WEBVTT
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Hi, I'm Ron Galen from Longmanow, Massachusetts and I play
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at long Winow Country Club.
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Welcome to Golf Smarter.
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Hi. This is Susannah McGhee from Sarasota, Florida. I play
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golf at the Preserve Golf Club at Tara in Bradenton.
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And this is Golf Smarter Number Oney twenty. Welcome.
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An average golfer isn't using a yardage book, probably doesn't
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know how to read the contour lines and the elevations.
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Our goal is to provide them a prescriptive answer that says,
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before you hit this t shot. Here's the aim point,
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not to say replace yardage books, maybe a different use case.
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And then what we've heard through a lot of folks
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is building intuition to play smart is really difficult in golf.
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If you don't play with a lot of really good players.
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If you're a fifteen handicap and you don't play with
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a lot of great golfers, how do you know you
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shouldn't shortside yourself? How do you know maybe you should
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take this different line? You just don't. And so people
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have used shot sense and have seen okay, it keeps
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angling me over here. Why is that like Okay, there's
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water over here. I need to shift over this way
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the pins over here? Why is it keep saying aim
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towards the fat part of the green? Now I'm better
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understanding this. One thing we get really comfortable with is
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the rangefinder of zapping the pin. It's one sixty two
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and you're like, all right, Like maybe I'll take a
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yard off and a yard to the left. We can
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do better than that now that we have all of this.
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Data, understanding and optimizing strategy for every shot using the
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shot Sense app. Featuring Brent Neville. This is Golf Smarter,
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sharing stories, tips and insights from great golf minds to
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help you lower your score and raise your golf IQ.
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Here's your host, Fred Green.
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Welcome to the Golf Smarter Podcast. Brent, Thanks Fred, happy
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to be here, Glad to have you on here. Let's
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get the mouth and first thing. On a Monday morning,
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we're recording this, so there's a lot of competition out there, right,
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there's like in what you're doing and we haven't even
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talked about what you're doing yet. But like when I
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started this podcast, there were two or three golf podcasts,
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it was easy to stand out and be on top.
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Here we are twenty years later in the podcast world,
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and there are a lot of podcasts. A couple of
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years ago, apps started coming out for your phone so
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that you have a GPS on your phone, and it
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was just a GPS. But it's getting crowded in that
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space too. There's a lot of apps there, and you've
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come up with something unique. And I know that your
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fingers are crossed that you are unique compared to everybody else,
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but I think you have a lot of reasons why
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it is. Let's talk about the shot sense app.
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Yeah, grab, yeah, thank you. Yeah. Definitely a crowded marketplace.
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Maybe I'll I think a step back and think about
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you know, how we view the golf landscape of like
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getting better lowering your scores. We think about it very
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simply as you can either hit the ball physically better
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or you can play smarter. Lots of innovation in the
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ability to physically hit it. New clubs, new swing AI,
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swing analyzer, launch monitors, simulators, all the golf specific fitness programs,
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Lots going on there, and then I think for a
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while that the playing smarter hadn't had as much innovation.
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There's a lot of golf GPS apps out there that
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maybe show you the satellite view and you can move
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a cursor around rangefinders really despite their price going up
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quite a bit in the last you know, five years
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or so, technology that's over twenty years old, and then
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you have some you know, larger course management let's like
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teach you how to think about it, or if you
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were to go to some clinic they can teach it.
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We kind of look at that and say, well, now
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that we have modern computing, now that we have a
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better understanding of how the analytics look, how can we
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leverage the supercomputer in your pocket to provide you the
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optimal strategy for every shot, for every course tailored to you.
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And that's really what we think. So you'll hear me
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say this quite a few times of de averaging these
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large data sets that were historically used to provide things.
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Good example is Strokes gained very very super helpful, very novel,
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but based on a very large average data set. That
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is what Brody had to do ten years ago when
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we didn't have all of this modern computing. So that's
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kind of where we're excited to go forward. Just that
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the capabilities that we have today that just frankly didn't
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exist two five, ten years ago.
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So you talk about Brody, you're talking about the Strokes Strokes,
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Strokes gained elements of the game.
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Yeah. Yeah, So so Brody came out with his book.
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Yeah yeah, Mark Brody. Sorry, that's okay. In about I
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think it was like twenty fourteen's about eleven years ago,
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you know, super novel research and all that. But but
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there are limitations to Strokes gained. Many of those he
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wasn't able to solve back then, and now with you know, computing,
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we can do that, and so a lot of what
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we do is just kind of trying to figure out
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instead of you know, a great example is seventeen at
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Sawgrass from the t one hundred and thirty yards strokes
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gained estimate I think is two point nine to eight.
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If you put that at one hundred and thirty yard
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part three at your local course, I'd argue that you
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know strokes Skain is going to say those are the
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exact same statistical you know score. I venture to say
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seventeen at Sawgrass is a little bit more difficult, and
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that's because of the unique playout of the green. Obviously,
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the hazards everywhere, and so that's a limitation of strokes
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stained is that it can't de average for your specific
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shot that's upcoming.
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I get it, I get it. So what was it
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that you decided this is where we need to create
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something that you know and you got it. You've got
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to believe I love the playing smart smarter part you've
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been talking about for for twenty years now.
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Yeah. Yeah, So started playing a lot of golf, played
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about I think close to two hundred rounds over two
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and a half years, and all at almost the same place.
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Went from an eight to a one and a half.
