1 00:00:00,360 --> 00:00:02,320 Speaker 1: The guys from paying They've kind of showed me how 2 00:00:02,400 --> 00:00:05,040 Speaker 1: much the equipment matters. I just love that I can 3 00:00:05,120 --> 00:00:05,760 Speaker 1: hit any shot. 4 00:00:05,840 --> 00:00:07,440 Speaker 2: I kind of want we're gonna be able to tell 5 00:00:07,480 --> 00:00:09,480 Speaker 2: some fun stories about what goes on here to help 6 00:00:09,520 --> 00:00:10,600 Speaker 2: golfers play better golf. 7 00:00:11,240 --> 00:00:13,920 Speaker 3: What's up, everybody, Welcome back to the Ping Proven Grounds Podcast. 8 00:00:14,000 --> 00:00:16,680 Speaker 3: I'm Shane Bacon. That's Marty Jerts and Marty. We've got 9 00:00:16,920 --> 00:00:21,240 Speaker 3: We've got a very polished guest today, a man that 10 00:00:21,320 --> 00:00:24,079 Speaker 3: has kind of changed the way we think about golf, 11 00:00:24,079 --> 00:00:28,040 Speaker 3: at least the way we interpret golf. Southsaya, the CEO 12 00:00:28,160 --> 00:00:31,080 Speaker 3: and co founder of Arcos, is with us. Sal, I 13 00:00:32,120 --> 00:00:35,280 Speaker 3: know wheeling on you guys a lot throughout the year 14 00:00:35,360 --> 00:00:38,479 Speaker 3: in terms of gaining information. So we thank you for 15 00:00:38,520 --> 00:00:40,200 Speaker 3: taking a couple of minutes to chat with us. 16 00:00:40,479 --> 00:00:42,400 Speaker 1: Yeah, I mean, thank you so much for having me 17 00:00:42,479 --> 00:00:45,720 Speaker 1: And I don't know, I mean, I'm thankful for the 18 00:00:45,720 --> 00:00:48,160 Speaker 1: words you use to describe me. Hopefully I can hold 19 00:00:48,159 --> 00:00:48,640 Speaker 1: true through that. 20 00:00:48,960 --> 00:00:52,960 Speaker 2: Sal, I've kind of loved technologies that bring kind of 21 00:00:55,840 --> 00:01:00,560 Speaker 2: the capabilities, tools, data that are traditionally only available to 22 00:01:00,560 --> 00:01:02,720 Speaker 2: tour players to the to the people, and I've always 23 00:01:02,760 --> 00:01:05,120 Speaker 2: kind of viewed. What you've built with Arcos is like 24 00:01:05,160 --> 00:01:08,080 Speaker 2: bringing shot link data to the people. How did how 25 00:01:08,120 --> 00:01:11,600 Speaker 2: did the idea of Arcos come to be? Tell us 26 00:01:11,640 --> 00:01:13,919 Speaker 2: give us a little history, and you guys are cooking 27 00:01:13,959 --> 00:01:15,840 Speaker 2: on your your new products laid there. We're going to 28 00:01:15,880 --> 00:01:17,520 Speaker 2: get into that. But give us the kind of the 29 00:01:17,600 --> 00:01:20,399 Speaker 2: short arc of of how Arcos came to be and 30 00:01:20,400 --> 00:01:21,720 Speaker 2: in your story. 31 00:01:21,440 --> 00:01:23,640 Speaker 1: There, sure, I mean, I would give you like a 32 00:01:23,720 --> 00:01:27,440 Speaker 1: brief summary. I was going to business school at Yale, 33 00:01:28,160 --> 00:01:29,920 Speaker 1: had a couple of co founders over there, and one 34 00:01:29,920 --> 00:01:31,600 Speaker 1: of the things for me, I would say, there were 35 00:01:31,600 --> 00:01:35,959 Speaker 1: like maybe two moments for me personally which led me 36 00:01:36,040 --> 00:01:40,319 Speaker 1: down this path. One of them was I was trying 37 00:01:40,360 --> 00:01:42,440 Speaker 1: to get better myself. I was an eight index at 38 00:01:42,440 --> 00:01:44,399 Speaker 1: the time, and I was like, how can I be 39 00:01:44,440 --> 00:01:48,440 Speaker 1: a five or two or better? And my background was 40 00:01:48,560 --> 00:01:52,480 Speaker 1: in data analytics and data architecture, and I was like, oh, 41 00:01:52,560 --> 00:01:54,840 Speaker 1: maybe if I track my data manually, if I can 42 00:01:54,880 --> 00:01:57,720 Speaker 1: track how many fairways I had, how many pots I'm having, 43 00:01:58,120 --> 00:02:02,120 Speaker 1: It'll help me get better. And I had a realization 44 00:02:02,720 --> 00:02:04,920 Speaker 1: that one it's very hard to track everything you're doing 45 00:02:04,920 --> 00:02:06,720 Speaker 1: while you're golfing, because you're out there to golf. You're 46 00:02:06,760 --> 00:02:10,559 Speaker 1: not out there to track and collect data. The second 47 00:02:10,919 --> 00:02:14,280 Speaker 1: is I wasn't collecting the right data. If I hit 48 00:02:14,360 --> 00:02:17,760 Speaker 1: a fairway and a drive by two hundred yards, that's 49 00:02:18,240 --> 00:02:20,320 Speaker 1: very different than a drive that went three hundred yards 50 00:02:20,320 --> 00:02:24,000 Speaker 1: and missed the fairway by three feet. And so so 51 00:02:24,040 --> 00:02:27,320 Speaker 1: I think, recognizing one, I'm not collecting the right data. 52 00:02:27,360 --> 00:02:30,480 Speaker 1: Two it's really painful led me to down the path 53 00:02:30,800 --> 00:02:35,440 Speaker 1: and led Arcoes down the path of like automating all 54 00:02:35,480 --> 00:02:38,560 Speaker 1: of that collection and simplifying it. But there's a second piece, 55 00:02:38,600 --> 00:02:41,800 Speaker 1: which was I read a survey in the National I 56 00:02:41,880 --> 00:02:44,240 Speaker 1: believe it was a National Golf Foundation. Need to go 57 00:02:44,320 --> 00:02:48,360 Speaker 1: back and confirm this, but they were trying to figure 58 00:02:48,400 --> 00:02:50,760 Speaker 1: out there's sixty seventy million golfers in the world. Why 59 00:02:50,760 --> 00:02:54,040 Speaker 1: do people golf? And the superficial I would say, like 60 00:02:54,040 --> 00:02:56,760 Speaker 1: the first level answers were, you know, I want to 61 00:02:56,760 --> 00:03:00,040 Speaker 1: break eighty. I want to break like I want to 62 00:03:00,080 --> 00:03:02,160 Speaker 1: be the shrew the best score I can. I want 63 00:03:02,200 --> 00:03:05,760 Speaker 1: to get a hole in one. I want to maybe 64 00:03:05,760 --> 00:03:08,040 Speaker 1: play a college golf forget on the PGA Tour or 65 00:03:08,160 --> 00:03:10,440 Speaker 1: when my member guests. A bunch of different reasons, but 66 00:03:10,520 --> 00:03:15,000 Speaker 1: all of them The conclusion of that study was all 67 00:03:15,040 --> 00:03:17,919 Speaker 1: of those basically translated like what would be realized if 68 00:03:17,919 --> 00:03:21,639 Speaker 1: you shot a better score if you played better golf? 69 00:03:21,880 --> 00:03:24,480 Speaker 1: And so their conclusion was eighty four percent of golfers 70 00:03:24,800 --> 00:03:27,040 Speaker 1: are playing golf to get better golf, and that better 71 00:03:27,080 --> 00:03:28,920 Speaker 1: golf happens on the golf course. So, like I would say, 72 00:03:28,960 --> 00:03:32,640 Speaker 1: connecting those two my personal journey and realizing I'm not unique, 73 00:03:32,639 --> 00:03:34,880 Speaker 1: I'm not the only one. There's millions of golfers out 74 00:03:34,920 --> 00:03:37,520 Speaker 1: there on the same path, and that improvement happens on 75 00:03:37,560 --> 00:03:39,280 Speaker 1: the golf course. And if we don't know what's happening 76 00:03:39,320 --> 00:03:42,120 Speaker 1: on the golf course, how can we improve Where I 77 00:03:42,120 --> 00:03:45,080 Speaker 1: would say, like kind of like the light bulb moments 78 00:03:45,080 --> 00:03:45,360 Speaker 1: for me. 79 00:03:47,520 --> 00:03:51,320 Speaker 3: Sal was there? What was the prototype when you originated 80 00:03:51,320 --> 00:03:55,119 Speaker 3: this idea? Like what did the first piece of let's 81 00:03:55,160 --> 00:03:57,440 Speaker 3: say R Coast technology look like. 82 00:03:58,200 --> 00:04:02,880 Speaker 1: I mean, it's funny we described we actually called it Frankenstein, 83 00:04:03,240 --> 00:04:06,520 Speaker 1: so it looked like it was like glue oozing out 84 00:04:06,520 --> 00:04:10,040 Speaker 1: of it. It was censors with wires. In fact, when 85 00:04:10,080 --> 00:04:13,040 Speaker 1: we launched years and years ago, it was like almost 86 00:04:13,040 --> 00:04:15,960 Speaker 1: ten years ago, actually a little more than ten years ago. 87 00:04:16,120 --> 00:04:20,719 Speaker 1: The first product I remember we went to Apple. We 88 00:04:20,760 --> 00:04:23,719 Speaker 1: had a close relationship with Apple, so I was like, Hey, 89 00:04:23,960 --> 00:04:26,200 Speaker 1: this would be awesome to have an Apple store, So 90 00:04:26,279 --> 00:04:28,840 Speaker 1: let me go pitch to Apple. And I showed up 91 00:04:28,880 --> 00:04:32,640 Speaker 1: with wires hanging and everything, and I think in the end, 92 00:04:32,760 --> 00:04:36,080 Speaker 1: like maybe that was endearing to them because they gave 93 00:04:36,160 --> 00:04:37,920 Speaker 1: us the chance and we actually launched. We were like, 94 00:04:38,880 --> 00:04:41,120 Speaker 1: I think we might have been and might still be 95 00:04:41,160 --> 00:04:43,440 Speaker 1: the only product that ever launched in Apple stores as 96 00:04:43,440 --> 00:04:47,440 Speaker 1: the first retail partner. And so it was. We called 97 00:04:47,440 --> 00:04:50,080 Speaker 1: it Frankenstein. It's still around somewhere. It looked very ugly, 98 00:04:50,120 --> 00:04:53,320 Speaker 1: it was very big. Since then, we've obviously, I mean 99 00:04:53,360 --> 00:04:56,440 Speaker 1: there's technology. I mean it's it's really a one direction 100 00:04:56,720 --> 00:05:01,280 Speaker 1: train where it gets simpler, more seamless, more like more invisible, 101 00:05:01,839 --> 00:05:03,400 Speaker 1: and that's the direction we went on. But the first 102 00:05:03,400 --> 00:05:05,400 Speaker 1: one was very visible. In fact, I would say even 103 00:05:05,440 --> 00:05:07,719 Speaker 1: before the one sensor that we put on, it was 104 00:05:07,760 --> 00:05:10,919 Speaker 1: a physical box you carried around. I remember I go 105 00:05:11,000 --> 00:05:13,760 Speaker 1: to golf courses and there's like wires everywhere and there's 106 00:05:13,800 --> 00:05:17,440 Speaker 1: a box somebody's holding and that's like analyzing all the 107 00:05:17,480 --> 00:05:21,560 Speaker 1: data that's coming off. So it's been quite a journey 108 00:05:21,560 --> 00:05:23,960 Speaker 1: and make me think of fond memories. 