1 00:00:00,720 --> 00:00:04,600 Speaker 1: This is Bloomberg Business of Sports. The world changing and 2 00:00:04,640 --> 00:00:06,840 Speaker 1: what are things we can do to transform our business 3 00:00:06,840 --> 00:00:09,719 Speaker 1: and engage our fans globally in different ways. People are 4 00:00:09,760 --> 00:00:12,680 Speaker 1: using their name and likeness to create more opportunities, more 5 00:00:12,720 --> 00:00:15,480 Speaker 1: states and companies. In order to turn the organization around, 6 00:00:15,560 --> 00:00:17,279 Speaker 1: we had to turn it around not only gets on 7 00:00:17,440 --> 00:00:20,959 Speaker 1: baseball operations side, but other business operations side well, and 8 00:00:21,079 --> 00:00:23,079 Speaker 1: any other sport is very difficult, but I like to 9 00:00:23,079 --> 00:00:26,560 Speaker 1: blub my horizons and be able to expand. Sports need 10 00:00:26,600 --> 00:00:29,040 Speaker 1: to be consumed a lot and not to the big 11 00:00:29,160 --> 00:00:33,640 Speaker 1: competitive advantage for intual property holders of sports content in 12 00:00:33,720 --> 00:00:37,920 Speaker 1: the media landscape. Bloomberg Business of Sports from Bloomberg Radio. 13 00:00:39,720 --> 00:00:42,280 Speaker 1: Hello every one, I'm Mike Lynch, I'm Scarlet Food and 14 00:00:42,280 --> 00:00:44,400 Speaker 1: for Jason Kelly. Over the next hour, we're going to 15 00:00:44,440 --> 00:00:46,559 Speaker 1: explore the big money issues in the world of sports 16 00:00:46,600 --> 00:00:49,240 Speaker 1: and talk to some of the biggest players in the industry. 17 00:00:49,280 --> 00:00:51,519 Speaker 1: And today I'm pleased to say we're going to be 18 00:00:51,560 --> 00:00:55,200 Speaker 1: speaking with the VP of Basketball Operations and Team Council 19 00:00:55,320 --> 00:00:59,000 Speaker 1: of the Boston Celtics and the Assistant GM Mike Zarin 20 00:00:59,400 --> 00:01:02,960 Speaker 1: and the Sea of Huddle Matt Mueller. Alright, So, I'm 21 00:01:02,960 --> 00:01:05,319 Speaker 1: gonna jump right in. Uh, this is Mike to Mike. 22 00:01:05,360 --> 00:01:06,959 Speaker 1: Mike Szarin and I grew up in the great town 23 00:01:06,959 --> 00:01:09,160 Speaker 1: of swamp Scott, a few about ten miles north of 24 00:01:09,160 --> 00:01:11,560 Speaker 1: Boston and a great place to be in the summertime 25 00:01:11,560 --> 00:01:14,080 Speaker 1: and year round. Mike, Uh, welcome to the show, and Matt, 26 00:01:14,080 --> 00:01:16,200 Speaker 1: welcome to the show as well. It's an honor to 27 00:01:16,200 --> 00:01:20,640 Speaker 1: be on with the swamp Scott legend. You're too kind, Matt. 28 00:01:20,720 --> 00:01:23,360 Speaker 1: Let me start with you, the CEO of Huddle. I've 29 00:01:23,360 --> 00:01:25,840 Speaker 1: been familiar with it for years, dealing with high school 30 00:01:25,880 --> 00:01:29,240 Speaker 1: athletes looking for specifically football, looking for highlights. It made 31 00:01:29,240 --> 00:01:32,640 Speaker 1: my life so much easier. Tell me about this marriage 32 00:01:32,640 --> 00:01:34,440 Speaker 1: with the NBA. Now you get twenty nine out of 33 00:01:34,480 --> 00:01:38,399 Speaker 1: thirty teams I think involved in huddle. Yeah, you bet so. 34 00:01:38,520 --> 00:01:40,839 Speaker 1: I think most people's first experience with huddle is really 35 00:01:40,840 --> 00:01:43,839 Speaker 1: through high school American football, whether it's you know, using 36 00:01:43,840 --> 00:01:46,039 Speaker 1: it themselves as an athlete or coach, or you know, 37 00:01:46,040 --> 00:01:49,120 Speaker 1: even more often you know, seeing their son or daughter, 38 00:01:49,400 --> 00:01:52,840 Speaker 1: or a nephew or a neighbor, you know, working through huddle. 39 00:01:52,920 --> 00:01:54,880 Speaker 1: But the beauty of what we built with huddle was 40 00:01:55,160 --> 00:01:57,600 Speaker 1: and solution that really helped teams find value out of 41 00:01:57,800 --> 00:01:59,960 Speaker 1: the combination of video and data, and as we grew, 42 00:02:00,440 --> 00:02:03,480 Speaker 1: we look to grow into more sports than just American football, 43 00:02:03,520 --> 00:02:06,400 Speaker 1: and basketball was a natural logical step for us. And 44 00:02:06,400 --> 00:02:09,280 Speaker 1: when we worked with basketball teams at all levels, NBA's 45 00:02:09,280 --> 00:02:11,320 Speaker 1: obviously and naturals are off for us, and so we 46 00:02:11,360 --> 00:02:13,960 Speaker 1: really put a strong focus on finding tools that would 47 00:02:13,960 --> 00:02:16,680 Speaker 1: help us deliver a lot of value for for NBA teams. 48 00:02:16,680 --> 00:02:18,680 Speaker 1: So it started with the product called sports Code, but 49 00:02:18,720 --> 00:02:20,880 Speaker 1: it's expanded to be so much more from there into 50 00:02:21,360 --> 00:02:23,640 Speaker 1: helping them find content from across the world, but also 51 00:02:23,800 --> 00:02:26,640 Speaker 1: you know, automate capture, look at instant replay, uh and 52 00:02:26,680 --> 00:02:28,600 Speaker 1: continue to find more value out of data and video. 