Speaker 1: Welcome back to another edition of the Frida Egg Podcast.
Speaker 1: My name is Garrett Morrison and this episode is brought
Speaker 1: to you by ourselves. So we're having a Black Friday
Speaker 1: sale that is this Friday at our pro shop on
Speaker 1: our website, so you can find it at proshop dot
Speaker 1: Thefrida Egg dot com and it's an automatic twenty percent
Speaker 1: discount off of everything that includes hats, shirts, headcovers, turvice tumblers.
Speaker 1: But I wanted to talk specifically about our photography prints.
Speaker 1: So these are drone shots of great golf courses. Most
Speaker 1: of the shots were taken by Andy Johnson. You can
Speaker 1: get them framed or mounted on metal. We've got pictures
Speaker 1: of bally Neil, Sand Valley, Prairie Dunes, Pasa Tiempo, and
Speaker 1: we've got a ton of new ones on the way.
Speaker 1: They look great. I've got one on my wall myself,
Speaker 1: and they make really good gifts. So this Friday, November
Speaker 1: twenty seventh, twenty percent off the whole store, including our prints.
Speaker 1: That's at the pro Shop, pro show dot Thefrida Egg
Speaker 1: dot com. All right, so on with our episode. My
Speaker 1: guest today is Matt Korshane along with his brother Will
Speaker 1: Korshane Matt founded Data Golf, which you can find at
Speaker 1: datagolf dot com. So Data Golf is really part of
Speaker 1: what has been a revolution in golf over the past
Speaker 1: couple of decades, and that has been this infusion of
Speaker 1: advanced data, statistics and analytics into golf, especially golf at
Speaker 1: the highest professional levels. Now part of that story is Shotlink,
Speaker 1: which provides a great deal of raw data that simply
Speaker 1: wasn't available in the nineteen nineties. And another part of
Speaker 1: it is the advent of strokes gained statistics, which were
Speaker 1: pioneered by the Columbia Business School professor Mark Brody. And basically,
Speaker 1: what strokes gained does is it compares a player's performance
Speaker 1: to the rest of the field, shot by shot, and
Speaker 1: in this way you can really isolate the value or
Speaker 1: quality of each shot a player hits. Now, in strokes gained,
Speaker 1: with each shot the player gains or loses a certain
Speaker 1: amount on the field. And what this has done is
Speaker 1: it's just given us a much better way to analyze
Speaker 1: the different skills that a player has. We really have
Speaker 1: a better idea of who the great drivers and iron
Speaker 1: players actually are because we've been able to zero in
Speaker 1: on the quality of each strike of the ball. But
Speaker 1: you know, strokes gained is like still in its infancy
Speaker 1: and it's just begun to shift our understanding of golf.
Speaker 1: I think where we're just scratching the surface, and that's
Speaker 1: the exciting arena in which Data Golf is working right now.
Speaker 1: Matt and Will Korshane are very young, but also very qualified,
Speaker 1: and they're doing a lot of interesting work. One of
Speaker 1: the things that I think Data Golf could change is
Speaker 1: how we view golf courses in golf course design now,
Speaker 1: I always think that golf course design will be an
Speaker 1: art and assessing golf course design will be an art
Speaker 1: as well. Know, no, data, I don't think can tell
Speaker 1: us if a golf course is good, but it can
Speaker 1: definitely tell us how. Data can definitely tell us how
Speaker 1: different tournament venues demand different skill sets from players. And
Speaker 1: I find that to be a really compelling question. And
Speaker 1: so that's what I wanted to talk with Matt Corshane
Speaker 1: about because Data Golf, the website that he and his
Speaker 1: brother have developed, has some great tools that use strokes
Speaker 1: gained data to tell you something about the golf courses
Speaker 1: on the PGA Tour, specifically this week's host of the
Speaker 1: Mayacoba Golf Classic, El kamal Ane Golf Club outside of Cancun, Mexico,
Speaker 1: is one of the biggest outliers in Data Golf's model.
Speaker 1: It's such a strange course. It just demands this extremely
Speaker 1: unusual type of performance from the top players. So I
Speaker 1: thought it was a good time to get at a
Speaker 1: question that I've had for a long time with the
Speaker 1: help of Matt Course Shane, what can statistics tell us
Speaker 1: about golf course architecture on the PGA Tour. I hope
Speaker 1: you enjoy I miss the green for example, I'm already
Speaker 1: upset when I find my ball in the bunker, I'm
Speaker 1: really upset.
Speaker 2: And when I.
Speaker 1: Find my ball in a brid egg Friday egg, the
Speaker 1: dreaded Frida egg fridagg Frida egg egg Frida egg bride
Speaker 1: egg Lie, I'm about ready to run off the golf course. So,
Speaker 1: just to find out a little bit about your background
Speaker 1: in golf, you were a pretty excellent golfer. You got
Speaker 1: down to a scratch handicap, You played some college golf
Speaker 1: at Queen's University as a competitive golfer. Did data ever
Speaker 1: enter your life?
Speaker 2: No, it really didn't.
Speaker 3: Yeah, I wasn't logging like the Strokes game stats or anything.
Speaker 3: I was not a and I'm still not. I'm very
Speaker 3: much a I mean, I'm a vivid head case on
Speaker 3: the golf course. Now, I think I'm very much a
Speaker 3: field player. I guess I would say I'm not like
Speaker 3: a yeah, technical robotic player by any means.
Speaker 1: That's an interesting contrast to me. So like if data
Speaker 1: were you know, some of these analytics were around back then,
Speaker 1: but they just weren't as commonly used, I guess, especially
Speaker 1: at the college golf level. Do you think anything would
Speaker 1: have changed.
Speaker 3: For you looking back when I was actually a serious
Speaker 3: player and didn't have because honestly, the way I think
Speaker 3: about it now is I just have on the list
Speaker 3: of the top ten things I need to improve in golf,
Speaker 3: understanding how to better use data is pretty far down
Speaker 3: that list. I need to stop like blocking my opening
Speaker 3: TV all ob before I start worrying about that stuff.
Speaker 3: But like, yeah, no, back in the day when I
Speaker 3: was good, it would be Yeah, it'd be super informalve
Speaker 3: just to get the basic numbers like the Strokes game stuff,
Speaker 3: just using Brodie's app, Like just understanding what you're losing
Speaker 3: strokes is super valuable, and I think it's often for
Speaker 3: most people it's counter intuitive. I think probably a lot
Speaker 3: of people would end myself included, would probably realize they're
Speaker 3: losing most shots, like on the long game, whenever you
Speaker 3: play poorly, or always thinking about all these pots you miss,
Speaker 3: et cetera. But really everybody misses a lot of potts,
Speaker 3: so I think, yeah, But anyway to answer the question,
Speaker 3: I think I would have taken advantage of this stuff.
Speaker 2: It was more easily accessible in ten years ago.
Speaker 1: Well. Hearing golfers talk about it, it's almost like, in
Speaker 1: addition to providing knowledge, these stats sometimes provide a kind
Speaker 1: of peace of mind, like you know what's wrong, yeah,
Speaker 1: or or you can contextualize your bad performances as just
Speaker 1: like this is what happens. You know, it's not a
Speaker 1: judgment on me as as a player. This is just
Speaker 1: what happens.
Speaker 2: Yeah.
Speaker 3: And it's also I kind of think that is the
Speaker 3: right way to think about it, because it's also debatable
Speaker 3: how like actionable this stuff is. Like if I find
Speaker 3: out that I am lacking, well, if I'm losing small
Speaker 3: good example of if I'm losing strokes, offer te it's
Speaker 3: like okay, like, yeah, if I hit.
Speaker 2: It further, that'd be great. But I mean there are
Speaker 2: examples of players.
Speaker 3: Who have like doing for Telly last week was getting
Speaker 3: a lot of attention for he's not a massive guy,
Speaker 3: and he managed to pick up some club at speech.
Speaker 2: So there are things you can do.
Speaker 3: But I've always thought, I think my brother and I've
Speaker 3: always thought that, Yeah, it's more like descriptive. It's nice
Speaker 3: to know where you're losing strokes and what is the
Speaker 3: difference between myself and like the number one player in
Speaker 3: the world.
Speaker 2: But I don't know how actionable a lot of it is.
Speaker 1: So right now you're doing a PhD at the University
Speaker 1: of British Columbia in economics. Where did your interest in
Speaker 1: economics come from?
Speaker 2: So my grandfather is, well, he's still alive.
Speaker 3: He was a pretty stop working but he was a
Speaker 3: pretty well known economist, so there was always that connection.