109 00:05:25,400 --> 00:05:28,320 Speaker 2: Sol how has used in your own product In this 110 00:05:28,400 --> 00:05:31,640 Speaker 2: I absolutely love is creating solutions to the problems that 111 00:05:31,720 --> 00:05:34,640 Speaker 2: you personally have yourself. Give us a few examples of 112 00:05:36,960 --> 00:05:41,640 Speaker 2: your own using your Arcos data to inform club feting 113 00:05:41,800 --> 00:05:44,080 Speaker 2: changes or where you need where you work on your 114 00:05:44,080 --> 00:05:45,240 Speaker 2: game strengths and weaknesses. 115 00:05:45,600 --> 00:05:47,600 Speaker 1: Yeah, one hundred percent. I mean it's I'd say, like 116 00:05:47,640 --> 00:05:50,440 Speaker 1: early on, it's really eye opening when you first start 117 00:05:50,560 --> 00:05:53,200 Speaker 1: using it. There's like different things that happen over different 118 00:05:53,240 --> 00:05:55,640 Speaker 1: stages of the data journey. So the first thing for 119 00:05:55,760 --> 00:05:59,320 Speaker 1: me was I realized my what I thought my club's went, 120 00:05:59,360 --> 00:06:01,480 Speaker 1: even though I was like eight or seven at the time. 121 00:06:01,680 --> 00:06:04,479 Speaker 1: Now I'm like scratch a lot better than scratch, like 122 00:06:04,480 --> 00:06:08,360 Speaker 1: a plus. But at that time, for me, there was 123 00:06:08,400 --> 00:06:10,760 Speaker 1: a cognitive bias, and that's true for a lot of golfers. 124 00:06:10,760 --> 00:06:12,720 Speaker 1: Like I remembered my best shot, so I was making 125 00:06:12,760 --> 00:06:16,359 Speaker 1: decisions like my eight iron goes one hundred and fifty yards, 126 00:06:16,360 --> 00:06:18,440 Speaker 1: I'm making that decision. Turns I was going one hundred 127 00:06:18,440 --> 00:06:21,039 Speaker 1: and forty two. That was my expected value, but that 128 00:06:21,120 --> 00:06:23,760 Speaker 1: was my expected distance, not one hundred and fifty. That 129 00:06:23,920 --> 00:06:28,679 Speaker 1: immediately changed my approach game. I also thought, like before 130 00:06:28,880 --> 00:06:31,480 Speaker 1: getting all this data, that putting was my problem, and 131 00:06:32,480 --> 00:06:35,040 Speaker 1: I realized, actually, I'm a really good putter. My problem 132 00:06:35,080 --> 00:06:37,599 Speaker 1: was my approach distance. Like the putt distances were too long. 133 00:06:37,880 --> 00:06:40,960 Speaker 1: I'm leaving myself fifty footers and that's the thing that 134 00:06:41,000 --> 00:06:42,880 Speaker 1: I can improve the fastest. So those were I would 135 00:06:42,880 --> 00:06:47,120 Speaker 1: say early aha moments. And since then, I mean one 136 00:06:47,160 --> 00:06:50,640 Speaker 1: of the things I would say, you realize is golf 137 00:06:50,640 --> 00:06:54,919 Speaker 1: improvement journey. While it can be pointing north, has like 138 00:06:55,720 --> 00:06:58,480 Speaker 1: I would say, natural variants built into it which you 139 00:06:58,520 --> 00:07:01,560 Speaker 1: want to identify. So anytime a certain facet of your game. 140 00:07:01,640 --> 00:07:03,400 Speaker 1: So for example, let's say I'm really good at putting, 141 00:07:03,680 --> 00:07:05,680 Speaker 1: but if I start realizing through data, like you know, 142 00:07:06,000 --> 00:07:08,480 Speaker 1: my lag putting is right now getting worse. Even if 143 00:07:08,520 --> 00:07:10,080 Speaker 1: I spend a little bit of time right there, I 144 00:07:10,160 --> 00:07:11,680 Speaker 1: can nip it in the butt so it doesn't become 145 00:07:11,720 --> 00:07:13,960 Speaker 1: a bigger problem. So I says, staying on top of 146 00:07:13,960 --> 00:07:18,040 Speaker 1: those things has been incredibly helpful on my journey personally, 147 00:07:18,040 --> 00:07:20,160 Speaker 1: and we hear that from a bunch of different users, 148 00:07:20,480 --> 00:07:24,600 Speaker 1: different members on the club fitting side. I mean, it's 149 00:07:25,680 --> 00:07:28,960 Speaker 1: you know, all the John case Solheim stories around how 150 00:07:29,080 --> 00:07:33,000 Speaker 1: his lie angle is actually based on Arcos Enforce data 151 00:07:33,000 --> 00:07:37,920 Speaker 1: and not just not just simulator data. For me, it's 152 00:07:38,000 --> 00:07:41,520 Speaker 1: been like, I mean, now I'm playing with a G 153 00:07:41,680 --> 00:07:46,880 Speaker 1: four forty K and when James and Paying fit me 154 00:07:47,000 --> 00:07:49,040 Speaker 1: like I sent him my Arco's dispersion data, which I 155 00:07:49,040 --> 00:07:51,360 Speaker 1: mean some of the stuff we're going to be exposing 156 00:07:51,920 --> 00:07:56,360 Speaker 1: to all our members in upcoming app releases. But we 157 00:07:56,440 --> 00:07:59,320 Speaker 1: knew the bias on the club like my by misdirection, 158 00:07:59,720 --> 00:08:01,920 Speaker 1: so I didn't have to go through any fitting honestly 159 00:08:03,240 --> 00:08:05,400 Speaker 1: based on Arcos and based on what we knew about 160 00:08:05,440 --> 00:08:09,160 Speaker 1: me previously on all the data paying head. But like 161 00:08:09,280 --> 00:08:11,480 Speaker 1: for me as a golfer, I can look at my 162 00:08:11,480 --> 00:08:13,920 Speaker 1: strokes gain stats with this new driver versus the last one. 163 00:08:13,920 --> 00:08:15,440 Speaker 1: I can look at my driving distance, I can look 164 00:08:15,440 --> 00:08:18,720 Speaker 1: at my mispatterns, and I feel more confident that I 165 00:08:18,800 --> 00:08:22,120 Speaker 1: made this choice the correct choice, so that this driver 166 00:08:22,360 --> 00:08:24,800 Speaker 1: is the right driver for me, because now I'm gaining. 167 00:08:25,960 --> 00:08:29,240 Speaker 1: I think it's like point four strokes more so so 168 00:08:29,320 --> 00:08:33,280 Speaker 1: I think all of those things, especially on the equipment side, 169 00:08:33,360 --> 00:08:36,400 Speaker 1: is not just the fitting, but also closing the loop, 170 00:08:36,520 --> 00:08:38,800 Speaker 1: Like the investment that I made, is it actually making 171 00:08:38,840 --> 00:08:41,080 Speaker 1: me better? And if it's not I can go back 172 00:08:41,120 --> 00:08:42,960 Speaker 1: to my fit, like, hey, here's the issue and we 173 00:08:43,000 --> 00:08:46,120 Speaker 1: can address it. So I think that's been incredibly helpful. 174 00:08:47,559 --> 00:08:50,080 Speaker 3: I mean, Sally, you're speaking kind of to my soul 175 00:08:50,160 --> 00:08:54,480 Speaker 3: on this stuff right now, because I feel like, and 176 00:08:54,520 --> 00:08:56,120 Speaker 3: I kind of am speaking like for the people I 177 00:08:56,160 --> 00:08:59,800 Speaker 3: play golf with my friends that maybe don't access you know, 178 00:09:00,160 --> 00:09:03,000 Speaker 3: maybe the technology you're data necessary all the time. They're 179 00:09:03,000 --> 00:09:04,719 Speaker 3: playing the wrong clubs in their bag. You know, they 180 00:09:04,720 --> 00:09:06,840 Speaker 3: have big gaps in their bag. They don't know where 181 00:09:06,840 --> 00:09:09,520 Speaker 3: they're good and bad. They're hitting a driver on a 182 00:09:09,520 --> 00:09:11,880 Speaker 3: hole where they're not good from forty yards and if 183 00:09:11,920 --> 00:09:14,400 Speaker 3: they pull the drive off, you know, they actually hit 184 00:09:14,400 --> 00:09:16,920 Speaker 3: a big drive, they're going to be forty yards out. 185 00:09:16,960 --> 00:09:22,360 Speaker 3: Like the enlighteningness of technology and golf right now is available, 186 00:09:22,360 --> 00:09:24,120 Speaker 3: and sometimes you just want to scream at a golfer 187 00:09:24,120 --> 00:09:26,640 Speaker 3: and go just try this stuff. It'll make you better. 