53 00:02:28,720 --> 00:02:31,280 Speaker 1: And it's been it's been exciting for us to reach 54 00:02:31,320 --> 00:02:33,720 Speaker 1: that kind of level of interest in NBA teams and 55 00:02:33,720 --> 00:02:35,800 Speaker 1: we're excited for what we continue to build for them. 56 00:02:35,880 --> 00:02:39,080 Speaker 1: And Huddle started off as a basketball specific product and 57 00:02:39,120 --> 00:02:41,400 Speaker 1: it's kind of really expanded to cover all kinds of 58 00:02:41,440 --> 00:02:44,840 Speaker 1: different things. Mike Sarin explained to us how the Boston 59 00:02:44,919 --> 00:02:47,840 Speaker 1: Celtics uses Huddle in the most basic form and in 60 00:02:47,880 --> 00:02:52,080 Speaker 1: the most complicated form. Well, I mean it's both basically 61 00:02:52,240 --> 00:02:55,400 Speaker 1: complicated at once, it's pretty easy to describe. On our bench, 62 00:02:55,440 --> 00:02:59,040 Speaker 1: we've got a guy who's coding the game and in 63 00:02:59,040 --> 00:03:02,040 Speaker 1: our video room and just same time, and they've got 64 00:03:02,280 --> 00:03:04,720 Speaker 1: you know, Huddle Sports Code software and we've been using 65 00:03:04,720 --> 00:03:07,680 Speaker 1: that for I don't know how long now, ten fifteen 66 00:03:07,760 --> 00:03:11,679 Speaker 1: years as our main video capture, recording and distribution platform 67 00:03:11,800 --> 00:03:14,280 Speaker 1: for NBA games. And you know, you can imagine all 68 00:03:14,320 --> 00:03:16,560 Speaker 1: the different kinds of video edits that we might need 69 00:03:16,600 --> 00:03:19,280 Speaker 1: to do, that we give to the players after the 70 00:03:19,280 --> 00:03:22,000 Speaker 1: game on iPads that we show in the locker room 71 00:03:22,040 --> 00:03:24,720 Speaker 1: at halftime, or even you know, more recently with some 72 00:03:24,840 --> 00:03:27,320 Speaker 1: NBA rules changes, that our coaches are looking at on 73 00:03:27,360 --> 00:03:29,480 Speaker 1: the bench to see if we should challenge a call 74 00:03:29,560 --> 00:03:32,919 Speaker 1: in real time. So that software platform has been hugely 75 00:03:32,960 --> 00:03:35,760 Speaker 1: useful for our coaching staff for a long time, and 76 00:03:35,800 --> 00:03:38,200 Speaker 1: they continue to innovate and provide great support for it. 77 00:03:38,280 --> 00:03:41,320 Speaker 1: So we're happy to be great partners of theirs. So Mike, 78 00:03:41,400 --> 00:03:43,560 Speaker 1: let me follow up on that. So, when Brad was 79 00:03:43,640 --> 00:03:46,040 Speaker 1: the coach, now he's SE's in the front office, obviously, 80 00:03:46,320 --> 00:03:48,600 Speaker 1: would he signal into the locker room? Look, I want 81 00:03:48,640 --> 00:03:50,840 Speaker 1: to look at when when they went to a zone here, 82 00:03:50,880 --> 00:03:52,119 Speaker 1: I want to look when they went to a full 83 00:03:52,120 --> 00:03:54,200 Speaker 1: court van demand press at halftime, I want to make 84 00:03:54,240 --> 00:03:57,200 Speaker 1: a demonstrate how we need to fix and break this 85 00:03:57,320 --> 00:03:59,640 Speaker 1: press or this trap or did he just walking there 86 00:03:59,640 --> 00:04:01,560 Speaker 1: and half time and just hit fast forward and go 87 00:04:01,600 --> 00:04:04,119 Speaker 1: to wherever he wants to go? No, no, no, They've 88 00:04:04,120 --> 00:04:06,160 Speaker 1: got it all cued up. So during the game, you know, 89 00:04:06,200 --> 00:04:09,080 Speaker 1: the coaches will be taking notes about which place to tag, 90 00:04:09,120 --> 00:04:12,120 Speaker 1: and you can sort of live tag those um on 91 00:04:12,160 --> 00:04:16,000 Speaker 1: the bench in that software. And then you know, I'm 92 00:04:16,040 --> 00:04:18,279 Speaker 1: not in too many coaches meetings at halftime. You mostly 93 00:04:18,279 --> 00:04:20,560 Speaker 1: want to let them do their jobs, but you know 94 00:04:20,600 --> 00:04:22,800 Speaker 1: they'll have it narrowed down to ten clips and then 95 00:04:22,960 --> 00:04:25,080 Speaker 1: cut out five or six of those so that they've 96 00:04:25,080 --> 00:04:26,760 Speaker 1: got the five or six to show at halftime. And 97 00:04:26,760 --> 00:04:30,120 Speaker 1: it's all pretty seamless. You don't really have time, you know, 98 00:04:30,600 --> 00:04:33,200 Speaker 1: at halftime to suddenly be editing the video then. But 99 00:04:33,320 --> 00:04:35,680 Speaker 1: one of the beauties of this product, um, and I 100 00:04:35,839 --> 00:04:37,359 Speaker 1: want to be too much of a sales pitch, but 101 00:04:37,400 --> 00:04:39,320 Speaker 1: one of the beauties of the product is how easy 102 00:04:39,320 --> 00:04:42,039 Speaker 1: it is to video and pick the particular edits that 103 00:04:42,080 --> 00:04:45,280 Speaker 1: you need to show in really quick time. Yeah, customize 104 00:04:45,320 --> 00:04:48,719 Speaker 1: things very quickly. Matt Mueller, Um Lynch, you was just saying, 105 00:04:48,760 --> 00:04:51,440 Speaker 1: how twenty nine out of thirty NBA teams use Huddle. 