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Speaker 1: Welcome back to another edition of the Frida Egg Podcast.

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Speaker 1: My name is Garrett Morrison and this episode is brought

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Speaker 1: to you by ourselves. So we're having a Black Friday

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Speaker 1: sale that is this Friday at our pro shop on

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Speaker 1: our website, so you can find it at proshop dot

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Speaker 1: Thefrida Egg dot com and it's an automatic twenty percent

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Speaker 1: discount off of everything that includes hats, shirts, headcovers, turvice tumblers.

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Speaker 1: But I wanted to talk specifically about our photography prints.

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Speaker 1: So these are drone shots of great golf courses. Most

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Speaker 1: of the shots were taken by Andy Johnson. You can

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Speaker 1: get them framed or mounted on metal. We've got pictures

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Speaker 1: of bally Neil, Sand Valley, Prairie Dunes, Pasa Tiempo, and

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Speaker 1: we've got a ton of new ones on the way.

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Speaker 1: They look great. I've got one on my wall myself,

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Speaker 1: and they make really good gifts. So this Friday, November

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Speaker 1: twenty seventh, twenty percent off the whole store, including our prints.

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Speaker 1: That's at the pro Shop, pro show dot Thefrida Egg

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Speaker 1: dot com. All right, so on with our episode. My

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Speaker 1: guest today is Matt Korshane along with his brother Will

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Speaker 1: Korshane Matt founded Data Golf, which you can find at

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Speaker 1: datagolf dot com. So Data Golf is really part of

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Speaker 1: what has been a revolution in golf over the past

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Speaker 1: couple of decades, and that has been this infusion of

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Speaker 1: advanced data, statistics and analytics into golf, especially golf at

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Speaker 1: the highest professional levels. Now part of that story is Shotlink,

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Speaker 1: which provides a great deal of raw data that simply

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Speaker 1: wasn't available in the nineteen nineties. And another part of

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Speaker 1: it is the advent of strokes gained statistics, which were

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Speaker 1: pioneered by the Columbia Business School professor Mark Brody. And basically,

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Speaker 1: what strokes gained does is it compares a player's performance

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Speaker 1: to the rest of the field, shot by shot, and

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Speaker 1: in this way you can really isolate the value or

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Speaker 1: quality of each shot a player hits. Now, in strokes gained,

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Speaker 1: with each shot the player gains or loses a certain

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Speaker 1: amount on the field. And what this has done is

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Speaker 1: it's just given us a much better way to analyze

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Speaker 1: the different skills that a player has. We really have

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Speaker 1: a better idea of who the great drivers and iron

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Speaker 1: players actually are because we've been able to zero in

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Speaker 1: on the quality of each strike of the ball. But

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Speaker 1: you know, strokes gained is like still in its infancy

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Speaker 1: and it's just begun to shift our understanding of golf.

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Speaker 1: I think where we're just scratching the surface, and that's

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Speaker 1: the exciting arena in which Data Golf is working right now.

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Speaker 1: Matt and Will Korshane are very young, but also very qualified,

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Speaker 1: and they're doing a lot of interesting work. One of

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Speaker 1: the things that I think Data Golf could change is

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Speaker 1: how we view golf courses in golf course design now,

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Speaker 1: I always think that golf course design will be an

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Speaker 1: art and assessing golf course design will be an art

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Speaker 1: as well. Know, no, data, I don't think can tell

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Speaker 1: us if a golf course is good, but it can

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Speaker 1: definitely tell us how. Data can definitely tell us how

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Speaker 1: different tournament venues demand different skill sets from players. And

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Speaker 1: I find that to be a really compelling question. And

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Speaker 1: so that's what I wanted to talk with Matt Corshane

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Speaker 1: about because Data Golf, the website that he and his

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Speaker 1: brother have developed, has some great tools that use strokes

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Speaker 1: gained data to tell you something about the golf courses

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Speaker 1: on the PGA Tour, specifically this week's host of the

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Speaker 1: Mayacoba Golf Classic, El kamal Ane Golf Club outside of Cancun, Mexico,

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Speaker 1: is one of the biggest outliers in Data Golf's model.

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Speaker 1: It's such a strange course. It just demands this extremely

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Speaker 1: unusual type of performance from the top players. So I

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Speaker 1: thought it was a good time to get at a

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Speaker 1: question that I've had for a long time with the

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Speaker 1: help of Matt Course Shane, what can statistics tell us

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Speaker 1: about golf course architecture on the PGA Tour. I hope

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Speaker 1: you enjoy I miss the green for example, I'm already

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Speaker 1: upset when I find my ball in the bunker, I'm

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Speaker 1: really upset.

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Speaker 2: And when I.

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Speaker 1: Find my ball in a brid egg Friday egg, the

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Speaker 1: dreaded Frida egg fridagg Frida egg egg Frida egg bride

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Speaker 1: egg Lie, I'm about ready to run off the golf course. So,

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Speaker 1: just to find out a little bit about your background

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Speaker 1: in golf, you were a pretty excellent golfer. You got

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Speaker 1: down to a scratch handicap, You played some college golf

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Speaker 1: at Queen's University as a competitive golfer. Did data ever

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Speaker 1: enter your life?

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Speaker 2: No, it really didn't.

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Speaker 3: Yeah, I wasn't logging like the Strokes game stats or anything.

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Speaker 3: I was not a and I'm still not. I'm very

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Speaker 3: much a I mean, I'm a vivid head case on

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Speaker 3: the golf course. Now, I think I'm very much a

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Speaker 3: field player. I guess I would say I'm not like

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Speaker 3: a yeah, technical robotic player by any means.

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Speaker 1: That's an interesting contrast to me. So like if data

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Speaker 1: were you know, some of these analytics were around back then,

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Speaker 1: but they just weren't as commonly used, I guess, especially

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Speaker 1: at the college golf level. Do you think anything would

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Speaker 1: have changed.

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Speaker 3: For you looking back when I was actually a serious

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Speaker 3: player and didn't have because honestly, the way I think

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Speaker 3: about it now is I just have on the list

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Speaker 3: of the top ten things I need to improve in golf,

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Speaker 3: understanding how to better use data is pretty far down

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Speaker 3: that list. I need to stop like blocking my opening

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Speaker 3: TV all ob before I start worrying about that stuff.

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Speaker 3: But like, yeah, no, back in the day when I

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Speaker 3: was good, it would be Yeah, it'd be super informalve

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Speaker 3: just to get the basic numbers like the Strokes game stuff,

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Speaker 3: just using Brodie's app, Like just understanding what you're losing

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Speaker 3: strokes is super valuable, and I think it's often for

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Speaker 3: most people it's counter intuitive. I think probably a lot

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Speaker 3: of people would end myself included, would probably realize they're

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Speaker 3: losing most shots, like on the long game, whenever you

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Speaker 3: play poorly, or always thinking about all these pots you miss,

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Speaker 3: et cetera. But really everybody misses a lot of potts,

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Speaker 3: so I think, yeah, But anyway to answer the question,

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Speaker 3: I think I would have taken advantage of this stuff.

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Speaker 2: It was more easily accessible in ten years ago.

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Speaker 1: Well. Hearing golfers talk about it, it's almost like, in

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Speaker 1: addition to providing knowledge, these stats sometimes provide a kind

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Speaker 1: of peace of mind, like you know what's wrong, yeah,

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Speaker 1: or or you can contextualize your bad performances as just

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Speaker 1: like this is what happens. You know, it's not a

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Speaker 1: judgment on me as as a player. This is just

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Speaker 1: what happens.

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Speaker 2: Yeah.

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Speaker 3: And it's also I kind of think that is the

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Speaker 3: right way to think about it, because it's also debatable

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Speaker 3: how like actionable this stuff is. Like if I find

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Speaker 3: out that I am lacking, well, if I'm losing small

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Speaker 3: good example of if I'm losing strokes, offer te it's

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Speaker 3: like okay, like, yeah, if I hit.

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Speaker 2: It further, that'd be great. But I mean there are

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Speaker 2: examples of players.

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Speaker 3: Who have like doing for Telly last week was getting

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Speaker 3: a lot of attention for he's not a massive guy,

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Speaker 3: and he managed to pick up some club at speech.

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Speaker 2: So there are things you can do.

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Speaker 3: But I've always thought, I think my brother and I've

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Speaker 3: always thought that, Yeah, it's more like descriptive. It's nice

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Speaker 3: to know where you're losing strokes and what is the

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Speaker 3: difference between myself and like the number one player in

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Speaker 3: the world.

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Speaker 2: But I don't know how actionable a lot of it is.

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Speaker 1: So right now you're doing a PhD at the University

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Speaker 1: of British Columbia in economics. Where did your interest in

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Speaker 1: economics come from?

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Speaker 2: So my grandfather is, well, he's still alive.

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Speaker 3: He was a pretty stop working but he was a

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Speaker 3: pretty well known economist, so there was always that connection.

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Speaker 3: I actually did my undergrad in biochemistry, which was I mean,

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Speaker 3: I was just a classic. I just went to university

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Speaker 3: not really knowing what to do, and I was like, oh,

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Speaker 3: I mean why not medical school?

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Speaker 2: Like that sounds reasonable.

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Speaker 3: It's embarrassing looking back on it, but then I eventually

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Speaker 3: realized I think it's like a few Coon courses and

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Speaker 3: just sort of liked the general approach to things. Just

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Speaker 3: using statistical methods, which are particularly the ones that are

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Speaker 3: prominent in economics, Just using those as a way to

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Speaker 3: understand the world is. It's just changed the way I

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Speaker 3: think about a lot of things, and I've really become

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Speaker 3: I really enjoy looking at the world through that lens.

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Speaker 1: So at some point you combined your background in golf

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Speaker 1: with your interest in statistics and informed data golf. How

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Speaker 1: did the idea for data golf come about?

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Speaker 3: Well, I guess the first thing I just say is,

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Speaker 3: so data Golf is run by myself from my younger brother, Will,

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Speaker 3: who's so he's two years younger. Yeah, he was working

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Speaker 3: at that point, and I was just in my second

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Speaker 3: year of the PhDs.

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Speaker 2: This is like four or five years ago. And then

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Speaker 2: the Yeah, the PJ Tour used to.

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Speaker 3: Have an academic program where you could access the shot

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Speaker 3: link data for free, and so we got that data

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Speaker 3: and we just started.

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Speaker 2: Yeah, we just started a Twitter account.

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Speaker 3: Started a WordPress blog and started doing some basic analyses

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Speaker 3: with the data, trying to just sort of answer.

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Speaker 2: It's funny to look back.

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Speaker 3: We still have all these blogs somewhere on our old website.

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Speaker 3: It's funny to look back on them. But yeah, just

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Speaker 3: answering questions like do players in fact, there's the same

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Speaker 3: wording out you shoot a really good round, it's hard

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Speaker 3: to follow it up, So we were sort of just

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Speaker 3: checking the degree to which that's all or true and yeah,

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Speaker 3: and then it's sort of just progressed from there.

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Speaker 1: Right for sure. So it's really evolved well past the

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Speaker 1: original blog. So the original Data Golf blog was basically

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Speaker 1: a word Press basic WordPress blog, and now it's this

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Speaker 1: very complex website with not only blog posts, but a

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Speaker 1: number of predictive models and interactive tools that kind of

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Speaker 1: make sense of the huge amount of data that's coming

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Speaker 1: out of professional golf right now. And so I wanted

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Speaker 1: to focus actually on the interactive tools and visualizations. So

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Speaker 1: you know, to someone like me, I find these very

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Speaker 1: useful and very informative just in general, How do you

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Speaker 1: decide what kinds of tools to build?

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Speaker 3: Yeah, so I think a lot of the tools come

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Speaker 3: naturally out of the model that we have, So really

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Speaker 3: like the centerpiece of the website and what we do

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Speaker 3: with Data Golf is this predictive model. We have of

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Speaker 3: golfer perform, where essentially the output of that model is

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Speaker 3: we're trying to, like, ultimately what matters in golf is

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Speaker 3: strokes gain, how many strokes you're beating the field by.

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Speaker 3: So that's ultimately what we're trying to predict with this model.

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Speaker 3: And along the way there will be things that I

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Speaker 3: don't know, sort to pop out of the model that

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Speaker 3: we can make into a webpage.

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Speaker 2: So a good example is true strokes gained.

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Speaker 3: So true strokes gained is it's regular strokes gain, which

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Speaker 3: is how many strokes beat the feeld by, except we're gonna.

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Speaker 2: Adjust that for the strength of that field.

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Speaker 3: So, for example, on the web dot com, if we

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Speaker 3: estimate that those fields are like a shot worse on

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Speaker 3: average than TJ tour field, So if you beat a

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Speaker 3: web dot com field by two shots, that's gonna be

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Speaker 3: worth like a true strokes gin of one because we're

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Speaker 3: saying this field is one shot.

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Speaker 2: Worse than average. So that's something that sort of comes

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Speaker 2: out of our model.

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Speaker 3: Another example of a page would be the course history

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Speaker 3: stuff or the course fit tool. We have that output

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Speaker 3: in our in our model, and at some point we say, wow,

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Speaker 3: this is interesting. I think general golf fans would like this.

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Speaker 3: Let's turn it into like an intuitive page that people

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Speaker 3: can can get something out of.

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Speaker 2: So where that's where a lot of them come from.

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Speaker 1: So I did want to dig a little bit deeper

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Speaker 1: into what you call the course fit tool, because I

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Speaker 1: think it does have a lot to say about what

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Speaker 1: one of our primary interests at the fried Egg is,

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Speaker 1: and that's golf course design and set up and how

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Speaker 1: design and set up influence play on the professional tours.

