they employ an.

647
00:35:09,040 --> 00:35:11,200
Speaker 4: Agent because they feel like there's a knowledge gap there

648
00:35:11,200 --> 00:35:15,560
Speaker 4: that the agent will fill. If a digital agent can

649
00:35:15,600 --> 00:35:17,919
Speaker 4: fill that gap, and then an agent isn't taking twenty

650
00:35:17,920 --> 00:35:19,640
Speaker 4: percent for really not doing a whole lot.

651
00:35:20,760 --> 00:35:24,280
Speaker 2: Yeah, And I feel quite strongly about this, particularly probably

652
00:35:24,320 --> 00:35:27,440
Speaker 2: with young athletes and young female athletes in particular, entering

653
00:35:27,560 --> 00:35:31,359
Speaker 2: into these high performance environments. When they come in and

654
00:35:31,960 --> 00:35:34,239
Speaker 2: if you get drafted, say in the AFOW, and you're

655
00:35:34,320 --> 00:35:38,520
Speaker 2: automatically on the lowest tier, an agent is not negotiating

656
00:35:38,719 --> 00:35:40,680
Speaker 2: to get any more pay. Yet some of them are

657
00:35:40,680 --> 00:35:43,880
Speaker 2: walking in and taking six percent or even ten percent,

658
00:35:44,000 --> 00:35:46,040
Speaker 2: some of them getting close to ten percent of your

659
00:35:46,080 --> 00:35:47,919
Speaker 2: contract value for actually doing.

660
00:35:47,880 --> 00:35:50,600
Speaker 4: No work for a negotiation that doesn't exist. Correct, Because

661
00:35:50,640 --> 00:35:52,600
Speaker 4: it's the CBA, you're not getting anything more.

662
00:35:52,800 --> 00:35:55,880
Speaker 2: Yeah, correct. So Yeah, I feel quite strongly about this.

663
00:35:55,960 --> 00:35:59,440
Speaker 2: So I'm quite interested to hear your thoughts on this. Ray. Yeah.

664
00:35:59,520 --> 00:36:01,719
Speaker 1: I mean, I didn't know that, which makes me even

665
00:36:01,760 --> 00:36:06,279
Speaker 1: more excited about this. The upside of this technology I

666
00:36:06,320 --> 00:36:09,400
Speaker 1: think is the democratization. So it gets it. It's a

667
00:36:09,400 --> 00:36:12,880
Speaker 1: shift in power in many ways. In some ways, I

668
00:36:13,040 --> 00:36:17,360
Speaker 1: really started to pay attention to this in twenty twenty,

669
00:36:17,400 --> 00:36:21,400
Speaker 1: twenty twenty one, there was a UK soccer player where

670
00:36:21,840 --> 00:36:25,279
Speaker 1: he ditched his agent and don't come at me in

671
00:36:25,280 --> 00:36:27,600
Speaker 1: the comments, agents, I'm just telling you the facts of

672
00:36:27,600 --> 00:36:30,680
Speaker 1: what's going on so you can make informed decisions. So

673
00:36:30,719 --> 00:36:33,399
Speaker 1: he ditched his agent and he hired an analytics firm

674
00:36:34,000 --> 00:36:37,080
Speaker 1: and they prove that he wasn't just good, he was

675
00:36:37,120 --> 00:36:40,360
Speaker 1: statistically irreplaceable because when he played all of these outcomes.

676
00:36:41,200 --> 00:36:43,360
Speaker 1: Now that's an expensive thing to do, right higher a

677
00:36:43,360 --> 00:36:47,680
Speaker 1: hole firm, But we're now seeing when this top technology

678
00:36:47,719 --> 00:36:49,759
Speaker 1: is now out in the hands of everyone. There was

679
00:36:49,800 --> 00:36:55,200
Speaker 1: a signal of a young man, you youth prospect, and

680
00:36:55,400 --> 00:36:58,640
Speaker 1: he used and he literally just used chatchbt's like you know,

681
00:36:58,680 --> 00:37:02,279
Speaker 1: the twenty dollars a month technic when he negotiated he's

682
00:37:02,440 --> 00:37:05,360
Speaker 1: moved to a new league, saying that the software was

683
00:37:05,520 --> 00:37:07,279
Speaker 1: has been his best agent to date.