Speaker 3: I actually did my undergrad in biochemistry, which was I mean,
Speaker 3: I was just a classic. I just went to university
Speaker 3: not really knowing what to do, and I was like, oh,
Speaker 3: I mean why not medical school?
Speaker 2: Like that sounds reasonable.
Speaker 3: It's embarrassing looking back on it, but then I eventually
Speaker 3: realized I think it's like a few Coon courses and
Speaker 3: just sort of liked the general approach to things. Just
Speaker 3: using statistical methods, which are particularly the ones that are
Speaker 3: prominent in economics, Just using those as a way to
Speaker 3: understand the world is. It's just changed the way I
Speaker 3: think about a lot of things, and I've really become
Speaker 3: I really enjoy looking at the world through that lens.
Speaker 1: So at some point you combined your background in golf
Speaker 1: with your interest in statistics and informed data golf. How
Speaker 1: did the idea for data golf come about?
Speaker 3: Well, I guess the first thing I just say is,
Speaker 3: so data Golf is run by myself from my younger brother, Will,
Speaker 3: who's so he's two years younger. Yeah, he was working
Speaker 3: at that point, and I was just in my second
Speaker 3: year of the PhDs.
Speaker 2: This is like four or five years ago. And then
Speaker 2: the Yeah, the PJ Tour used to.
Speaker 3: Have an academic program where you could access the shot
Speaker 3: link data for free, and so we got that data
Speaker 3: and we just started.
Speaker 2: Yeah, we just started a Twitter account.
Speaker 3: Started a WordPress blog and started doing some basic analyses
Speaker 3: with the data, trying to just sort of answer.
Speaker 2: It's funny to look back.
Speaker 3: We still have all these blogs somewhere on our old website.
Speaker 3: It's funny to look back on them. But yeah, just
Speaker 3: answering questions like do players in fact, there's the same
Speaker 3: wording out you shoot a really good round, it's hard
Speaker 3: to follow it up, So we were sort of just
Speaker 3: checking the degree to which that's all or true and yeah,
Speaker 3: and then it's sort of just progressed from there.
Speaker 1: Right for sure. So it's really evolved well past the
Speaker 1: original blog. So the original Data Golf blog was basically
Speaker 1: a word Press basic WordPress blog, and now it's this
Speaker 1: very complex website with not only blog posts, but a
Speaker 1: number of predictive models and interactive tools that kind of
Speaker 1: make sense of the huge amount of data that's coming
Speaker 1: out of professional golf right now. And so I wanted
Speaker 1: to focus actually on the interactive tools and visualizations. So
Speaker 1: you know, to someone like me, I find these very
Speaker 1: useful and very informative just in general, How do you
Speaker 1: decide what kinds of tools to build?
Speaker 3: Yeah, so I think a lot of the tools come
Speaker 3: naturally out of the model that we have, So really
Speaker 3: like the centerpiece of the website and what we do
Speaker 3: with Data Golf is this predictive model. We have of
Speaker 3: golfer perform, where essentially the output of that model is
Speaker 3: we're trying to, like, ultimately what matters in golf is
Speaker 3: strokes gain, how many strokes you're beating the field by.
Speaker 3: So that's ultimately what we're trying to predict with this model.
Speaker 3: And along the way there will be things that I
Speaker 3: don't know, sort to pop out of the model that
Speaker 3: we can make into a webpage.
Speaker 2: So a good example is true strokes gained.
Speaker 3: So true strokes gained is it's regular strokes gain, which
Speaker 3: is how many strokes beat the feeld by, except we're gonna.
Speaker 2: Adjust that for the strength of that field.
Speaker 3: So, for example, on the web dot com, if we
Speaker 3: estimate that those fields are like a shot worse on
Speaker 3: average than TJ tour field, So if you beat a
Speaker 3: web dot com field by two shots, that's gonna be
Speaker 3: worth like a true strokes gin of one because we're
Speaker 3: saying this field is one shot.
Speaker 2: Worse than average. So that's something that sort of comes
Speaker 2: out of our model.
Speaker 3: Another example of a page would be the course history
Speaker 3: stuff or the course fit tool. We have that output
Speaker 3: in our in our model, and at some point we say, wow,
Speaker 3: this is interesting. I think general golf fans would like this.
Speaker 3: Let's turn it into like an intuitive page that people
Speaker 3: can can get something out of.
Speaker 2: So where that's where a lot of them come from.
Speaker 1: So I did want to dig a little bit deeper
Speaker 1: into what you call the course fit tool, because I
Speaker 1: think it does have a lot to say about what
Speaker 1: one of our primary interests at the fried Egg is,
Speaker 1: and that's golf course design and set up and how
Speaker 1: design and set up influence play on the professional tours.
Speaker 1: But I mean, I think maybe before getting into course
Speaker 1: fit specifically, maybe we should lay the groundwork with this
Speaker 1: related but very different notion of course history. Can you
Speaker 1: tell me what course history is and some of the
Speaker 1: issues that you've had with it.
Speaker 2: Yeah.
Speaker 3: So, course istuy in the most general sense, is just
Speaker 3: how a player has performed at a specific course in
Speaker 3: the past. And then the way we analyze it is
Speaker 3: not just how like a player has performed in an
Speaker 3: absolute sense at a course, because, for example, Tiger, if
Speaker 3: you just define course a player's course history as just
Speaker 3: their total stroke seeing at that course, and somebody like
Speaker 3: Tiger is going to have the best course history everywhere.
Speaker 3: So course history, the way we define it is performance
Speaker 3: relative to expectation or baseline. So and by expectation or
Speaker 3: baseline that's going to be intuitally, that's like how a
Speaker 3: player performs across all courses in a given year, that's
Speaker 3: going to be their baseline. So it might be there
Speaker 3: might be a plus one strokes gained player. And then
Speaker 3: if there's a specific specific course, say I guess the National,
Speaker 3: where they've performed at an average of plus two strokes gained,
Speaker 3: we're gonna say they have good course history, dick, because
Speaker 3: they performed above their baseline. The main issue with course
Speaker 3: history in terms of trying to use it for actionable
Speaker 3: things like predicting performance, is that it's generally a small sample.
Speaker 3: If a guy has only played four rounds of the
Speaker 3: course and he's played really well, maybe he won the tournament,
Speaker 3: which is not not too uncommon, what can we really
Speaker 3: say from that, Like from our analysis, we've found like
Speaker 3: if a guy is performing one stroke better than expected
Speaker 3: at a course, historically, we're only going to improve his
Speaker 3: predictive performance going forward by maybe like two or three
Speaker 3: percent per round.
Speaker 2: Let's say, so, well, if he's if he's done that
Speaker 2: for four.
Speaker 3: Rounds, we would probably bump up his predicted skill by
Speaker 3: like zero point zero five if he's been one stroke planner.
Speaker 2: So it's a pretty small adjustment, but those things can matter.
Speaker 1: Yeah, it seems like your opinion on this has has
Speaker 1: shifted a little bit over time, but the general critique
Speaker 1: of how maybe the lay person uses course history is
Speaker 1: still valid. You know, when when you hear people talking
Speaker 1: about how a certain player has done well at a
Speaker 1: course in the past and is using that fact to
Speaker 1: say this player will definitely do well this week at
Speaker 1: the same course, that that's a vast oversimplification of how
Speaker 1: things actually work.
Speaker 2: Right, Yeah, it is.
Speaker 3: In general, making strong claims about predicting performance in golf
Speaker 3: is a bad idea, just because there's so much there's
Speaker 3: so much randomness in golf as we like, even just
Speaker 3: last week, like like Bryson losing to Lang, right, I guess,
Speaker 3: so like obviously this is a very unexpected outcome, but
Speaker 3: those things happen in golf. So but to your point
Speaker 3: about us changing our opinion, that's definitely true. I mean
Speaker 3: when we first started out on Twitter, we were more
Speaker 3: maybe rash and like combat of I guess to some
Speaker 3: debdeon and we took a pretty hard stance on course
Speaker 3: history saying it didn't matter. But I think we've definitely
Speaker 3: softened that. There are just examples like Phil Augusta. He
Speaker 3: now has like he's played like so many rounds there,
Speaker 3: like like sixty seventy rounds, and he's averaging almost a
Speaker 3: shot better than than expected Augusta, which is a huge
Speaker 3: that's a huge difference, and it can't be you can't
Speaker 3: just wave that away by saying, oh, that's just randomness.
Speaker 3: That's pretty meaningful. So, yeah, course history, when you get
Speaker 3: big examples, can matter for sure.
