so two points max first off ideas aren't worth a damn ever he's got ideas all right I could give a holy hoot about about ideas I mean I I I got people throw ideas at me all the friggin time you know I don't give a shit I just truly told give a shit right I want actions show me how I'm gonna turn something into an action how am I gonna make something better right and I I want to know ahead of time what that something is am I trying to improve customer attention trying to improve recovery time for an athlete who's got back-to-back games right III I know what I'm trying to do and I want to focus on that where ideas become great and you said it really well max is ideas are something I want to test so but I know what I want to test these of the event what outcome I'm trying to drive so it isn't just it is an ideation for the I eat for the sake of ideation its ideation around the idea that I need to drive an outcome I need to have athletes that are better prepare for the next game who can recover faster who are stronger and can you know it can play through a longer point of the season here we are in March Madness and we know that by the way that the teams that tend to rise to the top are the teams that have gone through a more rigorous schedule played tougher teams right they're better prepared for this and it's really hard for a mid-major team to get better prepared because they're playing a bunch of lollipop teams in their own conference so it's it's ideas really don't excite me ideation does around an environment that allows me to test ideas quickly fail fast in order to find those you know variables or metrics those data sources it just might be better predictors of performance yeah I like the idea of acting quickly failing quickly and learning quickly right you have this loop and what happens is and then I think every strand coach in the world is probably guilty of this is we get an idea and we just apply it you go home you know I think eccentric trainings this great idea and we're going to do an eccentric training block and I just apply it to my athletes and you don't know what the hell happened because you don't have any contextual metrics that you base your test on to actually learn from so you at the day go I think it worked you know they jump high but you're not comparing that to anything right they jump they've been the weight room for three months my god I hope they jump higher I hope they're stronger like I can sit in the weight room probably get stronger for three months and my thought is but let's have context and it's um I call them anchor data points they were always reflecting back on so for example if I have a key performance metric where I want to jump high I'll always track jumping high but then I can apply different interventions eccentric training power training strength training and I can see the stress response of these KPIs so now I've set an environment that we have our charter still there my charter being I'm going to improve my athletic development and that's my goal I'm basing that charter on the KPI of jumping high so key performance indicator of jumping high now I can apply different blocks and interventions with that anchor point over and over again and the example I give is I don't come home and ask my girlfriend how she's doing once every month I ask her every day and that's my anchor point right and I might try different things I might try cookie and I might try making dinner I might do the dishes I might stop forgetting our dates I might actually buy groceries for once well maybe she gets happier then I'll continue to buy groceries maybe I'll remember it's her birthday March 30th I remember that that's my put it on there right and so but the idea is we have in life the way life works we have these modular points where we call anchor points where we were self-reflect and we reflect off of others and we understand our progress in our own life environment based on these anchor points and we progress and we apply different interventions I want this job maybe I'll try having this idea outside of here maybe I'll play in a softball league and we're always reflecting it's not making me happier is that making me feel fulfilled and I don't understand why we don't take what we do every day and like subconsciously and apply it into the sports science world but lava is because it happens unconsciously because that's how our body has learned to evolve we have anchor points I want to survive I want to have kids lots of kids strong kids and I and I die so my kids can have my food and that's what we want as a body right your bison care about anything else and so that's why you walk with a limp after you get hurt you don't want perfect again it's a waste of energy to walk perfect right you can still have kids with a limp I hate to break it to you right we're not running from animals anymore and so we have all these anchor points in life let's apply that same model now and like you said it's like design thinking and actually having that architecture to outline it whether it's in that hypothesis canvas to force us to now consciously do it because we're not just interacting with ourselves now we're interacting with other systems other nodes of information to now have to work together in use in to achieve our company's charter interesting max there's a lot of a lot of key points in there the one that strikes me is measurement John Smail at Procter & Gamble I was there you still I say you are what you measure and you measure what you reward that was his way of saying as an organization that the compensation systems are critical and the story just walked through about what Kelsey right and what you guys are doing and how you increase your your happiness level right