684
00:37:07,880 --> 00:37:09,920
Speaker 5: Wow, twenty bucks a month bargain?

685
00:37:10,160 --> 00:37:10,760
Speaker 2: How good?

686
00:37:11,239 --> 00:37:14,880
Speaker 1: Yeah, I mean there's nuances in all of those contractual elements.

687
00:37:14,920 --> 00:37:16,960
Speaker 1: But Chloe, you just gave such a good example of

688
00:37:17,040 --> 00:37:19,280
Speaker 1: where I think that's totally right for disruption.

689
00:37:20,160 --> 00:37:22,600
Speaker 2: Are we starting to see a shift? Well, I feel

690
00:37:22,640 --> 00:37:24,879
Speaker 2: like we are. It's not even really a question. We

691
00:37:24,920 --> 00:37:27,760
Speaker 2: can now see athletes who aren't necessarily the best players

692
00:37:27,800 --> 00:37:33,160
Speaker 2: on the field have insane followings and draw huge commercial

693
00:37:33,239 --> 00:37:35,840
Speaker 2: value not really based on their performance.

694
00:37:36,239 --> 00:37:39,239
Speaker 1: This is the upside, I think, the exciting side. There's

695
00:37:39,280 --> 00:37:45,000
Speaker 1: this idea, a strange term called a paras social value.

696
00:37:45,719 --> 00:37:48,960
Speaker 1: So the whole premise is that Chloe, you might be

697
00:37:49,000 --> 00:37:52,160
Speaker 1: the best player on the team and I might just

698
00:37:52,200 --> 00:37:54,759
Speaker 1: be a benchuma of which there are signals around this,

699
00:37:55,440 --> 00:37:58,200
Speaker 1: but because of the way in which fans engage with

700
00:37:58,280 --> 00:38:02,880
Speaker 1: me and the authenticity, I might be earning way more than.

701
00:38:02,719 --> 00:38:05,160
Speaker 3: You because sponsors there.

702
00:38:05,200 --> 00:38:10,200
Speaker 1: So there's now technologies that lets brands use these tools

703
00:38:10,760 --> 00:38:14,440
Speaker 1: to determine if you if I am a trusted athletes

704
00:38:14,760 --> 00:38:17,680
Speaker 1: because we know that trusted athletes see seven times higher

705
00:38:17,719 --> 00:38:20,560
Speaker 1: return on their money and then investment, so they will

706
00:38:20,560 --> 00:38:23,120
Speaker 1: go through all of my comments, look at the sentiment,

707
00:38:23,239 --> 00:38:25,960
Speaker 1: get a sense of my kind of trustworthiness, and then

708
00:38:26,040 --> 00:38:31,640
Speaker 1: make judgments around sponsorship and support from there, so I

709
00:38:31,760 --> 00:38:35,080
Speaker 1: might have a higher parasocial value.

710
00:38:34,800 --> 00:38:36,920
Speaker 4: I think I think the perfect example of this kind

711
00:38:36,960 --> 00:38:39,279
Speaker 4: of athlete is alone O maa. We look at her,

712
00:38:39,520 --> 00:38:44,200
Speaker 4: she is She's become trusted, you know, for speaking out

713
00:38:44,239 --> 00:38:47,880
Speaker 4: about confidence in your body, you know, being who you

714
00:38:47,920 --> 00:38:49,800
Speaker 4: want to be on the field.

715
00:38:50,080 --> 00:38:51,240
Speaker 5: Exceptional rugby player.

716
00:38:51,320 --> 00:38:53,520
Speaker 4: I wouldn't necessarily say she's in the top one percent

717
00:38:53,880 --> 00:38:56,879
Speaker 4: of women rubby players in the world. She's definitely up there,

718
00:38:57,400 --> 00:39:01,479
Speaker 4: but there's better players than her that are nowhere near

719
00:39:02,239 --> 00:39:07,040
Speaker 4: her carrying her parasocial value. You know, she is kicking

720
00:39:07,160 --> 00:39:14,000
Speaker 4: goals financially, I'm assuming sex sure seems like it's side note.