Speaker 1: Now, so course history can matter a little bit. There's
Speaker 1: there's a small adjustment that you can make for a
Speaker 1: player's course history when you're trying to predict their performance
Speaker 1: in a given week. It seems to me that course
Speaker 1: fit in a lot of ways makes up for some
Speaker 1: of the deficiencies of course history. Is that basically right?
Speaker 2: Yeah, that's that's definitely right.
Speaker 3: You get you sort of with So with course fit,
Speaker 3: what we're gonna look at is which skills a given
Speaker 3: course favors. So like the skills, we're gonna focus on
Speaker 3: our driving distance, driving accuracy and strokes and putting around
Speaker 3: the green approach, and by focusing on skills, you kind
Speaker 3: of bypass this sample size problem. So if we know
Speaker 3: through our fancy statistical methods that Augusta favors people hit
Speaker 3: it further. And even if there's a guy who a
Speaker 3: first time round Augusta, so we have no course history
Speaker 3: date on him, but if we know he hits it
Speaker 3: above average distances, we can use course fit to make
Speaker 3: some judgments about how he's going to perform in that course.
Speaker 3: So yeah, by focusing on characteristics instead of specific players,
Speaker 3: which is what you do with course history, of course,
Speaker 3: it can be a lot more meaningful. Yeah, because you
Speaker 3: don't have a we have a ton of data to
Speaker 3: estimate how how much driving distance is favored at Augusta,
Speaker 3: we can use every player to understand that.
Speaker 1: Right. Yeah, So course fit just basically defined, is is
Speaker 1: kind of the degree to which a given golf course
Speaker 1: favors a specific skill set, Right, And those the skills
Speaker 1: that you measure are you know, maybe it can help
Speaker 1: me out here driving distance, driving accuracy, strokes gained, approach,
Speaker 1: strokes gained around the green, and strokes gained putting. Yeah,
Speaker 1: those are the kind of the five skills that you
Speaker 1: measure when you're assessing whether a course kind of fits
Speaker 1: a particular kind of player. So has anything kind of
Speaker 1: jumped out and surprised you since you started looking at
Speaker 1: course fit.
Speaker 3: I think the I mean, the main takeaways I think
Speaker 3: from course fit are like with the data that we have,
Speaker 3: it seems like the way that court, the way that
Speaker 3: PGA tour courses differ is along the dimensions of how
Speaker 3: much they favored driving distance and driving accuracy. Certainly we
Speaker 3: can make intuitiveur arguments for why different courses favor putting
Speaker 3: or around the green, but I think the reality is,
Speaker 3: like the data, especially the putting, it's just so there's
Speaker 3: so much variance that it's hard to it's hard to
Speaker 3: really say whether or not a course favors putting more
Speaker 3: than the average, and that sort of reflected in the tool.
Speaker 2: I think, I guess my takeaway like, I think I was.
Speaker 3: A bit surprised. Maybe how still important? Again, there are
Speaker 3: a lot of caveats here, but how important driving accuracy
Speaker 3: is just because there is the narrative of I mean,
Speaker 3: this is how I'm looking at it. Somebody else could
Speaker 3: look at it and say, oh, I'm surprised how much
Speaker 3: that distance is the most favored skill, And just for
Speaker 3: people who aren't looking at this thing. And basically the
Speaker 3: hierarchy is the way we have it is basically distance
Speaker 3: at the average course is the most particular power, along
Speaker 3: with approaching approach, and then it sort of goes.
Speaker 2: I guess driving.
Speaker 3: Accuracy, putting around the green are all pretty similar. Around
Speaker 3: the green is probably the smallest, but that's sort of
Speaker 3: the hierarchy. And then there's all sorts of interesting things
Speaker 3: that specific courses that you could get into.
Speaker 1: Yeah, and we'll get into that a little bit in
Speaker 1: a minute here, But first thing, maybe you could just
Speaker 1: describe the course fit tool a little bit. I think
Speaker 1: people should go and see if the visualization is really
Speaker 1: simple and effective, and it would be probably hard to
Speaker 1: describe over the radio basically what it looks like, but
Speaker 1: could you just tell me basically what the course fit
Speaker 1: tool is and how you built it.
Speaker 3: So the course fit tool, it's essentially what we're just
Speaker 3: trying to visualize, as you said earlier, the degree to
Speaker 3: which each course on the PGA Tour favors each skill,
Speaker 3: and so what we have on it is a basically
Speaker 3: have these shapes. Each shape represents the degree to which
Speaker 3: the course favors skills and so we have the average
Speaker 3: PGA Tour course on there, and then you can sort
Speaker 3: of easily see when we overlay another course on top
Speaker 3: of that whether or not that overlaid course favors distance
Speaker 3: more than the average course. Same with accuracy, et cetera.
Speaker 3: And yeah, I don't know if we want to get
Speaker 3: into the details of how these numbers are actually calculated.
Speaker 3: Is it's somewhat complicated, but the intuitive all we're really
Speaker 3: doing with this tool is like, if you take driving distance,
Speaker 3: for example, we're gonna before each event let's play on
Speaker 3: the PGA Tour or each round, we're gonna have an
Speaker 3: estimate of every player's driving distance and every player's driving accuracy,
Speaker 3: et cetera. And to estimating how much a course favors
Speaker 3: driving distance, we're basically just gonna compare two players, and
Speaker 3: this is important, two players who are similar in every
Speaker 3: dimension except distance.
Speaker 2: So there's one.
Speaker 3: Player, say that hits it ten years further than the other.
Speaker 3: So we're gonna take those two players, compare their stroke
Speaker 3: scheme in that round, and then you do that many
Speaker 3: times and eventually you're able to say, Okay, if you
Speaker 3: hit a ten yards further than somebody, that translates into
Speaker 3: whatever point three point four strokes gained around over that
Speaker 3: person holding everything else fixed.
Speaker 2: And it's right.
Speaker 1: You know, the visualization makes this very clear because this
Speaker 1: is an octagon. Basically, right, there are five skills, so
Speaker 1: there are there are five points on the octagon, and
Speaker 1: if one point is a little bit farther out than
Speaker 1: the others, and you can see quite quickly, okay, this
Speaker 1: this course tends to prefer that skill. And as you
Speaker 1: were indicating earlier, the most dynamic skills, the ones that
Speaker 1: seem to vary the most are driving distance and driving
Speaker 1: accuracy from course to course. The others are fairly constant,
Speaker 1: though with some significant exceptions. So this is a it's
Speaker 1: a really interesting tool. I use it all the time.
Speaker 1: And to be clear, you know, most people probably use
Speaker 1: this as a predictive tool, right to say, hey, the
Speaker 1: course this week seems to be a good fit for
Speaker 1: player X, All go ahead and place a bet on
Speaker 1: player X. But I'm definitely not like the usual data
Speaker 1: golf user. I don't think I'm a lot more interested.
Speaker 1: I don't bet. I'm really risk averse that way. So
Speaker 1: it's like I've never placed a bet on a golf tournament.
Speaker 1: But I'm more interested. The reason I look at this
Speaker 1: stuff is that I'm really interested in considering what something
Speaker 1: like this can tell us about courses on the PGA
Speaker 1: Tour and the skills that those courses prioritize. And I
Speaker 1: know that you know, all this stuff is there's always
Speaker 1: the caveat that data is noisy, and we can't draw
Speaker 1: any super firm conclusions about any giving course or any
Speaker 1: given trend in PGA Tour course design and set up
Speaker 1: from it. But there are some really interesting things in
Speaker 1: here that I can't help but think can tell us
Speaker 1: something about course architecture as it relates to PGA Tour
Speaker 1: player's performance. So, you know, just to start at one
Speaker 1: end of the spectrum, what does the course fit tool
Speaker 1: tell us about Bethpage Black, which is what's the host
Speaker 1: of the twenty nineteen PGA Championship. It's on one end
Speaker 1: of the spectrum for this tool.
Speaker 3: Right, Yeah, so best Page according to our stuff, is
Speaker 3: it favors driving distance more than any other course that
Speaker 3: the PJ Tour has played, and I guess.
Speaker 2: The last five years or so.