now here's the damnest your work I mean that is that is how you're rewarded right if you are rewarded by happiness and so you you learn to measure if you're smart right that you don't miss birthdays that you do dishes you you you help up around the house you do things and when you do those things the happiness meter goes up and when you don't do those things happiness meter goes down and you know because you're you're you're probably pulling not just once a day but as you walk by her throughout the day are on a weekend you're you're constantly knowing right if if you're liking your mom you know when mom's not happy you don't need to be a day to sign this and know mom's not happy and so then you you know you re engineer about okay what did I do wrong that causes unhappiness right and so life is a lot of there's a lot of life lessons that we can learn that we can apply to either our business our operations or sports whatever it might be that your your profession is in about the importance of capturing the right metrics and understanding how those metrics really drive you towards a desired outcome and the rewards you're gonna receive from those outcomes yeah and with those it's the right metrics right that's what not metrics the right metrics if I want to know if someone was happy I wouldn't go look at the weather I wouldn't you know check gas prices especially if I'm curious they're happy with me well maybe they might reflect if they're happy in general if they're happy with me right now I'm contextualizing I'm actually trying to look at I know a little bit more about what I should look at I don't know everything and so you might have metrics that you say you know I know science says this metric is good this metric is good maybe we want to explore of these couple of metrics over here because we think that either aid they're related to one of these metrics or they related to the main outcome itself and that gives you a way to then I have these key and core metrics that's not stacking the deck but it's no one you're gonna get insights out of it and then I have these exploratory metrics over here but you're gonna allow me then to dive and explore elsewhere and if you're a company those can be trade secrets they can be proprietary information if you're a trainer it can be ways to learn how different athletes adapt to make yourself better and again we're talking about a company and we're talking about trainer there's no difference when it comes to trade secrets right trainers keep their trade secrets and companies keep their trade secrets and as we talk about this it's really easy to see how these two environments where they're talking about company athletic development sports science personal training health and wellness are really universally governed by the same concepts because life itself is typically governed by these concepts and when we're playing those kind of home iterations to it you can really begin to quickly learn what's going on and whether or not those metrics that you we're good ARCA and whether or not you can learn new metrics and from that max you raise an interesting question or made a point here that's I might be very different in the sports world than it is in the business world and that is the ability to test and what I mean by that is you know the business world is full of concepts like a bee testing and see both custody and simulations and things like that when you're dealing with athletes individually I would imagine it's really hard to test athlete a with one technique and athlete B with another technique when both these athletes are trying to maximize their performance capabilities in order to maximize you know the money there can they can they can generate how do you deal with that so yes no one wants to get the shitty program yes that's correct yeah for the most part people don't and this I'll take people don't test like that and but here's my solution to us I think being a critic without solutions called being an asshole my solution to that is making it very agile and so we're not going to be able to you know test group a versus group B but what you can do if you're a coach and you have faith in because there are a lot of programs coaches use coaches probably use you know every offseason they might try a new program so there's no real difference in all honesty to try a new program on you know these seven athletes versus and then try a different one that you also trust on these seven athletes and part of that comes from the fact that we have science and evidence to show that both these programs are really good right but there's no one's actually broken down the minutiae of it and so yes you probably could do a and B testing because you have faith in both programs so it's not like either athletes getting the wrong program they're both getting programs that are going to probably elicit an outcome of performing better but who wants to perform the best the second asks the second aspect would be what kind of longitudinal data that you can collect very easily to understand typical progression of athletes for example if you coach and you coach for eight years you'll have you know eight different freshman classes theoretically and you'll begin to understand how a freshman typically progresses to a sophomore in what their key performance indicators typically trend ass and so you can now say okay last year we did this this year we do this I'm gonna see if my freshman class responds differently is this going to give us the perfect answer absolutely not no but without data you're just another person with an opinion that's not my quote I stole that quote but it's true because if we don't try and audit ourselves and try to understand the process of how is someone