106 00:04:51,520 --> 00:04:55,800 Speaker 1: Who's alone hold out and what are they using instead? Well, 107 00:04:55,880 --> 00:04:59,000 Speaker 1: there's there's a team in Dallas that that doesn't use 108 00:04:59,040 --> 00:05:01,960 Speaker 1: this for their game analysis from sports Code, but they do, 109 00:05:02,080 --> 00:05:04,440 Speaker 1: they do actually buy a variety of other products from us. 110 00:05:04,440 --> 00:05:06,839 Speaker 1: You know, Mike is talking about Huddle sports Code and 111 00:05:06,880 --> 00:05:09,040 Speaker 1: how you know teams canna use it to code events 112 00:05:09,080 --> 00:05:10,719 Speaker 1: and they use a you know, a different piece of 113 00:05:10,760 --> 00:05:14,040 Speaker 1: software for that, but they do buy other software pieces 114 00:05:14,040 --> 00:05:17,000 Speaker 1: that allow us to help their front office analyzed players, 115 00:05:17,040 --> 00:05:19,560 Speaker 1: you know, whether it's draft respects or free agency that 116 00:05:19,640 --> 00:05:22,080 Speaker 1: allow them to you know, more effectively bring together content 117 00:05:22,120 --> 00:05:24,440 Speaker 1: pieces and make decisions in that world. So when we 118 00:05:24,440 --> 00:05:26,359 Speaker 1: talked about, you know, that core piece of software that 119 00:05:26,440 --> 00:05:28,040 Speaker 1: that lynchment of what they do every day, it's a 120 00:05:28,120 --> 00:05:30,360 Speaker 1: sports co software and you know, we're excited about, you know, 121 00:05:30,400 --> 00:05:33,479 Speaker 1: the twenty three NBA teams that do use this. Hey, 122 00:05:33,520 --> 00:05:35,760 Speaker 1: Mike Zara and I when I think analytics, I think 123 00:05:35,800 --> 00:05:38,480 Speaker 1: of baseball teams, and I think of theo Epstein and 124 00:05:38,520 --> 00:05:42,080 Speaker 1: the basement over at Fenway Park. You know, fifteen years 125 00:05:42,080 --> 00:05:45,760 Speaker 1: ago with his little crew there, how does basketball use analytics? 126 00:05:45,800 --> 00:05:49,279 Speaker 1: And how's it marriage with huddle work? So, you know, 127 00:05:49,320 --> 00:05:52,280 Speaker 1: it's amazing how much our world has changed since I 128 00:05:52,360 --> 00:05:55,360 Speaker 1: started as an intern in two thousand three. There wasn't 129 00:05:55,400 --> 00:05:59,000 Speaker 1: anyone really doing full time basketball statistics work then, and 130 00:05:59,080 --> 00:06:00,600 Speaker 1: I was one of those luck enough to be one 131 00:06:00,600 --> 00:06:03,360 Speaker 1: of the first. Danny really took a chance on me 132 00:06:03,839 --> 00:06:07,560 Speaker 1: back then, and you know, for a long time we 133 00:06:07,680 --> 00:06:09,599 Speaker 1: just sort of had the box score to go off of. 134 00:06:09,680 --> 00:06:12,600 Speaker 1: Basketball is really different from baseball. You know, I've had 135 00:06:12,600 --> 00:06:15,279 Speaker 1: a few conversations with Bill James, and every time I 136 00:06:15,279 --> 00:06:17,680 Speaker 1: tell them how easy they have it, he laughs at me. 137 00:06:17,880 --> 00:06:22,599 Speaker 1: But you know, they baseball, it's all a sequence of 138 00:06:22,640 --> 00:06:25,560 Speaker 1: individual events. The guy throws a pitch, it goes somewhere 139 00:06:25,760 --> 00:06:27,919 Speaker 1: that someone either hits the wings are not and they 140 00:06:28,000 --> 00:06:30,320 Speaker 1: hit it or not, and then it goes somewhere. The 141 00:06:30,360 --> 00:06:34,640 Speaker 1: players interact occasionally on the base pass um, but it's not, 142 00:06:35,040 --> 00:06:37,720 Speaker 1: you know, particularly complicated the way the players interact for 143 00:06:37,800 --> 00:06:40,719 Speaker 1: each other. In basketball. There's just so many more things 144 00:06:40,800 --> 00:06:43,760 Speaker 1: happening at every moment during the game, and the players 145 00:06:43,760 --> 00:06:47,520 Speaker 1: are crashing into each other and interacting with multiple other 146 00:06:47,600 --> 00:06:49,599 Speaker 1: players and any given play. And so when you look 147 00:06:49,640 --> 00:06:53,839 Speaker 1: at the set of things that coaches talk about in basketball, 148 00:06:54,000 --> 00:06:55,760 Speaker 1: you can't look at a box score of a game 149 00:06:55,800 --> 00:06:59,360 Speaker 1: and know what happened in the game. Right. You can see, uh, 150 00:06:59,600 --> 00:07:02,240 Speaker 1: you know, Ason Tatum had points, but that doesn't really 151 00:07:02,279 --> 00:07:04,480 Speaker 1: let you picture anything about what happened in the game. 152 00:07:04,880 --> 00:07:07,200 Speaker 1: And so when I started, it was really difficult to 153 00:07:07,880 --> 00:07:09,479 Speaker 1: you know, really have a made you know, there were 154 00:07:09,560 --> 00:07:11,000 Speaker 1: some low hanging fruit, but it was hard to have 155 00:07:11,040 --> 00:07:13,760 Speaker 1: a major impact just because you know, I thought I 156 00:07:13,800 --> 00:07:15,640 Speaker 1: knew a bunch of about basketball, and my dad had 157 00:07:15,640 --> 00:07:18,440 Speaker 1: Celtic season tickets growing up. I would watch, you know, 158 00:07:18,560 --> 00:07:21,640 Speaker 1: either Lynchy or Bob lo Bell every night to see 159 00:07:21,680 --> 00:07:25,800 Speaker 1: what happened came um. You know, there's great wars between 160 00:07:25,840 --> 00:07:29,320 Speaker 1: the Boston TV stations back then. But I showed up 161 00:07:29,320 --> 00:07:32,520 Speaker 1: at my first practice as an intern, and the coaches 162 00:07:32,560 --> 00:07:35,200 Speaker 1: were speaking the language. I played high school basketball, but 163 00:07:35,400 --> 00:07:38,080 Speaker 1: for a weird little school, and I barely knew what 164 00:07:38,120 --> 00:07:40,040 Speaker 1: was going on watching the practice all the things they 165 00:07:40,040 --> 00:07:43,120 Speaker 1: were talking about. So one thing that we've done that 166 00:07:43,320 --> 00:07:45,160 Speaker 1: I think we're actually ahead of baseball and doing this. 167 00:07:45,240 --> 00:07:47,080 Speaker 1: Although they're catching up to us now is we were 168 00:07:47,080 --> 00:07:48,880 Speaker 1: the first team in the NBA in two thousand ten 169 00:07:49,000 --> 00:07:51,400 Speaker 1: to install a system of cameras and the rafters at 170 00:07:51,440 --> 00:07:54,360 Speaker 1: the garden to track the players movement X and y 171 00:07:54,440 --> 00:07:56,360 Speaker 1: at thirty frames a second and the ball X, y 172 00:07:56,400 --> 00:07:58,200 Speaker 1: and z. So we have up and down on the 173 00:07:58,200 --> 00:08:00,360 Speaker 1: ball and just the thoughts moving around the court for 174 00:08:00,400 --> 00:08:04,960 Speaker 1: the players. And now we actually know when someone sets 175 00:08:05,000 --> 00:08:07,440 Speaker 1: the screen how good they are at it, where it was, 176 00:08:07,720 --> 00:08:10,640 Speaker 1: what was the outcome. You know, before we the coaches 177 00:08:10,640 --> 00:08:12,880 Speaker 1: would talk about that stuff and the data we had 178 00:08:12,920 --> 00:08:15,480 Speaker 1: just didn't keep track of all those things. So the 179 00:08:15,480 --> 00:08:19,640 Speaker 1: world has really changed drastically. And what's come with that is, 180 00:08:20,200 --> 00:08:22,560 Speaker 1: you know, a single season of that data is like 181 00:08:22,800 --> 00:08:26,760 Speaker 1: three billion cells of data across the whole NBA. Every 182 00:08:26,760 --> 00:08:28,440 Speaker 1: team has it now. The league put it in for 183 00:08:28,480 --> 00:08:30,840 Speaker 1: all the teams in all the arenas in two fifteen, 184 00:08:31,200 --> 00:08:33,200 Speaker 1: and we get the data from every game and we've 185 00:08:33,200 --> 00:08:35,679 Speaker 1: got to process it overnight and you know, have it 186 00:08:35,720 --> 00:08:37,920 Speaker 1: available for our coaching staff the next morning or our 187 00:08:37,960 --> 00:08:40,480 Speaker 1: front office whenever we need to make decisions. And so 188 00:08:40,840 --> 00:08:43,360 Speaker 1: that level of complexity is just a whole new world. 189 00:08:43,360 --> 00:08:45,720 Speaker 1: You can't just come out of college with the basic 190 00:08:45,760 --> 00:08:48,839 Speaker 1: statistics degree and know how to deal with continuous motion 191 00:08:48,880 --> 00:08:51,880 Speaker 1: spatial data thirty frames a second over two thousand games 192 00:08:51,880 --> 00:08:58,000 Speaker 1: a year. So yeah, no, there's some colleges that do 193 00:08:58,160 --> 00:09:00,480 Speaker 1: teach that now, but it's a more rea sin thing. 194 00:09:00,679 --> 00:09:04,079 Speaker 1: And so you know, NBA teams that weren't hiring PhDs 195 00:09:04,080 --> 00:09:06,360 Speaker 1: in machine learning when I started, and now that's not 196 00:09:06,440 --> 00:09:09,360 Speaker 1: so uncommon, as odd as that may seem, but if 197 00:09:09,360 --> 00:09:10,839 Speaker 1: you want to have the best information, you've got to 198 00:09:10,840 --> 00:09:12,880 Speaker 1: be able to deal with that data. Now, it's really 199 00:09:13,000 --> 00:09:16,360 Speaker 1: the world has really changed drastically with regard to basketball stats, 200 00:09:16,360 --> 00:09:18,720 Speaker 1: and we still have so much we can learn because 201 00:09:18,720 --> 00:09:20,559 Speaker 1: we still only have five or six years really of 202 00:09:20,600 --> 00:09:24,240 Speaker 1: the whole league of this data. And you know, we 203 00:09:24,280 --> 00:09:26,720 Speaker 1: don't have full careers for a lot of guys. We 204 00:09:26,760 --> 00:09:30,359 Speaker 1: don't have anything from college or overseas of this complexity, 205 00:09:30,679 --> 00:09:32,640 Speaker 1: and there's a lot of guys, you know, everyone entering 206 00:09:32,679 --> 00:09:35,200 Speaker 1: the NBA comes from one of those places. So there's 