Speaker 3: So yeah, it just means when Bryce in, when any
Speaker 3: player that goes to bed Page flack, and if they
Speaker 3: hit it above average distances, instead of that advantage being
Speaker 3: worth save point four strokes beth Page, it might be
Speaker 3: worth zero point six strokes or point seven strokes for
Speaker 3: a round. So yeah, beth Page is the most extreme
Speaker 3: in that regard, and it's also yeah, pretty below average
Speaker 3: and how much it favors driving accuracy the thing also
Speaker 3: to note with like obviously guys who hit it far,
Speaker 3: there's almost there's partly a mechanical or partially a mechanical
Speaker 3: relationship between distance and accuracy, Like as you hit it further,
Speaker 3: it's just harder to hit more fairways. So there is
Speaker 3: a negative, a strong negative correlation between those two. So
Speaker 3: on our tool, like Bryson, like he ghets at above
Speaker 3: average distances, so we're getting it, he's getting a big
Speaker 3: bump for that, But he also is less accurate than average,
Speaker 3: so he's getting a and he's getting a bump for
Speaker 3: that as well at beth Page, because essentially you can
Speaker 3: read it as beth Page penalized accuracy less than the average.
Speaker 2: Was, which is not a great way to think about it, maybe.
Speaker 3: Because obviously there was a high penalty to miss fairway
Speaker 3: at beth Page, but all the tool is saying is
Speaker 3: that the benefit of being a PGA to a golfer
Speaker 3: who is five percent more accurate than average, that advantage
Speaker 3: was lessened at that page for whatever reason.
Speaker 2: We can speculate on that.
Speaker 1: Yeah, I know you don't love speculating about this kind
Speaker 1: of stuff. I might try to push you out of
Speaker 1: your comfort zone a few times.
Speaker 2: You know.
Speaker 1: Part of what makes this suggestion by the course fit
Speaker 1: tool about what skills Bethpage Black prioritizes, what makes it
Speaker 1: compelling is that it's kind of counterintuitive. You know. You
Speaker 1: look at beth Page as it was set up for
Speaker 1: the twenty nineteen PGA Championship, and you see really high
Speaker 1: rough you see really narrow fairways, and the assumption that
Speaker 1: most people would make is that that emphasizes accuracy. You
Speaker 1: have to hit the fairway or you're in trouble. But
Speaker 1: in fact, it seemed to be the opposite, where longer
Speaker 1: players had a distinct advantage. And obviously we can't draw
Speaker 1: strong conclusions from one or two particular results, but it
Speaker 1: seemed like it really seemed like Dustin Johnson and Brooks
Speaker 1: Kopka were the only players who had any chance of
Speaker 1: winning that week, and so is that something that caught
Speaker 1: your eye as well, that that there was a kind
Speaker 1: of counterintuitive result from the course fit tool based on
Speaker 1: what you would assume out of a course that was
Speaker 1: set up the way Beth Page was.
Speaker 3: Well, first, I would say the results I think are
Speaker 3: this is definitely something that's extreme. Like there's certainly a
Speaker 3: lot of signal there. There's no doubt that the I
Speaker 3: think probably there's three events in here, maybe because there's
Speaker 3: the maybe yeah, the two US opens and then that
Speaker 3: page had.
Speaker 1: A that's right, it's not not just the PGA Championship.
Speaker 1: Beth Page also had a Northern Trust or something. Yeah,
Speaker 1: so there is.
Speaker 3: Enough data to say to say things, and yeah, I
Speaker 3: think it was I think it was pretty surprising. Like generally,
Speaker 3: when you do things like this that are somewhat complicated statistically,
Speaker 3: you want like eighty five percent of your results to
Speaker 3: match up with intuition, so then you're like, okay, I'm
Speaker 3: not doing something completely insane here, and then you have
Speaker 3: fifteen or twenty percent where it's like, oh, that's like
Speaker 3: that's countertuitive and interesting.
Speaker 2: So yeah, so Beth Page, it.
Speaker 3: Does go this way where well, so just to bring
Speaker 3: up another example that didn't go this way. Firestone has
Speaker 3: fire Stones, not beth Page. But it's a long course
Speaker 3: that has narrow fairways with reasonably long rough and it
Speaker 3: has it favors distance, but it also favors accuracy, which
Speaker 3: is interesting.
Speaker 2: That what that just to reiterate what I said earlier.
Speaker 3: Like that means Firestone it's above average in terms of
Speaker 3: how much it favors distance, holding accuracy constant, but then
Speaker 3: also holding distance constant constant.
Speaker 2: It's still important to hit fairways.
Speaker 3: There's an above average benefit at Firestone for that as well.
Speaker 3: So I bring that up just because that to me
Speaker 3: is like more yeah, more into if I would have
Speaker 3: thought beth Page would have been like that. So I
Speaker 3: don't know what's different about beth Page. Maybe because it
Speaker 3: was so it was so long, like beth Pages, it
Speaker 3: was an extreme course like g it's longer than Firestone.
Speaker 1: Right. Another thing to mention about beth Page versus Firestone
Speaker 1: is that beth Page has you know, a lot more
Speaker 1: movement in its land and the greens are sort of
Speaker 1: sustained actually elevated, which would seem to you know, give
Speaker 1: a reward to players who end up closer to the
Speaker 1: green after their first shot on a on a par
Speaker 1: four or par five, because you have to get the
Speaker 1: ball all the way to the green. But I don't know,
Speaker 1: you know, there's more ground contour at Firestone than people
Speaker 1: give it credit for. And I'm not sure you can
Speaker 1: exactly run shots up at Firestone necessarily, but there might
Speaker 1: be might be something there, And I mean, I guess
Speaker 1: these are these are factors. This is where we get
Speaker 1: into speculation.
Speaker 3: Right, Yeah, No, I think it's important to keep in mind,
Speaker 3: like driving distance is not just when we're looking at
Speaker 3: a player who bombs it. It's true that he hits
Speaker 3: it further off at team, but then it's also true
Speaker 3: that it's just a powerful player in general. So from
Speaker 3: the rough he's hitting wedges, and like you can think
Speaker 3: about with driving distance as an app as a skill
Speaker 3: is going to be beneficial not.
Speaker 2: Just on t shots but also on approach shots.
Speaker 3: So yeah, that's and at that stage maybe that was
Speaker 3: playing a role as well.
Speaker 1: That's a great point. So on on the other end
Speaker 1: of the spectrum, we have this week's PGA Tour revenue
Speaker 1: El Kama Leone Golf Club, host of the Mya cob
Speaker 1: A Golf Classic. This is on the other end of
Speaker 1: the spectrum from Bethpage. Black tell me about the craziness
Speaker 1: of the data that this course produces.
Speaker 3: Not only is it the most extreme for in how
Speaker 3: much it rewards driving accuracy, it's also just of any
Speaker 3: attribute because again on the default view on the course
Speaker 3: fit stuff, you can compare across attributes, like you can
Speaker 3: say if the further out of dot is that means
Speaker 3: just in an absolute sense, that skill is getting rewarded
Speaker 3: more strokes gain than the other dots. So so driving
Speaker 3: accuracy at Alchemeleone is actually rewarded as much as driving.
Speaker 2: Distances at beth Page.
Speaker 3: And the reason it's not that's notable is because at
Speaker 3: the average course accuracy is rewarded less and distance, So
Speaker 3: that means at al chime Leone it's more of an
Speaker 3: outlier even than beth Page.
Speaker 2: Yeah, it's pretty crazy.
Speaker 3: And like I know, when we looking at like our
Speaker 3: predictive model stuff, when we're actually calculating how much we're
Speaker 3: going to adjust players skill levels, because that's how we
Speaker 3: actually put to use the course fits stuff. We basically
Speaker 3: have a player's baseline and then we're going to say, okay,
Speaker 3: based off course fit we're gonna move off of that
Speaker 3: baseline by whatever.
Speaker 2: Point two strokes or something.
Speaker 3: At alchemillion, there's adjustments that are like a stroke, which
Speaker 3: is insane. So we're moving a player's skill level a shot.
Speaker 3: I mean, that's like the most extreme guys. But just
Speaker 3: for context, that's the difference between like the fiftieth ranked
Speaker 3: player in the world in our rankings and like maybe
Speaker 3: the two hundredth or something like that. Like, it's a
Speaker 3: huge I got one shot around is obviously a massive difference.
Speaker 3: So that to be driven by course fit is pretty crazy.
Speaker 3: So yeah, this this week's course is the biggest outlier.
Speaker 1: It exerts a huge influence on the results in a
Speaker 1: certain way. And I guess one way to put it
Speaker 1: is that if you have a clear best player in
Speaker 1: the field, you know, somebody whose past performance is really
Speaker 1: really awesome, a lot better than everybody else in the field,
Speaker 1: going into the Maacoba Golf Classic, they are a little
Speaker 1: bit less likely to win there than usual. Would that
Speaker 1: be fair to say?
Speaker 3: It would be fair to say as long as they
Speaker 3: possess the typical top ranked player skill set Brendan Todd
Speaker 3: ever to be the best player in the world, then
Speaker 3: we might actually predict him to be better.