developing then we're just strictly relying on confirmation bias I mean my program was great you know Pat some guys in the back that jumped higher and we did awesome if we're truly into understanding what's best then we'll actually try and you know measure some of these progress some of this some of these KPIs over time in the example I give and it's unfortunate and fortunate I don't mean anything bad by this either we're on a salary right and so what happens when you're on a salary is no matter really what happens assuming you're doing your job you're gonna keep your job but if you look at a start-up a startup has one option and that's to make money or go out of business right they don't really have the luxury of oh we're just gonna you know hang out and not saying coaches hang up or not we're just gonna you know keep this path we're going on as a coach you know how do I apply a similar model well I start up the bank my startup is you can go from worth zero dollars to worth a hundred you know million two billion dollars in one year at the coach we don't have that same environment because we're not producing something tangible which doesn't always it doesn't have the same capitalistic Drive right the invisible hand pushing us the same way the free market does with you know devices and so we don't always follow the same path that these startups have done yet that same path and same model might provide better insights so max you've hit something I found very interesting confirmation bias if if you don't take the time before you execute a test understand the variables that you're gonna test what happens is if you after the test is over you go back and try to triage what the drivers were that impact and confirmation bias and revisionist history and all these other things that make humans really poor decision-makers get in the way and so but before as a coach I would imagine before as a coach what you'd want to do is is set up ahead of time we're gonna test the following things to see if they have impact by thoroughly like the hypothesis development canvas right they'll really understand against what you're really going to test and then when you've done that test you you will you would have much more confidence in the results of that test versus trying to say wow Jimmy Jimmy jumped two inches higher this year thank God what did he do let's figure out and revision it wasn't what he ate was it where he slept oh he played a lot of video games that must be it he is the video games made him jump higher right so it's I think a lot of sports in particular even more than the business for a lot of sports is based on on heuristics and gut feel it's run by a priesthood of former athletes who are were great because of their own skills and capabilities and it maybe had very little do with her development and I don't want to pick on Michael Jordan but no Michael Jordan was notoriously a poor coach and a poor judge of talent he made some of the most industries when the worst draft choices industry has ever seen and that's because he mistakenly thought that everybody was like him that he revision history about well what made me great were the following thing so I'm gonna look for people like that instead of reversing the course and saying okay let's figure out ahead of time what makes what will make you a better plant player and then trying these tests across a number of different players to figure out okay which of these things actually had impact so sports I think has gotten much better Moneyball sort of opened that people's eyes to it now we're seeing now more and more team who are realizing that that data science is as a discipline it's not something you apply after the fact but in order to really uncover what's the real drivers of performance you have to sit down before you do the test to really understand what it is you're testing because then you can learn from the tests and and let's be honest right learning is a process of exploring and failing and if you don't try and fail enough times if you don't have enough might moments you'll never have any break to a moment and I think what people don't understand is they hear the word fail and assumed oh we did a six-month program and failed nope failure can occur in one day and that's okay right you can use for example I'm going to use this piece of technology as motivation for biofeedback to increase my athletes and tint and the amount of effort they put into the weight room that's right hypothesis you can test that in one day you print out that piece of technology the athletes don't respond well you'd have learned something now okay that technology didn't bring about the motivation I thought why was that you can do reflect and that revision because you had the infrastructure beforehand on maybe notes that you may have taken and scribbled down on your pad or observations from the coaches I am I but you know what the athletes weren't very invested because the technology took too long to set up right it wasn't the technology's fault it was the process of given technology available to act and utilize on so maybe you retest again with it set up beforehand or a piece of technology that's much easier to use and the intent increases so now you say okay it's not the technology's fault it's the application of how we're using the technology at the same time we hear a lot of things like I'm gonna take a little bit of pivot not too far though is in the baseball world you see technology being more used more and more as a tool and it's helping guide immediate actions on the field whether it's not it's a you know spin rates its arm velocities with accelerometers or some sort of measurement they decide to use but that's not necessarily collecting data that's