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Speaker 1: But I mean, I think maybe before getting into course

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Speaker 1: fit specifically, maybe we should lay the groundwork with this

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Speaker 1: related but very different notion of course history. Can you

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Speaker 1: tell me what course history is and some of the

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Speaker 1: issues that you've had with it.

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Speaker 2: Yeah.

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Speaker 3: So, course istuy in the most general sense, is just

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Speaker 3: how a player has performed at a specific course in

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Speaker 3: the past. And then the way we analyze it is

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Speaker 3: not just how like a player has performed in an

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Speaker 3: absolute sense at a course, because, for example, Tiger, if

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Speaker 3: you just define course a player's course history as just

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Speaker 3: their total stroke seeing at that course, and somebody like

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Speaker 3: Tiger is going to have the best course history everywhere.

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Speaker 3: So course history, the way we define it is performance

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Speaker 3: relative to expectation or baseline. So and by expectation or

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Speaker 3: baseline that's going to be intuitally, that's like how a

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Speaker 3: player performs across all courses in a given year, that's

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Speaker 3: going to be their baseline. So it might be there

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Speaker 3: might be a plus one strokes gained player. And then

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Speaker 3: if there's a specific specific course, say I guess the National,

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Speaker 3: where they've performed at an average of plus two strokes gained,

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Speaker 3: we're gonna say they have good course history, dick, because

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Speaker 3: they performed above their baseline. The main issue with course

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Speaker 3: history in terms of trying to use it for actionable

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Speaker 3: things like predicting performance, is that it's generally a small sample.

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Speaker 3: If a guy has only played four rounds of the

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Speaker 3: course and he's played really well, maybe he won the tournament,

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Speaker 3: which is not not too uncommon, what can we really

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Speaker 3: say from that, Like from our analysis, we've found like

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Speaker 3: if a guy is performing one stroke better than expected

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Speaker 3: at a course, historically, we're only going to improve his

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Speaker 3: predictive performance going forward by maybe like two or three

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Speaker 3: percent per round.

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Speaker 2: Let's say, so, well, if he's if he's done that

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Speaker 2: for four.

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Speaker 3: Rounds, we would probably bump up his predicted skill by

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Speaker 3: like zero point zero five if he's been one stroke planner.

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Speaker 2: So it's a pretty small adjustment, but those things can matter.

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Speaker 1: Yeah, it seems like your opinion on this has has

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Speaker 1: shifted a little bit over time, but the general critique

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Speaker 1: of how maybe the lay person uses course history is

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Speaker 1: still valid. You know, when when you hear people talking

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Speaker 1: about how a certain player has done well at a

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Speaker 1: course in the past and is using that fact to

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Speaker 1: say this player will definitely do well this week at

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Speaker 1: the same course, that that's a vast oversimplification of how

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Speaker 1: things actually work.

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Speaker 2: Right, Yeah, it is.

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Speaker 3: In general, making strong claims about predicting performance in golf

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Speaker 3: is a bad idea, just because there's so much there's

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Speaker 3: so much randomness in golf as we like, even just

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Speaker 3: last week, like like Bryson losing to Lang, right, I guess,

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Speaker 3: so like obviously this is a very unexpected outcome, but

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Speaker 3: those things happen in golf. So but to your point

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Speaker 3: about us changing our opinion, that's definitely true. I mean

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Speaker 3: when we first started out on Twitter, we were more

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Speaker 3: maybe rash and like combat of I guess to some

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Speaker 3: debdeon and we took a pretty hard stance on course

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Speaker 3: history saying it didn't matter. But I think we've definitely

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Speaker 3: softened that. There are just examples like Phil Augusta. He

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Speaker 3: now has like he's played like so many rounds there,

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Speaker 3: like like sixty seventy rounds, and he's averaging almost a

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Speaker 3: shot better than than expected Augusta, which is a huge

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Speaker 3: that's a huge difference, and it can't be you can't

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Speaker 3: just wave that away by saying, oh, that's just randomness.

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Speaker 3: That's pretty meaningful. So, yeah, course history, when you get

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Speaker 3: big examples, can matter for sure.

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Speaker 1: Now, so course history can matter a little bit. There's

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Speaker 1: there's a small adjustment that you can make for a

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Speaker 1: player's course history when you're trying to predict their performance

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Speaker 1: in a given week. It seems to me that course

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Speaker 1: fit in a lot of ways makes up for some

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Speaker 1: of the deficiencies of course history. Is that basically right?

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Speaker 2: Yeah, that's that's definitely right.

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Speaker 3: You get you sort of with So with course fit,

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Speaker 3: what we're gonna look at is which skills a given

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Speaker 3: course favors. So like the skills, we're gonna focus on

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Speaker 3: our driving distance, driving accuracy and strokes and putting around

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Speaker 3: the green approach, and by focusing on skills, you kind

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Speaker 3: of bypass this sample size problem. So if we know

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Speaker 3: through our fancy statistical methods that Augusta favors people hit

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Speaker 3: it further. And even if there's a guy who a

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Speaker 3: first time round Augusta, so we have no course history

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Speaker 3: date on him, but if we know he hits it

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Speaker 3: above average distances, we can use course fit to make

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Speaker 3: some judgments about how he's going to perform in that course.

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Speaker 3: So yeah, by focusing on characteristics instead of specific players,

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Speaker 3: which is what you do with course history, of course,

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Speaker 3: it can be a lot more meaningful. Yeah, because you

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Speaker 3: don't have a we have a ton of data to

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Speaker 3: estimate how how much driving distance is favored at Augusta,

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Speaker 3: we can use every player to understand that.

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Speaker 1: Right. Yeah, So course fit just basically defined, is is

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Speaker 1: kind of the degree to which a given golf course

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Speaker 1: favors a specific skill set, Right, And those the skills

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Speaker 1: that you measure are you know, maybe it can help

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Speaker 1: me out here driving distance, driving accuracy, strokes gained, approach,

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Speaker 1: strokes gained around the green, and strokes gained putting. Yeah,

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Speaker 1: those are the kind of the five skills that you

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Speaker 1: measure when you're assessing whether a course kind of fits

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Speaker 1: a particular kind of player. So has anything kind of

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Speaker 1: jumped out and surprised you since you started looking at

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Speaker 1: course fit.

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Speaker 3: I think the I mean, the main takeaways I think

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Speaker 3: from course fit are like with the data that we have,

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Speaker 3: it seems like the way that court, the way that

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Speaker 3: PGA tour courses differ is along the dimensions of how

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Speaker 3: much they favored driving distance and driving accuracy. Certainly we

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Speaker 3: can make intuitiveur arguments for why different courses favor putting

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Speaker 3: or around the green, but I think the reality is,

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Speaker 3: like the data, especially the putting, it's just so there's

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Speaker 3: so much variance that it's hard to it's hard to

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Speaker 3: really say whether or not a course favors putting more

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Speaker 3: than the average, and that sort of reflected in the tool.

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Speaker 2: I think, I guess my takeaway like, I think I was.

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Speaker 3: A bit surprised. Maybe how still important? Again, there are

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Speaker 3: a lot of caveats here, but how important driving accuracy

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Speaker 3: is just because there is the narrative of I mean,

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Speaker 3: this is how I'm looking at it. Somebody else could

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Speaker 3: look at it and say, oh, I'm surprised how much

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Speaker 3: that distance is the most favored skill, And just for

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Speaker 3: people who aren't looking at this thing. And basically the

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Speaker 3: hierarchy is the way we have it is basically distance

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Speaker 3: at the average course is the most particular power, along

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Speaker 3: with approaching approach, and then it sort of goes.

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Speaker 2: I guess driving.

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Speaker 3: Accuracy, putting around the green are all pretty similar. Around

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Speaker 3: the green is probably the smallest, but that's sort of

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Speaker 3: the hierarchy. And then there's all sorts of interesting things

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Speaker 3: that specific courses that you could get into.

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Speaker 1: Yeah, and we'll get into that a little bit in

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Speaker 1: a minute here, But first thing, maybe you could just

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Speaker 1: describe the course fit tool a little bit. I think

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Speaker 1: people should go and see if the visualization is really

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Speaker 1: simple and effective, and it would be probably hard to

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Speaker 1: describe over the radio basically what it looks like, but

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Speaker 1: could you just tell me basically what the course fit

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Speaker 1: tool is and how you built it.

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Speaker 3: So the course fit tool, it's essentially what we're just

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Speaker 3: trying to visualize, as you said earlier, the degree to

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Speaker 3: which each course on the PGA Tour favors each skill,

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Speaker 3: and so what we have on it is a basically

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Speaker 3: have these shapes. Each shape represents the degree to which

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Speaker 3: the course favors skills and so we have the average

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Speaker 3: PGA Tour course on there, and then you can sort

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Speaker 3: of easily see when we overlay another course on top

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Speaker 3: of that whether or not that overlaid course favors distance

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Speaker 3: more than the average course. Same with accuracy, et cetera.

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Speaker 3: And yeah, I don't know if we want to get

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Speaker 3: into the details of how these numbers are actually calculated.

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Speaker 3: Is it's somewhat complicated, but the intuitive all we're really

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Speaker 3: doing with this tool is like, if you take driving distance,

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Speaker 3: for example, we're gonna before each event let's play on

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Speaker 3: the PGA Tour or each round, we're gonna have an

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Speaker 3: estimate of every player's driving distance and every player's driving accuracy,

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Speaker 3: et cetera. And to estimating how much a course favors

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Speaker 3: driving distance, we're basically just gonna compare two players, and

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Speaker 3: this is important, two players who are similar in every

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Speaker 3: dimension except distance.

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Speaker 2: So there's one.

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Speaker 3: Player, say that hits it ten years further than the other.

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Speaker 3: So we're gonna take those two players, compare their stroke

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Speaker 3: scheme in that round, and then you do that many

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Speaker 3: times and eventually you're able to say, Okay, if you

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Speaker 3: hit a ten yards further than somebody, that translates into

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Speaker 3: whatever point three point four strokes gained around over that

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Speaker 3: person holding everything else fixed.

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Speaker 2: And it's right.

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Speaker 1: You know, the visualization makes this very clear because this

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Speaker 1: is an octagon. Basically, right, there are five skills, so

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Speaker 1: there are there are five points on the octagon, and

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Speaker 1: if one point is a little bit farther out than

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Speaker 1: the others, and you can see quite quickly, okay, this

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Speaker 1: this course tends to prefer that skill. And as you

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Speaker 1: were indicating earlier, the most dynamic skills, the ones that

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Speaker 1: seem to vary the most are driving distance and driving

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Speaker 1: accuracy from course to course. The others are fairly constant,

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Speaker 1: though with some significant exceptions. So this is a it's

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Speaker 1: a really interesting tool. I use it all the time.

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Speaker 1: And to be clear, you know, most people probably use

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Speaker 1: this as a predictive tool, right to say, hey, the

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Speaker 1: course this week seems to be a good fit for

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Speaker 1: player X, All go ahead and place a bet on

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Speaker 1: player X. But I'm definitely not like the usual data

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Speaker 1: golf user. I don't think I'm a lot more interested.

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Speaker 1: I don't bet. I'm really risk averse that way. So

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Speaker 1: it's like I've never placed a bet on a golf tournament.

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Speaker 1: But I'm more interested. The reason I look at this

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Speaker 1: stuff is that I'm really interested in considering what something

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Speaker 1: like this can tell us about courses on the PGA

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Speaker 1: Tour and the skills that those courses prioritize. And I

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Speaker 1: know that you know, all this stuff is there's always

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Speaker 1: the caveat that data is noisy, and we can't draw

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Speaker 1: any super firm conclusions about any giving course or any

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Speaker 1: given trend in PGA Tour course design and set up

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Speaker 1: from it. But there are some really interesting things in

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Speaker 1: here that I can't help but think can tell us

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Speaker 1: something about course architecture as it relates to PGA Tour

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Speaker 1: player's performance. So, you know, just to start at one

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Speaker 1: end of the spectrum, what does the course fit tool

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Speaker 1: tell us about Bethpage Black, which is what's the host

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Speaker 1: of the twenty nineteen PGA Championship. It's on one end

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Speaker 1: of the spectrum for this tool.

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Speaker 3: Right, Yeah, so best Page according to our stuff, is

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Speaker 3: it favors driving distance more than any other course that

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Speaker 3: the PJ Tour has played, and I guess.

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Speaker 2: The last five years or so.