207 00:09:35,200 --> 00:09:37,280 Speaker 1: still a lot more we'd love to do. But the 208 00:09:37,600 --> 00:09:40,680 Speaker 1: basketball stats world is drastically, drastically different than it was 209 00:09:40,720 --> 00:09:43,520 Speaker 1: in two thousand three, and I think it will continue 210 00:09:43,520 --> 00:09:46,440 Speaker 1: to get more complexed and I think that's interesting, this 211 00:09:46,520 --> 00:09:49,559 Speaker 1: idea that things have gotten more complex over the last 212 00:09:49,920 --> 00:09:53,160 Speaker 1: ten twenty years. That you guys work a lot with 213 00:09:53,240 --> 00:09:55,319 Speaker 1: high school teams with college teams, so you have a 214 00:09:55,360 --> 00:09:59,480 Speaker 1: lot of student athletes who are familiar with what huddle 215 00:09:59,559 --> 00:10:01,720 Speaker 1: does and the software and being able to break apart 216 00:10:01,800 --> 00:10:04,720 Speaker 1: plays and do some analysis on specific place. How do 217 00:10:04,760 --> 00:10:08,880 Speaker 1: you see those student athletes graduating to college athletes and 218 00:10:09,160 --> 00:10:13,320 Speaker 1: pro athletes kind of requiring and needing that level of 219 00:10:13,360 --> 00:10:19,840 Speaker 1: analytics and that level of technology as they further their careers. Yeah, 220 00:10:19,880 --> 00:10:22,920 Speaker 1: I think just the it's it's really similar short of 221 00:10:22,960 --> 00:10:25,440 Speaker 1: what Mike said, the baseline of an athlete that's come in, 222 00:10:25,520 --> 00:10:28,880 Speaker 1: it's just changed in terms of their technological needs or 223 00:10:28,880 --> 00:10:32,360 Speaker 1: technological needs or their expectations of what of what a 224 00:10:32,360 --> 00:10:34,800 Speaker 1: team should have. When you when we talk to basketball 225 00:10:34,800 --> 00:10:37,679 Speaker 1: teams now, um, you know, ten twenty years ago, an 226 00:10:37,679 --> 00:10:40,240 Speaker 1: athlete may watch video, They may watch a VHS tape. 227 00:10:40,360 --> 00:10:42,839 Speaker 1: Right if for anyone who watched, uh, you know the 228 00:10:42,880 --> 00:10:45,520 Speaker 1: last dance you saw Michael Jordan watching, uh, you know 229 00:10:45,640 --> 00:10:49,440 Speaker 1: VAHS tapes and just think about now, an athlete would expect, um, 230 00:10:49,559 --> 00:10:53,200 Speaker 1: you know, playlists specific to their upcoming opponent UH delivered 231 00:10:53,200 --> 00:10:56,160 Speaker 1: to their cell phone oftentimes you know, on you know, 232 00:10:56,200 --> 00:10:58,440 Speaker 1: before they board the plane to the next flight or 233 00:10:58,559 --> 00:11:00,679 Speaker 1: on their drive home, and they want to think about, 234 00:11:00,920 --> 00:11:02,679 Speaker 1: you know, how can they start preparing right away for 235 00:11:02,720 --> 00:11:05,560 Speaker 1: their upcoming opponents and and you're right there. You know, 236 00:11:05,600 --> 00:11:07,880 Speaker 1: the technological needs are bigger and the data needs are 237 00:11:07,880 --> 00:11:10,640 Speaker 1: also bigger. And our whole job with Huddle and as 238 00:11:10,679 --> 00:11:13,559 Speaker 1: we partner with teams is to find, you know, how 239 00:11:13,559 --> 00:11:16,400 Speaker 1: do we deliver actionable insights that are simple and digestible 240 00:11:16,440 --> 00:11:18,440 Speaker 1: from that data. Because your players aren't going to be 241 00:11:18,480 --> 00:11:20,439 Speaker 1: a PhD in machine learning and you can't just give 242 00:11:20,480 --> 00:11:23,920 Speaker 1: them positional data. You need to give them data and 243 00:11:23,960 --> 00:11:26,120 Speaker 1: digestible chunks, you know that that can tell them what 244 00:11:26,160 --> 00:11:28,160 Speaker 1: to do when they see something happen on the court, 245 00:11:28,200 --> 00:11:31,480 Speaker 1: and how they can react to you know, what their 246 00:11:31,480 --> 00:11:33,600 Speaker 1: opponents are doing and UH and the opportunities that are 247 00:11:33,600 --> 00:11:35,760 Speaker 1: available to them. And and really that's where we target 248 00:11:35,800 --> 00:11:37,160 Speaker 1: and how we think we can deliver a lot of 249 00:11:37,240 --> 00:11:40,280 Speaker 1: value for you for the back office is helping simplify 250 00:11:40,320 --> 00:11:43,439 Speaker 1: that data and turn that into solutions for them right away. 251 00:11:43,440 --> 00:11:46,640 Speaker 1: On the you know, on the court, we have so 252 00:11:46,720 --> 00:11:50,960 Speaker 1: many players now who who are are shocked. Um, well 253 00:11:51,000 --> 00:11:54,240 Speaker 1: they're not shocked. They want this data instantly after the game, 254 00:11:54,280 --> 00:11:55,920 Speaker 1: and that was definitely not the case when I started. 255 00:11:55,960 --> 00:11:59,080 Speaker 1: They've been conditioned by Huddle to need it. That's really good. 256 00:11:59,720 --> 00:12:02,319 Speaker 1: That was gonna be my question, how recept of other players. 