using technology as a performance tool and I think there's a distinction between the two the two are not mutually exclusive you can still use it as a performance tool but that performance data if the infrastructure is not there to store a file and reflect and analyze it's only being used one-sided and so people think oh we're doing sports science we're doing data science because we're collecting data well that's not I can go count ants that's collecting data but that's not you know I don't unless I count ants every day and say oh my game populations decreasing right and kind of a here's a really easy way to think of it in my opinion you have cookies in the fridge right and every day I go and every week will say my mom makes cookies this doesn't happen I wish it did be very cool but I love your mom and we didn't eat cookies every week but in the fridge I go when I count how many cookies there were right and using data I'd say oh twelve cookies if there's any cookies at all I can eat right that's using technology and that moment but doing data Sciences well you know what she's gonna make you know twelve and a couple of days and I have two days left and there's six cookies I can eat three today and three tomorrow because now you're doing prescriptive analytics right because you are prescribing an action based on the information you collected it's based on historical data because you know that every seventh day the cookies are coming no I just take it as I'm using technology as a tool I might only eat one cookie and forever be leaving six cookies on the table right and so there's hid don't want to do that no we don't but we trick ourselves I think we see that not saying baseball does is but I'm saying we've see that in all domains where we use technology we say oh technology good we had someone use technology that's data science no that's not data science that's using technology to help Tripp augment training using data Sciences understand the information that happened during the training process looking at it contextually to them prescribed saying I'm going to do this exercise or this exercise based on the collection and maturation of the information so instead of cookies here I eat one cookie it's a historic Lee I know there's going to be twelve cookies every seven days I have two days left I can eat three cookies now I can hide two and tell my sister Amelia oh there's only one left very weird I don't know who ate data - well let max let me let me let me wrap up with a very interesting challenge that I think all all data scientists face wellmaybe all citizens of data science face and I say did as citizens of data science I mean people who understand how to use the results of data science not necessarily people who are creating the data science and here's here's the challenge that if you if you make your decisions just based on the numbers alone you're likely to end up with suboptimal results and the reason why that happens is because there's lots of outside variables that have huge influence especially when it comes to humans and even machines to a certain extent let me give you an example know baseball is is infatuated with cyber metrics and numbers right everybody is making decisions we're seeing this now in the current offseason you know who was signing contracts and who has given given money and they're using they're using the numbers to show you know how much is that person really worth and and organizations are getting really surgical and their ability to figure out that that person is not worth a you know a six year contract for you know 84 million dollars they're worth a two-year contract for 36 and that's the best way I'm gonna you know pay but minimize my risks and so then the numbers are really drive and allow that but it isn't just the big data that helps to make decisions and in fact I would argue the insights carried from the small data is equally important especially in sports and I think this is a challenge in other parts of the business is the numbers itself the data itself doesn't tell the full story and in particular think about how does an organization leverage the small data the observed data to really help make a better decision so right now in baseball for example in this offseason the teams became infatuated with using numbers to figure out who were they going to offer contracts to how much they were going to pay him for how long and we saw really the contracts in most cases really shrinking and value in size cuz people are using the numbers and comparing that to say always so and so it only got this you're only going to get this and numbers are great but they miss some of the smaller aspects that really differentiate good athletes from great athletes and those are things like fortitude part you know effort resilience these these kind of things that aren't you can't find that in the number so somebody's ability to a closer write who goes out there in the eighth-inning and and just has a shit performance gets beat up all over the place comes back in it still has to lead and and does that person have the guts the fortitude to go back out there after us bad eighth-inning and go do it again who can fight through when they're tired it's late in the game now you've been playing it's a you know 48 minute game you've been playing forty minutes already you've hardly had a break and you're down by two the balls in your hand a three-pointer is gonna win it what are you gonna do my numbers don't measure that it's theirs these these these other metrics out there like fortitude at heart and such that you actually can start to measure they don't show up a numbers where they come from the inside some subject matter experts to say yeah that person has fight and in fact