188 00:09:27,080 --> 00:09:29,440 Speaker 1: Yeah, I mean, I totally agree with you that. I 189 00:09:29,480 --> 00:09:32,840 Speaker 1: will also say that there have been, like I mean, 190 00:09:32,960 --> 00:09:35,440 Speaker 1: maybe I'll pivot into like some of the things that 191 00:09:35,559 --> 00:09:40,200 Speaker 1: keep people from adopting like a technology like arcos are 192 00:09:41,120 --> 00:09:42,800 Speaker 1: some of the behavior changes you have to make. So 193 00:09:42,840 --> 00:09:46,720 Speaker 1: for example, till I would say a week ago, you 194 00:09:46,800 --> 00:09:50,160 Speaker 1: have to put sensors on all your clubs to track 195 00:09:50,240 --> 00:09:52,199 Speaker 1: all that data, and that to a lot of people 196 00:09:52,280 --> 00:09:54,640 Speaker 1: sounded like I would say, they were like put golfers 197 00:09:54,640 --> 00:09:58,560 Speaker 1: in two camps. One the really elite golfers, we're really 198 00:09:58,559 --> 00:10:02,880 Speaker 1: finicky about every little like how the grip should feel 199 00:10:02,960 --> 00:10:05,920 Speaker 1: where they're holding the club, like how the vibration the 200 00:10:05,920 --> 00:10:09,839 Speaker 1: shock should feel. So while Arcos did not change any 201 00:10:09,880 --> 00:10:12,880 Speaker 1: performance characteristics of the club, like it does look different 202 00:10:12,880 --> 00:10:14,959 Speaker 1: with the censor on there, and if it throws people off, 203 00:10:15,440 --> 00:10:17,800 Speaker 1: like the really elite golfers we were not able to attract. 204 00:10:17,960 --> 00:10:21,319 Speaker 1: And then similarly, the second set of golfers that we 205 00:10:21,320 --> 00:10:24,199 Speaker 1: were not able to attract, were you know, like pairing 206 00:10:24,360 --> 00:10:26,680 Speaker 1: fourteen censors on my club. That just sounds like a 207 00:10:26,679 --> 00:10:30,360 Speaker 1: lot of work And I'm just out there at golf 208 00:10:30,640 --> 00:10:32,920 Speaker 1: and I totally get that. And that's like I would say, 209 00:10:33,000 --> 00:10:36,920 Speaker 1: listening to those two key messages from our potential members 210 00:10:37,040 --> 00:10:39,320 Speaker 1: or even members who've tried it and then said it 211 00:10:40,800 --> 00:10:44,120 Speaker 1: let us down this path, which we just launched Arcos 212 00:10:44,160 --> 00:10:47,040 Speaker 1: Air about a week ago to address, like, Shane, exactly 213 00:10:47,080 --> 00:10:49,199 Speaker 1: what you're saying where it takes so much of the 214 00:10:49,240 --> 00:10:51,920 Speaker 1: friction away. All you have to do is put arcos 215 00:10:51,920 --> 00:10:54,080 Speaker 1: Air in your pocket and go play, and it's going 216 00:10:54,120 --> 00:10:57,760 Speaker 1: to do all the work for you. And we believe 217 00:10:57,840 --> 00:10:59,800 Speaker 1: that that's going to be a game changer in terms 218 00:10:59,840 --> 00:11:03,280 Speaker 1: of getting that as you mentioned, data enlightenment, enlightenment to 219 00:11:03,480 --> 00:11:05,520 Speaker 1: a much broader set of golfers out there. 220 00:11:07,240 --> 00:11:10,600 Speaker 2: So arcos Air you're talking about is uh is the 221 00:11:10,679 --> 00:11:14,280 Speaker 2: ability to capture shots on the course. I've used I've 222 00:11:14,400 --> 00:11:15,800 Speaker 2: used it kind of when it was in the R 223 00:11:15,840 --> 00:11:20,280 Speaker 2: and D space, and it's kind of magical in the 224 00:11:20,280 --> 00:11:22,959 Speaker 2: way it works. You get done, you're like, hey, I 225 00:11:23,080 --> 00:11:25,240 Speaker 2: might need to edit a putter to putt distance or two, 226 00:11:25,320 --> 00:11:27,079 Speaker 2: but you're like, whoa, this thing just gotten my whole 227 00:11:27,160 --> 00:11:29,440 Speaker 2: round Now, I don't need to If I don't want to, 228 00:11:30,040 --> 00:11:32,600 Speaker 2: I can just not even look at my phone play 229 00:11:32,600 --> 00:11:35,680 Speaker 2: golf in a very kind of frictionless way. As you mentioned, 230 00:11:36,000 --> 00:11:39,000 Speaker 2: gathers all the data. Was arcos Air part of your 231 00:11:39,120 --> 00:11:42,520 Speaker 2: kind of kind of the technological grand plan. You needed 232 00:11:42,559 --> 00:11:46,200 Speaker 2: all this training data or was this Hey, Over the 233 00:11:46,240 --> 00:11:50,000 Speaker 2: ten plus years that ARCO has been around, new capabilities 234 00:11:50,040 --> 00:11:52,560 Speaker 2: came to be and and you guys engineered your way 235 00:11:52,920 --> 00:11:56,120 Speaker 2: towards it. How did that Arcos Air evolution come to be? 236 00:11:56,200 --> 00:11:57,280 Speaker 2: Technologically speaking? 237 00:11:57,760 --> 00:11:59,720 Speaker 1: Yeah, I would say it was a full circle moment 238 00:11:59,760 --> 00:12:02,640 Speaker 1: in this sense for Arcos because we actually started with 239 00:12:02,880 --> 00:12:06,040 Speaker 1: censor less first, and we realized we couln't solve it. 240 00:12:06,120 --> 00:12:09,240 Speaker 1: The technology wasn't there and the data wasn't there. So 241 00:12:11,120 --> 00:12:13,400 Speaker 1: when Arcos first came out, I actually shared, like when 242 00:12:13,440 --> 00:12:16,599 Speaker 1: when we were launching Arcos here a week and a 243 00:12:16,640 --> 00:12:19,400 Speaker 1: half ago, I shared this message with the entire company, 244 00:12:19,400 --> 00:12:23,320 Speaker 1: which was, it's it's it's in a sensor realization of 245 00:12:23,400 --> 00:12:27,000 Speaker 1: some vision that I had shared vision team had had 246 00:12:27,800 --> 00:12:30,400 Speaker 1: years and years ago. And we tried it and I'd say, 247 00:12:30,400 --> 00:12:32,240 Speaker 1: I drove the enginet. It's crazy. I'm like, hey, how 248 00:12:32,240 --> 00:12:35,000 Speaker 1: can Shazam figure out which song it is with just 249 00:12:35,080 --> 00:12:37,719 Speaker 1: the phone? And there's like a billion songs out there 250 00:12:37,720 --> 00:12:39,240 Speaker 1: and you can't figure out there's a golf shot that 251 00:12:39,280 --> 00:12:41,640 Speaker 1: was taken with which club. Turns out it was I 252 00:12:41,720 --> 00:12:43,360 Speaker 1: was wrong and the team was right, and we couldn't 253 00:12:43,400 --> 00:12:46,320 Speaker 1: do it that And so then we went down the 254 00:12:46,360 --> 00:12:51,080 Speaker 1: censored path of like we need something on the club 255 00:12:51,120 --> 00:12:54,679 Speaker 1: to understand when a shot is taken and obviously it's 256 00:12:54,679 --> 00:12:57,400 Speaker 1: Census plus GPS. We're not guessing where the SHOT's going. 257 00:12:57,600 --> 00:13:00,959 Speaker 1: We're detecting impact and location, and then we record the location. 258 00:13:01,040 --> 00:13:02,720 Speaker 1: Then when you take your next shot, that's how we 259 00:13:02,720 --> 00:13:05,320 Speaker 1: know where a previous shot ended up. So by virtue 260 00:13:05,360 --> 00:13:08,640 Speaker 1: of that and the by virtue of the fact that 261 00:13:08,679 --> 00:13:11,000 Speaker 1: we've recorded one and a half billion shots, we started 262 00:13:11,000 --> 00:13:15,319 Speaker 1: recording all kinds of data around those shots, all the 263 00:13:15,520 --> 00:13:21,040 Speaker 1: IMU motion sensor data from accelerometer, gyroscope, magnetometer, GPS, and 264 00:13:21,160 --> 00:13:25,920 Speaker 1: it was it's recorded a super high fidelity. And I 265 00:13:25,960 --> 00:13:29,560 Speaker 1: think like the analogy here is similar to how like 266 00:13:29,600 --> 00:13:32,000 Speaker 1: every time I drive my Tesla, I'm actually training the 267 00:13:32,040 --> 00:13:36,320 Speaker 1: Tesla self driver less vehicle, like the self driving thing, 268 00:13:36,640 --> 00:13:41,360 Speaker 1: which is amazing. Similarly, the hundreds of thousands of users 269 00:13:42,000 --> 00:13:45,720 Speaker 1: members that have been playing with Arcos and played with Arcos, 270 00:13:46,400 --> 00:13:51,840 Speaker 1: they helped us train the sensor less model, which is 271 00:13:52,040 --> 00:13:55,960 Speaker 1: Arcos Air that you don't need sensors on clubs. And 272 00:13:56,360 --> 00:13:58,840 Speaker 1: like I would say, that's probably the easiest way for 273 00:13:58,880 --> 00:14:01,560 Speaker 1: me to describe it beyond saying I mean, just like 274 00:14:01,600 --> 00:14:04,360 Speaker 1: I can't describe our chat GPD works but it does, 275 00:14:04,760 --> 00:14:08,160 Speaker 1: or clock works but it does. Similarly, this is the 276 00:14:08,280 --> 00:14:10,600 Speaker 1: secret is in the data capture and the data collection. 277 00:14:10,960 --> 00:14:13,440 Speaker 1: Once you capture the right data, which is the hard part, 278 00:14:13,880 --> 00:14:16,240 Speaker 1: then you can build the right models on it to 279 00:14:16,480 --> 00:14:19,680 Speaker 1: predict when the shots taken versus not when it's a 280 00:14:19,720 --> 00:14:22,320 Speaker 1: practice saying when you're just hanging out over there. And 281 00:14:22,720 --> 00:14:24,880 Speaker 1: the other thing that's really interesting is these models are 282 00:14:24,880 --> 00:14:26,600 Speaker 1: going to get better and better and smarter and smarter. 