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Speaker 3: So yeah, it just means when Bryce in, when any

438
00:21:05,960 --> 00:21:07,720
Speaker 3: player that goes to bed Page flack, and if they

439
00:21:07,800 --> 00:21:10,960
Speaker 3: hit it above average distances, instead of that advantage being

440
00:21:11,000 --> 00:21:13,560
Speaker 3: worth save point four strokes beth Page, it might be

441
00:21:13,600 --> 00:21:15,920
Speaker 3: worth zero point six strokes or point seven strokes for

442
00:21:15,920 --> 00:21:18,360
Speaker 3: a round. So yeah, beth Page is the most extreme

443
00:21:18,400 --> 00:21:21,480
Speaker 3: in that regard, and it's also yeah, pretty below average

444
00:21:21,480 --> 00:21:24,440
Speaker 3: and how much it favors driving accuracy the thing also

445
00:21:24,520 --> 00:21:27,040
Speaker 3: to note with like obviously guys who hit it far,

446
00:21:27,160 --> 00:21:30,200
Speaker 3: there's almost there's partly a mechanical or partially a mechanical

447
00:21:30,200 --> 00:21:32,680
Speaker 3: relationship between distance and accuracy, Like as you hit it further,

448
00:21:32,720 --> 00:21:35,960
Speaker 3: it's just harder to hit more fairways. So there is

449
00:21:36,000 --> 00:21:38,480
Speaker 3: a negative, a strong negative correlation between those two. So

450
00:21:39,240 --> 00:21:41,520
Speaker 3: on our tool, like Bryson, like he ghets at above

451
00:21:41,520 --> 00:21:43,320
Speaker 3: average distances, so we're getting it, he's getting a big

452
00:21:43,359 --> 00:21:46,240
Speaker 3: bump for that, But he also is less accurate than average,

453
00:21:46,359 --> 00:21:48,080
Speaker 3: so he's getting a and he's getting a bump for

454
00:21:48,119 --> 00:21:50,600
Speaker 3: that as well at beth Page, because essentially you can

455
00:21:50,680 --> 00:21:55,080
Speaker 3: read it as beth Page penalized accuracy less than the average.

456
00:21:54,800 --> 00:21:56,720
Speaker 2: Was, which is not a great way to think about it, maybe.

457
00:21:56,520 --> 00:21:59,399
Speaker 3: Because obviously there was a high penalty to miss fairway

458
00:21:59,400 --> 00:22:01,639
Speaker 3: at beth Page, but all the tool is saying is

459
00:22:01,680 --> 00:22:04,720
Speaker 3: that the benefit of being a PGA to a golfer

460
00:22:04,720 --> 00:22:08,000
Speaker 3: who is five percent more accurate than average, that advantage

461
00:22:08,040 --> 00:22:10,440
Speaker 3: was lessened at that page for whatever reason.

462
00:22:10,720 --> 00:22:12,119
Speaker 2: We can speculate on that.

463
00:22:13,480 --> 00:22:16,719
Speaker 1: Yeah, I know you don't love speculating about this kind

464
00:22:16,760 --> 00:22:18,800
Speaker 1: of stuff. I might try to push you out of

465
00:22:18,800 --> 00:22:20,119
Speaker 1: your comfort zone a few times.

466
00:22:21,000 --> 00:22:21,159
Speaker 2: You know.

467
00:22:21,240 --> 00:22:24,639
Speaker 1: Part of what makes this suggestion by the course fit

468
00:22:24,720 --> 00:22:29,080
Speaker 1: tool about what skills Bethpage Black prioritizes, what makes it

469
00:22:29,119 --> 00:22:32,760
Speaker 1: compelling is that it's kind of counterintuitive. You know. You

470
00:22:32,800 --> 00:22:35,480
Speaker 1: look at beth Page as it was set up for

471
00:22:35,520 --> 00:22:39,120
Speaker 1: the twenty nineteen PGA Championship, and you see really high

472
00:22:39,200 --> 00:22:43,760
Speaker 1: rough you see really narrow fairways, and the assumption that

473
00:22:43,800 --> 00:22:47,520
Speaker 1: most people would make is that that emphasizes accuracy. You

474
00:22:47,600 --> 00:22:50,199
Speaker 1: have to hit the fairway or you're in trouble. But

475
00:22:50,320 --> 00:22:54,040
Speaker 1: in fact, it seemed to be the opposite, where longer

476
00:22:54,080 --> 00:22:57,280
Speaker 1: players had a distinct advantage. And obviously we can't draw

477
00:22:57,600 --> 00:23:00,760
Speaker 1: strong conclusions from one or two particular results, but it

478
00:23:00,880 --> 00:23:03,880
Speaker 1: seemed like it really seemed like Dustin Johnson and Brooks

479
00:23:03,920 --> 00:23:05,960
Speaker 1: Kopka were the only players who had any chance of

480
00:23:05,960 --> 00:23:10,040
Speaker 1: winning that week, and so is that something that caught

481
00:23:10,040 --> 00:23:12,480
Speaker 1: your eye as well, that that there was a kind

482
00:23:12,520 --> 00:23:15,480
Speaker 1: of counterintuitive result from the course fit tool based on

483
00:23:15,520 --> 00:23:17,399
Speaker 1: what you would assume out of a course that was

484
00:23:17,440 --> 00:23:18,960
Speaker 1: set up the way Beth Page was.

485
00:23:19,560 --> 00:23:21,400
Speaker 3: Well, first, I would say the results I think are

486
00:23:21,880 --> 00:23:24,840
Speaker 3: this is definitely something that's extreme. Like there's certainly a

487
00:23:24,840 --> 00:23:27,000
Speaker 3: lot of signal there. There's no doubt that the I

488
00:23:27,080 --> 00:23:30,040
Speaker 3: think probably there's three events in here, maybe because there's

489
00:23:30,040 --> 00:23:32,320
Speaker 3: the maybe yeah, the two US opens and then that

490
00:23:32,400 --> 00:23:32,880
Speaker 3: page had.

491
00:23:32,760 --> 00:23:35,400
Speaker 1: A that's right, it's not not just the PGA Championship.

492
00:23:35,440 --> 00:23:38,040
Speaker 1: Beth Page also had a Northern Trust or something. Yeah,

493
00:23:38,320 --> 00:23:38,919
Speaker 1: so there is.

494
00:23:39,000 --> 00:23:41,600
Speaker 3: Enough data to say to say things, and yeah, I

495
00:23:41,600 --> 00:23:43,680
Speaker 3: think it was I think it was pretty surprising. Like generally,

496
00:23:43,720 --> 00:23:47,000
Speaker 3: when you do things like this that are somewhat complicated statistically,

497
00:23:47,040 --> 00:23:49,880
Speaker 3: you want like eighty five percent of your results to

498
00:23:49,880 --> 00:23:51,960
Speaker 3: match up with intuition, so then you're like, okay, I'm

499
00:23:51,960 --> 00:23:54,000
Speaker 3: not doing something completely insane here, and then you have

500
00:23:54,080 --> 00:23:56,560
Speaker 3: fifteen or twenty percent where it's like, oh, that's like

501
00:23:56,600 --> 00:23:57,880
Speaker 3: that's countertuitive and interesting.

502
00:23:58,000 --> 00:23:59,919
Speaker 2: So yeah, so Beth Page, it.

503
00:24:00,560 --> 00:24:02,960
Speaker 3: Does go this way where well, so just to bring

504
00:24:03,000 --> 00:24:07,040
Speaker 3: up another example that didn't go this way. Firestone has

505
00:24:06,520 --> 00:24:08,960
Speaker 3: fire Stones, not beth Page. But it's a long course

506
00:24:08,960 --> 00:24:11,760
Speaker 3: that has narrow fairways with reasonably long rough and it

507
00:24:11,840 --> 00:24:15,920
Speaker 3: has it favors distance, but it also favors accuracy, which

508
00:24:16,000 --> 00:24:16,480
Speaker 3: is interesting.

509
00:24:16,600 --> 00:24:19,879
Speaker 2: That what that just to reiterate what I said earlier.

510
00:24:19,640 --> 00:24:23,439
Speaker 3: Like that means Firestone it's above average in terms of

511
00:24:23,440 --> 00:24:26,400
Speaker 3: how much it favors distance, holding accuracy constant, but then

512
00:24:26,440 --> 00:24:29,000
Speaker 3: also holding distance constant constant.

513
00:24:29,040 --> 00:24:30,679
Speaker 2: It's still important to hit fairways.

514
00:24:30,720 --> 00:24:33,600
Speaker 3: There's an above average benefit at Firestone for that as well.

515
00:24:33,640 --> 00:24:36,159
Speaker 3: So I bring that up just because that to me

516
00:24:36,320 --> 00:24:38,280
Speaker 3: is like more yeah, more into if I would have

517
00:24:38,280 --> 00:24:40,159
Speaker 3: thought beth Page would have been like that. So I

518
00:24:40,200 --> 00:24:43,320
Speaker 3: don't know what's different about beth Page. Maybe because it

519
00:24:43,400 --> 00:24:46,399
Speaker 3: was so it was so long, like beth Pages, it

520
00:24:46,440 --> 00:24:48,560
Speaker 3: was an extreme course like g it's longer than Firestone.

521
00:24:49,119 --> 00:24:53,160
Speaker 1: Right. Another thing to mention about beth Page versus Firestone

522
00:24:53,760 --> 00:24:56,800
Speaker 1: is that beth Page has you know, a lot more

523
00:24:57,000 --> 00:24:59,600
Speaker 1: movement in its land and the greens are sort of

524
00:24:59,600 --> 00:25:03,600
Speaker 1: sustained actually elevated, which would seem to you know, give

525
00:25:03,640 --> 00:25:06,480
Speaker 1: a reward to players who end up closer to the

526
00:25:06,520 --> 00:25:08,520
Speaker 1: green after their first shot on a on a par

527
00:25:08,640 --> 00:25:10,800
Speaker 1: four or par five, because you have to get the

528
00:25:10,840 --> 00:25:13,000
Speaker 1: ball all the way to the green. But I don't know,

529
00:25:13,040 --> 00:25:16,320
Speaker 1: you know, there's more ground contour at Firestone than people

530
00:25:16,359 --> 00:25:18,560
Speaker 1: give it credit for. And I'm not sure you can

531
00:25:18,600 --> 00:25:21,920
Speaker 1: exactly run shots up at Firestone necessarily, but there might

532
00:25:21,960 --> 00:25:23,960
Speaker 1: be might be something there, And I mean, I guess

533
00:25:23,960 --> 00:25:25,639
Speaker 1: these are these are factors. This is where we get

534
00:25:25,680 --> 00:25:26,879
Speaker 1: into speculation.

535
00:25:26,520 --> 00:25:29,400
Speaker 3: Right, Yeah, No, I think it's important to keep in mind,

536
00:25:29,400 --> 00:25:32,239
Speaker 3: like driving distance is not just when we're looking at

537
00:25:32,240 --> 00:25:33,880
Speaker 3: a player who bombs it. It's true that he hits

538
00:25:33,880 --> 00:25:35,640
Speaker 3: it further off at team, but then it's also true

539
00:25:35,640 --> 00:25:38,280
Speaker 3: that it's just a powerful player in general. So from

540
00:25:38,320 --> 00:25:40,520
Speaker 3: the rough he's hitting wedges, and like you can think

541
00:25:40,520 --> 00:25:43,280
Speaker 3: about with driving distance as an app as a skill

542
00:25:43,400 --> 00:25:44,560
Speaker 3: is going to be beneficial not.

543
00:25:44,520 --> 00:25:46,800
Speaker 2: Just on t shots but also on approach shots.

544
00:25:46,840 --> 00:25:48,840
Speaker 3: So yeah, that's and at that stage maybe that was

545
00:25:49,160 --> 00:25:50,480
Speaker 3: playing a role as well.

546
00:25:51,000 --> 00:25:53,800
Speaker 1: That's a great point. So on on the other end

547
00:25:53,840 --> 00:25:56,760
Speaker 1: of the spectrum, we have this week's PGA Tour revenue

548
00:25:56,920 --> 00:26:00,280
Speaker 1: El Kama Leone Golf Club, host of the Mya cob

549
00:26:00,320 --> 00:26:03,240
Speaker 1: A Golf Classic. This is on the other end of

550
00:26:03,280 --> 00:26:07,280
Speaker 1: the spectrum from Bethpage. Black tell me about the craziness

551
00:26:07,480 --> 00:26:10,040
Speaker 1: of the data that this course produces.

552
00:26:10,600 --> 00:26:13,119
Speaker 3: Not only is it the most extreme for in how

553
00:26:13,200 --> 00:26:16,680
Speaker 3: much it rewards driving accuracy, it's also just of any

554
00:26:16,720 --> 00:26:19,440
Speaker 3: attribute because again on the default view on the course

555
00:26:19,440 --> 00:26:23,000
Speaker 3: fit stuff, you can compare across attributes, like you can

556
00:26:23,040 --> 00:26:25,280
Speaker 3: say if the further out of dot is that means

557
00:26:25,320 --> 00:26:28,159
Speaker 3: just in an absolute sense, that skill is getting rewarded

558
00:26:28,160 --> 00:26:30,840
Speaker 3: more strokes gain than the other dots. So so driving

559
00:26:30,880 --> 00:26:34,920
Speaker 3: accuracy at Alchemeleone is actually rewarded as much as driving.

560
00:26:34,640 --> 00:26:36,119
Speaker 2: Distances at beth Page.

561
00:26:36,160 --> 00:26:38,199
Speaker 3: And the reason it's not that's notable is because at

562
00:26:38,200 --> 00:26:41,840
Speaker 3: the average course accuracy is rewarded less and distance, So

563
00:26:42,000 --> 00:26:44,760
Speaker 3: that means at al chime Leone it's more of an

564
00:26:44,800 --> 00:26:46,359
Speaker 3: outlier even than beth Page.

565
00:26:46,800 --> 00:26:47,720
Speaker 2: Yeah, it's pretty crazy.

566
00:26:47,720 --> 00:26:49,480
Speaker 3: And like I know, when we looking at like our

567
00:26:49,520 --> 00:26:52,399
Speaker 3: predictive model stuff, when we're actually calculating how much we're

568
00:26:52,440 --> 00:26:54,840
Speaker 3: going to adjust players skill levels, because that's how we

569
00:26:54,880 --> 00:26:57,199
Speaker 3: actually put to use the course fits stuff. We basically

570
00:26:57,280 --> 00:26:59,960
Speaker 3: have a player's baseline and then we're going to say, okay,

571
00:27:00,320 --> 00:27:01,920
Speaker 3: based off course fit we're gonna move off of that

572
00:27:01,960 --> 00:27:03,119
Speaker 3: baseline by whatever.