there's one pro team that actually what they do in the minor leagues they actually put their players into situations that are almost no win because they want to see what they're gonna do do they give up or do they fight back and and you know what you again you can't batting average then tell you that if somebody's gonna get up and that you're gonna give up it's a ninth in and you think you've lost you know what I don't want that person out there and so think about in sports how do you complement the data that you can see coming off of devices with the data that experience coach can say that that person's got something extra there they got the fight they have the fortitude they have the resilience when they're down they keep battling they don't give up and you know from experience from from playing and coaching I know from playing and coaching the guy is going to give up you know who they are I don't want them on the court right it made me the best player from a numbers perspective hell if that was the case Carmelo Anthony would be an all-star every time his numbers are always great the guide lacks heart but he doesn't know how to win so think about how as an organization a sporting organization you use the metrics to help give you a baseline but don't forget about the the soft metrics the servable things that you got to tell you that somebody has something special that is an awesome way to bring this together because subject matter experts those are people who have been in the trenches who see it firsthand date is here to augment you in your decisions it's not here to override you it's not here to take your place and so in coaches fear data it's the silliest thing ever because it's giving more ammo to a gunslinger that's all it does right it's not going to win the battle right it's just the bullets you got to still aim it in fire and so when we look at it in regards to performance and athletic development all these numbers they'll never be right ever they'll never be 100% perfect but neither will you and so what we're trying to do is help your decisions with more information that you can process into your brain that you might otherwise not be able to quantify so it's giving that paintbrush not just the color red but given all the colors to you and so now you can make whatever painting you want and you're not constrained by things you can't measure yourself I could add one point max to bill on that data won't make a shitty coach good but it will make a good coach great yeah yeah I couldn't agree more well dad thank you for being on here I really appreciate and for everyone who's listening this is going on prime March Madness time and so to pull away the dean of big data from March Madness who for people listening he made his bracket on the Google cloud using AI and so it only he so I was thanking him to come here and only he would be the one to I guess take I don't say take the fun out of it but try and grid the family bracket for used it all augmented decision-making he possibly can like it the data will make won't make somebody shitty good and I'm still not good Google Cloud couldn't help me I still at the bottom of the family pool it's great to have you in I guess every minute here is worth double being that's March Madness time thanks max for the opportunity it's a fun conversation alright thank you guys for listening really appreciate it and [Music] [Applause] [Music] you

>>Now, this is siliconangle.com exclusive coverage of Sapphire. This is the cube, our flagship program where we go out to the events, extract the signal from the noise. This is our fourth year of the cube born four years ago at SAP and EMC world. And we uh, we call it the ESPN of tech back in the day. And uh, you know, we always joke that, you know, there wasn't any deep dive commentary, but we're pleased to actually have what ESPN would love to have on Adam silver, the deputy commissioner of the NBA. Um, Chris Berman and all the folks at the, at ESPN would eat their heart out to have, have you here. So welcome to the cube. Oh, thank you. It's great to be off. So SAP Owsley's how about the future of business and this modern era of, of a business with technology, real time NBA has been very progressive. >>You guys have a modernization going on now. The franchisors are changing, the fan base is changing. You're here at SAP. What are you talking about here at SAP with bill McDermott and the team and what does the NBA look at is as a sports franchise, a sports league, and with a lot of franchises it's moving and changing and the old contracts and the old rules aren't, aren't really relevant when you have Twitter and you have unlimited media, frictionless sharing. How do you, how do you view all that? Well, we're here looking for solutions. I mean frankly I that we know what we don't know. I mean just the specific example with HANA and SAP is that when David stern, I first met, sat down with bill McDermott a few years ago and said we have this issue with our stats database and we want to find a way to allow fans wherever they're located to engage deeply with those statistics, weather for predictive behavior and to analyze what they think is going to happen in a particular game or to make relative comparisons, you know, between players, among players in the league. >>And he explained to us what Hannah was and we ended up meeting with one of Bill's teams@sapwhoultimatelydesignedanewstatisticaldatabasesystemforuswhereyoucangoonnba.com and access, um, any of the kinds of permutations, statistics I've been talking about. And, and frankly, one of the reasons we got there, to your point about how the world is evolving around us is that there were lots of other sites that I won't name that were doing that, you