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Didn't change my swing, didn't change my clubs. You're obviously
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playing a lot, you're you're putting a lot, and you're
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you're very familiar. But but what it really was is
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I knew every play on every shot very rarely, you
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know golf. The magic thing with golf and going new
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courses that every you know lie is different or every
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pin position. If you play a course two hundred times,
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you've you haven't seen everything, but you've seen a lot
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of it. And so from you know, my background kind
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of engineering, there's a couple holes where I didn't know
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the right play. Even after all of that first hold
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is a great example. Ob right and a bunch of
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trees left? Is it? Driver? But aimed towards the trees?
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You obviously what you know, don't want to hit? Ob
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But how far into the trees do I want to aim?
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Or do I hit a three wood? Obviously with three
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wood you're gonna lose some distance and have a longer approach,
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like you know, how does that all work? And you
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can start trying to do strokes gain but it's a
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little tricky. And so that kind of started the quest
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to say, is there a way to mathematically model this
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out and go for you know, our goal of saying,
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can we provide the optimal strategy for every player on
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every course, for every shot? And so that's where shots
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since golf was born.
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How did you figure that out?
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Uh? Lots of lots lots of math and problem solving.
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So my background is engineering and a lot of what
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I'll call it frontier technology, satellites, autonomous vehicles, UH, GEO,
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spatial analysis, augmented reality UH. And so at the end
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of the day, those are all so new that you
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may think you're going to solve something, but you end
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up having to solve quite a few different problems along
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the way before you can actually get to it. And
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that's really what what we faced here is, Okay, you
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want to do this one thing and you're like, oh man,
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these these trees like trees end up impacting strategy quite
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a bit. Or you know, how do I get the
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data source for every hole to be accurate? Okay, that's
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pretty tough, but you know, if we give the users
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the ability to draw out of bounce or to adjust
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the trees, that can can help. And so it was
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all those we'll say like micro problem solvings that laddered
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up to to help us do that, and so ultimately
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our patent pending kind of core you know differentiator is
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what we call our shot Sense decision engine, and that's
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what helps, you know, tell the users the optimal strategy.
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And so there's four main inputs to that. One. The
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first is your club characteristics. That's how far you carry it,
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the total if there's a rollout, your dispersion length with rotation,
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and then ultimately shot shape you know, big fade or
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small draw something like that or you know maybe if
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you hit a straight ball you're one of the lucky few.
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And you know, so that's everything about your your shot.
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Then let me let me stop you there before you
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get to number two. There's four of them, right, yeah,
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So how does it know your club characteristics?
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Yep. Yeah. So when you start or when you join
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the app or download it, the first thing you'll be
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taken through is our interactive onboarding and the first portion
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of that is where we figure out what's in your bag.
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So ask you. I think it's about seven to ten questions,
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and we trained a machine learning model to predict your
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club distances, your dispersion, and then shot shape. We've trained
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that on tens of thousands of shots. And dispersion is
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this wildly fascinating topic that I think a lot of
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people may not understand all of the nuances there. And
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so what we found in early research is one of
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the quickest ways people get their dispersion is they do
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the track Man combine. Super fun, super helpful, but it's
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six shots at one location with one club. Statistically speaking,
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if you want to get closer to like a ninetieth
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percentile confidence, give it one hundred and forty shots. No
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one's going to the simulator and hitting one hundred and
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forty seven irons. And so what you find is there's
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somewhere in between of we hit six shots from TrackMan
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combine and we get this estimate on our dispersion that's
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clearly not good enough, and then one hundred and forty
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No one's going to do that. And so this is
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a perfect use case of machine learning is to say, okay,
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how far do you estimate you hit your driver and
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seven iron? What's your handicap? Where do you typically miss?
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And using all the trained data of people that have
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a similar characteristic of you, here's what we project it.
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So that's where we start. However, if you actually know
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your data, if you have a launch monitor or if
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you're a you know, a college golfer or pro, you
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obvious are going to know that data so you can
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manually edit all of that and that really gets kind
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of what is the truth about your clubs if you
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do know that.
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So when I started using the app myself, I went
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through some of the onboarding. I didn't complete it. Obviously,
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I need to do more. I kind of got stuck
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because I'd carry a five hybrid and it didn't allow
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me to put that in there. You have five iron
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and four hybrid, Now what do I do? I'm stuck?
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Yeah, so we have different labels. I think we have woods, hybrids, irons,
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and wedges. Yeah, so you can go you can put
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a four hybrid and then you can change the name
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of it to a five hybrid and then they carry both.
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So then you can select like two hybrid and three
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hybrid and four hybrid. And then if you or you're saying,
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do you carry like a two, three, four and five hybrid?
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No, I carry a four and five hybrid, A three
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and five.
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Wood, gotcha. Yeah, so I think you can do driver,
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three wood, five wood, and then I'd recommend you do
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like a three hybrid and a four hybrid, and then
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you can change the names of those to you know,
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reflect what you actually have. So it's it's a good
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point of feedback, you know, easy to add additional names there.
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But yeah, so raypious feedback.
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Oh you're welcome.
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Good.
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All right, So we got club character you said there
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were four four elements. Yeah, and the first one was
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club characteristic, which was fascinating in itself. Where are we
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going next?
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Yeah? Yeah, so now we know, you know, to simplify
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the clubs, we know how you hit the ball uniquely
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to you. The next one, obviously with golf and the
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piece that's really I think when you think about the averaging,
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stuff is missed is every hole is unique, and so