283 00:14:26,720 --> 00:14:29,360 Speaker 1: The first time you play with AIR is going to 284 00:14:29,360 --> 00:14:32,880 Speaker 1: be the worst experience you'll ever have. Every successive time 285 00:14:32,920 --> 00:14:34,560 Speaker 1: is going to get better and better, and like what 286 00:14:34,600 --> 00:14:36,280 Speaker 1: it's going to look like a year from now is 287 00:14:36,760 --> 00:14:38,360 Speaker 1: obviously going to be way better what it is now. 288 00:14:38,400 --> 00:14:40,320 Speaker 1: But the more it learns about you, more it learns 289 00:14:40,320 --> 00:14:42,080 Speaker 1: about your golf behaviors, the better it's going to be. 290 00:14:43,440 --> 00:14:46,000 Speaker 3: Is this the biggest leap for you guys to date 291 00:14:46,080 --> 00:14:47,960 Speaker 3: in terms of the technology Arcos AIR? 292 00:14:48,960 --> 00:14:52,120 Speaker 1: I think from a capturing of data standpoint, there's no 293 00:14:52,160 --> 00:14:56,440 Speaker 1: doubt that for the for every golfer out there has 294 00:14:56,520 --> 00:14:59,680 Speaker 1: not been a technology that's existed so far and the 295 00:14:59,680 --> 00:15:02,360 Speaker 1: exist in the history of golf that allows them to 296 00:15:02,400 --> 00:15:05,480 Speaker 1: capture data this seamlessly so cool. 297 00:15:05,560 --> 00:15:09,680 Speaker 3: I mean, I've seen I'm buddies with some Marcos. I'm 298 00:15:09,720 --> 00:15:11,600 Speaker 3: some of my Arcos pals out there, and I've seen 299 00:15:11,680 --> 00:15:13,560 Speaker 3: them kind of messing around with it the last few months, 300 00:15:13,560 --> 00:15:16,560 Speaker 3: and I've got a sense from everybody this is the 301 00:15:16,600 --> 00:15:18,520 Speaker 3: next generation of what we're going to be doing. So 302 00:15:18,920 --> 00:15:20,560 Speaker 3: very cool to kind of see it out and about 303 00:15:20,600 --> 00:15:22,560 Speaker 3: and uh and available for the public now. 304 00:15:22,880 --> 00:15:23,280 Speaker 1: Awesome. 305 00:15:25,000 --> 00:15:27,680 Speaker 2: And so how does the you know, I think I 306 00:15:27,720 --> 00:15:30,400 Speaker 2: was a little surprised when you guys launched a rangefinder, right, 307 00:15:30,440 --> 00:15:32,880 Speaker 2: so tell us a little bit about the rangefinder you 308 00:15:32,880 --> 00:15:37,080 Speaker 2: guys launched, and what is the interplay into Arcos Era 309 00:15:37,280 --> 00:15:40,400 Speaker 2: just the whole uh, you know, maybe accuracy, the pin 310 00:15:40,560 --> 00:15:42,120 Speaker 2: location and things of that nature. 311 00:15:42,720 --> 00:15:44,480 Speaker 1: Yeah, So, I mean, I would say it ties back 312 00:15:45,040 --> 00:15:49,160 Speaker 1: to her mission, which is to improve the dedicated performance 313 00:15:49,200 --> 00:15:53,040 Speaker 1: of dedicated golfers at every level through seamless data collection 314 00:15:53,680 --> 00:15:57,360 Speaker 1: and actionable intelligence. And when we look at the actionable 315 00:15:57,400 --> 00:16:03,360 Speaker 1: intelligence part, one of the things that we realized was 316 00:16:04,400 --> 00:16:07,840 Speaker 1: one of the most love features of our experience is 317 00:16:08,160 --> 00:16:12,360 Speaker 1: us giving you insights on how when you look at 318 00:16:12,360 --> 00:16:14,480 Speaker 1: a certain shot and let's say you have one hundred 319 00:16:14,480 --> 00:16:16,520 Speaker 1: and fifty yards, what do you do from there? Where 320 00:16:16,520 --> 00:16:20,560 Speaker 1: should you play, where should you aim? And we factor 321 00:16:20,640 --> 00:16:24,240 Speaker 1: in your game. We factor in all the elements around it. 322 00:16:24,760 --> 00:16:26,960 Speaker 1: What's the wind doing, what direction is it coming from, 323 00:16:27,040 --> 00:16:29,640 Speaker 1: what's the humidity? Are you playing in Arizona where it's 324 00:16:29,680 --> 00:16:32,440 Speaker 1: like fifteen hundred feet above sea level VEREREUS sea level 325 00:16:32,440 --> 00:16:34,480 Speaker 1: which might be your home course? Take all that into 326 00:16:34,480 --> 00:16:40,840 Speaker 1: account and listening to our members, we heard that, you know, 327 00:16:40,920 --> 00:16:42,560 Speaker 1: like we would love to see all this information and 328 00:16:42,640 --> 00:16:44,880 Speaker 1: rangefinder because we're actually lasering the pin it's a part 329 00:16:44,920 --> 00:16:48,160 Speaker 1: of for experience, and so I would say for us, 330 00:16:48,160 --> 00:16:53,640 Speaker 1: it was we either integrate with other laser technologies or 331 00:16:53,680 --> 00:16:57,200 Speaker 1: build our own. And one of the things we realized 332 00:16:57,400 --> 00:17:02,080 Speaker 1: was this requires a connected experience, smart device, and it's 333 00:17:02,120 --> 00:17:05,560 Speaker 1: the same thing as like Tesla could integrate itself driving 334 00:17:05,600 --> 00:17:09,199 Speaker 1: technology into other cars or built their own. And the 335 00:17:09,240 --> 00:17:13,119 Speaker 1: reason Tesla did their own is because the other cars 336 00:17:13,119 --> 00:17:15,480 Speaker 1: are not built ground up as a connected experience. So 337 00:17:15,520 --> 00:17:18,000 Speaker 1: what we've done is built the smart laser as a 338 00:17:18,040 --> 00:17:20,960 Speaker 1: connected experience, so it works absolutely seamlessly with your app. 339 00:17:21,320 --> 00:17:25,119 Speaker 1: It's always like we were experts in Bluetooth, So there's 340 00:17:25,160 --> 00:17:28,879 Speaker 1: a live weatherfeed that's going into the smart laser. It 341 00:17:28,920 --> 00:17:30,600 Speaker 1: knows what hole you're on when you're going to the 342 00:17:30,640 --> 00:17:33,960 Speaker 1: next hole. It's got all this hole switching automatic hole 343 00:17:33,960 --> 00:17:36,520 Speaker 1: switching algorithms built. And we also know what direction the 344 00:17:36,560 --> 00:17:39,720 Speaker 1: wind's coming, what direction you're going, and how what's the 345 00:17:39,760 --> 00:17:43,440 Speaker 1: interplay of that, what the gust is doing. And so 346 00:17:43,920 --> 00:17:46,280 Speaker 1: with all of that, when you laser a flag, we're 347 00:17:46,320 --> 00:17:49,000 Speaker 1: able to give you And then for a user, it's 348 00:17:49,040 --> 00:17:51,440 Speaker 1: like a super simple there's no difference we are using 349 00:17:51,520 --> 00:17:54,439 Speaker 1: a smart laser Arco smart laser versus using any other 350 00:17:54,520 --> 00:17:57,080 Speaker 1: laser in terms of the user experience. You just point 351 00:17:57,119 --> 00:17:59,879 Speaker 1: and click. The information you get out of it is 352 00:18:00,560 --> 00:18:04,240 Speaker 1: five times more valuable. And the reason it is five 353 00:18:04,280 --> 00:18:07,040 Speaker 1: times I would say, yeah, five times more valuable, is 354 00:18:07,040 --> 00:18:10,280 Speaker 1: the other rangefinders only give you nineteen percent the answer. 355 00:18:10,480 --> 00:18:14,560 Speaker 1: So when you look at what goes into figuring out 356 00:18:15,920 --> 00:18:20,760 Speaker 1: how far lay, there are various factors. One is slope, 357 00:18:20,760 --> 00:18:23,240 Speaker 1: which is the only innovation that's happened in lasers since 358 00:18:24,119 --> 00:18:26,760 Speaker 1: before smart laser, and that when you look at it 359 00:18:26,760 --> 00:18:28,960 Speaker 1: accounts for only nineteen percent the answer. The rest of 360 00:18:29,000 --> 00:18:31,280 Speaker 1: the eighty one percent the answer because we have now 361 00:18:31,320 --> 00:18:34,399 Speaker 1: millions of apps taken we have that data, so basing 362 00:18:34,400 --> 00:18:37,040 Speaker 1: it on all the data I have the rest eighty 363 00:18:37,080 --> 00:18:40,080 Speaker 1: one percent, the answer is what's the wind doing, what's augusting, 364 00:18:40,160 --> 00:18:42,760 Speaker 1: what's the wind direction, what's the temperature? Is it cold, 365 00:18:42,800 --> 00:18:44,800 Speaker 1: is it hot? Because it changes things? And what's your 366 00:18:44,840 --> 00:18:48,840 Speaker 1: altitude and elevation and actually barometric pressure too because when 367 00:18:48,840 --> 00:18:52,280 Speaker 1: you got in Arizona where the air center like, stuff changes. 368 00:18:52,320 --> 00:18:56,560 Speaker 1: So all those factors are accounted for in the smart 369 00:18:56,600 --> 00:18:58,760 Speaker 1: laser that no other laser is doing. And then in 370 00:18:58,800 --> 00:19:01,439 Speaker 1: addition to that, the other thing we're able to do, 371 00:19:01,480 --> 00:19:04,359 Speaker 1: like one of the things smart you mentioned is when 372 00:19:04,359 --> 00:19:07,399 Speaker 1: we zap, we can set your pin location and we 373 00:19:07,440 --> 00:19:09,480 Speaker 1: also show you an arc on the green where the 374 00:19:09,480 --> 00:19:10,840 Speaker 1: pin is, so in the case that you don't have 375 00:19:10,880 --> 00:19:13,080 Speaker 1: pin sheets, you can see how far deep or how 376 00:19:13,119 --> 00:19:17,600 Speaker 1: short like, so it's an immense value. In addition to that, 377 00:19:17,960 --> 00:19:20,960 Speaker 1: because it's a connected experience, we also give you green maps, 378 00:19:20,960 --> 00:19:22,480 Speaker 1: so you can look at the map of the whole. 