know, they didn't necessarily have the official data. And I thought this isn't a question of sending out an army of lawyers to shut them down. This is a question of out competing them instead of how can anybody be doing a better job than the MBA. We own this data, we have the richest data, we have the deepest data, we haven't in real time, fans should want to come to NBC. Dot to get that information. The best defense is a good offense in this case. >>Right? Exactly, exactly. And so that's our relationship with SAP. And even just the several hours I've spent already here today, sort of in the green room behind the scenes talking to bill and his executive team. It's like, all right, here are some other business issues we have. We've talked about China earlier today. We've talked about how to connect with our fans and look, you know, that only a minuscule percentage of our fans actually experience our game in person. You know, that's just the nature of it. So it's really through technology, through innovation that we're going to connect with fans on a global basis. That's why I'm here. So what was your >>comment on the keynote about bill McDermott share with the folks here about his history >>with basketball and so, Oh, so anyways, with bill, isn't that interesting? Coincidentally, when we first sat down with bill, he mentioned that his grandfather was Bobby McDermott, Bobby McDermott I'd heard of. But honestly I hadn't thought a lot about, I went back to the office and we have an archivist named Paul Hirsch Heimer and our office say, Paul, tell me everything you can about Bobby McDorman. He goes, Bobby McDermott. And like he'd just off the top of his head, he goes, he goes, Bob McDermott, you might not realize this, but in 1946 he was named the greatest basketball player in the history of the then NBL, the predecessor league to the NBA. He was a five 11 guard. He averaged over 20 points a game. And back then this was like obviously more than 35 years before the three point shot. This was before anybody was averaging 20 points a game. And he was, I think the three time MVP. He won two championships. I could go on and on, but it's incredible. And you know, this morning I actually, my friend and colleague Paul Hirsch Heimer found an old Bobby McDermott trading card. I don't even have any boundaries, which I presented to bill. But you know, it's, it's like the coincidence that that's our relationship now. It's, it's this frankly a really cool >>kind of gesture to be fantastic. And bill McDermott, it's just a great guy. And the keynote up there, they had that little anchor desk, kind of like the cube format. >>And don't forget JBS basketball credentials. The Harvard basketball team, >>Twitter saying, I'm negotiating with JBS age. People thought I was really serious about coming on and anchoring the cubes a JV. If you're watching, we want you drafted by the Atlanta Hawks McDermott on his keynote, Dermot on his keynote, talked about an in business example. You know, talking about, always talks about, you know, if people using this and got the bartender, how they're instrumenting the tab and it's just a gut crest. Great example of instrumentation measurement data that they couldn't get before internet of things. Whatever industrial internet is GE calls it. I want to ask you something a little bit more about the NBA in this regard because the NBA has really done great strides of, I won't say cleaning up the game, but looking at the integrity of the players off the court and on the court. And that's been something that uh, you know, stern has done extremely well, but now you have, you have the ability to instrument the, the actual athletes. They're on Twitter, they're building their own direct fan basis. So, so the question is how do you guys look at that as an opportunity? And challenge, how do you guys, cause now you have more media there, they're self promoting, >>right? And look, I don't want to suggest by any means we can control, you know what they do, but we can monitor it. We can do it to a certain extent. And what we said to our players mean your employees. Just like I'm an employee of a company and I think, you know, and there are certain limits. I mean especially one thing we can do is say, you know, during 45 minutes before the game, through the game, certain period afterwards when we require media availability, we don't want them going off and tweeting in the corner. We want them talking to media and they recognize that's part of their job. We monitor it to a certain extent, but we also are realistic. You know, I think that we understand that it's an opportunity for them to connect directly with their fans or in certain cases people aren't their fans. But we, and we also understand that it's an expectation of fans in this day and age that they're going to have that direct access to our athletes. And I and, and I think it's synergistic. I mean it's, it's, it's effective. I mean recognizing that it's warts and all that. Players get themselves in trouble. League executives get themselves in trouble and owners get themselves in trouble increases. >>I know as well. I know you've got to run, so do you want to kind of get one last question and I'll see the TV contracts over the years have been pretty much territorial couple of networks and now you have cable, now you have unlimited, now you have NFL TV, MLB TV, NBA vertical and programming where you can control your own destiny, you have different inside looks, all that data. How you're looking at the future of media in that regard where now you have unlimited outlets potential. Can you talk about how you're looking at that and maybe some of the tech approaches that you did? >>Yeah, well I would just say it's, it's going to be a