573
00:27:02,880 --> 00:27:04,320
Speaker 2: Point two strokes or something.

574
00:27:04,560 --> 00:27:07,480
Speaker 3: At alchemillion, there's adjustments that are like a stroke, which

575
00:27:07,520 --> 00:27:10,520
Speaker 3: is insane. So we're moving a player's skill level a shot.

576
00:27:10,560 --> 00:27:12,600
Speaker 3: I mean, that's like the most extreme guys. But just

577
00:27:12,640 --> 00:27:16,240
Speaker 3: for context, that's the difference between like the fiftieth ranked

578
00:27:16,240 --> 00:27:18,639
Speaker 3: player in the world in our rankings and like maybe

579
00:27:18,640 --> 00:27:20,560
Speaker 3: the two hundredth or something like that. Like, it's a

580
00:27:20,640 --> 00:27:23,560
Speaker 3: huge I got one shot around is obviously a massive difference.

581
00:27:23,560 --> 00:27:26,399
Speaker 3: So that to be driven by course fit is pretty crazy.

582
00:27:26,440 --> 00:27:29,080
Speaker 3: So yeah, this this week's course is the biggest outlier.

583
00:27:29,640 --> 00:27:32,720
Speaker 1: It exerts a huge influence on the results in a

584
00:27:33,119 --> 00:27:35,280
Speaker 1: certain way. And I guess one way to put it

585
00:27:35,320 --> 00:27:38,720
Speaker 1: is that if you have a clear best player in

586
00:27:38,760 --> 00:27:41,680
Speaker 1: the field, you know, somebody whose past performance is really

587
00:27:41,680 --> 00:27:44,439
Speaker 1: really awesome, a lot better than everybody else in the field,

588
00:27:44,880 --> 00:27:48,560
Speaker 1: going into the Maacoba Golf Classic, they are a little

589
00:27:48,560 --> 00:27:52,560
Speaker 1: bit less likely to win there than usual. Would that

590
00:27:52,600 --> 00:27:53,320
Speaker 1: be fair to say?

591
00:27:53,840 --> 00:27:55,360
Speaker 3: It would be fair to say as long as they

592
00:27:55,359 --> 00:27:59,600
Speaker 3: possess the typical top ranked player skill set Brendan Todd

593
00:27:59,600 --> 00:28:01,639
Speaker 3: ever to be the best player in the world, then

594
00:28:02,000 --> 00:28:03,879
Speaker 3: we might actually predict him to be better.

595
00:28:03,880 --> 00:28:05,240
Speaker 2: But in general, in general, Yeah.

596
00:28:05,119 --> 00:28:07,840
Speaker 3: Your statement is true, Like this week, the skilled attribution

597
00:28:08,000 --> 00:28:10,200
Speaker 3: is going to be compressed just because it tends to

598
00:28:10,200 --> 00:28:13,400
Speaker 3: be the case that the best players do hit it far.

599
00:28:13,640 --> 00:28:15,920
Speaker 3: And so the best players on average will be getting

600
00:28:15,920 --> 00:28:18,760
Speaker 3: a negative bump this week, and worst players on average

601
00:28:18,760 --> 00:28:19,680
Speaker 3: will be getting a positive.

602
00:28:19,800 --> 00:28:20,960
Speaker 2: Yeah, bringing everybody together.

603
00:28:21,240 --> 00:28:23,240
Speaker 1: So I guess a way to put is, if Rory

604
00:28:23,280 --> 00:28:28,480
Speaker 1: were playing this week, then you would give him less

605
00:28:28,560 --> 00:28:31,879
Speaker 1: of a positive positive adjustment than usual. You would you

606
00:28:31,880 --> 00:28:34,720
Speaker 1: would be less strong on the idea that he might

607
00:28:34,760 --> 00:28:37,600
Speaker 1: win than you usually would be at the at the

608
00:28:37,680 --> 00:28:39,160
Speaker 1: average PGA tour venue.

609
00:28:39,240 --> 00:28:40,040
Speaker 2: That's the right way to say.

610
00:28:40,120 --> 00:28:42,880
Speaker 3: Yeah, I would probably say relative to his baseline, like

611
00:28:42,960 --> 00:28:45,160
Speaker 3: it's average still level, we're giving him a negative bump.

612
00:28:45,160 --> 00:28:47,040
Speaker 3: But what you're saying makes makes sense to It's we're

613
00:28:47,080 --> 00:28:49,080
Speaker 3: still we still like Rory. We just like him less

614
00:28:49,120 --> 00:28:49,560
Speaker 3: than normal.

615
00:28:49,680 --> 00:28:51,200
Speaker 1: He's still going to be good. It's not like he's

616
00:28:51,200 --> 00:28:53,280
Speaker 1: all of a sudden, you know, yeah, the worst player

617
00:28:53,320 --> 00:28:56,040
Speaker 1: in the field or something. Yeah, I mean I find

618
00:28:56,040 --> 00:28:58,920
Speaker 1: that interesting. Does that mean does that mean that there's

619
00:28:58,960 --> 00:29:01,360
Speaker 1: just that randomness kind of plays more of a role

620
00:29:01,600 --> 00:29:05,000
Speaker 1: at a venue like this week's I.

621
00:29:04,960 --> 00:29:06,920
Speaker 3: Wouldn't say I wouldn't say that there are some courses

622
00:29:06,920 --> 00:29:08,480
Speaker 3: where that does seem to be true, but I think

623
00:29:08,520 --> 00:29:10,680
Speaker 3: it's just the issue is just when I when we

624
00:29:10,720 --> 00:29:13,040
Speaker 3: say high skill gear, what we mean like high skill

625
00:29:13,040 --> 00:29:15,000
Speaker 3: on the PGA Tour these days, that generally means you

626
00:29:15,080 --> 00:29:18,120
Speaker 3: hit it far, just because that's the way that skills

627
00:29:18,160 --> 00:29:21,360
Speaker 3: are rewarded. So when you go to Camillone, it's true

628
00:29:21,400 --> 00:29:24,200
Speaker 3: that higher skilled players are getting a negative bump, but

629
00:29:24,240 --> 00:29:26,320
Speaker 3: it's it's not necessarily due to randomness. It's just because

630
00:29:26,440 --> 00:29:29,160
Speaker 3: driving accuracy is now way more important because if you

631
00:29:29,160 --> 00:29:32,080
Speaker 3: miss a fairway at that course, you're generally taking a drop.

632
00:29:32,160 --> 00:29:33,840
Speaker 2: So yeah, I wouldn't say it's randomous.

633
00:29:33,880 --> 00:29:35,920
Speaker 3: I would say it's it's a different skill set that's

634
00:29:35,960 --> 00:29:39,760
Speaker 3: been tested, and so that brings the skill distribution relative

635
00:29:39,800 --> 00:29:42,000
Speaker 3: to like the typical tour event closer together.

636
00:29:43,160 --> 00:29:45,000
Speaker 1: You mentioned what I think is one of the key

637
00:29:45,040 --> 00:29:47,280
Speaker 1: factors here, and that's that if you miss a fair

638
00:29:47,320 --> 00:29:51,280
Speaker 1: way at this course, then you're basically in the bush,

639
00:29:51,360 --> 00:29:54,720
Speaker 1: like it's a lost ball, and so there's a really

640
00:29:54,720 --> 00:29:58,240
Speaker 1: heavy penalty for missing fairways. But at the same time,

641
00:29:58,480 --> 00:30:01,240
Speaker 1: you know, I think Sometimes people get confused here because

642
00:30:01,280 --> 00:30:04,760
Speaker 1: then they say, well, doesn't that mean that penal setups

643
00:30:04,800 --> 00:30:07,600
Speaker 1: where there's where there's a clear kind of immedia penalty

644
00:30:07,600 --> 00:30:10,440
Speaker 1: for missing a fairway, doesn't that mean that those would

645
00:30:10,520 --> 00:30:14,080
Speaker 1: favor accuracy above all else. But of course we just

646
00:30:14,080 --> 00:30:17,320
Speaker 1: talked about Beth Page, where there was a real penalty

647
00:30:17,400 --> 00:30:21,880
Speaker 1: for missing a fairway, but distance tended to be more

648
00:30:21,880 --> 00:30:25,560
Speaker 1: predictive there. And so how would you reconcile those two?

649
00:30:25,600 --> 00:30:28,640
Speaker 1: Is it because they're like different levels of penalty, right

650
00:30:28,640 --> 00:30:31,840
Speaker 1: that you know that taking a drop is a lot

651
00:30:31,840 --> 00:30:33,280
Speaker 1: different from hitting it out of the rough.

652
00:30:33,800 --> 00:30:35,320
Speaker 3: It could be that it could just be a matter

653
00:30:35,320 --> 00:30:37,240
Speaker 3: of degree, although I think it's more than that. I

654
00:30:37,240 --> 00:30:39,160
Speaker 3: think it's I mean, maybe it's also has to do

655
00:30:39,200 --> 00:30:41,760
Speaker 3: with the fact that at death Page, maybe it didn't

656
00:30:41,760 --> 00:30:43,120
Speaker 3: matter so much if you were five years off the

657
00:30:43,160 --> 00:30:45,000
Speaker 3: fairway or if you were twenty yards off. I don't

658
00:30:45,000 --> 00:30:46,600
Speaker 3: even I don't know if that statement is true. It

659
00:30:46,640 --> 00:30:49,640
Speaker 3: still matters a bit, but alchimately, oh maybe it matters

660
00:30:49,760 --> 00:30:51,960
Speaker 3: a bit more like the fact that you're two yards

661
00:30:52,000 --> 00:30:53,560
Speaker 3: off the fairway. I don't know how many yards you

662
00:30:53,560 --> 00:30:55,600
Speaker 3: have to deal with there, two yards or three yards,

663
00:30:55,600 --> 00:30:56,440
Speaker 3: and then you're in the junk.

664
00:30:56,640 --> 00:30:59,000
Speaker 2: Maybe that that's part of it, also part of the

665
00:30:59,080 --> 00:30:59,600
Speaker 2: people what we were.

666
00:30:59,520 --> 00:31:01,960
Speaker 3: Saying earlier, where at best page, when you miss a fairway,

667
00:31:02,560 --> 00:31:05,040
Speaker 3: you are hacking it out of deep rough where bombers

668
00:31:05,080 --> 00:31:08,680
Speaker 3: still have an advantage, where whereas at UG mileone you're

669
00:31:09,040 --> 00:31:11,520
Speaker 3: you're just dropping it in pretty light rough. I don't

670
00:31:11,560 --> 00:31:14,080
Speaker 3: there's not sick rough at this course, so maybe that

671
00:31:14,160 --> 00:31:17,480
Speaker 3: advantage is gone for distance, But a lot of it's

672
00:31:17,480 --> 00:31:19,800
Speaker 3: got to just be the fact that this week's course

673
00:31:19,840 --> 00:31:20,240
Speaker 3: is shorter.

674
00:31:21,080 --> 00:31:23,280
Speaker 1: Yeah, I mean, the causes are really hard to identify,

675
00:31:23,400 --> 00:31:25,560
Speaker 1: but you know, I want to expand this out a

676
00:31:25,560 --> 00:31:29,680
Speaker 1: little bit, Matt, that el kamal Leone is part of

677
00:31:29,720 --> 00:31:33,400
Speaker 1: a set of courses on the PGA Tour that I've

678
00:31:33,440 --> 00:31:36,240
Speaker 1: kind of discovered through the course fit tool on your

679
00:31:36,240 --> 00:31:40,160
Speaker 1: website that I like to call the web Tour Web

680
00:31:40,200 --> 00:31:42,960
Speaker 1: with two b's. You know, they're just courses where web

681
00:31:43,000 --> 00:31:46,840
Speaker 1: Simpson seems to seems to perform particularly well, and players

682
00:31:46,840 --> 00:31:50,120
Speaker 1: of Web Simson's ILK as in, you know, really good

683
00:31:50,200 --> 00:31:53,680
Speaker 1: in every skill except for driving distance. You may also

684
00:31:53,720 --> 00:31:56,000
Speaker 1: say that Brendan Todd is kind of along this model

685
00:31:56,000 --> 00:31:57,720
Speaker 1: as well, at least within the past year and a

686
00:31:57,760 --> 00:32:01,800
Speaker 1: half or so. So in addition to El kamala ayone,

687
00:32:01,880 --> 00:32:06,440
Speaker 1: you have Harbor Town Golf Links, you have Wyley Country Club,

688
00:32:06,560 --> 00:32:10,480
Speaker 1: you have TPC, Sawgrass, you have Colonial, and then maybe

689
00:32:10,480 --> 00:32:15,600
Speaker 1: to a slightly lesser degree, you have Sedgefield Innisbrook, Copperhead,

690
00:32:15,800 --> 00:32:19,000
Speaker 1: the side of the Valspar Championship. Sedgefield is the side

691
00:32:19,040 --> 00:32:23,040
Speaker 1: of the Windham Championship. To an extent, Sea Island, which

692
00:32:23,160 --> 00:32:27,000
Speaker 1: was last week's venue for the RSM Classic, maybe Sherwood,

693
00:32:27,360 --> 00:32:32,000
Speaker 1: one time host of the Zozo this year, maybe Merefield Village.