379 00:19:23,240 --> 00:19:25,080 Speaker 1: You can you get like map of the whole, but 380 00:19:25,080 --> 00:19:27,240 Speaker 1: you also get all the green slopes for that. I 381 00:19:27,320 --> 00:19:31,160 Speaker 1: think we have over nine thousand courses with green maps 382 00:19:31,200 --> 00:19:35,080 Speaker 1: and it's ever increasing. And then you get AI strategy 383 00:19:35,080 --> 00:19:38,240 Speaker 1: with it, which is and I won't get into a 384 00:19:38,359 --> 00:19:41,600 Speaker 1: strategy with the idea being that it's a smart laser 385 00:19:41,880 --> 00:19:44,920 Speaker 1: that's always going to be improving. It's a connected technology. 386 00:19:44,920 --> 00:19:46,840 Speaker 1: We're going to be releasing more and more updates. It's 387 00:19:46,840 --> 00:19:48,800 Speaker 1: going to be more exciting stuff you can do with it. 388 00:19:49,280 --> 00:19:50,760 Speaker 1: You get green maps, and you'll get a bunch of 389 00:19:50,760 --> 00:19:52,720 Speaker 1: software stuff with it, and it's up to your house. 390 00:19:52,720 --> 00:19:55,199 Speaker 1: You going to interact with it, you know. 391 00:19:55,280 --> 00:19:57,720 Speaker 3: It's it's so interesting you say that about the kind 392 00:19:57,760 --> 00:20:00,359 Speaker 3: of the progression of the range finder. I mean, I 393 00:20:00,400 --> 00:20:03,280 Speaker 3: remember my uncle, who you know, at any given time 394 00:20:04,040 --> 00:20:06,320 Speaker 3: in his prime was anywhere between a three and a 395 00:20:06,400 --> 00:20:10,080 Speaker 3: ten handicap, and I remember the sky Caddy days, and 396 00:20:10,119 --> 00:20:12,280 Speaker 3: he would have the sky Caddy on the cart and 397 00:20:12,320 --> 00:20:16,400 Speaker 3: it would say front and back, And I remember always 398 00:20:16,440 --> 00:20:20,200 Speaker 3: thinking that was more helpful to him than shooting the pen, 399 00:20:20,480 --> 00:20:22,440 Speaker 3: because all of a sudden, it gave him thirty or 400 00:20:22,480 --> 00:20:24,720 Speaker 3: forty yards to play with, because he's not necessarily going 401 00:20:24,760 --> 00:20:27,080 Speaker 3: to hit an eight iron to one forty every time, 402 00:20:27,119 --> 00:20:28,840 Speaker 3: but having an idea of what the green looked like, 403 00:20:29,280 --> 00:20:32,520 Speaker 3: and to your point, shooting the flags sometimes can be 404 00:20:32,560 --> 00:20:34,320 Speaker 3: a detriment to the player if you have no other 405 00:20:34,400 --> 00:20:36,480 Speaker 3: information right, because you don't know if it's on the 406 00:20:36,480 --> 00:20:37,800 Speaker 3: front of the green, in the back of the green, 407 00:20:38,160 --> 00:20:40,040 Speaker 3: what the wind's doing. So all of a sudden, you're 408 00:20:40,160 --> 00:20:43,640 Speaker 3: I was messing with the laser last week, and it's incredible. 409 00:20:43,680 --> 00:20:46,840 Speaker 3: I mean, the fact that it can give you adjusted 410 00:20:46,880 --> 00:20:49,760 Speaker 3: distances is so cool. I mean, the fact that it's 411 00:20:49,760 --> 00:20:52,240 Speaker 3: so much smarter than every other laser sal I mean, 412 00:20:52,400 --> 00:20:54,520 Speaker 3: it just seems like a layup in terms of that 413 00:20:54,640 --> 00:20:56,439 Speaker 3: next step of what this technology can be. 414 00:20:57,440 --> 00:20:59,280 Speaker 1: Thank you totally agree. Couldn't agree more. 415 00:21:00,040 --> 00:21:03,920 Speaker 2: Let's say somebody, let's say somebody tried arcos uh three 416 00:21:04,000 --> 00:21:07,040 Speaker 2: four years ago and they had the sensors on there, 417 00:21:07,480 --> 00:21:09,359 Speaker 2: maybe they maybe they didn't like the field, the grips, 418 00:21:09,359 --> 00:21:12,840 Speaker 2: what have you, and and we're we're a little bit 419 00:21:12,880 --> 00:21:15,080 Speaker 2: of a like you said, maybe a better player nitpicky 420 00:21:15,440 --> 00:21:18,000 Speaker 2: in terms of feel and things of that nature. Fast 421 00:21:18,040 --> 00:21:20,119 Speaker 2: forward to today, because I feel like you guys are 422 00:21:20,119 --> 00:21:22,240 Speaker 2: really turn up the heat here with the green scans 423 00:21:22,280 --> 00:21:25,840 Speaker 2: and the AI caddy and the uh the range smart 424 00:21:25,920 --> 00:21:29,000 Speaker 2: range finder now and now you have air give you know, 425 00:21:29,080 --> 00:21:32,680 Speaker 2: what is somebody uh maybe coming into the arcos ecosystem 426 00:21:32,720 --> 00:21:35,280 Speaker 2: today going to experience relative to you know, let's say 427 00:21:35,280 --> 00:21:38,040 Speaker 2: they came in three four years ago and tried it out. 428 00:21:38,720 --> 00:21:41,680 Speaker 1: I think what they will experience is probably like I'll say, 429 00:21:41,720 --> 00:21:49,359 Speaker 1: two fundamental shifts, excuse me. One is the seamlessness of 430 00:21:51,000 --> 00:21:54,800 Speaker 1: capturing your data is dramatically different, like in the past 431 00:21:54,800 --> 00:21:57,040 Speaker 1: you had to I mean, just let me just walk 432 00:21:57,080 --> 00:22:02,440 Speaker 1: you through the pairing process with sensors, which I'm not 433 00:22:02,520 --> 00:22:05,639 Speaker 1: saying don't do it. It just requires like people are 434 00:22:05,640 --> 00:22:07,520 Speaker 1: still doing it, it just requires a lot more work. 435 00:22:07,880 --> 00:22:11,159 Speaker 1: You have to install fourteen sensors on each club, You 436 00:22:11,160 --> 00:22:12,480 Speaker 1: have to put the clubs in the bag, then you 437 00:22:12,480 --> 00:22:14,720 Speaker 1: have to put them out one by one and scan them. 438 00:22:15,359 --> 00:22:17,439 Speaker 1: And that's like a ten to fifteen minute process to 439 00:22:17,520 --> 00:22:21,240 Speaker 1: get set up. And for a lot of people, like they, 440 00:22:21,520 --> 00:22:23,879 Speaker 1: it might that just might sound like too much, like 441 00:22:23,920 --> 00:22:26,480 Speaker 1: because you don't remember to do your golf things till 442 00:22:26,560 --> 00:22:28,560 Speaker 1: you get to the golf course and by that time 443 00:22:28,600 --> 00:22:31,720 Speaker 1: you're focused on just playing your around. Now that has 444 00:22:31,760 --> 00:22:34,560 Speaker 1: gone down to like how long does it take you 445 00:22:34,600 --> 00:22:37,120 Speaker 1: to pay an AirPod? Like five seconds? It's like gone 446 00:22:37,119 --> 00:22:40,840 Speaker 1: down to that. So that's a fundamental shift. Like and 447 00:22:40,880 --> 00:22:46,800 Speaker 1: then similarly, while you're playing with sensors, we recommend you 448 00:22:46,880 --> 00:22:50,040 Speaker 1: carry your clubs upside down, don't throw them in anger 449 00:22:50,240 --> 00:22:53,200 Speaker 1: or whatever, like it could trigger false positives, generally won't. 450 00:22:54,200 --> 00:22:57,000 Speaker 1: But with air, because we don't have anything on clubs, 451 00:22:57,520 --> 00:22:59,080 Speaker 1: you can filling them, you can do whatever you want 452 00:22:59,080 --> 00:23:00,680 Speaker 1: to do, like do not you don't need to change 453 00:23:00,720 --> 00:23:04,160 Speaker 1: your behavior, and so so I think that's one piece 454 00:23:04,200 --> 00:23:09,479 Speaker 1: that's going to be really fundamentally different. The other is 455 00:23:09,520 --> 00:23:14,359 Speaker 1: the insights that you're getting, whether it's the smart laser 456 00:23:14,520 --> 00:23:17,560 Speaker 1: where like now it's more integrated into your golf experience, 457 00:23:18,040 --> 00:23:20,360 Speaker 1: or like when you go into the app, like what 458 00:23:20,400 --> 00:23:23,800 Speaker 1: you'll see is we've made a lot of experiences better 459 00:23:23,840 --> 00:23:26,000 Speaker 1: and in the next couple of months they're going to 460 00:23:26,080 --> 00:23:28,600 Speaker 1: be like just the insights that you're going to get 461 00:23:28,920 --> 00:23:31,360 Speaker 1: are going to blow your mind and out. I mean 462 00:23:31,520 --> 00:23:33,639 Speaker 1: like maybe maybe in a couple months will be chatting 463 00:23:33,640 --> 00:23:37,919 Speaker 1: about that, but the there's mind blowing insights coming based 464 00:23:37,960 --> 00:23:39,920 Speaker 1: on the virtue of all the data we've collected, which 465 00:23:39,960 --> 00:23:43,960 Speaker 1: will and those insights are going to be unbelievably actionable, 466 00:23:43,960 --> 00:23:47,760 Speaker 1: and they're already are. It's the most actionable insights platform 467 00:23:47,840 --> 00:23:51,439 Speaker 1: in golf. It's most accessible to every golfer from we 468 00:23:51,480 --> 00:23:54,919 Speaker 1: have players Marty like you, and like Mason Howell and 469 00:23:54,920 --> 00:23:58,639 Speaker 1: others like really elite paying athletes that are utilizing this 470 00:23:58,720 --> 00:24:02,359 Speaker 1: data where we do unbelievably detailed analysis for them, but 471 00:24:02,400 --> 00:24:04,640 Speaker 1: at the same time we're able to distill those insights 472 00:24:04,680 --> 00:24:08,040 Speaker 1: and make them super actionable, so you give so you 473 00:24:08,040 --> 00:24:11,800 Speaker 1: can act upon it, and like that is also very 474 00:24:11,840 --> 00:24:13,600 Speaker 1: different than would have been three four years ago. 475 00:24:15,000 --> 00:24:18,240 Speaker 3: So how about your relationship with Ping and John k specifically, 476 00:24:18,280 --> 00:24:19,960 Speaker 3: how did that? How did that come about? 