721
00:39:13,480 --> 00:39:14,760
Speaker 5: Did you see.

722
00:39:14,560 --> 00:39:19,160
Speaker 4: She's been in Milano, Cortina for the Winter Olympics. She

723
00:39:19,200 --> 00:39:20,520
Speaker 4: didn't interview the Cookie Monster.

724
00:39:20,800 --> 00:39:21,640
Speaker 2: Oh you said it to me.

725
00:39:21,680 --> 00:39:22,319
Speaker 5: I haven't watched it.

726
00:39:22,360 --> 00:39:23,040
Speaker 3: She's made it.

727
00:39:23,239 --> 00:39:26,360
Speaker 4: She's mad when you're when you're sharing cookies with the

728
00:39:26,360 --> 00:39:28,480
Speaker 4: cookie Monster, that's it iconic.

729
00:39:28,680 --> 00:39:29,800
Speaker 5: But yeah, sorry.

730
00:39:30,040 --> 00:39:32,560
Speaker 4: Back to Alona Mark. She I feel like she is

731
00:39:32,600 --> 00:39:35,759
Speaker 4: that perfect example. She's a trusted athlete, Her message resonates

732
00:39:37,000 --> 00:39:40,560
Speaker 4: and she's benefiting from it. I wonder, I do wonder

733
00:39:40,600 --> 00:39:44,360
Speaker 4: ask the question does that place more pressure on the

734
00:39:44,480 --> 00:39:49,000
Speaker 4: athlete on the field. Do they feel the weight of

735
00:39:49,040 --> 00:39:53,279
Speaker 4: that fandom and that connection when they step onto the

736
00:39:53,280 --> 00:39:54,560
Speaker 4: field to do their job.

737
00:39:54,800 --> 00:39:56,800
Speaker 2: Because yeah, a lot of people who would potentially be

738
00:39:56,880 --> 00:39:59,680
Speaker 2: following and engaging with these people see that they're an

739
00:39:59,719 --> 00:40:02,920
Speaker 2: elite athlete and often make assumptions that you're famous, you

740
00:40:02,960 --> 00:40:05,040
Speaker 2: have a huge following, you must be the best athlete

741
00:40:05,040 --> 00:40:05,560
Speaker 2: on your team.

742
00:40:06,120 --> 00:40:09,560
Speaker 1: I would think that's what's shifting. I think there's new

743
00:40:09,640 --> 00:40:12,760
Speaker 1: metrics for new times. So to be successful in sport,

744
00:40:12,880 --> 00:40:14,640
Speaker 1: you don't even have to be playing the top tier

745
00:40:14,800 --> 00:40:18,080
Speaker 1: sports anymore. You could be an athlete in a fifth tier,

746
00:40:18,120 --> 00:40:21,719
Speaker 1: a club in a fifth tier and be successful if

747
00:40:21,760 --> 00:40:26,720
Speaker 1: success is about visibility and the financial benefits of sport

748
00:40:26,760 --> 00:40:27,759
Speaker 1: and winning the algorithm.

749
00:40:28,120 --> 00:40:30,759
Speaker 2: Let's move on to our final theme, and we'll make

750
00:40:30,800 --> 00:40:35,560
Speaker 2: this one pretty quick. Who gets protected this? I feel

751
00:40:35,560 --> 00:40:39,840
Speaker 2: like ree is probably the most important part of this conversation.

752
00:40:39,920 --> 00:40:40,680
Speaker 2: What does it look like?

753
00:40:41,600 --> 00:40:44,200
Speaker 1: Yeah, you just touched on it in a way of saying,

754
00:40:44,880 --> 00:40:49,560
Speaker 1: bears that the more visibility that you get, the more

755
00:40:49,840 --> 00:40:56,800
Speaker 1: targetability And it's just like a tragic, deplorable reality for

756
00:40:57,680 --> 00:41:03,439
Speaker 1: women athletes. The experience of harm at scale I think

757
00:41:03,600 --> 00:41:07,879
Speaker 1: now is enormous and how sophisticated that's getting Deacon did