257 00:12:02,520 --> 00:12:05,000 Speaker 1: The Usually nobody wants to see themselves on film because 258 00:12:05,360 --> 00:12:07,520 Speaker 1: it always looks worse than you would even imagine it 259 00:12:07,640 --> 00:12:09,720 Speaker 1: was when you are actually playing on real time. But 260 00:12:09,760 --> 00:12:12,680 Speaker 1: now you're throwing these this, this data and this analytics, 261 00:12:12,960 --> 00:12:15,120 Speaker 1: and that's a lot for them to digest. You know. 262 00:12:15,360 --> 00:12:19,800 Speaker 1: One thing I think that's a relatively common misconception about um, 263 00:12:19,880 --> 00:12:23,880 Speaker 1: you know stats, uh in basketball, is that everybody has 264 00:12:23,920 --> 00:12:26,760 Speaker 1: to become a statistician to understand them. I think most 265 00:12:26,800 --> 00:12:29,520 Speaker 1: of our messages to the players sound very similar to 266 00:12:29,520 --> 00:12:32,280 Speaker 1: the messages we would have delivered twenty years ago. They're 267 00:12:32,320 --> 00:12:35,880 Speaker 1: just better informed, right, so we're more sure that we're 268 00:12:35,960 --> 00:12:38,440 Speaker 1: right about telling you someone to go under a screen 269 00:12:38,559 --> 00:12:43,199 Speaker 1: or send someone to his left hand. Um, it's still basketball. 270 00:12:43,240 --> 00:12:44,959 Speaker 1: The rules are still the same, they're still playing the 271 00:12:45,000 --> 00:12:48,280 Speaker 1: same game, and so you know the types of messages 272 00:12:48,320 --> 00:12:50,160 Speaker 1: you might deliver to a player about what to do 273 00:12:50,280 --> 00:12:52,640 Speaker 1: haven't changed that much. Now there's some players who are 274 00:12:52,640 --> 00:12:56,560 Speaker 1: more interested in more advanced things, and for them, um, 275 00:12:56,600 --> 00:12:58,800 Speaker 1: you know, Bryan Scalabrini is a good example. He was 276 00:12:58,800 --> 00:13:01,320 Speaker 1: one of the first guys to really want more numbers 277 00:13:01,400 --> 00:13:03,000 Speaker 1: on the team. And maybe it's because he had more 278 00:13:03,000 --> 00:13:04,760 Speaker 1: time on his hands because he wasn't playing as much 279 00:13:07,840 --> 00:13:13,520 Speaker 1: out there happens, But with guys like that, you just 280 00:13:13,559 --> 00:13:16,520 Speaker 1: have to make sure that what if the guy's taken 281 00:13:16,679 --> 00:13:19,120 Speaker 1: five three, you know, and he hits three of them, 282 00:13:19,160 --> 00:13:22,280 Speaker 1: it doesn't mean he's a six three point shoot. So um. 283 00:13:22,320 --> 00:13:24,920 Speaker 1: We spend a lot of time actually teaching coaches who 284 00:13:24,960 --> 00:13:28,440 Speaker 1: also aren't generally staff guys and players what information not 285 00:13:28,600 --> 00:13:31,120 Speaker 1: to look at. That's almost as important as knowing what 286 00:13:31,200 --> 00:13:34,600 Speaker 1: information to look at. I think that's fascinating because you're right. 287 00:13:34,640 --> 00:13:36,720 Speaker 1: You can you can drown and go drown in all 288 00:13:36,760 --> 00:13:38,760 Speaker 1: the data and you can go down rabbit holes really 289 00:13:38,840 --> 00:13:41,080 Speaker 1: quickly if you don't distill it to what is the 290 00:13:41,080 --> 00:13:43,840 Speaker 1: most relevant. Um, Matt, I want to get a sense 291 00:13:43,840 --> 00:13:46,720 Speaker 1: from you as well. Obviously you have these relationships with 292 00:13:46,760 --> 00:13:49,200 Speaker 1: twenty nine out of thirty NBA teams, and and these 293 00:13:49,200 --> 00:13:52,880 Speaker 1: are are steady clients. But um, in terms of your 294 00:13:53,000 --> 00:13:56,080 Speaker 1: business with high school teams and college teams, many of 295 00:13:56,120 --> 00:13:59,040 Speaker 1: which canceled or truncated their seasons over the last few 296 00:13:59,120 --> 00:14:02,600 Speaker 1: years because of the panic, What was business like overall? 297 00:14:03,080 --> 00:14:05,679 Speaker 1: You know, the realities COVID was really challenging for people. 298 00:14:05,720 --> 00:14:08,760 Speaker 1: But um, looking just across you know, the high school landscape, 299 00:14:09,200 --> 00:14:15,120 Speaker 1: seventy of high states played sports on a slightly modified schedule, 300 00:14:15,600 --> 00:14:17,640 Speaker 1: uh and played you know, in the normal season. So 301 00:14:17,640 --> 00:14:19,240 Speaker 1: football happened in the fall. It may have started a 302 00:14:19,280 --> 00:14:21,280 Speaker 1: few weeks later than normal and ran a little longer, 303 00:14:21,520 --> 00:14:24,680 Speaker 1: but almost everyone played uh and really across the US, UM, 304 00:14:24,720 --> 00:14:27,760 Speaker 1: what we saw is most most um high schools ended 305 00:14:27,800 --> 00:14:29,360 Speaker 1: up playing their season again. It might have been in 306 00:14:29,400 --> 00:14:31,480 Speaker 1: a different, different time window. Right if you look to 307 00:14:31,520 --> 00:14:34,040 Speaker 1: California when they were playing football in March, it felt 308 00:14:34,040 --> 00:14:36,280 Speaker 1: different for a lot of those athletes, but they did it. 309 00:14:36,800 --> 00:14:39,400 Speaker 1: Um and UH and really we saw that same thing 310 00:14:39,400 --> 00:14:41,880 Speaker 1: at most most of our teams across the world. You know, 311 00:14:41,920 --> 00:14:44,560 Speaker 1: we're not just in the US. We serve um, you know, 312 00:14:44,920 --> 00:14:47,920 Speaker 1: teams across we serve forty different sports across eighty different countries. 