477 00:24:22,240 --> 00:24:25,119 Speaker 1: So I would say, Uh, it's a great relationship. I 478 00:24:25,119 --> 00:24:28,439 Speaker 1: love John, He's such a great leader. That came about 479 00:24:28,600 --> 00:24:31,720 Speaker 1: I met him Mary. I don't know if you were 480 00:24:31,720 --> 00:24:35,119 Speaker 1: there when I first met him. I remember going golfing 481 00:24:35,160 --> 00:24:37,200 Speaker 1: with him, Uh, and there were a couple other people, 482 00:24:37,680 --> 00:24:40,760 Speaker 1: and he used Arcos for the first time, and he 483 00:24:40,800 --> 00:24:44,720 Speaker 1: immediately saw the power of the data. And I think 484 00:24:44,720 --> 00:24:48,760 Speaker 1: it harkens back to Ping's roots. Ping has always been 485 00:24:48,800 --> 00:24:52,680 Speaker 1: at the forefront of utilizing data to improve golfer's performance, 486 00:24:53,080 --> 00:24:56,320 Speaker 1: with the whole I mean fitting concept really essentially being 487 00:24:56,320 --> 00:24:59,320 Speaker 1: introduced by Pings. So I would say this keep is 488 00:24:59,720 --> 00:25:03,600 Speaker 1: in keeping with the tradition that Ping has of making 489 00:25:03,640 --> 00:25:08,200 Speaker 1: sure that Ping is using the latest and greatest of 490 00:25:08,240 --> 00:25:11,520 Speaker 1: any technology possible as long as it helps golfers play 491 00:25:11,560 --> 00:25:13,480 Speaker 1: their best. So I think there is a lot of 492 00:25:13,480 --> 00:25:16,600 Speaker 1: synergy there. There's a lot of autenticity because we truly 493 00:25:16,880 --> 00:25:22,040 Speaker 1: that's what we do is making sure like everything we're doing, 494 00:25:22,040 --> 00:25:25,000 Speaker 1: every action we're taking, every decision we're making internally as 495 00:25:25,000 --> 00:25:28,960 Speaker 1: a product team as a company is advancing or mission 496 00:25:29,000 --> 00:25:32,240 Speaker 1: of improving the performance of every dedicated golfer at every level. 497 00:25:32,480 --> 00:25:36,480 Speaker 1: And and so I think we bonded over that shared vision, 498 00:25:36,520 --> 00:25:40,840 Speaker 1: shared mission. Since then we've become great friends. We have 499 00:25:40,920 --> 00:25:44,399 Speaker 1: a Actually I'm going to do something cool, which is 500 00:25:44,440 --> 00:25:47,639 Speaker 1: so every match John, every time John and I play, 501 00:25:48,080 --> 00:25:51,359 Speaker 1: we play for what I call the Saad Solheim Cup. 502 00:25:52,440 --> 00:25:54,919 Speaker 1: Also no, no, it's not called Solheim Cup, but we 503 00:25:54,960 --> 00:25:57,560 Speaker 1: have a little cup. It's an Oakmond Cup, and whoever 504 00:25:57,600 --> 00:26:01,640 Speaker 1: wins it, they get it. And all rounds from every 505 00:26:01,720 --> 00:26:04,280 Speaker 1: time we've played are recorded in arcos. So I'm gonna 506 00:26:04,320 --> 00:26:08,040 Speaker 1: have Claude go in and create like a little storybook. 507 00:26:09,080 --> 00:26:11,760 Speaker 1: So it's it's really fun. We both look at our data. 508 00:26:12,000 --> 00:26:15,639 Speaker 1: We're both data centric and John is very like I 509 00:26:15,680 --> 00:26:19,000 Speaker 1: would say among golf leaders, he is unique in terms 510 00:26:19,040 --> 00:26:21,879 Speaker 1: of how tech forward he is from a vision standpoint, 511 00:26:22,600 --> 00:26:24,119 Speaker 1: and I think part of it is also that he 512 00:26:24,240 --> 00:26:28,119 Speaker 1: was an early investor in Tesla and so he's always 513 00:26:28,119 --> 00:26:29,920 Speaker 1: been kind of ahead of the curve on that. 514 00:26:30,920 --> 00:26:32,480 Speaker 3: Who's leading this series. 515 00:26:33,080 --> 00:26:35,600 Speaker 1: Currently, I'm I'm leading the series, but he holds the 516 00:26:35,600 --> 00:26:38,239 Speaker 1: cup right now. Okay, I I gotta get that back. 517 00:26:38,720 --> 00:26:40,240 Speaker 3: We're gonna have to figure out who the next round is. 518 00:26:40,280 --> 00:26:41,119 Speaker 1: Maybe we'll get it. 519 00:26:41,160 --> 00:26:43,320 Speaker 3: We'll get Marty to roll out with a camera. Marty, 520 00:26:43,359 --> 00:26:46,280 Speaker 3: I wanted to ask you about Arcos. I mean, we've 521 00:26:46,320 --> 00:26:48,760 Speaker 3: been doing this podcast for a few years now. You 522 00:26:49,000 --> 00:26:52,560 Speaker 3: say the word Arcos probably every episode in some capacity. 523 00:26:52,600 --> 00:26:54,800 Speaker 3: I mean how important it is in terms of R 524 00:26:54,840 --> 00:26:57,359 Speaker 3: and D and what ping does as the research in 525 00:26:57,560 --> 00:27:00,479 Speaker 3: kind of the you know, the the every day golfer. 526 00:27:00,800 --> 00:27:03,080 Speaker 3: How impactful has Arcos been for you guys? 527 00:27:04,080 --> 00:27:07,359 Speaker 2: It's just been gold Shane because before having the Arcos 528 00:27:07,440 --> 00:27:09,600 Speaker 2: data set, and I think we've captured sol three hundred 529 00:27:09,600 --> 00:27:12,919 Speaker 2: and twenty million shots from from you know, Pink of 530 00:27:13,240 --> 00:27:16,840 Speaker 2: customers out there playing Pink product using Arcos together, and 531 00:27:16,880 --> 00:27:20,600 Speaker 2: we can data mind that we're always sitting in conference rooms. 532 00:27:21,040 --> 00:27:24,399 Speaker 2: Uh is Shane asking the question? Well, I wonder we 533 00:27:24,720 --> 00:27:27,119 Speaker 2: have this great PGA tour data from shot link. I 534 00:27:27,119 --> 00:27:30,400 Speaker 2: wonder what our what our actual you know, customers are 535 00:27:30,400 --> 00:27:31,440 Speaker 2: doing out on the golf course. 536 00:27:31,520 --> 00:27:31,720 Speaker 1: Right. 537 00:27:32,119 --> 00:27:34,919 Speaker 2: So before Arcos, it was we were guestimating or we 538 00:27:34,920 --> 00:27:37,720 Speaker 2: were having to go recall memories, and each of us 539 00:27:37,760 --> 00:27:40,520 Speaker 2: have our own cognitive bias that were laying on layering 540 00:27:40,560 --> 00:27:42,800 Speaker 2: onto that. When I play in pro ams, I observe back. 541 00:27:42,920 --> 00:27:46,199 Speaker 2: So it's been gold to have that information. Uh, I 542 00:27:46,200 --> 00:27:49,320 Speaker 2: mean a couple examples there, Shane or uh, you know, 543 00:27:49,400 --> 00:27:52,119 Speaker 2: I think the thriver concept. What percentage of the time 544 00:27:52,200 --> 00:27:54,480 Speaker 2: do players use their fairy woods off the te versus 545 00:27:54,480 --> 00:27:57,280 Speaker 2: the fairway? And this is different for players like us 546 00:27:57,320 --> 00:27:59,399 Speaker 2: than players who drive the ball to fifty or two 547 00:27:59,480 --> 00:28:03,359 Speaker 2: hundred yards. There's how much are folks using their highest 548 00:28:03,400 --> 00:28:06,800 Speaker 2: lofted wedge around the green out of the rough versus 549 00:28:06,800 --> 00:28:12,159 Speaker 2: the fringe, different versus the bunker, different terrain scenarios. That informs, 550 00:28:12,640 --> 00:28:15,960 Speaker 2: for example, our groove designs. We want to we wanna 551 00:28:16,520 --> 00:28:21,000 Speaker 2: optimize our groove designs for more out of the rough conditions, 552 00:28:21,040 --> 00:28:24,679 Speaker 2: bunker conditions. It changes how we do fitting protocols, how 553 00:28:24,720 --> 00:28:27,920 Speaker 2: much time we spend green side versus around the green, 554 00:28:28,160 --> 00:28:31,520 Speaker 2: tons of gapping information, tons of Hey, we can go 555 00:28:31,560 --> 00:28:34,919 Speaker 2: in and say this SFT players are generally drive the 556 00:28:34,960 --> 00:28:37,159 Speaker 2: ball only two hundred and twenty five yards. We can 557 00:28:37,200 --> 00:28:39,760 Speaker 2: do a different face design, we can change our durability 558 00:28:39,760 --> 00:28:42,640 Speaker 2: standards based off of that. So instead of guessing, now, 559 00:28:42,640 --> 00:28:45,120 Speaker 2: we have all this data to to kind of back 560 00:28:45,160 --> 00:28:47,960 Speaker 2: it up. One other very fun pichae that always comes 561 00:28:47,960 --> 00:28:51,440 Speaker 2: to my mind is that Carston Solheim in the early 562 00:28:51,560 --> 00:28:54,160 Speaker 2: days of club fitting, he would you would you would 563 00:28:55,000 --> 00:28:57,240 Speaker 2: you know, get your club fitting specs and on there 564 00:28:57,280 --> 00:29:00,680 Speaker 2: it said, hey, here's a little stats tracking. Go track 565 00:29:00,720 --> 00:29:03,680 Speaker 2: whether you miss the ball right or left and if 566 00:29:03,720 --> 00:29:06,680 Speaker 2: you're if you if you see a pattern there, uh, 567 00:29:06,840 --> 00:29:09,360 Speaker 2: send your clubs in and we'll adjust your color code. Right, 568 00:29:09,760 --> 00:29:13,560 Speaker 2: this is the very early days of Hey, there's club fitting, 569 00:29:13,600 --> 00:29:16,320 Speaker 2: then there's how how do you perform on the golf course? 