694
00:32:32,080 --> 00:32:34,240
Speaker 1: I mean those kind of got less and less a

695
00:32:34,320 --> 00:32:36,800
Speaker 1: part of the Web tour as I went along, but

696
00:32:36,920 --> 00:32:42,200
Speaker 1: especially that kind of top group of courses Harbor Town, Wyley, TPC, Sawgrass, Colonial,

697
00:32:42,560 --> 00:32:45,920
Speaker 1: El kamala Ayone. At all of these courses you see

698
00:32:45,960 --> 00:32:51,160
Speaker 1: an unusual emphasis on driving accuracy and an unusual d

699
00:32:51,360 --> 00:32:55,600
Speaker 1: emphasis on driving distance, as in the skill of driving

700
00:32:55,680 --> 00:32:59,440
Speaker 1: distance is less predictive of success at these courses than

701
00:32:59,560 --> 00:33:02,360
Speaker 1: average on the PGA tour, and the skill of driving

702
00:33:02,560 --> 00:33:06,120
Speaker 1: accuracy tends to be more predictive. El kama aone is

703
00:33:06,200 --> 00:33:10,080
Speaker 1: just the most extreme example of this. But have you

704
00:33:10,200 --> 00:33:13,520
Speaker 1: noticed the Web tour before. Have you noticed this class

705
00:33:13,560 --> 00:33:16,080
Speaker 1: of courses on the PGA tour and if so, what

706
00:33:16,120 --> 00:33:17,160
Speaker 1: do you what do you make of it.

707
00:33:17,840 --> 00:33:20,040
Speaker 3: I'm not gonna say I would have come across this

708
00:33:20,080 --> 00:33:23,120
Speaker 3: cluster of courses before looking at before doing this analysis,

709
00:33:23,120 --> 00:33:25,520
Speaker 3: although at the same time I think it does. It

710
00:33:25,520 --> 00:33:27,240
Speaker 3: does make sense. Like I think most of these courses

711
00:33:27,280 --> 00:33:30,400
Speaker 3: are are shorter, and I mean they're also there are

712
00:33:30,400 --> 00:33:32,240
Speaker 3: also differently a few of them. I think of Sedgefield

713
00:33:32,280 --> 00:33:34,920
Speaker 3: and east Lake is also one that rewards accuracy more

714
00:33:34,920 --> 00:33:37,920
Speaker 3: like those are pretty like Eastlake is pretty brutal rough Sedgfield.

715
00:33:38,080 --> 00:33:40,440
Speaker 3: I'm not sure exactly why it favors. It's just it's

716
00:33:40,520 --> 00:33:43,960
Speaker 3: a shorter course rough pretty tough there. But I mean again,

717
00:33:44,000 --> 00:33:46,000
Speaker 3: but as we have at best day, it's not necessarily

718
00:33:46,000 --> 00:33:48,600
Speaker 3: a simple relationship like thicker rough does not necessarily mean

719
00:33:48,640 --> 00:33:52,280
Speaker 3: it favors favors accuracy, although I do think the combination

720
00:33:52,360 --> 00:33:55,200
Speaker 3: of thick rough with a shorter course maybe does. It

721
00:33:55,280 --> 00:33:57,240
Speaker 3: might just be the interaction of really thick rough with

722
00:33:57,560 --> 00:33:59,720
Speaker 3: a really long course that gets you that effect where

723
00:33:59,720 --> 00:34:03,400
Speaker 3: it distance but not accuracy. Yeah, And Harbordtown, I think

724
00:34:03,480 --> 00:34:05,280
Speaker 3: is just the course where it just takes driver out

725
00:34:05,280 --> 00:34:07,760
Speaker 3: of your hands on a lot of holes, which which

726
00:34:07,800 --> 00:34:08,359
Speaker 3: is fine.

727
00:34:08,400 --> 00:34:10,760
Speaker 2: I mean, I think it's honestly what I thought after

728
00:34:10,800 --> 00:34:11,680
Speaker 2: the when.

729
00:34:11,560 --> 00:34:14,080
Speaker 3: Golf came back after the three month COVID break and

730
00:34:14,120 --> 00:34:16,480
Speaker 3: Bryson was doing his thing and getting all that attention, Like,

731
00:34:16,960 --> 00:34:19,200
Speaker 3: I was kind of struck by how many courses It

732
00:34:19,239 --> 00:34:21,319
Speaker 3: seemed like the first few weeks he was playing, he

733
00:34:21,360 --> 00:34:24,239
Speaker 3: was playing golf courses that didn't really favor bombers that much.

734
00:34:24,280 --> 00:34:25,320
Speaker 2: Like I was sort of struck by it.

735
00:34:25,360 --> 00:34:28,000
Speaker 3: I was thinking, Yeah, like, obviously we all know distance,

736
00:34:28,040 --> 00:34:29,640
Speaker 3: like the best players in the game bombit, and it's

737
00:34:29,680 --> 00:34:30,760
Speaker 3: clearly a huge advantage.

738
00:34:30,760 --> 00:34:33,600
Speaker 2: But there are still courses on the PGA Tour, like the.

739
00:34:33,600 --> 00:34:36,719
Speaker 3: Travelers River Highlands that it's not that it's still it's

740
00:34:36,719 --> 00:34:38,440
Speaker 3: always an advantage to hit it far, but they are

741
00:34:38,480 --> 00:34:41,839
Speaker 3: there are courses on tour that, yeah, favor accuracy still,

742
00:34:41,880 --> 00:34:43,560
Speaker 3: so that I remember being struck by that when the

743
00:34:43,600 --> 00:34:44,320
Speaker 3: golf came back.

744
00:34:44,560 --> 00:34:48,719
Speaker 1: Yeah, and Detroit Golf Club where Bryson won, is not

745
00:34:48,800 --> 00:34:54,160
Speaker 1: a course that necessarily strongly prefers distance in the way

746
00:34:54,160 --> 00:34:56,400
Speaker 1: that we see a lot of PGA Tour courses do.

747
00:34:56,719 --> 00:34:57,560
Speaker 1: Am I right about that?

748
00:34:58,160 --> 00:35:01,480
Speaker 3: Yeah, I mean, according to tool, that's true. I feel

749
00:35:01,520 --> 00:35:04,080
Speaker 3: like I've heard people say, I mean, I'm not sure

750
00:35:04,120 --> 00:35:06,200
Speaker 3: who I should trust. I guess I've heard people say,

751
00:35:06,560 --> 00:35:08,799
Speaker 3: like I think I've heard people say before that event that, oh,

752
00:35:08,800 --> 00:35:11,640
Speaker 3: Bryson's gonna make a mockery of this, of this layout.

753
00:35:12,040 --> 00:35:13,840
Speaker 2: And I don't think I watched a shot at that event,

754
00:35:13,880 --> 00:35:14,160
Speaker 2: so I.

755
00:35:14,120 --> 00:35:18,840
Speaker 1: Can't The most exciting thing that happened was Bryson yelling

756
00:35:18,840 --> 00:35:21,360
Speaker 1: at a cameraman. But I think what people were saying

757
00:35:21,400 --> 00:35:23,560
Speaker 1: is that he's going to bombit past the fairway bunkers

758
00:35:23,600 --> 00:35:26,359
Speaker 1: that were put in during the renovation that were put

759
00:35:26,360 --> 00:35:29,319
Speaker 1: out there to kind of, like, you know, give give

760
00:35:29,360 --> 00:35:32,040
Speaker 1: tour players something to think about, and he just was

761
00:35:32,080 --> 00:35:34,759
Speaker 1: longer than them. I don't know. I mean, there's a

762
00:35:34,800 --> 00:35:37,640
Speaker 1: lot of these factors that tend to come into play

763
00:35:37,800 --> 00:35:40,080
Speaker 1: at these courses, and obviously a player like Bryson can

764
00:35:40,200 --> 00:35:42,920
Speaker 1: do well anywhere, you know, he's he's got other skills.

765
00:35:43,480 --> 00:35:47,240
Speaker 1: But you mentioned some of the characteristics that these courses

766
00:35:47,280 --> 00:35:51,600
Speaker 1: have in common. These courses that actually succeed in prioritizing

767
00:35:51,680 --> 00:35:55,200
Speaker 1: driving accuracy, and that's something that so many people are

768
00:35:55,239 --> 00:35:58,120
Speaker 1: interested in seeing come back to the PGA Tour in

769
00:35:58,160 --> 00:36:01,799
Speaker 1: a bigger way, you know, more of an emphasis on

770
00:36:01,880 --> 00:36:04,080
Speaker 1: driving accuracy. They're they're trying to figure out how in

771
00:36:04,080 --> 00:36:06,040
Speaker 1: the heck do we do this? Do we roll back equipment,

772
00:36:06,120 --> 00:36:08,920
Speaker 1: do we do things to courses? But it seems to

773
00:36:08,960 --> 00:36:11,160
Speaker 1: me that this set of courses gives a pretty good

774
00:36:11,160 --> 00:36:15,520
Speaker 1: example of how to do that. But it doesn't necessarily

775
00:36:15,560 --> 00:36:19,200
Speaker 1: explain why these courses behave in this way, you know,

776
00:36:19,440 --> 00:36:22,560
Speaker 1: super clearly. But one thing that I see in common

777
00:36:23,120 --> 00:36:25,760
Speaker 1: is just course length. A lot of these are shorter courses.

778
00:36:26,200 --> 00:36:28,960
Speaker 1: So do you think that's like a strong factor.

779
00:36:29,640 --> 00:36:31,680
Speaker 3: Yeah, a lot of times you have we can, we can,

780
00:36:31,719 --> 00:36:33,760
Speaker 3: we can come up with these more complex theories about

781
00:36:33,760 --> 00:36:37,000
Speaker 3: how courses will favor certain skill sets. But yeah, often

782
00:36:37,040 --> 00:36:38,799
Speaker 3: those things are hard to like tease out of the data.

783
00:36:38,800 --> 00:36:40,920
Speaker 3: But yeah, something like course length. I think it's pretty

784
00:36:40,960 --> 00:36:45,640
Speaker 3: clear in the data that longer courses, shocking, yeah, favor

785
00:36:45,719 --> 00:36:48,480
Speaker 3: driving distance, and and yeah, these shorter courses, I mean,

786
00:36:48,920 --> 00:36:51,600
Speaker 3: I don't know that people would be particularly happy with

787
00:36:51,640 --> 00:36:54,239
Speaker 3: this set of courses, being like if this is the

788
00:36:54,239 --> 00:36:56,359
Speaker 3: template that it's like, oh, yeah, these are you want this,

789
00:36:56,719 --> 00:36:59,320
Speaker 3: these skills to be rewarded in this way, like, Okay,

790
00:37:00,320 --> 00:37:00,839
Speaker 3: that's your course.

791
00:37:00,840 --> 00:37:03,160
Speaker 2: It's like, I don't know if people because because Sawgas is.

792
00:37:03,160 --> 00:37:05,360
Speaker 3: Sort of a some of these courses I think of

793
00:37:05,400 --> 00:37:09,279
Speaker 3: as kind of random or not not rewarding skill that much.

794
00:37:09,320 --> 00:37:11,600
Speaker 3: But that might also be like a bias because we're

795
00:37:11,680 --> 00:37:14,200
Speaker 3: used to seeing we're just not like Rory won't play

796
00:37:14,239 --> 00:37:15,680
Speaker 3: as well at that course, and we're used to thinking

797
00:37:15,719 --> 00:37:16,960
Speaker 3: of Rory as the best player in the world.

798
00:37:17,040 --> 00:37:18,480
Speaker 2: So you have to like.

799
00:37:18,600 --> 00:37:20,960
Speaker 3: Maybe come to terms with the reason Rory's the best

800
00:37:20,960 --> 00:37:23,360
Speaker 3: play in the world is because his language has an advantage.

801
00:37:24,160 --> 00:37:26,480
Speaker 2: But the one course, and I'm surprised.

802
00:37:26,960 --> 00:37:28,839
Speaker 3: I'm sure that Friday did write some articles about it

803
00:37:29,200 --> 00:37:31,480
Speaker 3: the course a few weeks back where it was short

804
00:37:31,520 --> 00:37:33,160
Speaker 3: and it was it played incredibly tough.

805
00:37:33,719 --> 00:37:38,160
Speaker 2: The Houston Open, the Sure Memorial Park, Memorial Park, Yeah, I'll.

806
00:37:38,040 --> 00:37:41,600
Speaker 1: Be I'll be fascinated to see how that comes out.

807
00:37:41,800 --> 00:37:44,120
Speaker 3: That wasn't particularly penal off the tee, right, that was

808
00:37:44,200 --> 00:37:46,080
Speaker 3: just it was around the greens that were giving people

809
00:37:46,280 --> 00:37:46,960
Speaker 3: a lot of trouble.