Speaker 2: But in general, in general, Yeah.
Speaker 3: Your statement is true, Like this week, the skilled attribution
Speaker 3: is going to be compressed just because it tends to
Speaker 3: be the case that the best players do hit it far.
Speaker 3: And so the best players on average will be getting
Speaker 3: a negative bump this week, and worst players on average
Speaker 3: will be getting a positive.
Speaker 2: Yeah, bringing everybody together.
Speaker 1: So I guess a way to put is, if Rory
Speaker 1: were playing this week, then you would give him less
Speaker 1: of a positive positive adjustment than usual. You would you
Speaker 1: would be less strong on the idea that he might
Speaker 1: win than you usually would be at the at the
Speaker 1: average PGA tour venue.
Speaker 2: That's the right way to say.
Speaker 3: Yeah, I would probably say relative to his baseline, like
Speaker 3: it's average still level, we're giving him a negative bump.
Speaker 3: But what you're saying makes makes sense to It's we're
Speaker 3: still we still like Rory. We just like him less
Speaker 3: than normal.
Speaker 1: He's still going to be good. It's not like he's
Speaker 1: all of a sudden, you know, yeah, the worst player
Speaker 1: in the field or something. Yeah, I mean I find
Speaker 1: that interesting. Does that mean does that mean that there's
Speaker 1: just that randomness kind of plays more of a role
Speaker 1: at a venue like this week's I.
Speaker 3: Wouldn't say I wouldn't say that there are some courses
Speaker 3: where that does seem to be true, but I think
Speaker 3: it's just the issue is just when I when we
Speaker 3: say high skill gear, what we mean like high skill
Speaker 3: on the PGA Tour these days, that generally means you
Speaker 3: hit it far, just because that's the way that skills
Speaker 3: are rewarded. So when you go to Camillone, it's true
Speaker 3: that higher skilled players are getting a negative bump, but
Speaker 3: it's it's not necessarily due to randomness. It's just because
Speaker 3: driving accuracy is now way more important because if you
Speaker 3: miss a fairway at that course, you're generally taking a drop.
Speaker 2: So yeah, I wouldn't say it's randomous.
Speaker 3: I would say it's it's a different skill set that's
Speaker 3: been tested, and so that brings the skill distribution relative
Speaker 3: to like the typical tour event closer together.
Speaker 1: You mentioned what I think is one of the key
Speaker 1: factors here, and that's that if you miss a fair
Speaker 1: way at this course, then you're basically in the bush,
Speaker 1: like it's a lost ball, and so there's a really
Speaker 1: heavy penalty for missing fairways. But at the same time,
Speaker 1: you know, I think Sometimes people get confused here because
Speaker 1: then they say, well, doesn't that mean that penal setups
Speaker 1: where there's where there's a clear kind of immedia penalty
Speaker 1: for missing a fairway, doesn't that mean that those would
Speaker 1: favor accuracy above all else. But of course we just
Speaker 1: talked about Beth Page, where there was a real penalty
Speaker 1: for missing a fairway, but distance tended to be more
Speaker 1: predictive there. And so how would you reconcile those two?
Speaker 1: Is it because they're like different levels of penalty, right
Speaker 1: that you know that taking a drop is a lot
Speaker 1: different from hitting it out of the rough.
Speaker 3: It could be that it could just be a matter
Speaker 3: of degree, although I think it's more than that. I
Speaker 3: think it's I mean, maybe it's also has to do
Speaker 3: with the fact that at death Page, maybe it didn't
Speaker 3: matter so much if you were five years off the
Speaker 3: fairway or if you were twenty yards off. I don't
Speaker 3: even I don't know if that statement is true. It
Speaker 3: still matters a bit, but alchimately, oh maybe it matters
Speaker 3: a bit more like the fact that you're two yards
Speaker 3: off the fairway. I don't know how many yards you
Speaker 3: have to deal with there, two yards or three yards,
Speaker 3: and then you're in the junk.
Speaker 2: Maybe that that's part of it, also part of the
Speaker 2: people what we were.
Speaker 3: Saying earlier, where at best page, when you miss a fairway,
Speaker 3: you are hacking it out of deep rough where bombers
Speaker 3: still have an advantage, where whereas at UG mileone you're
Speaker 3: you're just dropping it in pretty light rough. I don't
Speaker 3: there's not sick rough at this course, so maybe that
Speaker 3: advantage is gone for distance, But a lot of it's
Speaker 3: got to just be the fact that this week's course
Speaker 3: is shorter.
Speaker 1: Yeah, I mean, the causes are really hard to identify,
Speaker 1: but you know, I want to expand this out a
Speaker 1: little bit, Matt, that el kamal Leone is part of
Speaker 1: a set of courses on the PGA Tour that I've
Speaker 1: kind of discovered through the course fit tool on your
Speaker 1: website that I like to call the web Tour Web
Speaker 1: with two b's. You know, they're just courses where web
Speaker 1: Simpson seems to seems to perform particularly well, and players
Speaker 1: of Web Simson's ILK as in, you know, really good
Speaker 1: in every skill except for driving distance. You may also
Speaker 1: say that Brendan Todd is kind of along this model
Speaker 1: as well, at least within the past year and a
Speaker 1: half or so. So in addition to El kamala ayone,
Speaker 1: you have Harbor Town Golf Links, you have Wyley Country Club,
Speaker 1: you have TPC, Sawgrass, you have Colonial, and then maybe
Speaker 1: to a slightly lesser degree, you have Sedgefield Innisbrook, Copperhead,
Speaker 1: the side of the Valspar Championship. Sedgefield is the side
Speaker 1: of the Windham Championship. To an extent, Sea Island, which
Speaker 1: was last week's venue for the RSM Classic, maybe Sherwood,
Speaker 1: one time host of the Zozo this year, maybe Merefield Village.
Speaker 1: I mean those kind of got less and less a
Speaker 1: part of the Web tour as I went along, but
Speaker 1: especially that kind of top group of courses Harbor Town, Wyley, TPC, Sawgrass, Colonial,
Speaker 1: El kamala Ayone. At all of these courses you see
Speaker 1: an unusual emphasis on driving accuracy and an unusual d
Speaker 1: emphasis on driving distance, as in the skill of driving
Speaker 1: distance is less predictive of success at these courses than
Speaker 1: average on the PGA tour, and the skill of driving
Speaker 1: accuracy tends to be more predictive. El kama aone is
Speaker 1: just the most extreme example of this. But have you
Speaker 1: noticed the Web tour before. Have you noticed this class
Speaker 1: of courses on the PGA tour and if so, what
Speaker 1: do you what do you make of it.
Speaker 3: I'm not gonna say I would have come across this
Speaker 3: cluster of courses before looking at before doing this analysis,
Speaker 3: although at the same time I think it does. It
Speaker 3: does make sense. Like I think most of these courses
Speaker 3: are are shorter, and I mean they're also there are
Speaker 3: also differently a few of them. I think of Sedgefield
Speaker 3: and east Lake is also one that rewards accuracy more
Speaker 3: like those are pretty like Eastlake is pretty brutal rough Sedgfield.
Speaker 3: I'm not sure exactly why it favors. It's just it's
Speaker 3: a shorter course rough pretty tough there. But I mean again,
Speaker 3: but as we have at best day, it's not necessarily
Speaker 3: a simple relationship like thicker rough does not necessarily mean
Speaker 3: it favors favors accuracy, although I do think the combination
Speaker 3: of thick rough with a shorter course maybe does. It
Speaker 3: might just be the interaction of really thick rough with
Speaker 3: a really long course that gets you that effect where
Speaker 3: it distance but not accuracy. Yeah, And Harbordtown, I think
Speaker 3: is just the course where it just takes driver out
Speaker 3: of your hands on a lot of holes, which which
Speaker 3: is fine.
Speaker 2: I mean, I think it's honestly what I thought after
Speaker 2: the when.
Speaker 3: Golf came back after the three month COVID break and
Speaker 3: Bryson was doing his thing and getting all that attention, Like,
Speaker 3: I was kind of struck by how many courses It
Speaker 3: seemed like the first few weeks he was playing, he
Speaker 3: was playing golf courses that didn't really favor bombers that much.
Speaker 2: Like I was sort of struck by it.
Speaker 3: I was thinking, Yeah, like, obviously we all know distance,
Speaker 3: like the best players in the game bombit, and it's
Speaker 3: clearly a huge advantage.
Speaker 2: But there are still courses on the PGA Tour, like the.