570 00:29:16,800 --> 00:29:19,600 Speaker 2: And really that's what Arcos has helped us do is 571 00:29:19,640 --> 00:29:22,480 Speaker 2: to bring we're gonna we're gonna change our club fitting 572 00:29:22,520 --> 00:29:25,640 Speaker 2: protocols to better match how you play on the course. 573 00:29:26,160 --> 00:29:29,400 Speaker 2: But the but the optimal club fitting is never ending. 574 00:29:29,560 --> 00:29:32,120 Speaker 2: Right the tour club fitting, the truck is there every week, 575 00:29:32,520 --> 00:29:35,360 Speaker 2: so you're always kind of have this never ending and 576 00:29:35,520 --> 00:29:37,719 Speaker 2: uh it's been great to have Arcos to help us 577 00:29:37,840 --> 00:29:40,600 Speaker 2: kind of be at the intersection of those two pieces. 578 00:29:41,440 --> 00:29:44,640 Speaker 3: Yeah, I mean it's uh it, I think to your point, 579 00:29:44,800 --> 00:29:47,480 Speaker 3: I mean, getting the information on the everyday golfer has 580 00:29:47,520 --> 00:29:50,400 Speaker 3: always been something that an OEM would want to attain 581 00:29:50,440 --> 00:29:52,280 Speaker 3: and the fact that now it exists, I mean what 582 00:29:52,400 --> 00:29:53,760 Speaker 3: we were two billion shots? 583 00:29:54,040 --> 00:29:54,239 Speaker 1: Is that? 584 00:29:54,280 --> 00:29:56,440 Speaker 3: Is that where we're at right now in terms. 585 00:29:56,200 --> 00:29:59,880 Speaker 4: Of maybe a billion and half we actually had all shots. 586 00:30:00,400 --> 00:30:03,000 Speaker 4: We have a map of the world over there. Lives 587 00:30:03,040 --> 00:30:06,160 Speaker 4: happen anytime there's a shot taken. It's like it's like 588 00:30:06,240 --> 00:30:09,719 Speaker 4: lights going off. So we have also like seven hundred 589 00:30:09,760 --> 00:30:11,040 Speaker 4: shots a minute right now. 590 00:30:11,080 --> 00:30:12,040 Speaker 1: And this is not even golf. 591 00:30:12,440 --> 00:30:15,840 Speaker 3: N Have you changed anything in your bag or maybe 592 00:30:16,120 --> 00:30:19,000 Speaker 3: adjusted a gap in your bag because of Arco's data 593 00:30:19,040 --> 00:30:22,200 Speaker 3: that you probably maybe wouldn't have tried out or wouldn't 594 00:30:22,240 --> 00:30:26,320 Speaker 3: have ventured into maybe a different wedge. Sure, Martin you 595 00:30:26,320 --> 00:30:28,400 Speaker 3: you is there is there any like locks maybe you 596 00:30:28,440 --> 00:30:28,920 Speaker 3: threw in there? 597 00:30:29,080 --> 00:30:30,880 Speaker 1: Yeah, I'll tell you like a few changes. I mean 598 00:30:31,120 --> 00:30:34,280 Speaker 1: I can go through like fitting like at nauseum here, 599 00:30:34,320 --> 00:30:39,000 Speaker 1: but like I'll give you two examples for my home course, 600 00:30:39,040 --> 00:30:43,280 Speaker 1: which is Tamarack Country Club. What data showed me was 601 00:30:43,320 --> 00:30:47,920 Speaker 1: I use my hybrid on there are two long part 602 00:30:47,960 --> 00:30:50,320 Speaker 1: threes and that's basically where I'm using my hybrid, and 603 00:30:50,720 --> 00:30:53,040 Speaker 1: on both those the penal misses on the left when 604 00:30:53,080 --> 00:30:57,160 Speaker 1: you look at a stroke standpoint, and my hybrid would 605 00:30:57,240 --> 00:31:01,560 Speaker 1: miss left more often. So I switched to the four 606 00:31:01,640 --> 00:31:06,240 Speaker 1: iron crossover and now my like so my scoring average 607 00:31:06,240 --> 00:31:08,800 Speaker 1: on those two holes improved. So that was one specific, 608 00:31:08,840 --> 00:31:12,240 Speaker 1: core specific change based on data that I made, and 609 00:31:12,280 --> 00:31:15,280 Speaker 1: it's been like, Actually I showed John Kay that the 610 00:31:16,000 --> 00:31:18,840 Speaker 1: missing my hybrid more left be is there any other thing? 611 00:31:18,880 --> 00:31:20,600 Speaker 1: He's like, Oh, we have this new club like this 612 00:31:20,760 --> 00:31:23,960 Speaker 1: specifically that you won't miss more left with that, and 613 00:31:24,160 --> 00:31:28,280 Speaker 1: it's been unbelievable. So that's been huge helped me with 614 00:31:28,400 --> 00:31:34,160 Speaker 1: my club championship and then my wedge gaping. So my 615 00:31:35,760 --> 00:31:38,000 Speaker 1: I used to have a fifty to fifty four and sixty, 616 00:31:38,560 --> 00:31:40,120 Speaker 1: now I have based on that gaping, I have a 617 00:31:40,200 --> 00:31:42,640 Speaker 1: forty eight fifty four and sixty and that works better 618 00:31:42,880 --> 00:31:45,480 Speaker 1: based on where my pitching wedges and to even those out. 619 00:31:45,440 --> 00:31:47,480 Speaker 1: Otherwise I was trying to hit a lot of field 620 00:31:47,480 --> 00:31:50,760 Speaker 1: shots with a fifty degree, and that's generally for somebody 621 00:31:50,760 --> 00:31:53,920 Speaker 1: who's not who's scoring doesn't depend on living. I'm not 622 00:31:54,000 --> 00:31:57,680 Speaker 1: practicing my fifty degree like nine o'clock, ten o'clock, eleven o'clock. 623 00:31:58,040 --> 00:32:00,480 Speaker 1: So like there's an easier way to solve make the 624 00:32:00,480 --> 00:32:02,160 Speaker 1: gap in a little bit. So it's only maybe you 625 00:32:02,200 --> 00:32:06,520 Speaker 1: have two swings and maybe just one. Yep, those are 626 00:32:06,520 --> 00:32:08,680 Speaker 1: two quick examples that come right on the top of 627 00:32:08,720 --> 00:32:09,080 Speaker 1: my head. 628 00:32:09,280 --> 00:32:11,400 Speaker 2: I love that. So I think there's the big data 629 00:32:11,440 --> 00:32:14,320 Speaker 2: side Shane and sal which is, okay, let's look at 630 00:32:14,320 --> 00:32:17,560 Speaker 2: on average what these groupings do. But sal brought uh 631 00:32:18,160 --> 00:32:20,600 Speaker 2: brought up a couple of examples where he comes into 632 00:32:20,640 --> 00:32:24,000 Speaker 2: our fitters and we look at his data ahead of time. 633 00:32:24,040 --> 00:32:26,800 Speaker 2: And this is this is the ultimate. This is where 634 00:32:26,800 --> 00:32:29,720 Speaker 2: I think every golfer should be is to be able 635 00:32:29,800 --> 00:32:32,320 Speaker 2: to bring your data in. So start collecting the data. 636 00:32:32,320 --> 00:32:34,400 Speaker 2: It's lower friction now with air than it's ever been. 637 00:32:34,880 --> 00:32:38,360 Speaker 2: Start bringing your data into your fitter and using that 638 00:32:38,400 --> 00:32:41,240 Speaker 2: to inform the process, not not to eliminate the fitting, 639 00:32:41,240 --> 00:32:44,280 Speaker 2: but it's just one better piece of information that's from 640 00:32:44,280 --> 00:32:47,600 Speaker 2: your en course. Salas brought up another great example where 641 00:32:48,680 --> 00:32:50,040 Speaker 2: you know, for a good chunk of the year, sow. 642 00:32:50,080 --> 00:32:52,000 Speaker 2: You're playing a lot of your golfer your one home 643 00:32:52,000 --> 00:32:54,800 Speaker 2: golf course, you can optimize your bag for that one 644 00:32:54,920 --> 00:32:58,080 Speaker 2: golf course, right versus another golfer that's traveling around a 645 00:32:58,120 --> 00:33:00,120 Speaker 2: little bit more. Might little need a little bit more 646 00:33:00,720 --> 00:33:02,280 Speaker 2: diversity in their club setup. 647 00:33:03,200 --> 00:33:07,000 Speaker 3: Yeah, it makes it makes total sense. It's kind of 648 00:33:07,040 --> 00:33:09,400 Speaker 3: like Arcos feels kind of like that therapist for your 649 00:33:09,400 --> 00:33:11,200 Speaker 3: golf game, you know it just it's kind of like 650 00:33:11,400 --> 00:33:13,800 Speaker 3: in the background, just consistently helping you out to get 651 00:33:13,960 --> 00:33:15,560 Speaker 3: a little better, maybe when you don't even need to 652 00:33:15,880 --> 00:33:18,880 Speaker 3: know you need to get better, Marty, I'm imagining. I 653 00:33:18,880 --> 00:33:21,040 Speaker 3: mean you mentioned the Thriver earlier, but I mean you 654 00:33:21,480 --> 00:33:24,520 Speaker 3: basically through Arcos you discovered a whole new golf club 655 00:33:24,560 --> 00:33:26,880 Speaker 3: that I know you've leaned on heavily over the last 656 00:33:26,920 --> 00:33:27,440 Speaker 3: few years. 