758
00:41:07,880 --> 00:41:11,279
Speaker 1: a study so deacons in Australian University that said that

759
00:41:11,320 --> 00:41:15,040
Speaker 1: eighty seven percent of women athletes experience gendered online harm

760
00:41:15,520 --> 00:41:21,960
Speaker 1: in the past year and there are discountless experiences. For me,

761
00:41:22,080 --> 00:41:24,200
Speaker 1: this is a shift to say that online harm this

762
00:41:24,320 --> 00:41:28,120
Speaker 1: is not an edge case. It's becoming part of the brutal,

763
00:41:28,360 --> 00:41:33,120
Speaker 1: horrific conditions that women experience in doing this work. And

764
00:41:33,160 --> 00:41:35,719
Speaker 1: the change is changing in that the nature of that

765
00:41:35,800 --> 00:41:40,480
Speaker 1: harm is getting more sophisticated. We know that in Australia

766
00:41:41,280 --> 00:41:44,120
Speaker 1: pornographic videos make up ninety eight percent of deep fake

767
00:41:44,239 --> 00:41:47,040
Speaker 1: material and ninety nine percent of that imagery depicts women

768
00:41:47,080 --> 00:41:52,759
Speaker 1: and girls. We're seeing gambling really amplify a lot of

769
00:41:53,080 --> 00:41:58,000
Speaker 1: the harm. You can now get trolled online through bots

770
00:41:59,400 --> 00:42:03,200
Speaker 1: that are game related. We know that college athletes are

771
00:42:03,200 --> 00:42:06,200
Speaker 1: saying as soon as gambling came in, the experience of

772
00:42:06,239 --> 00:42:11,520
Speaker 1: harm disproportionately on women became significant and noticeable. It is

773
00:42:11,600 --> 00:42:14,080
Speaker 1: absolutely unrelenting.

774
00:42:13,840 --> 00:42:17,120
Speaker 4: And it's interesting the amount of athletes that speak to

775
00:42:17,160 --> 00:42:20,480
Speaker 4: the fact that they turn off everything when it comes

776
00:42:20,480 --> 00:42:24,240
Speaker 4: time to compete or leading up to important events because

777
00:42:24,960 --> 00:42:29,000
Speaker 4: they can't avoid it. Whatever the algorithm and AI are

778
00:42:29,040 --> 00:42:32,479
Speaker 4: doing in regards to protecting it's not enough.

779
00:42:33,360 --> 00:42:36,280
Speaker 2: Can we take a look really at what sporting bodies

780
00:42:36,280 --> 00:42:39,839
Speaker 2: are doing, like taking responsibility I guess into their own

781
00:42:39,880 --> 00:42:42,399
Speaker 2: hands to protect their players. Yeah.

782
00:42:42,440 --> 00:42:44,480
Speaker 1: I think it used to be a sense of well,

783
00:42:44,560 --> 00:42:47,600
Speaker 1: the athlete needs to sort that out. This is an

784
00:42:47,600 --> 00:42:51,840
Speaker 1: individual issue. I'm saying that it's now this is industrialized

785
00:42:51,880 --> 00:42:55,720
Speaker 1: abuse at scale, and so sport are starting to really

786
00:42:55,800 --> 00:43:01,160
Speaker 1: shift of building more tech safety operations systems. This isn't

787
00:43:01,200 --> 00:43:05,760
Speaker 1: like around the edge because the abuse is beyond human scale.

788
00:43:06,520 --> 00:43:11,000
Speaker 1: So you can't go through every single comment, although Chloe

789
00:43:11,000 --> 00:43:15,360
Speaker 1: can challenge, accepted and then you know, respond back to that.

790
00:43:15,480 --> 00:43:18,600
Speaker 1: So it's using the same technology back. I know Wimbledon

791
00:43:20,000 --> 00:43:22,640
Speaker 1: they had a tool they were using, like a threat

792
00:43:22,640 --> 00:43:26,560
Speaker 1: mattrix to detect and flag abusive content. They had it

793
00:43:26,600 --> 00:43:29,000
Speaker 1: in the Olympics. I think they actually had it at Tokyo.