Speaker 3: Travelers River Highlands that it's not that it's still it's
Speaker 3: always an advantage to hit it far, but they are
Speaker 3: there are courses on tour that, yeah, favor accuracy still,
Speaker 3: so that I remember being struck by that when the
Speaker 3: golf came back.
Speaker 1: Yeah, and Detroit Golf Club where Bryson won, is not
Speaker 1: a course that necessarily strongly prefers distance in the way
Speaker 1: that we see a lot of PGA Tour courses do.
Speaker 1: Am I right about that?
Speaker 3: Yeah, I mean, according to tool, that's true. I feel
Speaker 3: like I've heard people say, I mean, I'm not sure
Speaker 3: who I should trust. I guess I've heard people say,
Speaker 3: like I think I've heard people say before that event that, oh,
Speaker 3: Bryson's gonna make a mockery of this, of this layout.
Speaker 2: And I don't think I watched a shot at that event,
Speaker 2: so I.
Speaker 1: Can't The most exciting thing that happened was Bryson yelling
Speaker 1: at a cameraman. But I think what people were saying
Speaker 1: is that he's going to bombit past the fairway bunkers
Speaker 1: that were put in during the renovation that were put
Speaker 1: out there to kind of, like, you know, give give
Speaker 1: tour players something to think about, and he just was
Speaker 1: longer than them. I don't know. I mean, there's a
Speaker 1: lot of these factors that tend to come into play
Speaker 1: at these courses, and obviously a player like Bryson can
Speaker 1: do well anywhere, you know, he's he's got other skills.
Speaker 1: But you mentioned some of the characteristics that these courses
Speaker 1: have in common. These courses that actually succeed in prioritizing
Speaker 1: driving accuracy, and that's something that so many people are
Speaker 1: interested in seeing come back to the PGA Tour in
Speaker 1: a bigger way, you know, more of an emphasis on
Speaker 1: driving accuracy. They're they're trying to figure out how in
Speaker 1: the heck do we do this? Do we roll back equipment,
Speaker 1: do we do things to courses? But it seems to
Speaker 1: me that this set of courses gives a pretty good
Speaker 1: example of how to do that. But it doesn't necessarily
Speaker 1: explain why these courses behave in this way, you know,
Speaker 1: super clearly. But one thing that I see in common
Speaker 1: is just course length. A lot of these are shorter courses.
Speaker 1: So do you think that's like a strong factor.
Speaker 3: Yeah, a lot of times you have we can, we can,
Speaker 3: we can come up with these more complex theories about
Speaker 3: how courses will favor certain skill sets. But yeah, often
Speaker 3: those things are hard to like tease out of the data.
Speaker 3: But yeah, something like course length. I think it's pretty
Speaker 3: clear in the data that longer courses, shocking, yeah, favor
Speaker 3: driving distance, and and yeah, these shorter courses, I mean,
Speaker 3: I don't know that people would be particularly happy with
Speaker 3: this set of courses, being like if this is the
Speaker 3: template that it's like, oh, yeah, these are you want this,
Speaker 3: these skills to be rewarded in this way, like, Okay,
Speaker 3: that's your course.
Speaker 2: It's like, I don't know if people because because Sawgas is.
Speaker 3: Sort of a some of these courses I think of
Speaker 3: as kind of random or not not rewarding skill that much.
Speaker 3: But that might also be like a bias because we're
Speaker 3: used to seeing we're just not like Rory won't play
Speaker 3: as well at that course, and we're used to thinking
Speaker 3: of Rory as the best player in the world.
Speaker 2: So you have to like.
Speaker 3: Maybe come to terms with the reason Rory's the best
Speaker 3: play in the world is because his language has an advantage.
Speaker 2: But the one course, and I'm surprised.
Speaker 3: I'm sure that Friday did write some articles about it
Speaker 3: the course a few weeks back where it was short
Speaker 3: and it was it played incredibly tough.
Speaker 2: The Houston Open, the Sure Memorial Park, Memorial Park, Yeah, I'll.
Speaker 1: Be I'll be fascinated to see how that comes out.
Speaker 3: That wasn't particularly penal off the tee, right, that was
Speaker 3: just it was around the greens that were giving people
Speaker 3: a lot of trouble.
Speaker 1: Very much around the greens. Yeah, I mean, I think
Speaker 1: that the more tournaments that are played there, the better
Speaker 1: of an idea we're going to get for it. But certainly,
Speaker 1: you know, since the greens were fairly new, they were
Speaker 1: quite firm, and so I'm not sure if in future
Speaker 1: years it'll be the same kind of dynamic. And you know,
Speaker 1: there are some pros who are frustrated by it, so
Speaker 1: that might play into future setups there as well. But
Speaker 1: I think it is a good example of how, you know,
Speaker 1: narrow courses with with clear penalties off the tee are
Speaker 1: not necessarily the only solution, and I think, you know,
Speaker 1: Wileye probably demonstrates that as well. I don't think of
Speaker 1: Wildleye as being particularly penal off the tee it. You know,
Speaker 1: there are some palm trees out there, but it's it's
Speaker 1: fairly wide open. Other than that, it's just a shorter course.
Speaker 2: Yeah.
Speaker 3: Wiley is an interesting example though, where if you look
Speaker 3: at its diagram on the course fit page, it sort
Speaker 3: of is compressed in every respect, so it doesn't rewards
Speaker 3: driving distance class and driving accuracy lass. Oh and also
Speaker 3: slightly approached.
Speaker 2: Which I mean again it's hard to.
Speaker 3: Say exactly what that means, but in theory that might
Speaker 3: not be a great thing because it means that you're
Speaker 3: not rewarding skill as much at Wiley as another course,
Speaker 3: but again that's only like part of the equation. I
Speaker 3: think when we because like, realistically, if you want to
Speaker 3: reward skin as much as possible in the PGA Tour,
Speaker 3: you probably would want a course where where greens are
Speaker 3: super soft and it's just target golf, because yeah, it
Speaker 3: does take like when there's no randomness in the sense
Speaker 3: that there's no bounces that can make a a good
Speaker 3: shot turned into.
Speaker 2: A bad shot.
Speaker 3: Then yeah, like a soft course like that probably does
Speaker 3: reward skill the most. But as viewers, like, nobody wants
Speaker 3: to watch that, so there's other there's other considerations, and
Speaker 3: so I think, yeah, while I it does level the
Speaker 3: playing field, it does seem like there's some element of
Speaker 3: randomness there, but like that could be fine, depends on
Speaker 3: what your preferences are.
Speaker 1: That's maybe not a bad thing. Yeah, I mean, there's
Speaker 1: an assumption that what we really want are courses that
Speaker 1: reward skill in a clear way. But I think that
Speaker 1: once we take that to the logical extreme, what's the
Speaker 1: course that rewards skill the most that we might not
Speaker 1: like what's on the other side of that door.
Speaker 3: Yeah, maybe maybe that brings us to like top golf,
Speaker 3: and you just get tour pros hitting shots into baskets,
Speaker 3: and that's that's the most that's the most skilled thing
Speaker 3: for rewarding approach shots the.
Speaker 1: Most reliable test of skill. Yeah, totally. There's all the
Speaker 1: other factors have have kind of been leveled. So you know,
Speaker 1: speaking of course, is that that seem to have high
Speaker 1: variance is another way to put what we're talking about.
Speaker 1: You did a really good deep dive earlier this year,
Speaker 1: I believe on TPC Sawgrass. Tell me about what you
Speaker 1: found there. What are some of your thoughts about TPC
Speaker 1: Sawgrass as a course that you know tests certain skills
Speaker 1: or doesn't test certain skills from PGA tow or players.
Speaker 3: Yeah, Sawgrass is a course that rewards accuracy more so
Speaker 3: than compared to the other four attributes, I think, and
Speaker 3: any other thing.
Speaker 2: About saw Grass is and this isn't really.
Speaker 3: Reflected in the course fit tool, is that at Sawgrass,
Speaker 3: not only is it the case that if you're one
Speaker 3: shot better than the average player at the average PG two,
Speaker 3: of course when you go to Sawgrass, you're only going
Speaker 3: to be point seven or point eight shots better. So
Speaker 3: it's it is reducing the advantage that skilled players have.
Speaker 3: And then it's also it's also adding in variants in
Speaker 3: the sense that Sawgrass is just a course where if
Speaker 3: the same player plays there one hundred times the variants
Speaker 3: in their score. So they're going to average seventy, let's say,
Speaker 3: but they're gonna sometimes she's seventy five, sometimes.
Speaker 2: She's sixty five.