313 00:14:48,320 --> 00:14:49,800 Speaker 1: So we got a really good feel for what it 314 00:14:49,840 --> 00:14:51,480 Speaker 1: looked like. And while there were some teams that you 315 00:14:51,480 --> 00:14:54,480 Speaker 1: know canceled or short in their seasons, most teams across 316 00:14:54,520 --> 00:14:57,800 Speaker 1: the world, especially in the elite level of sport, right 317 00:14:57,840 --> 00:14:59,600 Speaker 1: the professional teams across the world, they found a way 318 00:14:59,640 --> 00:15:02,040 Speaker 1: to play uh And so huddle was really important for 319 00:15:02,080 --> 00:15:04,200 Speaker 1: them in terms of staying connected to their team when 320 00:15:04,200 --> 00:15:06,640 Speaker 1: they were at home, when they couldn't have team meetings 321 00:15:06,640 --> 00:15:09,280 Speaker 1: in person or wouldn't there you know, the facility was closed, 322 00:15:09,400 --> 00:15:11,320 Speaker 1: being able to stay connected with their athletes and find 323 00:15:11,320 --> 00:15:13,800 Speaker 1: ways to communicate around you know, upcoming opponents or just 324 00:15:13,960 --> 00:15:16,880 Speaker 1: offseason training schedules. Huddle took a whole new form of 325 00:15:16,960 --> 00:15:20,000 Speaker 1: communication for them. And so Matt, let me follow up 326 00:15:20,000 --> 00:15:23,480 Speaker 1: in that, can high school teams monetize I assume you 327 00:15:23,600 --> 00:15:25,720 Speaker 1: have some fixed cameras at some places that they don't 328 00:15:25,760 --> 00:15:27,680 Speaker 1: even have, they don't even have to be manned, and 329 00:15:27,720 --> 00:15:29,960 Speaker 1: that people can just sort of it's live streaming, is 330 00:15:29,960 --> 00:15:33,720 Speaker 1: that right? Yeah? We actually provide that at all levels, um, 331 00:15:33,800 --> 00:15:36,320 Speaker 1: but at the high schools, we provide them the ability 332 00:15:36,320 --> 00:15:38,200 Speaker 1: to live stream and they can choose to monetize that 333 00:15:38,240 --> 00:15:39,600 Speaker 1: if they want, or they can make it free to 334 00:15:39,600 --> 00:15:41,960 Speaker 1: connect to their fans. Again, that was really important over 335 00:15:41,960 --> 00:15:44,800 Speaker 1: this last year is you know, many many fans weren't 336 00:15:44,800 --> 00:15:47,800 Speaker 1: able to attend games. We trund a different version, but 337 00:15:47,840 --> 00:15:51,240 Speaker 1: the same type of automated system for you know NBA teams, uh, 338 00:15:51,320 --> 00:15:53,800 Speaker 1: you know. And it's funny we actually provided in the 339 00:15:53,840 --> 00:15:56,720 Speaker 1: French basketball leagues where the Boston Suffic you know, just 340 00:15:56,800 --> 00:15:59,120 Speaker 1: drafted someone over this last year. And that content isn't 341 00:15:59,160 --> 00:16:01,800 Speaker 1: something you'll just you know find biosmosis, so being able 342 00:16:01,840 --> 00:16:04,720 Speaker 1: to automatically stream it, not have a camera person. They're 343 00:16:04,720 --> 00:16:07,440 Speaker 1: available for you, but haven't immediately dropped into your tools 344 00:16:07,880 --> 00:16:09,840 Speaker 1: and get analysis from it right away, or make it 345 00:16:09,840 --> 00:16:13,560 Speaker 1: available for you know, the scouting platform or just you know, 346 00:16:13,600 --> 00:16:15,760 Speaker 1: for your fans. It's been something that's been really popular 347 00:16:15,840 --> 00:16:18,240 Speaker 1: for us over the last last couple of years. Final 348 00:16:18,320 --> 00:16:20,880 Speaker 1: question here to Mike Sarin, and I'm gonna you know, 349 00:16:20,960 --> 00:16:23,080 Speaker 1: touch upon the Boston love that you and Lynch you 350 00:16:23,120 --> 00:16:25,560 Speaker 1: share here as a native of swamp scott and you 351 00:16:25,600 --> 00:16:29,640 Speaker 1: mentioned how you grew up a Celtics fan. You start 352 00:16:29,680 --> 00:16:32,880 Speaker 1: off as this in paid intern fourteen years ago. Um 353 00:16:32,960 --> 00:16:35,960 Speaker 1: season tickets. I mean I imagine that you're not the 354 00:16:36,000 --> 00:16:37,880 Speaker 1: first person that you won't be the last person to 355 00:16:38,040 --> 00:16:40,120 Speaker 1: work for the team that you grew up rooting for. 356 00:16:40,240 --> 00:16:43,200 Speaker 1: But I wonder was that transition hard for you to 357 00:16:44,640 --> 00:16:47,280 Speaker 1: to to work for a team