794
00:43:29,160 --> 00:43:31,800
Speaker 1: It was like anticipatory, so a lot of the stuff

795
00:43:31,800 --> 00:43:34,320
Speaker 1: didn't get to the athletes. A lot of it still does,

796
00:43:34,480 --> 00:43:38,520
Speaker 1: but it cuts it out beforehand. There's a lot happening

797
00:43:38,600 --> 00:43:40,960
Speaker 1: in that space. I think the shift for me is

798
00:43:41,000 --> 00:43:42,680
Speaker 1: that sports seeing.

799
00:43:42,520 --> 00:43:47,719
Speaker 3: Abuse as you know, a workplace issue, but.

800
00:43:47,719 --> 00:43:50,840
Speaker 1: Also that it's been operationalized like a match day, like

801
00:43:50,920 --> 00:43:54,560
Speaker 1: a genuine everyday risks. It's part of the service that

802
00:43:54,600 --> 00:43:56,400
Speaker 1: you get now it As an athlete you get physio

803
00:43:56,480 --> 00:44:00,279
Speaker 1: strength and conditioning, but increasingly you may now get you know,

804
00:44:01,160 --> 00:44:05,600
Speaker 1: safety operations. From a technical perspective, the one is shoe

805
00:44:05,680 --> 00:44:08,120
Speaker 1: I do have though, is a lot of these interventions

806
00:44:08,239 --> 00:44:13,480
Speaker 1: are not gender specific, and they should be if ninety

807
00:44:13,680 --> 00:44:16,719
Speaker 1: whatever percent or the majority of the harm is experienced

808
00:44:16,719 --> 00:44:19,480
Speaker 1: by women. This is the reverse in saying the algorithm

809
00:44:19,880 --> 00:44:22,920
Speaker 1: that writes these things should be gender specific, given then

810
00:44:23,160 --> 00:44:25,480
Speaker 1: disproportionate nature of the harm for women.

811
00:44:26,680 --> 00:44:29,359
Speaker 2: Can you hit us with a so what? Now what?

812
00:44:29,600 --> 00:44:31,359
Speaker 2: I kind of hate them, but I kind of love them.

813
00:44:31,880 --> 00:44:33,920
Speaker 1: I want to go back to the coaching question because

814
00:44:34,040 --> 00:44:36,880
Speaker 1: I would be curious about this myself if I was playing.

815
00:44:37,440 --> 00:44:41,359
Speaker 1: Would you rather is this that's again that the young people.

816
00:44:41,080 --> 00:44:44,880
Speaker 3: Play, Isn't it? Okay? I just got cool all of

817
00:44:44,920 --> 00:44:45,360
Speaker 3: a sudden.

818
00:44:46,840 --> 00:44:52,040
Speaker 1: Scots went up a coach that had really great people

819
00:44:52,080 --> 00:44:57,200
Speaker 1: management but was like poor tactically, but could start using

820
00:44:57,640 --> 00:45:00,960
Speaker 1: AI tools and these tools to fill the gaps technically,

821
00:45:01,680 --> 00:45:07,800
Speaker 1: Or a brilliant tactical coach that struggled with interpersonal skills

822
00:45:07,800 --> 00:45:08,760
Speaker 1: and people management.

823
00:45:09,600 --> 00:45:11,520
Speaker 2: I want to say the first one because to me,

824
00:45:12,800 --> 00:45:16,680
Speaker 2: I genuinely feel like good coaches are just they just

825
00:45:16,760 --> 00:45:19,120
Speaker 2: need to be good people managers. I just feel like,

826
00:45:19,239 --> 00:45:23,440
Speaker 2: obviously your team needs to have good systems and you

827
00:45:23,520 --> 00:45:25,399
Speaker 2: need to be able to execute that on game day.