Speaker 3: That variance is higher at Sawgrass as well, and that's
Speaker 3: sort of a that's a separate thing to relate this
Speaker 3: to whyl Whyli is actually a course where even though
Speaker 3: that skill advantage gets reduced, it's it's a low variance course.
Speaker 3: So if the same player plays there a bunch, it's
Speaker 3: going to be a tighter bound around their average score.
Speaker 3: It's kind of a tricky thing to think through, but
Speaker 3: the upshot for saw Gas is that, yeah, it's a
Speaker 3: it's a very random course. It does I think it
Speaker 3: does reward accuracy more than average, but that's about it.
Speaker 3: And it also has this added element of just variance,
Speaker 3: which I think everybody who watches it what you agree
Speaker 3: with that, like, there's there are there is potential for
Speaker 3: big numbers in that course, and there is more randomness
Speaker 3: in that respect.
Speaker 2: So it's Yeah, the takeaway.
Speaker 3: With Sawgas is that it's just not only is performance unpredictable,
Speaker 3: there's also just more variance in general.
Speaker 1: And so yeah, I wonder why that. And this is
Speaker 1: getting into the architecture questions that you know, data is
Speaker 1: not necessarily gonna tell us clear answers on but I
Speaker 1: I wonder why that. What design characteristics of TPC sawgrass
Speaker 1: are at play here? Could it be the really severe
Speaker 1: penalties at certain places in the course. Could it be
Speaker 1: the tininess of the targets on the greens? Do you
Speaker 1: think these things could have have an influence? I mean
Speaker 1: not just the greens themselves, but like the sections of
Speaker 1: the greens where pins might be are super super small.
Speaker 2: I think that's it.
Speaker 3: I think so yeah, I think you can have variance,
Speaker 3: and variants can be a good thing or it can
Speaker 3: be a bad I mean, if you have at sawgrass,
Speaker 3: like if you have a shot that if it misses,
Speaker 3: if a player misses this spot by two yards, it
Speaker 3: ends up rolling down a hill, and it's super penalizing
Speaker 3: compared to a course that's soft. You miss your spot
Speaker 3: by two years, you just have a pieto that's two
Speaker 3: years longer.
Speaker 2: It's not a big deal.
Speaker 3: So that that can certainly add variance, and that would
Speaker 3: probably be considered good varians because it's it's making the
Speaker 3: margin between a good shot and or a marginal shot
Speaker 3: and a good shot is now that that's creating a
Speaker 3: bigger difference scores, which is probably what we want. And yeah,
Speaker 3: I think Sawgrass does that to something to be obviously,
Speaker 3: seventeen is like a good example of probably variants you
Speaker 3: might not want.
Speaker 2: It's hard to.
Speaker 3: Say, like if somebody's playing really well and goes to
Speaker 3: seventeen and makes a seven, their tournament is all but
Speaker 3: over because of that, and that's sort of that that's
Speaker 3: another big source of variance.
Speaker 1: Shout out Sergio.
Speaker 3: Yeah, and in general, I think it's just because Sawgrass
Speaker 3: is sagas generally plays super firm too. And like there's
Speaker 3: speaking of surgery, there's that year where you like eight
Speaker 3: putted hole or eight the part three and like you
Speaker 3: just that's probably bad variance.
Speaker 2: I think we would agree.
Speaker 3: That that's bad rans The greens were just they sort
Speaker 3: of maybe put them over the edge. And when I
Speaker 3: think of Sawgas, I just think of it might not
Speaker 3: necessarily be rewarding skill as much as we like, but
Speaker 3: it's maybe it is it's hard to say, but it's
Speaker 3: very entertaining golf to watch.
Speaker 1: Absolutely, yeah, I mean, I think it's good to be
Speaker 1: clear that, you know, when we're asking questions about how
Speaker 1: PGA Tour venues can reward or not reward different skills
Speaker 1: and talk about the kind of variety that we might
Speaker 1: want to see in PGA Tour venues, that's not necessarily
Speaker 1: a commentary about good architecture. The question of what makes
Speaker 1: good architecture and what makes a PGA Tour venue that
Speaker 1: should be part of the rotation, those are two separate questions, right,
Speaker 1: And I think that finding that balance between good architecture
Speaker 1: for the masses and something compelling to watch in a
Speaker 1: PGA Tour event, that's really something that these courses have
Speaker 1: to think about, right because they're they're just hosting a
Speaker 1: PGA Tour event one week of the year. The rest
Speaker 1: of the rest of the year, they're you know, open
Speaker 1: to the membership or open to the public. But personally,
Speaker 1: you know, what, what I really want to see, you know,
Speaker 1: I love, you know, these questions about good architecture I'm
Speaker 1: very interested in and I think that's the highest priority
Speaker 1: for a golf course. But I'm also somebody. I'm also
Speaker 1: a PGA Tour fan, and I want to see as
Speaker 1: much variety in these venues as possible. I want to see,
Speaker 1: you know, many different types of courses that test different skills.
Speaker 1: And that's why I get excited when I see a
Speaker 1: course like Harbor Town, because it's just not the usual
Speaker 1: PGA Tour venue. I mean, you're you're a golf fan,
Speaker 1: do you do you kind of feel the same way.
Speaker 2: Yeah? Yeah, I mean I feel the same way. I yeah.
Speaker 3: I think there's a clear trade off to be made
Speaker 3: between setting up a course of rewards skill and setting
Speaker 3: up a course to maybe not a clear trade up,
Speaker 3: but there is a trade off between rewarding skill and
Speaker 3: making the golf entertaining to watch. Like, I really can't
Speaker 3: watch the PGA Tour play when they play a course
Speaker 3: where it's soft and it's a diverty fest, it's just
Speaker 3: very uninteresting to.
Speaker 2: Watch for me. Like, I don't necessarily.
Speaker 3: Appreciate architecture per se, but I mean I like watching
Speaker 3: golfers play firm golf courses just because it's tough.
Speaker 2: It's just testing players. I like seeing.
Speaker 3: I like I like see courses where there's like penalties
Speaker 3: off the tee, just because I think especially down the stretch,
Speaker 3: Like just from my own experiences playing golf, I like
Speaker 3: seeing it's compelling to watch players try and perform under
Speaker 3: the gun, and hitting a good drive under pressure is
Speaker 3: I think one of the harder things to do in golf.
Speaker 3: So I mean that to me is interesting. But yeah,
Speaker 3: in general, variety I think, is you need it on
Speaker 3: the PGA Tour like I don't, And I think, unfortunately,
Speaker 3: it'd be one thing if there's no variety. But they
Speaker 3: had settled on firm golf courses as the thing they're
Speaker 3: going to focus on, but they've settled on on softer
Speaker 3: birdy fest at least for non majors and non well
Speaker 3: pretty much just non majors, And yeah, I think that's
Speaker 3: pretty uninteresting golf for a lot of fans.
Speaker 1: And it changes the complexion of the top the set
Speaker 1: of top players in the world, because presumably if there
Speaker 1: were more courses on the PGA Tour that had some
Speaker 1: of these Web Tour characteristics that we've been talking about
Speaker 1: Web with two b's, there would be different players in
Speaker 1: the top ten. I mean, it would just be the
Speaker 1: skill set of the top players in the world might
Speaker 1: be a little more varied.
Speaker 2: Yeah, I think it would be more varied.
Speaker 3: But then and then at some point we're just going
Speaker 3: to get to like this subjective question of how much
Speaker 3: which skills should be rewarded, because I still like Dustin Johnson.
Speaker 3: Be able to hit the ball as far as he
Speaker 3: does is and then obviously an incredible skill that should
Speaker 3: be rewarded. And then the question is just how much,
Speaker 3: because yeah, Web's also skilled in various ways. So it's
Speaker 3: it's a tough if the fine line sort of you
Speaker 3: have to figure out what exactly your priorities are.
Speaker 1: I guess absolutely, and and DJ, it should be said
Speaker 1: as well as Rory have significant skills across the board,
Speaker 1: whereas somebody like Web, you know, you could say that
Speaker 1: he just doesn't really have the skill of driving distance
Speaker 1: at an elite level, and so and he gets docked
Speaker 1: for that, and some might say that that's exactly right,
Speaker 1: you know. So somebody like DJ, his balanced game, which
Speaker 1: includes that extraordinary ability with the driver, is certainly well
Speaker 1: rewarded and and should be. I think that's a good
Speaker 1: place to wrap up our discussion. Of course, fit you also,
Speaker 1: I should mention, did a deep dive into augusta National
Speaker 1: recently for your website before the Masters, and we're not
Speaker 1: going to go into that in depth, but I just
Speaker 1: wanted to mention it and just recommend that people go
Speaker 1: and check that out because I think it's absolutely fascinating.