that you grew up 358 00:16:47,280 --> 00:16:50,320 Speaker 1: loving and needing to use your head, not your heart, 359 00:16:50,640 --> 00:16:53,240 Speaker 1: to make decisions that might be painful in the short 360 00:16:53,320 --> 00:16:56,360 Speaker 1: term but are good in the long term. I mean, 361 00:16:57,080 --> 00:17:01,360 Speaker 1: you know what's funny. I've I've kept my dad's had 362 00:17:01,360 --> 00:17:03,880 Speaker 1: these tickets, you know, before I was born, at the 363 00:17:03,880 --> 00:17:06,800 Speaker 1: old Arena and then at the new Arena, and um 364 00:17:06,840 --> 00:17:09,760 Speaker 1: I've been insistent that during home games and some one 365 00:17:09,800 --> 00:17:11,679 Speaker 1: of the worst things about this past year was not 366 00:17:11,680 --> 00:17:13,760 Speaker 1: being able to do this. But but during home games, 367 00:17:14,359 --> 00:17:16,600 Speaker 1: um I sit up in the balcony near the back 368 00:17:16,720 --> 00:17:19,480 Speaker 1: with him, in the same seats we've always sat in, 369 00:17:20,000 --> 00:17:22,439 Speaker 1: and I probably shout something to get me in trouble 370 00:17:22,480 --> 00:17:27,080 Speaker 1: with the NBA. Um U up there, we know everyone 371 00:17:27,119 --> 00:17:30,520 Speaker 1: and it's all friends and UH, and that sort of 372 00:17:30,640 --> 00:17:33,240 Speaker 1: enabled me to continue just being a big fan of 373 00:17:33,280 --> 00:17:35,520 Speaker 1: the team. Um I got myself in a little trouble 374 00:17:35,600 --> 00:17:38,080 Speaker 1: this past year just because I was the only Celtics 375 00:17:38,080 --> 00:17:40,160 Speaker 1: fan in the arena for some of our games, because 376 00:17:40,160 --> 00:17:44,080 Speaker 1: there weren't any fans and I'm sitting their courtside like 377 00:17:44,119 --> 00:17:48,959 Speaker 1: the strain. But you know, I think that the beauty 378 00:17:49,000 --> 00:17:52,240 Speaker 1: of being a sports fan is, Um, you want a 379 00:17:52,280 --> 00:17:55,960 Speaker 1: team to do well. And I think you know, Danny 380 00:17:56,160 --> 00:18:00,399 Speaker 1: and and Doc and Brad, Um, I've all sort of 381 00:18:00,400 --> 00:18:03,200 Speaker 1: realized that I'm a crazy fan, but I am also 382 00:18:03,600 --> 00:18:05,560 Speaker 1: wanting the team to do really well. It's not about 383 00:18:05,560 --> 00:18:08,480 Speaker 1: me ever, and so um. You know, if you think 384 00:18:08,480 --> 00:18:11,360 Speaker 1: you're doing what's right for the team's success, and we've 385 00:18:11,359 --> 00:18:16,160 Speaker 1: had some success, Um, it's not hard to be a fan. Also, Um, 386 00:18:16,200 --> 00:18:18,239 Speaker 1: you know the championship rings up from a wage has 387 00:18:18,240 --> 00:18:21,120 Speaker 1: got my dad's last name on it too, um. And 388 00:18:21,160 --> 00:18:25,199 Speaker 1: so you know that that moment um made anything I 389 00:18:25,240 --> 00:18:27,760 Speaker 1: had to give up. You know, any naivete I had 390 00:18:27,760 --> 00:18:30,520 Speaker 1: about about from being on the outside giving that up 391 00:18:30,600 --> 00:18:32,520 Speaker 1: wasn't so hard because I got to be a part 392 00:18:32,560 --> 00:18:34,119 Speaker 1: of the team's success. And it's just been I've been 393 00:18:34,160 --> 00:18:37,720 Speaker 1: so lucky. Um. I can't believe our our owners and 394 00:18:37,720 --> 00:18:40,960 Speaker 1: and Danny took a chance on me when I wasn't 395 00:18:40,960 --> 00:18:44,639 Speaker 1: really a basketball guy, and so um it's I I 396 00:18:44,680 --> 00:18:47,040 Speaker 1: feel totally blessed every day to have this job. I 397 00:18:47,119 --> 00:18:49,560 Speaker 1: love that answer. And staying true to yourself. And I 398 00:18:49,560 --> 00:18:51,880 Speaker 1: wish we could have one of the Huddle cameras captured 399 00:18:51,960 --> 00:18:59,880 Speaker 1: you up in the rafters making those comments. All right, 400 00:19:00,280 --> 00:19:02,800 Speaker 1: I want to thank our guest Mike Sarin, Assistant GM 401 00:19:03,000 --> 00:19:07,000 Speaker 1: VP UH Basketball Operations Team Council of the Boston Celtics, 402 00:19:07,000 --> 00:19:10,520 Speaker 1: and Matt Mueller, CEO of Huddle. You have been listening 403 00:19:10,520 --> 00:19:13,240 Speaker 1: to Bloomberg Business of Sports. We are here every week 404 00:19:13,280 --> 00:19:16,000 Speaker 1: at the same time, and of course we're online wherever 405 00:19:16,040 --> 00:19:18,800 Speaker 1: you get your podcasts. You can catch those Mondays, Wednesdays 406 00:19:18,880 --> 00:19:22,320 Speaker 1: and Thursdays. I'm Scarlet Poo and on Twitter, I'm at 407 00:19:22,359 --> 00:19:24,919 Speaker 1: Scarlet Pooh and I'm Mike Lnch. You can follow me 408 00:19:24,960 --> 00:19:27,520 Speaker 1: at got Swampscott. By the way, at Lynch e w 409 00:19:27,760 --> 00:19:30,479 Speaker 1: c VB. You're listening to Bloomberg Business to Sports from 410 00:19:30,480 --> 00:19:32,359 Speaker 1: Bloomberg Radio around the world.