810
00:37:47,360 --> 00:37:49,680
Speaker 1: Very much around the greens. Yeah, I mean, I think

811
00:37:49,719 --> 00:37:51,719
Speaker 1: that the more tournaments that are played there, the better

812
00:37:51,800 --> 00:37:55,400
Speaker 1: of an idea we're going to get for it. But certainly,

813
00:37:55,520 --> 00:37:58,839
Speaker 1: you know, since the greens were fairly new, they were

814
00:37:58,920 --> 00:38:01,120
Speaker 1: quite firm, and so I'm not sure if in future

815
00:38:01,160 --> 00:38:03,759
Speaker 1: years it'll be the same kind of dynamic. And you know,

816
00:38:03,800 --> 00:38:06,640
Speaker 1: there are some pros who are frustrated by it, so

817
00:38:07,160 --> 00:38:10,560
Speaker 1: that might play into future setups there as well. But

818
00:38:10,640 --> 00:38:12,719
Speaker 1: I think it is a good example of how, you know,

819
00:38:13,360 --> 00:38:16,759
Speaker 1: narrow courses with with clear penalties off the tee are

820
00:38:16,800 --> 00:38:19,920
Speaker 1: not necessarily the only solution, and I think, you know,

821
00:38:19,960 --> 00:38:22,440
Speaker 1: Wileye probably demonstrates that as well. I don't think of

822
00:38:22,520 --> 00:38:26,279
Speaker 1: Wildleye as being particularly penal off the tee it. You know,

823
00:38:26,320 --> 00:38:28,640
Speaker 1: there are some palm trees out there, but it's it's

824
00:38:28,680 --> 00:38:31,240
Speaker 1: fairly wide open. Other than that, it's just a shorter course.

825
00:38:32,120 --> 00:38:32,319
Speaker 2: Yeah.

826
00:38:32,360 --> 00:38:34,839
Speaker 3: Wiley is an interesting example though, where if you look

827
00:38:34,840 --> 00:38:37,399
Speaker 3: at its diagram on the course fit page, it sort

828
00:38:37,400 --> 00:38:40,400
Speaker 3: of is compressed in every respect, so it doesn't rewards

829
00:38:40,480 --> 00:38:43,279
Speaker 3: driving distance class and driving accuracy lass. Oh and also

830
00:38:43,280 --> 00:38:44,520
Speaker 3: slightly approached.

831
00:38:44,400 --> 00:38:46,480
Speaker 2: Which I mean again it's hard to.

832
00:38:46,400 --> 00:38:48,359
Speaker 3: Say exactly what that means, but in theory that might

833
00:38:48,400 --> 00:38:51,239
Speaker 3: not be a great thing because it means that you're

834
00:38:51,280 --> 00:38:54,000
Speaker 3: not rewarding skill as much at Wiley as another course,

835
00:38:54,040 --> 00:38:57,000
Speaker 3: but again that's only like part of the equation. I

836
00:38:57,000 --> 00:38:59,360
Speaker 3: think when we because like, realistically, if you want to

837
00:38:59,400 --> 00:39:01,520
Speaker 3: reward skin as much as possible in the PGA Tour,

838
00:39:02,000 --> 00:39:04,719
Speaker 3: you probably would want a course where where greens are

839
00:39:04,760 --> 00:39:07,279
Speaker 3: super soft and it's just target golf, because yeah, it

840
00:39:07,360 --> 00:39:09,520
Speaker 3: does take like when there's no randomness in the sense

841
00:39:09,560 --> 00:39:13,000
Speaker 3: that there's no bounces that can make a a good

842
00:39:13,000 --> 00:39:13,760
Speaker 3: shot turned into.

843
00:39:13,600 --> 00:39:14,080
Speaker 2: A bad shot.

844
00:39:14,160 --> 00:39:17,200
Speaker 3: Then yeah, like a soft course like that probably does

845
00:39:17,239 --> 00:39:19,719
Speaker 3: reward skill the most. But as viewers, like, nobody wants

846
00:39:19,760 --> 00:39:22,520
Speaker 3: to watch that, so there's other there's other considerations, and

847
00:39:22,560 --> 00:39:25,360
Speaker 3: so I think, yeah, while I it does level the

848
00:39:25,360 --> 00:39:27,640
Speaker 3: playing field, it does seem like there's some element of

849
00:39:27,680 --> 00:39:30,319
Speaker 3: randomness there, but like that could be fine, depends on

850
00:39:30,360 --> 00:39:31,600
Speaker 3: what your preferences are.

851
00:39:32,080 --> 00:39:33,759
Speaker 1: That's maybe not a bad thing. Yeah, I mean, there's

852
00:39:33,760 --> 00:39:36,960
Speaker 1: an assumption that what we really want are courses that

853
00:39:37,120 --> 00:39:41,080
Speaker 1: reward skill in a clear way. But I think that

854
00:39:41,120 --> 00:39:43,279
Speaker 1: once we take that to the logical extreme, what's the

855
00:39:43,320 --> 00:39:46,040
Speaker 1: course that rewards skill the most that we might not

856
00:39:46,400 --> 00:39:48,479
Speaker 1: like what's on the other side of that door.

857
00:39:49,040 --> 00:39:52,480
Speaker 3: Yeah, maybe maybe that brings us to like top golf,

858
00:39:52,480 --> 00:39:54,840
Speaker 3: and you just get tour pros hitting shots into baskets,

859
00:39:54,840 --> 00:39:57,239
Speaker 3: and that's that's the most that's the most skilled thing

860
00:39:57,280 --> 00:39:58,759
Speaker 3: for rewarding approach shots the.

861
00:39:58,800 --> 00:40:01,680
Speaker 1: Most reliable test of skill. Yeah, totally. There's all the

862
00:40:01,680 --> 00:40:05,120
Speaker 1: other factors have have kind of been leveled. So you know,

863
00:40:05,160 --> 00:40:08,040
Speaker 1: speaking of course, is that that seem to have high

864
00:40:08,160 --> 00:40:11,760
Speaker 1: variance is another way to put what we're talking about.

865
00:40:12,560 --> 00:40:15,160
Speaker 1: You did a really good deep dive earlier this year,

866
00:40:15,200 --> 00:40:18,600
Speaker 1: I believe on TPC Sawgrass. Tell me about what you

867
00:40:18,680 --> 00:40:21,480
Speaker 1: found there. What are some of your thoughts about TPC

868
00:40:21,600 --> 00:40:26,040
Speaker 1: Sawgrass as a course that you know tests certain skills

869
00:40:26,080 --> 00:40:28,640
Speaker 1: or doesn't test certain skills from PGA tow or players.

870
00:40:29,040 --> 00:40:32,480
Speaker 3: Yeah, Sawgrass is a course that rewards accuracy more so

871
00:40:32,560 --> 00:40:35,560
Speaker 3: than compared to the other four attributes, I think, and

872
00:40:35,640 --> 00:40:36,239
Speaker 3: any other thing.

873
00:40:36,160 --> 00:40:38,520
Speaker 2: About saw Grass is and this isn't really.

874
00:40:38,360 --> 00:40:42,640
Speaker 3: Reflected in the course fit tool, is that at Sawgrass,

875
00:40:42,920 --> 00:40:44,480
Speaker 3: not only is it the case that if you're one

876
00:40:44,480 --> 00:40:47,279
Speaker 3: shot better than the average player at the average PG two,

877
00:40:47,280 --> 00:40:48,799
Speaker 3: of course when you go to Sawgrass, you're only going

878
00:40:48,880 --> 00:40:50,880
Speaker 3: to be point seven or point eight shots better. So

879
00:40:50,880 --> 00:40:54,520
Speaker 3: it's it is reducing the advantage that skilled players have.

880
00:40:55,160 --> 00:40:57,840
Speaker 3: And then it's also it's also adding in variants in

881
00:40:57,880 --> 00:41:00,359
Speaker 3: the sense that Sawgrass is just a course where if

882
00:41:00,360 --> 00:41:02,880
Speaker 3: the same player plays there one hundred times the variants

883
00:41:02,880 --> 00:41:05,000
Speaker 3: in their score. So they're going to average seventy, let's say,

884
00:41:05,000 --> 00:41:06,960
Speaker 3: but they're gonna sometimes she's seventy five, sometimes.

885
00:41:06,760 --> 00:41:07,439
Speaker 2: She's sixty five.

886
00:41:07,640 --> 00:41:10,200
Speaker 3: That variance is higher at Sawgrass as well, and that's

887
00:41:10,200 --> 00:41:12,560
Speaker 3: sort of a that's a separate thing to relate this

888
00:41:12,600 --> 00:41:15,200
Speaker 3: to whyl Whyli is actually a course where even though

889
00:41:15,239 --> 00:41:18,440
Speaker 3: that skill advantage gets reduced, it's it's a low variance course.

890
00:41:18,480 --> 00:41:20,359
Speaker 3: So if the same player plays there a bunch, it's

891
00:41:20,360 --> 00:41:22,399
Speaker 3: going to be a tighter bound around their average score.

892
00:41:23,120 --> 00:41:24,799
Speaker 3: It's kind of a tricky thing to think through, but

893
00:41:25,120 --> 00:41:27,319
Speaker 3: the upshot for saw Gas is that, yeah, it's a

894
00:41:27,400 --> 00:41:29,759
Speaker 3: it's a very random course. It does I think it

895
00:41:29,760 --> 00:41:31,960
Speaker 3: does reward accuracy more than average, but that's about it.

896
00:41:32,040 --> 00:41:35,120
Speaker 3: And it also has this added element of just variance,

897
00:41:35,120 --> 00:41:37,239
Speaker 3: which I think everybody who watches it what you agree

898
00:41:37,280 --> 00:41:39,319
Speaker 3: with that, like, there's there are there is potential for

899
00:41:39,360 --> 00:41:41,840
Speaker 3: big numbers in that course, and there is more randomness

900
00:41:41,840 --> 00:41:42,520
Speaker 3: in that respect.

901
00:41:42,560 --> 00:41:44,799
Speaker 2: So it's Yeah, the takeaway.

902
00:41:44,440 --> 00:41:47,520
Speaker 3: With Sawgas is that it's just not only is performance unpredictable,

903
00:41:47,520 --> 00:41:49,400
Speaker 3: there's also just more variance in general.

904
00:41:49,640 --> 00:41:52,000
Speaker 1: And so yeah, I wonder why that. And this is

905
00:41:52,000 --> 00:41:56,279
Speaker 1: getting into the architecture questions that you know, data is

906
00:41:56,320 --> 00:41:59,840
Speaker 1: not necessarily gonna tell us clear answers on but I

907
00:42:00,120 --> 00:42:03,719
Speaker 1: I wonder why that. What design characteristics of TPC sawgrass

908
00:42:03,760 --> 00:42:05,880
Speaker 1: are at play here? Could it be the really severe

909
00:42:06,040 --> 00:42:08,879
Speaker 1: penalties at certain places in the course. Could it be

910
00:42:09,440 --> 00:42:12,279
Speaker 1: the tininess of the targets on the greens? Do you

911
00:42:12,280 --> 00:42:14,719
Speaker 1: think these things could have have an influence? I mean

912
00:42:14,760 --> 00:42:18,000
Speaker 1: not just the greens themselves, but like the sections of

913
00:42:18,040 --> 00:42:21,360
Speaker 1: the greens where pins might be are super super small.

914
00:42:21,960 --> 00:42:22,719
Speaker 2: I think that's it.

915
00:42:22,880 --> 00:42:26,160
Speaker 3: I think so yeah, I think you can have variance,

916
00:42:26,200 --> 00:42:27,759
Speaker 3: and variants can be a good thing or it can

917
00:42:27,800 --> 00:42:29,440
Speaker 3: be a bad I mean, if you have at sawgrass,

918
00:42:29,480 --> 00:42:32,120
Speaker 3: like if you have a shot that if it misses,

919
00:42:32,120 --> 00:42:34,239
Speaker 3: if a player misses this spot by two yards, it

920
00:42:34,400 --> 00:42:36,480
Speaker 3: ends up rolling down a hill, and it's super penalizing

921
00:42:36,640 --> 00:42:39,000
Speaker 3: compared to a course that's soft. You miss your spot

922
00:42:39,040 --> 00:42:40,880
Speaker 3: by two years, you just have a pieto that's two

923
00:42:40,920 --> 00:42:41,439
Speaker 3: years longer.

924
00:42:41,600 --> 00:42:42,319
Speaker 2: It's not a big deal.

925
00:42:42,360 --> 00:42:44,239
Speaker 3: So that that can certainly add variance, and that would

926
00:42:44,239 --> 00:42:47,239
Speaker 3: probably be considered good varians because it's it's making the

927
00:42:47,920 --> 00:42:50,000
Speaker 3: margin between a good shot and or a marginal shot

928
00:42:50,040 --> 00:42:52,279
Speaker 3: and a good shot is now that that's creating a

929
00:42:52,320 --> 00:42:55,040
Speaker 3: bigger difference scores, which is probably what we want. And yeah,

930
00:42:55,040 --> 00:42:57,760
Speaker 3: I think Sawgrass does that to something to be obviously,

931
00:42:57,760 --> 00:43:00,799
Speaker 3: seventeen is like a good example of probably variants you

932
00:43:00,880 --> 00:43:01,520
Speaker 3: might not want.

933
00:43:01,560 --> 00:43:02,319
Speaker 2: It's hard to.

934
00:43:02,239 --> 00:43:04,000
Speaker 3: Say, like if somebody's playing really well and goes to

935
00:43:04,040 --> 00:43:07,360
Speaker 3: seventeen and makes a seven, their tournament is all but

936
00:43:07,880 --> 00:43:10,440
Speaker 3: over because of that, and that's sort of that that's

937
00:43:10,440 --> 00:43:11,720
Speaker 3: another big source of variance.

938
00:43:12,160 --> 00:43:13,040
Speaker 1: Shout out Sergio.

939
00:43:13,800 --> 00:43:15,920
Speaker 3: Yeah, and in general, I think it's just because Sawgrass

940
00:43:15,960 --> 00:43:19,080
Speaker 3: is sagas generally plays super firm too. And like there's

941
00:43:19,360 --> 00:43:21,640
Speaker 3: speaking of surgery, there's that year where you like eight

942
00:43:21,680 --> 00:43:24,120
Speaker 3: putted hole or eight the part three and like you

943
00:43:24,280 --> 00:43:26,640
Speaker 3: just that's probably bad variance.