Speaker 1: So there's just something that that people can check out
Speaker 1: that we won't discuss here. That's a reason to go
Speaker 1: to datagolf dot com and do some re So I
Speaker 1: just wanted to to wrap up with it with a
Speaker 1: few random questions. These don't need to be long in
Speaker 1: depth answers, but a few points of curiosity outside of
Speaker 1: this course fit discussion that we've been having so kind
Speaker 1: of a lightning round. When a player is having a
Speaker 1: breakout season, are there any key characteristics that you see,
Speaker 1: like things that change in their game?
Speaker 3: Generally, if a player is playing well recently, and all
Speaker 3: I know is that they're playing well, I haven't looked
Speaker 3: at deeper into the data, it's probably due to the
Speaker 3: putting around the green stuff, just because that's the stuff
Speaker 3: that varies a lot round around. But if they're truly
Speaker 3: having a breakout season, one where our model is actually
Speaker 3: gonna say, okay, this guy's being elevated to a new level.
Speaker 3: I mean I think it's usually it's usually driven by
Speaker 3: either off the t stuff or approach stuff.
Speaker 2: Yeah.
Speaker 3: The easiest way is, like for Telly, like somebody who's
Speaker 3: just gained ten yards and that matters. That's an easy
Speaker 3: way to do it.
Speaker 1: Who is the best player on the PGA Tour without
Speaker 1: a major? H? And why is it? John Rahm?
Speaker 2: Oh right, wrong?
Speaker 3: Yeah, obviously is wrong. Yeah, obviously obviously it's wrong. Yeah,
Speaker 3: I guess I was thinking, do I want to say,
Speaker 3: who's like, who deserves one at this point?
Speaker 1: And not like the best career, not the best career, yeah,
Speaker 1: but like more you know, who is currently you know,
Speaker 1: based on their recent performance? What would it clearly be wrong?
Speaker 1: I mean I don't know, is that the clear answer
Speaker 1: to the question.
Speaker 3: I mean yeah, to me as someone who's looking at
Speaker 3: I don't really think of players who perform well in
Speaker 3: majors versus.
Speaker 2: Those who don't. I just think of golfers who are
Speaker 2: good versus golfers who are not.
Speaker 3: So I'm obviously rom has not had that many chances
Speaker 3: to win majors, or maybe any good chances. But he's
Speaker 3: rom Is We've been saying wrong. Ram has been underrated
Speaker 3: he's probably not underrated anymore, given that he's the number
Speaker 3: two is replaying in the world. But he's just he
Speaker 3: started his careers as close to Tiger in the last
Speaker 3: twenty years as anybody else has in terms of just
Speaker 3: strokes gained and number of top fives, and he just
Speaker 3: didn't have a transition period. He just came came out
Speaker 3: on the PJ Tour and was already playing like the
Speaker 3: top five planet world. So he's going to get a
Speaker 3: major soon.
Speaker 1: Yeah, this game is unbelievable. Yeah, it can't it can't belong.
Speaker 1: People are very high right now in Dustin Johnson's ability
Speaker 1: to win multiple additional majors. What would you say to
Speaker 1: that all that excitement, Yeah.
Speaker 3: I would say the best player in the world has
Speaker 3: Maybe you don't even have to trust us. You can
Speaker 3: trust Betty Markets, trust anything. They have about a nine
Speaker 3: or ten percent chance, if they're clearly the best player
Speaker 3: in the world, a nine or ten percent chance to
Speaker 3: win a major. So that means if a guy plays,
Speaker 3: if DJ plays ten majors and he's aging, that means
Speaker 3: we've expect him to win one. So still it's like
Speaker 3: you can't assign majors to too many golfers because there's
Speaker 3: only four of them each year, so it's it's always
Speaker 3: unlikely to win majors.
Speaker 1: Yeah, yeah, there's there would be a good hobby in
Speaker 1: listening to golf podcasts and tabulating the amount of future
Speaker 1: majors that are given out.
Speaker 2: Yeah yeah, we'd be in the thousands.
Speaker 1: Yeah, exactly. So you know you're on golf Twitter on occasion.
Speaker 1: So I'm sure you've seen how people use statistics to
Speaker 1: try to win arguments. What is the what was common
Speaker 1: mistake that you see when people use stats in this way.
Speaker 3: I think the biggest one is confusing correlation with causation.
Speaker 3: I have an economics background, so that's I am very
Speaker 3: sensitive to correlation versus causation. And I mean a good example,
Speaker 3: I saw AUGUSTA somebody posted just on number three the
Speaker 3: scoring average for players who had hit it more than
Speaker 3: three hundred yards on the whole versus the scoring average
Speaker 3: for players who had hit less than two forty. And
Speaker 3: the conclusion they were trying to say was, oh, look,
Speaker 3: you should be hitting driver here to go for the
Speaker 3: green because the scoring average is better for the guys
Speaker 3: who hit it further. But that's the flawed for many reasons,
Speaker 3: and the biggest one is that the guys who are
Speaker 3: hitting at two forty on that whole were like Larry Mize,
Speaker 3: Samuel Aisle, among others, and the guys who are hitting
Speaker 3: at three hundred, like Bryce and Rory the best players
Speaker 3: in the world. So that's correlation versus causation because you're
Speaker 3: picking up we want to know the causal effect of
Speaker 3: hitting driver on that hole versus not hitting driver. But
Speaker 3: when you do that analysis, you're picking up all sorts
Speaker 3: of things, like the fact that Rory is just a
Speaker 3: better golfer in every way than my So you're, yeah,
Speaker 3: that's Twitter is not a good space for statistical arguments.
Speaker 1: You're you're comparing Rory McElroy to a retired golf pro yah, yeah,
Speaker 1: which which is meaningless. Yeah yeah, all right. So you know,
Speaker 1: this is maybe a big question, but I'm just curious,
Speaker 1: like when you're looking at the future of data golf
Speaker 1: and what kinds of new questions you want to answer.
Speaker 1: What do you think the new frontiers are in golf stats?
Speaker 1: What do you think the questions are that that we
Speaker 1: haven't answered yet, and maybe we could find methods to
Speaker 1: answer them.
Speaker 3: Yeah, I think I actually have a good answer, because
Speaker 3: we're hopefully for next year we're going to be we're
Speaker 3: going to have official access to shot link data. We're
Speaker 3: the actual shot level stuff, or sort of, because right
Speaker 3: now everything on our site is just round level stuff
Speaker 3: just because you're not allowed to without an official license,
Speaker 3: you can't display shot link data.
Speaker 2: So if that hurdles passed.
Speaker 3: I think yeah, I think the next step for golf
Speaker 3: analysis is just building on Brody's stuff with strokes gained
Speaker 3: and trying to if we really trying basically, because right
Speaker 3: now in the PGA Tour, the way strokes gain is
Speaker 3: calculated is just you have a generic baseline function that says,
Speaker 3: from this distance and this lie, we expect to a
Speaker 3: player to take this many shots and then you get
Speaker 3: strokes gain from that. What we would like to do
Speaker 3: is have a model that and it would be difficult,
Speaker 3: but it would have a different baseline function for every hole.
Speaker 3: So like, maybe there's a hole where going this would
Speaker 3: be very subtle and potentially not possible, but maybe there's
Speaker 3: a hole where when you're two hundred yards out in
Speaker 3: the right side of the fairway, that's actually significantly worse
Speaker 3: than being two hundre yards out in the left side
Speaker 3: of the fairway and right now with strokes gained, that's
Speaker 3: treated the same.
Speaker 2: But if you had again it would be difficult.
Speaker 3: But you couldn't theory be able to say, no, I
Speaker 3: guess from this angle or from this spot, players take
Speaker 3: whatever three point four shots to get down from this
Speaker 3: spot it's three point one. And I think once you
Speaker 3: have that whole specific baseline function, there's all sorts of things.
Speaker 3: The course, your your assessment of course fit could totally change.
Speaker 3: You could drill down to a whole level. You could
Speaker 3: say yeah, you could say a lot of a lot
Speaker 3: of interesting things. So I think that's sort of the
Speaker 3: next direction. That then that has implications for prediction and
Speaker 3: golf and for yeah, for for a lot of things.
Speaker 2: So I think that's the next one.
Speaker 1: All right, Well, Matt, good luck with your with your
Speaker 1: future work. And thank you so much for talking to
Speaker 1: me today.
Speaker 2: Yeah, thanks a lot for having me on. Enjoyed it.
Speaker 3: Hmmm.