828
00:45:25,400 --> 00:45:29,919
Speaker 2: But gosh, if you don't have an effective communicator, then

829
00:45:30,120 --> 00:45:31,919
Speaker 2: you're in a bit of trouble. I think I would

830
00:45:31,920 --> 00:45:35,320
Speaker 2: probably struggle with the good people management, poor tactical coach,

831
00:45:35,920 --> 00:45:40,080
Speaker 2: even if they're using AI. In terms of my day

832
00:45:40,080 --> 00:45:42,840
Speaker 2: to day feedback on the training track, that's what would

833
00:45:43,040 --> 00:45:45,200
Speaker 2: That's what would bother me. You know, like, all well

834
00:45:45,239 --> 00:45:46,840
Speaker 2: and good if you can communicate to me, but what

835
00:45:46,880 --> 00:45:48,960
Speaker 2: are you actually communicating to me about? So maybe I

836
00:45:49,080 --> 00:45:50,560
Speaker 2: changed my mind in god, it should be I don't

837
00:45:50,600 --> 00:45:51,480
Speaker 2: know you'll do.

838
00:45:51,719 --> 00:45:55,359
Speaker 4: So maybe you need maybe you need the coach that

839
00:45:55,480 --> 00:46:01,799
Speaker 4: has the personal connections that uses AI to increase their

840
00:46:01,840 --> 00:46:04,239
Speaker 4: knowledge base, which I think is important. And if we

841
00:46:04,320 --> 00:46:08,160
Speaker 4: remove this from the elite sphere and talk about club

842
00:46:08,200 --> 00:46:11,960
Speaker 4: coach like community coaches, that's where I love this use

843
00:46:12,000 --> 00:46:16,280
Speaker 4: of like an undernines coach being like, what's the best

844
00:46:16,360 --> 00:46:19,080
Speaker 4: thing for me to teach my undernines and getting the basics,

845
00:46:19,560 --> 00:46:22,880
Speaker 4: because let's be honest, undernines is about wrangling children and

846
00:46:22,920 --> 00:46:25,120
Speaker 4: getting them to listen to you, So that part is

847
00:46:25,120 --> 00:46:28,280
Speaker 4: more important than teaching the kids some kind of shape

848
00:46:28,360 --> 00:46:31,120
Speaker 4: or structure on the field to chat like that. However,

849
00:46:31,640 --> 00:46:36,040
Speaker 4: my issue with that lack of tactical coach. So if

850
00:46:36,040 --> 00:46:39,040
Speaker 4: we're talking the tactical coach that maybe suffers in the

851
00:46:39,040 --> 00:46:43,320
Speaker 4: interpersonal skill space, I trust that coach to make more

852
00:46:43,440 --> 00:46:46,799
Speaker 4: in the heat decisions, in heat of the moment we

853
00:46:46,840 --> 00:46:48,719
Speaker 4: need to make a change here, what's happening on the

854
00:46:48,719 --> 00:46:54,200
Speaker 4: field right now, Whereas the more personal coach, they're kind

855
00:46:54,239 --> 00:46:55,600
Speaker 4: of just going to be like, well, I can't really

856
00:46:55,640 --> 00:46:57,799
Speaker 4: talk to you right now and figure out how to

857
00:46:57,880 --> 00:47:02,120
Speaker 4: change this. So that's the only negative. I'm all about.

858
00:47:02,680 --> 00:47:06,120
Speaker 4: Good person, good coach, you need to have that connection piece.

859
00:47:06,960 --> 00:47:10,200
Speaker 4: But I do think that's the one moment, the moment

860
00:47:10,400 --> 00:47:15,200
Speaker 4: in the game where you have to make decisions.

861
00:47:14,680 --> 00:47:16,000
Speaker 5: That's the gap.

862
00:47:16,520 --> 00:47:19,319
Speaker 1: Maybe a bot will be able to fill that gap soon.

863
00:47:20,440 --> 00:47:23,920
Speaker 1: Dare I say it coming to a field.

864
00:47:23,680 --> 00:47:26,400
Speaker 4: Near you just literally you know when you look up

865
00:47:26,440 --> 00:47:29,280
Speaker 4: at the at the coaching box and there's fourteen people

866
00:47:29,320 --> 00:47:31,360
Speaker 4: in there, half of them are in front of a laptop.

867
00:47:31,800 --> 00:47:33,799
Speaker 4: One of them will be this is what's happening? What

868
00:47:33,840 --> 00:47:34,600
Speaker 4: do we do next?

869
00:47:34,800 --> 00:47:36,720
Speaker 3: That could be one bot soon anyway.