944
00:43:26,680 --> 00:43:28,239
Speaker 2: I think we would agree.

945
00:43:27,920 --> 00:43:30,160
Speaker 3: That that's bad rans The greens were just they sort

946
00:43:30,200 --> 00:43:33,160
Speaker 3: of maybe put them over the edge. And when I

947
00:43:33,160 --> 00:43:35,239
Speaker 3: think of Sawgas, I just think of it might not

948
00:43:35,280 --> 00:43:37,840
Speaker 3: necessarily be rewarding skill as much as we like, but

949
00:43:37,880 --> 00:43:39,799
Speaker 3: it's maybe it is it's hard to say, but it's

950
00:43:39,880 --> 00:43:41,040
Speaker 3: very entertaining golf to watch.

951
00:43:41,680 --> 00:43:44,279
Speaker 1: Absolutely, yeah, I mean, I think it's good to be

952
00:43:44,400 --> 00:43:48,839
Speaker 1: clear that, you know, when we're asking questions about how

953
00:43:48,920 --> 00:43:53,759
Speaker 1: PGA Tour venues can reward or not reward different skills

954
00:43:54,239 --> 00:43:56,359
Speaker 1: and talk about the kind of variety that we might

955
00:43:56,400 --> 00:43:59,840
Speaker 1: want to see in PGA Tour venues, that's not necessarily

956
00:43:59,880 --> 00:44:04,120
Speaker 1: a commentary about good architecture. The question of what makes

957
00:44:04,160 --> 00:44:07,600
Speaker 1: good architecture and what makes a PGA Tour venue that

958
00:44:07,600 --> 00:44:11,279
Speaker 1: should be part of the rotation, those are two separate questions, right,

959
00:44:12,160 --> 00:44:15,960
Speaker 1: And I think that finding that balance between good architecture

960
00:44:16,000 --> 00:44:20,840
Speaker 1: for the masses and something compelling to watch in a

961
00:44:20,840 --> 00:44:24,400
Speaker 1: PGA Tour event, that's really something that these courses have

962
00:44:24,440 --> 00:44:26,560
Speaker 1: to think about, right because they're they're just hosting a

963
00:44:26,560 --> 00:44:28,560
Speaker 1: PGA Tour event one week of the year. The rest

964
00:44:28,560 --> 00:44:31,000
Speaker 1: of the rest of the year, they're you know, open

965
00:44:31,040 --> 00:44:34,120
Speaker 1: to the membership or open to the public. But personally,

966
00:44:34,160 --> 00:44:36,279
Speaker 1: you know, what, what I really want to see, you know,

967
00:44:36,280 --> 00:44:39,880
Speaker 1: I love, you know, these questions about good architecture I'm

968
00:44:40,000 --> 00:44:43,280
Speaker 1: very interested in and I think that's the highest priority

969
00:44:43,280 --> 00:44:45,600
Speaker 1: for a golf course. But I'm also somebody. I'm also

970
00:44:45,640 --> 00:44:47,719
Speaker 1: a PGA Tour fan, and I want to see as

971
00:44:47,800 --> 00:44:50,840
Speaker 1: much variety in these venues as possible. I want to see,

972
00:44:50,920 --> 00:44:53,840
Speaker 1: you know, many different types of courses that test different skills.

973
00:44:54,120 --> 00:44:55,840
Speaker 1: And that's why I get excited when I see a

974
00:44:55,840 --> 00:44:59,880
Speaker 1: course like Harbor Town, because it's just not the usual

975
00:45:00,160 --> 00:45:02,640
Speaker 1: PGA Tour venue. I mean, you're you're a golf fan,

976
00:45:02,719 --> 00:45:04,160
Speaker 1: do you do you kind of feel the same way.

977
00:45:04,440 --> 00:45:07,279
Speaker 2: Yeah? Yeah, I mean I feel the same way. I yeah.

978
00:45:07,280 --> 00:45:08,799
Speaker 3: I think there's a clear trade off to be made

979
00:45:08,880 --> 00:45:11,520
Speaker 3: between setting up a course of rewards skill and setting

980
00:45:11,560 --> 00:45:13,680
Speaker 3: up a course to maybe not a clear trade up,

981
00:45:13,680 --> 00:45:15,600
Speaker 3: but there is a trade off between rewarding skill and

982
00:45:15,640 --> 00:45:19,280
Speaker 3: making the golf entertaining to watch. Like, I really can't

983
00:45:19,320 --> 00:45:22,239
Speaker 3: watch the PGA Tour play when they play a course

984
00:45:22,280 --> 00:45:23,920
Speaker 3: where it's soft and it's a diverty fest, it's just

985
00:45:24,040 --> 00:45:25,080
Speaker 3: very uninteresting to.

986
00:45:25,000 --> 00:45:26,960
Speaker 2: Watch for me. Like, I don't necessarily.

987
00:45:26,600 --> 00:45:29,319
Speaker 3: Appreciate architecture per se, but I mean I like watching

988
00:45:29,320 --> 00:45:31,879
Speaker 3: golfers play firm golf courses just because it's tough.

989
00:45:31,920 --> 00:45:33,560
Speaker 2: It's just testing players. I like seeing.

990
00:45:34,120 --> 00:45:36,240
Speaker 3: I like I like see courses where there's like penalties

991
00:45:36,239 --> 00:45:39,480
Speaker 3: off the tee, just because I think especially down the stretch,

992
00:45:39,600 --> 00:45:41,560
Speaker 3: Like just from my own experiences playing golf, I like

993
00:45:41,640 --> 00:45:44,839
Speaker 3: seeing it's compelling to watch players try and perform under

994
00:45:44,840 --> 00:45:47,480
Speaker 3: the gun, and hitting a good drive under pressure is

995
00:45:47,840 --> 00:45:49,600
Speaker 3: I think one of the harder things to do in golf.

996
00:45:49,800 --> 00:45:52,839
Speaker 3: So I mean that to me is interesting. But yeah,

997
00:45:52,880 --> 00:45:56,239
Speaker 3: in general, variety I think, is you need it on

998
00:45:56,280 --> 00:45:58,960
Speaker 3: the PGA Tour like I don't, And I think, unfortunately,

999
00:45:59,000 --> 00:46:00,839
Speaker 3: it'd be one thing if there's no variety. But they

1000
00:46:00,880 --> 00:46:04,640
Speaker 3: had settled on firm golf courses as the thing they're

1001
00:46:04,680 --> 00:46:07,040
Speaker 3: going to focus on, but they've settled on on softer

1002
00:46:07,160 --> 00:46:09,879
Speaker 3: birdy fest at least for non majors and non well

1003
00:46:10,000 --> 00:46:12,200
Speaker 3: pretty much just non majors, And yeah, I think that's

1004
00:46:12,200 --> 00:46:14,640
Speaker 3: pretty uninteresting golf for a lot of fans.

1005
00:46:14,960 --> 00:46:18,719
Speaker 1: And it changes the complexion of the top the set

1006
00:46:18,760 --> 00:46:21,239
Speaker 1: of top players in the world, because presumably if there

1007
00:46:21,280 --> 00:46:24,319
Speaker 1: were more courses on the PGA Tour that had some

1008
00:46:24,360 --> 00:46:27,799
Speaker 1: of these Web Tour characteristics that we've been talking about

1009
00:46:27,880 --> 00:46:31,400
Speaker 1: Web with two b's, there would be different players in

1010
00:46:31,560 --> 00:46:33,719
Speaker 1: the top ten. I mean, it would just be the

1011
00:46:34,000 --> 00:46:37,480
Speaker 1: skill set of the top players in the world might

1012
00:46:37,480 --> 00:46:38,879
Speaker 1: be a little more varied.

1013
00:46:39,200 --> 00:46:41,000
Speaker 2: Yeah, I think it would be more varied.

1014
00:46:42,040 --> 00:46:43,480
Speaker 3: But then and then at some point we're just going

1015
00:46:43,520 --> 00:46:45,600
Speaker 3: to get to like this subjective question of how much

1016
00:46:45,880 --> 00:46:48,720
Speaker 3: which skills should be rewarded, because I still like Dustin Johnson.

1017
00:46:48,840 --> 00:46:50,560
Speaker 3: Be able to hit the ball as far as he

1018
00:46:50,600 --> 00:46:52,720
Speaker 3: does is and then obviously an incredible skill that should

1019
00:46:52,719 --> 00:46:55,160
Speaker 3: be rewarded. And then the question is just how much,

1020
00:46:55,560 --> 00:46:59,000
Speaker 3: because yeah, Web's also skilled in various ways. So it's

1021
00:46:59,120 --> 00:47:01,319
Speaker 3: it's a tough if the fine line sort of you

1022
00:47:01,400 --> 00:47:03,879
Speaker 3: have to figure out what exactly your priorities are.

1023
00:47:03,960 --> 00:47:06,839
Speaker 1: I guess absolutely, and and DJ, it should be said

1024
00:47:06,880 --> 00:47:10,440
Speaker 1: as well as Rory have significant skills across the board,

1025
00:47:11,360 --> 00:47:13,560
Speaker 1: whereas somebody like Web, you know, you could say that

1026
00:47:13,640 --> 00:47:16,480
Speaker 1: he just doesn't really have the skill of driving distance

1027
00:47:16,560 --> 00:47:19,920
Speaker 1: at an elite level, and so and he gets docked

1028
00:47:19,920 --> 00:47:22,960
Speaker 1: for that, and some might say that that's exactly right,

1029
00:47:23,040 --> 00:47:26,359
Speaker 1: you know. So somebody like DJ, his balanced game, which

1030
00:47:26,400 --> 00:47:30,480
Speaker 1: includes that extraordinary ability with the driver, is certainly well

1031
00:47:30,520 --> 00:47:33,600
Speaker 1: rewarded and and should be. I think that's a good

1032
00:47:33,600 --> 00:47:37,560
Speaker 1: place to wrap up our discussion. Of course, fit you also,

1033
00:47:37,600 --> 00:47:40,280
Speaker 1: I should mention, did a deep dive into augusta National

1034
00:47:41,480 --> 00:47:44,120
Speaker 1: recently for your website before the Masters, and we're not

1035
00:47:44,120 --> 00:47:45,560
Speaker 1: going to go into that in depth, but I just

1036
00:47:45,600 --> 00:47:48,040
Speaker 1: wanted to mention it and just recommend that people go

1037
00:47:48,640 --> 00:47:51,400
Speaker 1: and check that out because I think it's absolutely fascinating.

1038
00:47:51,760 --> 00:47:54,879
Speaker 1: So there's just something that that people can check out

1039
00:47:54,920 --> 00:47:58,040
Speaker 1: that we won't discuss here. That's a reason to go

1040
00:47:58,040 --> 00:48:01,480
Speaker 1: to datagolf dot com and do some re So I

1041
00:48:01,560 --> 00:48:03,320
Speaker 1: just wanted to to wrap up with it with a

1042
00:48:03,360 --> 00:48:06,920
Speaker 1: few random questions. These don't need to be long in

1043
00:48:06,960 --> 00:48:11,200
Speaker 1: depth answers, but a few points of curiosity outside of

1044
00:48:11,239 --> 00:48:14,319
Speaker 1: this course fit discussion that we've been having so kind

1045
00:48:14,320 --> 00:48:17,600
Speaker 1: of a lightning round. When a player is having a

1046
00:48:17,600 --> 00:48:21,440
Speaker 1: breakout season, are there any key characteristics that you see,

1047
00:48:21,719 --> 00:48:23,160
Speaker 1: like things that change in their game?

1048
00:48:24,200 --> 00:48:27,279
Speaker 3: Generally, if a player is playing well recently, and all

1049
00:48:27,280 --> 00:48:28,640
Speaker 3: I know is that they're playing well, I haven't looked

1050
00:48:28,640 --> 00:48:31,400
Speaker 3: at deeper into the data, it's probably due to the

1051
00:48:31,400 --> 00:48:33,440
Speaker 3: putting around the green stuff, just because that's the stuff

1052
00:48:33,480 --> 00:48:36,880
Speaker 3: that varies a lot round around. But if they're truly

1053
00:48:36,920 --> 00:48:38,879
Speaker 3: having a breakout season, one where our model is actually

1054
00:48:38,920 --> 00:48:40,960
Speaker 3: gonna say, okay, this guy's being elevated to a new level.

1055
00:48:41,480 --> 00:48:43,640
Speaker 3: I mean I think it's usually it's usually driven by

1056
00:48:44,520 --> 00:48:46,360
Speaker 3: either off the t stuff or approach stuff.

1057
00:48:46,520 --> 00:48:46,719
Speaker 2: Yeah.

1058
00:48:46,760 --> 00:48:48,920
Speaker 3: The easiest way is, like for Telly, like somebody who's

1059
00:48:49,040 --> 00:48:51,600
Speaker 3: just gained ten yards and that matters. That's an easy

1060
00:48:51,600 --> 00:48:52,160
Speaker 3: way to do it.

1061
00:48:53,400 --> 00:48:55,279
Speaker 1: Who is the best player on the PGA Tour without

1062
00:48:55,280 --> 00:48:59,160
Speaker 1: a major? H? And why is it? John Rahm?

1063
00:49:00,120 --> 00:49:01,239
Speaker 2: Oh right, wrong?