657 00:33:28,000 --> 00:33:30,800 Speaker 2: Yeah, yeah, absolutely, I think you're you're seeing this on 658 00:33:30,840 --> 00:33:34,720 Speaker 2: the PGA Tour. You know, all the number of players play, 659 00:33:34,840 --> 00:33:38,959 Speaker 2: whether it's a a twelve degree Thriver build from US 660 00:33:39,720 --> 00:33:43,000 Speaker 2: Mini Driver, a type of concept category kind of came 661 00:33:43,040 --> 00:33:46,080 Speaker 2: to fruition out of that. Another big one for US, 662 00:33:46,080 --> 00:33:49,720 Speaker 2: Shane is just looking at what percentage of the time 663 00:33:49,840 --> 00:33:54,320 Speaker 2: golfers are in the fairway versus the rough versus hitting 664 00:33:54,320 --> 00:33:57,360 Speaker 2: shots off of tee. So again this might be obvious 665 00:33:57,360 --> 00:34:00,960 Speaker 2: in hindsight, but through the Arcos data, we found that 666 00:34:01,040 --> 00:34:04,000 Speaker 2: players hit their mid irons a third of the time 667 00:34:04,080 --> 00:34:06,480 Speaker 2: off of tee on par threes, a third of the 668 00:34:06,520 --> 00:34:08,680 Speaker 2: time in the fairway, and a third of the time 669 00:34:08,680 --> 00:34:11,799 Speaker 2: in the rough. This is super valuable because now when 670 00:34:11,840 --> 00:34:15,600 Speaker 2: we're fitting iron, when we're fitting irons, it's totally okay. 671 00:34:15,680 --> 00:34:17,960 Speaker 2: Not only is it okay, but we actually encourage you 672 00:34:18,440 --> 00:34:21,120 Speaker 2: to hit a good number of shots with our seven iron, 673 00:34:21,160 --> 00:34:24,480 Speaker 2: our AFS fitting club off of tee because yeah, that 674 00:34:24,600 --> 00:34:27,000 Speaker 2: might be obviously in hindsight now, but without this data, 675 00:34:27,000 --> 00:34:29,560 Speaker 2: we didn't have this enlightenment that you're hitting that on 676 00:34:29,760 --> 00:34:32,680 Speaker 2: par three's a good percentage of the time. You're also 677 00:34:32,760 --> 00:34:36,040 Speaker 2: spending a lot of time from one hundred and twenty 678 00:34:36,080 --> 00:34:39,200 Speaker 2: to one hundred and seventy yards. Sally, I'd love to 679 00:34:39,239 --> 00:34:40,640 Speaker 2: get your take on this, because I know you're a 680 00:34:40,640 --> 00:34:45,439 Speaker 2: golf course architecture history buff, but we see a lot 681 00:34:45,480 --> 00:34:49,400 Speaker 2: of people hitting their approach shots centered around one forty 682 00:34:49,440 --> 00:34:53,560 Speaker 2: plus or minus twenty yards or so, what what are 683 00:34:53,600 --> 00:34:56,480 Speaker 2: your thoughts on that from a golf course architecture standpoint, 684 00:34:56,480 --> 00:34:59,719 Speaker 2: maybe at the viewpoint of the macro level history of 685 00:34:59,719 --> 00:35:01,279 Speaker 2: golf course architecture as well. 686 00:35:01,920 --> 00:35:03,880 Speaker 1: Yeah, I like, I will say the same thing. It 687 00:35:03,960 --> 00:35:05,719 Speaker 1: was really interesting we were looking at the same thing 688 00:35:05,719 --> 00:35:08,239 Speaker 1: and looking at the distribution of shots, and it really 689 00:35:08,280 --> 00:35:11,239 Speaker 1: centers around like doesn't matter what your skill level is, 690 00:35:11,280 --> 00:35:14,000 Speaker 1: Like at least in the amateur database. It's almost like 691 00:35:14,040 --> 00:35:17,680 Speaker 1: people are normalizing to like outlay the t's where I'm like, 692 00:35:17,719 --> 00:35:23,000 Speaker 1: subconsciously is like almost like happening where it's really like 693 00:35:23,080 --> 00:35:27,040 Speaker 1: one forty eight yards is like where we find the peak, 694 00:35:27,760 --> 00:35:29,759 Speaker 1: like the wh when you look at the curve and 695 00:35:29,840 --> 00:35:33,279 Speaker 1: so like we looked at like a twenty. When you 696 00:35:33,280 --> 00:35:35,719 Speaker 1: look at twenty handicappers scratch golfer, it's kind of the same, 697 00:35:36,080 --> 00:35:39,520 Speaker 1: Like they're hitting their appro shots. The highest frequency is 698 00:35:39,560 --> 00:35:44,000 Speaker 1: from one hundred and fifty yards, and like it's like 699 00:35:44,080 --> 00:35:47,000 Speaker 1: I haven't spent time thinking about how are humans adjusting 700 00:35:47,040 --> 00:35:50,480 Speaker 1: to the T boxes subconsciously so that that's happening, But 701 00:35:50,520 --> 00:35:53,120 Speaker 1: it is like true for every skill set, which is wild, 702 00:35:53,480 --> 00:35:56,279 Speaker 1: like the thing matches up. I mean, I don't know, 703 00:35:56,440 --> 00:36:02,720 Speaker 1: it's just like from our course architecture standpoint. Maybe, I mean, honestly, 704 00:36:02,760 --> 00:36:05,040 Speaker 1: it shouldn't be that way, Like somebody who's are twenty 705 00:36:05,040 --> 00:36:07,920 Speaker 1: handicappers should not be hitting their pro shots from one 706 00:36:07,960 --> 00:36:10,640 Speaker 1: hundred and fifty on average. They should be a little 707 00:36:10,680 --> 00:36:14,080 Speaker 1: closer than that. But somehow people are normalizing and maybe 708 00:36:14,080 --> 00:36:17,520 Speaker 1: part of it is people are just making their tea 709 00:36:17,600 --> 00:36:19,640 Speaker 1: decisions so that they're hitting their pro shots from the 710 00:36:19,640 --> 00:36:23,880 Speaker 1: same spot that the longer driver's hitting. And I'll just 711 00:36:23,960 --> 00:36:26,080 Speaker 1: spend a little bit more time thinking about it. But 712 00:36:26,400 --> 00:36:28,600 Speaker 1: that's the only thing come up right off the bat. 713 00:36:28,680 --> 00:36:29,560 Speaker 1: But it is interesting. 714 00:36:30,840 --> 00:36:33,719 Speaker 2: Yeah, I've kind of come to I think golf courses 715 00:36:34,239 --> 00:36:38,160 Speaker 2: sell need probably a bit more diversity in the tea boxes, 716 00:36:38,239 --> 00:36:40,839 Speaker 2: right So I think when the USJ did the play 717 00:36:40,840 --> 00:36:43,640 Speaker 2: it Forward initiative and and a lot of the courses 718 00:36:43,680 --> 00:36:46,960 Speaker 2: just didn't have tea boxes forward enough for folks to 719 00:36:47,040 --> 00:36:48,840 Speaker 2: tee off at. But I always find that there's a 720 00:36:49,200 --> 00:36:51,120 Speaker 2: there's a lot of different nooks and crannies you can 721 00:36:51,160 --> 00:36:52,960 Speaker 2: go to and you look at just kind of organic 722 00:36:54,280 --> 00:36:56,120 Speaker 2: pattern data out on the golf course, and that's the 723 00:36:56,160 --> 00:36:59,480 Speaker 2: interesting one that folks bunch up around one fifty, but 724 00:36:59,600 --> 00:37:03,520 Speaker 2: from a upfitting standpoint. This is super important Shane and 725 00:37:03,560 --> 00:37:06,799 Speaker 2: sal because we want to make sure we don't have 726 00:37:06,880 --> 00:37:09,080 Speaker 2: too big of yardage gaps in there. And with a 727 00:37:09,120 --> 00:37:12,719 Speaker 2: modernization of losts. You know, some of our models have 728 00:37:12,719 --> 00:37:15,680 Speaker 2: seven iron loss that are stronger than previous years. We 729 00:37:15,840 --> 00:37:20,960 Speaker 2: can't forget about that distribution where golfers play a lot 730 00:37:20,960 --> 00:37:22,680 Speaker 2: of golf and hit a lot of shots, which is 731 00:37:22,760 --> 00:37:27,640 Speaker 2: one thirty to one seventy centered around one Sal mentioned. 732 00:37:27,640 --> 00:37:30,560 Speaker 2: So we've used that to informed our our gapping, our 733 00:37:30,640 --> 00:37:35,200 Speaker 2: loss and second figuration quite quite strongly. 734 00:37:36,680 --> 00:37:36,879 Speaker 1: Sal. 735 00:37:37,000 --> 00:37:39,000 Speaker 3: Well, congrats on Arcos, Ayer, I mean, I know you 736 00:37:39,040 --> 00:37:40,680 Speaker 3: guys are fired up for it. I know it's It's 737 00:37:40,719 --> 00:37:43,560 Speaker 3: obviously a big leap for Arcos. As I mentioned, the 738 00:37:43,600 --> 00:37:46,440 Speaker 3: lasers awesome for anybody out there that is in the 739 00:37:46,440 --> 00:37:49,719 Speaker 3: market for a new laser, I would definitely give the Arcos. 740 00:37:50,120 --> 00:37:51,120 Speaker 3: Is it smart lasers? 741 00:37:51,120 --> 00:37:52,000 Speaker 1: That was that where we're smart? 742 00:37:52,760 --> 00:37:55,520 Speaker 3: Yeah, the smart laser is definitely one to consider. I mean, 743 00:37:55,640 --> 00:37:58,120 Speaker 3: obviously it gives you so many more data points, which 744 00:37:58,160 --> 00:37:59,800 Speaker 3: is something we're always trying to attain. 745 00:38:00,719 --> 00:38:00,879 Speaker 1: Sal. 746 00:38:00,920 --> 00:38:02,400 Speaker 3: We gotta get out and play this year, by the way, 747 00:38:02,400 --> 00:38:03,080 Speaker 3: We've got we got. 748 00:38:02,920 --> 00:38:03,680 Speaker 4: To sleep out and play. 749 00:38:03,760 --> 00:38:05,960 Speaker 3: Uh, get Musso out and uh and we'll go get 750 00:38:05,960 --> 00:38:06,440 Speaker 3: a round. 751 00:38:06,239 --> 00:38:08,359 Speaker 1: In or something. Let's do it. That'll be fun. 752 00:38:09,280 --> 00:38:09,360 Speaker 2: Uh. 753 00:38:09,440 --> 00:38:11,160 Speaker 1: Sal I appreciate the time. This is the thing. 754 00:38:11,280 --> 00:38:12,360 Speaker 3: Proven Grounds Podcast