870
00:47:36,880 --> 00:47:41,759
Speaker 1: But the macro point I think is that the algorithms,

871
00:47:41,760 --> 00:47:43,160
Speaker 1: so remember the rules.

872
00:47:42,840 --> 00:47:44,160
Speaker 3: Inside of these technologies.

873
00:47:44,200 --> 00:47:49,080
Speaker 1: Isn't necessarily the villain, but it's not neutral because algorithms

874
00:47:49,080 --> 00:47:52,600
Speaker 1: are designed. So you know, the algorithms that decide which

875
00:47:52,719 --> 00:47:55,520
Speaker 1: kid to scout and why is designed? So is that

876
00:47:55,640 --> 00:48:00,440
Speaker 1: designed with girls' skills, girls body development in mind? I

877
00:48:00,480 --> 00:48:03,920
Speaker 1: don't think it's necessary about women's sport just rejecting AI

878
00:48:04,000 --> 00:48:06,359
Speaker 1: and algorithms, and it's too late anyway, it's in here

879
00:48:06,400 --> 00:48:09,920
Speaker 1: and now. But can we set the rules of the machine,

880
00:48:10,400 --> 00:48:13,200
Speaker 1: because it's already shaping the game. And this is where

881
00:48:13,280 --> 00:48:17,040
Speaker 1: it feels really inconsequential. But I say, every click trains

882
00:48:17,080 --> 00:48:20,160
Speaker 1: the algorithm. Just go out and search for women's sport,

883
00:48:20,360 --> 00:48:23,359
Speaker 1: click on it. Did you see the Billy Jean king

884
00:48:24,040 --> 00:48:25,960
Speaker 1: little thing where it's her tapping on the screen a

885
00:48:26,000 --> 00:48:29,040
Speaker 1: million times saying how many likes for women's sports? So

886
00:48:29,640 --> 00:48:33,480
Speaker 1: every micro action is very consequential in shaping the future,

887
00:48:33,640 --> 00:48:37,920
Speaker 1: even digitally. So click on the links, share them, subscribe,

888
00:48:38,480 --> 00:48:41,440
Speaker 1: every time we do that, we're training the data and

889
00:48:41,480 --> 00:48:44,080
Speaker 1: I think that anyone can go and do this now.

890
00:48:44,239 --> 00:48:45,840
Speaker 4: Yeah, So those call outs that we make at the

891
00:48:45,920 --> 00:48:48,040
Speaker 4: end of every podcast, at the end of every post,

892
00:48:48,320 --> 00:48:51,480
Speaker 4: they mean something. We're not just asking you to lift

893
00:48:51,840 --> 00:48:53,560
Speaker 4: us up. We're asking you to lift all the women's

894
00:48:53,560 --> 00:48:54,000
Speaker 4: sport up.

895
00:48:54,120 --> 00:48:55,880
Speaker 2: Yes, I love that. On that note, though, can you

896
00:48:55,920 --> 00:48:58,600
Speaker 2: give us a review or give us a five star? Ready?

897
00:48:58,719 --> 00:48:59,439
Speaker 5: Clicky?

898
00:48:59,560 --> 00:49:03,279
Speaker 2: Thank you so much? Quick, we've gone well over time

899
00:49:03,320 --> 00:49:05,360
Speaker 2: read but quick shout out to one of the listeners

900
00:49:05,400 --> 00:49:06,279
Speaker 2: that you had to chat with.

901
00:49:06,640 --> 00:49:08,960
Speaker 3: Yes, very quickly. Bluebell Nichols.

902
00:49:09,000 --> 00:49:12,280
Speaker 1: She's a sports journal and a UK semi pro rugby player.

903
00:49:12,360 --> 00:49:15,160
Speaker 1: She reached out to me. We had a wild and

904
00:49:15,239 --> 00:49:19,640
Speaker 1: amazing chat that went across a variety of different things.