1064
00:49:01,320 --> 00:49:04,560
Speaker 3: Yeah, obviously is wrong. Yeah, obviously obviously it's wrong. Yeah,

1065
00:49:04,560 --> 00:49:06,000
Speaker 3: I guess I was thinking, do I want to say,

1066
00:49:06,040 --> 00:49:08,239
Speaker 3: who's like, who deserves one at this point?

1067
00:49:08,280 --> 00:49:11,680
Speaker 1: And not like the best career, not the best career, yeah,

1068
00:49:11,880 --> 00:49:15,120
Speaker 1: but like more you know, who is currently you know,

1069
00:49:15,120 --> 00:49:18,799
Speaker 1: based on their recent performance? What would it clearly be wrong?

1070
00:49:18,840 --> 00:49:20,960
Speaker 1: I mean I don't know, is that the clear answer

1071
00:49:20,960 --> 00:49:21,439
Speaker 1: to the question.

1072
00:49:21,520 --> 00:49:23,960
Speaker 3: I mean yeah, to me as someone who's looking at

1073
00:49:24,320 --> 00:49:26,400
Speaker 3: I don't really think of players who perform well in

1074
00:49:26,440 --> 00:49:27,200
Speaker 3: majors versus.

1075
00:49:27,040 --> 00:49:28,759
Speaker 2: Those who don't. I just think of golfers who are

1076
00:49:28,920 --> 00:49:30,200
Speaker 2: good versus golfers who are not.

1077
00:49:30,400 --> 00:49:33,920
Speaker 3: So I'm obviously rom has not had that many chances

1078
00:49:33,960 --> 00:49:36,000
Speaker 3: to win majors, or maybe any good chances. But he's

1079
00:49:36,800 --> 00:49:39,280
Speaker 3: rom Is We've been saying wrong. Ram has been underrated

1080
00:49:39,360 --> 00:49:42,439
Speaker 3: he's probably not underrated anymore, given that he's the number

1081
00:49:42,440 --> 00:49:44,160
Speaker 3: two is replaying in the world. But he's just he

1082
00:49:44,239 --> 00:49:46,919
Speaker 3: started his careers as close to Tiger in the last

1083
00:49:46,960 --> 00:49:48,799
Speaker 3: twenty years as anybody else has in terms of just

1084
00:49:48,800 --> 00:49:51,480
Speaker 3: strokes gained and number of top fives, and he just

1085
00:49:51,520 --> 00:49:53,640
Speaker 3: didn't have a transition period. He just came came out

1086
00:49:53,640 --> 00:49:55,880
Speaker 3: on the PJ Tour and was already playing like the

1087
00:49:55,960 --> 00:49:57,960
Speaker 3: top five planet world. So he's going to get a

1088
00:49:58,000 --> 00:49:58,439
Speaker 3: major soon.

1089
00:49:58,560 --> 00:50:01,800
Speaker 1: Yeah, this game is unbelievable. Yeah, it can't it can't belong.

1090
00:50:03,239 --> 00:50:06,320
Speaker 1: People are very high right now in Dustin Johnson's ability

1091
00:50:06,360 --> 00:50:10,319
Speaker 1: to win multiple additional majors. What would you say to

1092
00:50:10,320 --> 00:50:12,440
Speaker 1: that all that excitement, Yeah.

1093
00:50:12,239 --> 00:50:15,480
Speaker 3: I would say the best player in the world has

1094
00:50:15,600 --> 00:50:17,640
Speaker 3: Maybe you don't even have to trust us. You can

1095
00:50:17,640 --> 00:50:19,920
Speaker 3: trust Betty Markets, trust anything. They have about a nine

1096
00:50:20,000 --> 00:50:22,200
Speaker 3: or ten percent chance, if they're clearly the best player

1097
00:50:22,200 --> 00:50:23,600
Speaker 3: in the world, a nine or ten percent chance to

1098
00:50:23,640 --> 00:50:25,879
Speaker 3: win a major. So that means if a guy plays,

1099
00:50:25,920 --> 00:50:28,520
Speaker 3: if DJ plays ten majors and he's aging, that means

1100
00:50:28,520 --> 00:50:31,680
Speaker 3: we've expect him to win one. So still it's like

1101
00:50:32,239 --> 00:50:34,239
Speaker 3: you can't assign majors to too many golfers because there's

1102
00:50:34,239 --> 00:50:37,040
Speaker 3: only four of them each year, so it's it's always

1103
00:50:37,080 --> 00:50:38,120
Speaker 3: unlikely to win majors.

1104
00:50:38,440 --> 00:50:41,160
Speaker 1: Yeah, yeah, there's there would be a good hobby in

1105
00:50:41,239 --> 00:50:44,840
Speaker 1: listening to golf podcasts and tabulating the amount of future

1106
00:50:44,880 --> 00:50:46,640
Speaker 1: majors that are given out.

1107
00:50:47,480 --> 00:50:49,319
Speaker 2: Yeah yeah, we'd be in the thousands.

1108
00:50:49,440 --> 00:50:53,760
Speaker 1: Yeah, exactly. So you know you're on golf Twitter on occasion.

1109
00:50:53,840 --> 00:50:57,120
Speaker 1: So I'm sure you've seen how people use statistics to

1110
00:50:57,160 --> 00:51:00,439
Speaker 1: try to win arguments. What is the what was common

1111
00:51:00,520 --> 00:51:04,200
Speaker 1: mistake that you see when people use stats in this way.

1112
00:51:04,800 --> 00:51:08,000
Speaker 3: I think the biggest one is confusing correlation with causation.

1113
00:51:08,440 --> 00:51:11,520
Speaker 3: I have an economics background, so that's I am very

1114
00:51:11,560 --> 00:51:16,000
Speaker 3: sensitive to correlation versus causation. And I mean a good example,

1115
00:51:16,000 --> 00:51:18,719
Speaker 3: I saw AUGUSTA somebody posted just on number three the

1116
00:51:18,760 --> 00:51:22,040
Speaker 3: scoring average for players who had hit it more than

1117
00:51:22,040 --> 00:51:23,960
Speaker 3: three hundred yards on the whole versus the scoring average

1118
00:51:23,960 --> 00:51:26,120
Speaker 3: for players who had hit less than two forty. And

1119
00:51:26,520 --> 00:51:28,160
Speaker 3: the conclusion they were trying to say was, oh, look,

1120
00:51:28,160 --> 00:51:30,120
Speaker 3: you should be hitting driver here to go for the

1121
00:51:30,160 --> 00:51:31,919
Speaker 3: green because the scoring average is better for the guys

1122
00:51:31,960 --> 00:51:34,760
Speaker 3: who hit it further. But that's the flawed for many reasons,

1123
00:51:34,760 --> 00:51:36,360
Speaker 3: and the biggest one is that the guys who are

1124
00:51:36,440 --> 00:51:38,840
Speaker 3: hitting at two forty on that whole were like Larry Mize,

1125
00:51:39,160 --> 00:51:41,279
Speaker 3: Samuel Aisle, among others, and the guys who are hitting

1126
00:51:41,320 --> 00:51:43,400
Speaker 3: at three hundred, like Bryce and Rory the best players

1127
00:51:43,400 --> 00:51:45,960
Speaker 3: in the world. So that's correlation versus causation because you're

1128
00:51:46,000 --> 00:51:48,520
Speaker 3: picking up we want to know the causal effect of

1129
00:51:48,640 --> 00:51:51,279
Speaker 3: hitting driver on that hole versus not hitting driver. But

1130
00:51:51,320 --> 00:51:53,279
Speaker 3: when you do that analysis, you're picking up all sorts

1131
00:51:53,280 --> 00:51:55,560
Speaker 3: of things, like the fact that Rory is just a

1132
00:51:55,560 --> 00:51:58,840
Speaker 3: better golfer in every way than my So you're, yeah,

1133
00:51:58,920 --> 00:52:03,120
Speaker 3: that's Twitter is not a good space for statistical arguments.

1134
00:52:03,400 --> 00:52:08,200
Speaker 1: You're you're comparing Rory McElroy to a retired golf pro yah, yeah,

1135
00:52:08,480 --> 00:52:13,360
Speaker 1: which which is meaningless. Yeah yeah, all right. So you know,

1136
00:52:13,440 --> 00:52:16,960
Speaker 1: this is maybe a big question, but I'm just curious,

1137
00:52:17,040 --> 00:52:19,800
Speaker 1: like when you're looking at the future of data golf

1138
00:52:19,800 --> 00:52:22,640
Speaker 1: and what kinds of new questions you want to answer.

1139
00:52:23,239 --> 00:52:26,240
Speaker 1: What do you think the new frontiers are in golf stats?

1140
00:52:26,320 --> 00:52:28,400
Speaker 1: What do you think the questions are that that we

1141
00:52:28,480 --> 00:52:31,279
Speaker 1: haven't answered yet, and maybe we could find methods to

1142
00:52:31,320 --> 00:52:31,879
Speaker 1: answer them.

1143
00:52:32,239 --> 00:52:33,839
Speaker 3: Yeah, I think I actually have a good answer, because

1144
00:52:33,840 --> 00:52:36,319
Speaker 3: we're hopefully for next year we're going to be we're

1145
00:52:36,320 --> 00:52:39,600
Speaker 3: going to have official access to shot link data. We're

1146
00:52:40,280 --> 00:52:42,400
Speaker 3: the actual shot level stuff, or sort of, because right

1147
00:52:42,440 --> 00:52:44,480
Speaker 3: now everything on our site is just round level stuff

1148
00:52:44,520 --> 00:52:46,880
Speaker 3: just because you're not allowed to without an official license,

1149
00:52:46,920 --> 00:52:48,160
Speaker 3: you can't display shot link data.

1150
00:52:48,160 --> 00:52:49,920
Speaker 2: So if that hurdles passed.

1151
00:52:49,680 --> 00:52:53,200
Speaker 3: I think yeah, I think the next step for golf

1152
00:52:53,239 --> 00:52:57,719
Speaker 3: analysis is just building on Brody's stuff with strokes gained

1153
00:52:57,760 --> 00:53:00,799
Speaker 3: and trying to if we really trying basically, because right

1154
00:53:00,800 --> 00:53:02,480
Speaker 3: now in the PGA Tour, the way strokes gain is

1155
00:53:02,520 --> 00:53:06,120
Speaker 3: calculated is just you have a generic baseline function that says,

1156
00:53:06,160 --> 00:53:08,360
Speaker 3: from this distance and this lie, we expect to a

1157
00:53:08,440 --> 00:53:10,080
Speaker 3: player to take this many shots and then you get

1158
00:53:10,080 --> 00:53:12,000
Speaker 3: strokes gain from that. What we would like to do

1159
00:53:12,080 --> 00:53:13,960
Speaker 3: is have a model that and it would be difficult,

1160
00:53:14,000 --> 00:53:16,840
Speaker 3: but it would have a different baseline function for every hole.

1161
00:53:17,000 --> 00:53:19,279
Speaker 3: So like, maybe there's a hole where going this would

1162
00:53:19,320 --> 00:53:21,960
Speaker 3: be very subtle and potentially not possible, but maybe there's

1163
00:53:22,000 --> 00:53:23,640
Speaker 3: a hole where when you're two hundred yards out in

1164
00:53:23,640 --> 00:53:25,719
Speaker 3: the right side of the fairway, that's actually significantly worse

1165
00:53:25,760 --> 00:53:27,160
Speaker 3: than being two hundre yards out in the left side

1166
00:53:27,160 --> 00:53:29,520
Speaker 3: of the fairway and right now with strokes gained, that's

1167
00:53:29,560 --> 00:53:30,280
Speaker 3: treated the same.

1168
00:53:30,360 --> 00:53:32,720
Speaker 2: But if you had again it would be difficult.

1169
00:53:32,719 --> 00:53:35,000
Speaker 3: But you couldn't theory be able to say, no, I

1170
00:53:35,000 --> 00:53:38,120
Speaker 3: guess from this angle or from this spot, players take

1171
00:53:38,280 --> 00:53:40,040
Speaker 3: whatever three point four shots to get down from this

1172
00:53:40,080 --> 00:53:42,319
Speaker 3: spot it's three point one. And I think once you

1173
00:53:42,440 --> 00:53:45,799
Speaker 3: have that whole specific baseline function, there's all sorts of things.

1174
00:53:45,800 --> 00:53:49,080
Speaker 3: The course, your your assessment of course fit could totally change.

1175
00:53:49,320 --> 00:53:52,000
Speaker 3: You could drill down to a whole level. You could

1176
00:53:52,040 --> 00:53:54,200
Speaker 3: say yeah, you could say a lot of a lot

1177
00:53:54,200 --> 00:53:56,319
Speaker 3: of interesting things. So I think that's sort of the

1178
00:53:56,360 --> 00:53:58,880
Speaker 3: next direction. That then that has implications for prediction and

1179
00:53:58,960 --> 00:54:01,239
Speaker 3: golf and for yeah, for for a lot of things.

1180
00:54:01,239 --> 00:54:02,760
Speaker 2: So I think that's the next one.

1181
00:54:03,760 --> 00:54:06,520
Speaker 1: All right, Well, Matt, good luck with your with your

1182
00:54:06,520 --> 00:54:08,480
Speaker 1: future work. And thank you so much for talking to

1183
00:54:08,480 --> 00:54:08,880
Speaker 1: me today.

1184
00:54:09,360 --> 00:54:10,920
Speaker 2: Yeah, thanks a lot for having me on. Enjoyed it.

1185
00:54:34,360 --> 00:54:35,240
Speaker 3: Hmmm.