balance. We recognize that people still want aggregators or editors and that you mentioned ESPN at the top of the show. I mean people are still going to go to ESPN and expect to get the best highlights, you know, presumably the game of the week or the game of the night or whatever else. But in addition to that, there are some parents who only want to consume NBA, don't want to sit through sports center and get the hockey scores first or the baseball scores or whatever else. And for those people, there's NBA TV, there's nba.com and other outlets and like thousands of others that we didn't create. So I think for us it's a realization that you need to do both. And that some fans, you know are out there have want to consume us, you know, in an incredibly deep ways and get down to like the nitty gritty statistics and others just want to have the highlights and you've got to serve all those fans. >>Well, we get the hook from your handler. Adam silver, a deputy, >>how can my man, Jeff, I don't have a handler. This is fantastic. I appreciate, appreciate you taking the time. So my question, uh, I wanted to ask was, uh, so obviously SAP Sapphire, it's a, it's a technology event in some ways, but it's also a business event. And in this market we talked a lot about the technology but less maybe sometimes about the business value of all this technology. And you mentioned an example earlier about making data and predictive analytics available to um, all the fans out there who might not be my knife to go to an event. So can you translate that, how does that translate to business value for the NDA and more broadly when you're looking at areas where data might provide business value, how do you identify those areas where you want to focus on build new capabilities? Um, focusing on, you know, the data is, is the underlying kind of enabler and the technology is the enabler, but really how do you identify where the business value is? So, >>you know, I'll say one easy example again, just going back to the stats database that, that SAP HANA built for us. Um, we've already seen that we've doubled the amount of time that our fans spent on mba.com looking at statistics than we did before we had an Ohana database. So that's just a simple example and clear. There's all kinds of ways of monetizing that traffic when you dealt with there. But I think from a more general standpoint, it's about increasing engagement. Um, as I said in response to an earlier question, um, one of the fundamental things we look at in terms of television viewership is duration. And we found that from when the time I still got involved in the NBA a little over 20 years ago, let's say the average fan was watching two and a half hour game, 50 minutes. The average fan is now watching around 40 minutes just because it's the nature of the number of options they have. >>And if through deep data we can increase that engagement, we find other ways that the people remain interested in the game, frankly, that they may be a knick fan, but if their team is down 15 points and there's two minutes left, they're turning the channel. On the other hand, if they're engaged and they're thinking, all right, you know, what does Carmelo Anthony do typically with a minute left in the game, what are his fourth quarter statistics? How does he behave when a team is down? Ken, statistically a team overcome a deficit of 15 points in that many minutes. We find all those kinds of new approaches to the game help us monetize and help by increasing the level of passion, passion, and, and, and depth of fandom, you know, for our consumers. >>and in terms of actually making those decisions, how is the ability to, to do those kinds of analytics internally? How has that impacted how the NBA operates in terms of, you know, sometimes making data driven decisions and, and, and, and using data to, to kind of do business is that there's a cultural and a people issue? >>Well, yeah. Uh, I'll give you another example. And this is on the business side and that is ticket pricing. I mean, through analytics we've entered into a new world of flexible ticket pricing where it's dynamic pricing price. So now you know in the, in the old days, which weren't so long ago, you know, if you bought a season ticket package for the Orlando magic here in Orlando, of course you know it was the same price for every ticket. Now the teams that recognize that people that there's different values for different games, it's just a function of data and it's based on demand for those particular games. People may care more about seeing the Miami he played than they will another team that I won't mention at least in that particular season. And then secondly, teams are also realizing that people just like with the airlines, people will pay a certain price for the ability to lock in that seat two weeks before the game and they're going to pay a different price to get a seat an hour before the game. And so by mining all that data, we're in essence able to increase the yield from any particular game. >>Fantastic. Well Adam, I appreciate you taking the time to answer. It's only one question we didn't know we get the hook. So short of a Adam silver. Thanks for coming on because this is the NBA onside. The cue we call the ESPN of tech and we copied ESPN on Twitter. Thanks for coming. Thanks for coming on. The cube and NBA is transforming. I'll say digital media is, I was exploding and I'll see with technology like SAP, they're going to start doing new things. Thanks for coming on the really appreciate it. We'll be right back with our guests and deep dive into SAP and all the action here on the ground. This is exclusive coverage from siliconangle.com and Wiki bond. This is the Cuba right back after this short break.