905
00:49:20,320 --> 00:49:23,040
Speaker 1: She runs a platform called She's the Champ. It's a

906
00:49:23,080 --> 00:49:26,480
Speaker 1: podcast about the women's championship rugby, so the level below

907
00:49:26,560 --> 00:49:30,200
Speaker 1: the professional league. For me, I think that platform is

908
00:49:30,200 --> 00:49:32,839
Speaker 1: a signal in and of itself about it new kinds

909
00:49:32,840 --> 00:49:35,080
Speaker 1: of sports models. We should do a podcast on this

910
00:49:36,320 --> 00:49:38,200
Speaker 1: that it doesn't have to be top tier for people

911
00:49:38,239 --> 00:49:40,440
Speaker 1: to be interested or to people be engaging in it.

912
00:49:40,680 --> 00:49:44,840
Speaker 1: But she's also historian. So we totally nerded out about

913
00:49:45,000 --> 00:49:49,280
Speaker 1: futures and history and how assumptions can were.

914
00:49:49,160 --> 00:49:50,400
Speaker 5: You guys like Ying and Yang?

915
00:49:50,600 --> 00:49:55,000
Speaker 1: Oh, it was lots of great futurists I know are historians. Yeah, okay,

916
00:49:55,040 --> 00:49:58,320
Speaker 1: because the crossover is saying, we have assumptions about the past,

917
00:49:58,400 --> 00:50:01,560
Speaker 1: we have assumptions about the press, and that dictates about

918
00:50:01,600 --> 00:50:03,680
Speaker 1: the future, that dictates how we act in the present.

919
00:50:03,880 --> 00:50:07,920
Speaker 1: So she gave this really amazing example of a Viking

920
00:50:08,400 --> 00:50:10,200
Speaker 1: I'm going to get this so wrong, Bluebell, sorry, but

921
00:50:10,480 --> 00:50:13,440
Speaker 1: a Viking burial and it had weapons around it, and

922
00:50:13,480 --> 00:50:16,000
Speaker 1: it assumed for hundreds of years that it was a man,

923
00:50:16,160 --> 00:50:19,200
Speaker 1: and DNA testing told us that it was a woman,

924
00:50:20,360 --> 00:50:25,000
Speaker 1: and it got kind of critiqued as well, that's an anomaly.

925
00:50:25,360 --> 00:50:28,640
Speaker 1: But essentially it unlocked the whole conversation of saying, maybe

926
00:50:28,640 --> 00:50:31,000
Speaker 1: what we've thought about we've got wrong. And it's the

927
00:50:31,040 --> 00:50:33,520
Speaker 1: same in thinking about the future. It hasn't happened. We

928
00:50:33,560 --> 00:50:36,640
Speaker 1: have assumptions that lock us into beliefs about what is possible.

929
00:50:36,719 --> 00:50:42,160
Speaker 1: So check out She's the Chat podcast and just get

930
00:50:42,200 --> 00:50:44,880
Speaker 1: around women's sport and let's shape the algorithm together.

931
00:50:45,280 --> 00:50:47,239
Speaker 2: Yes, I love it. Thank you so much. Fore for

932
00:50:47,280 --> 00:50:49,520
Speaker 2: your time. Shout out to Bluebell, love the work you're doing.

933
00:50:49,719 --> 00:50:51,799
Speaker 2: We will see you guys next time on the next

934
00:50:51,840 --> 00:50:53,320
Speaker 2: episode of The Next.

935
00:50:54,040 --> 00:50:55,720
Speaker 5: It's my Favorite Life the Next.

936
00:50:57,440 --> 00:50:59,759
Speaker 2: Thanks so much for listening. If you got something out

937
00:50:59,800 --> 00:51:02,279
Speaker 2: of this episode, I would absolutely love it if you

938
00:51:02,320 --> 00:51:04,359
Speaker 2: could send it on to one person who you think

939
00:51:04,520 --> 00:51:08,319
Speaker 2: might enjoy it. Otherwise, subscribe, give us a review, and

940
00:51:08,400 --> 00:51:10,760
Speaker 2: make sure you follow us on Instagram at the Female

941
00:51:10,760 --> 00:51:13,839
Speaker 2: Athlete Project to stay up to date with podcast episodes,

942
00:51:13,880 --> 00:51:17,360
Speaker 2: merch drops, and of course news and stories about epic

943
00:51:17,440 --> 00:51:18,440
Speaker 2: female athletes.