Qlik Connect 2024 | Martin Tombs | Qlik & Martin Sahlin | Stretch Qonnect

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Martin Tombs

VP of Cloud

Qlik

Martin Sahlin

CEO

Stretch Qonnect

Qlik and Stretch help drive Malmo Redhawks’ team success with cutting-edge data analytics

QlikTech International AB is getting the ball rolling with data analytics in sports by partnering with Stretch AB to maximize the potential of Swedish professional ice hockey team, the Malmö Redhawks.The Redhawks are one of several sports teams to whom Qlik has delivered cutting edge data analytics software aimed at improving player performance and recovery. Modern sports teams will need to embrace the technology or face falling behind, according to Martin Sahlin (pictured, left), founder and chief executive officer of Stretch.Martin Tombs of Qlik and Martin Sahlin of Stretch talk with theCUBE about how data-powered insights are the next evolution in sports

play_circle_outline Revolutionizing Malmo Redhawks: Leveraging Data Analytics for Enhanced Performance, Practice, and Injury Prevention in Ice Hockey

play_circle_outline Emphasis on the benefits of structured and unstructured data handling.

play_circle_outline The importance of data-driven decision making and predictive analytics in sports.

play_circle_outline Examining the Transformative Influence of GenAI on Time Management and Team Performance: A Path to Success

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Martin Tombs | Qlik & Martin Sahlin | Stretch Qonnect

Martin Tombs

VP of Cloud Qlik

Martin Sahlin

CEO Stretch Qonnect

search

>> Good morning, Qlik community, and welcome back to beautiful Orlando, Florida. We are here steaming through the day at Qlik Connect. Very excited for our live coverage all day long. My name's Savannah Peterson. You're watching theCUBE. I'm joined by my co-host and CUBE co-founder John Furrier. John, lovely to spend the day with you.

>> I love the data culture here at the event, data value. This next segment's going to be fun.

>> I am personally really excited because I know we both like to play sports. I'm an athlete. We're going to be talking about analytics and sports, and we have a pair of Martins here to tell us all about it. Martin and Martin, thank you so much for being here. We appreciate you. You've got big smiles. You look like you came off the keynote feeling real excited.

>> Yep. Super cool. Really interesting as always, but this was good.

>> I feel like the energy in here, I mean totally packed house, good vibes, even that happy hour last night, you must be feeling great. I want to learn a little bit more about this relationship. So Martin, you're at Qlik, GTM side, why did you bring this Martin on stage with us today?

>> So Martin has one of the best stories we've got in sport today.

>> Can't wait to hear it.

>> I can't wait to hear it. It's about ice hockey. Now, I've got to bring him because I know literally nothing about ice hockey. I'm from the UK. It doesn't kind of translate over to me. So he's going to be the expert walking through that, whereas I'm kind of like a cycling background and football, that's a football, a round ball football for us in the other side of the pond.

>> Really everywhere else in the world.

>> Yeah, it's really everywhere else in the world. That's absolutely correct. But he's here to just go through how analytics has transformed the Malmo team. I think I probably should let you do a little bit of a .

>> Let's hear it. Now I want to know. The Malmo Redhawks. Shout out to the Malmo Redhawks. What have you done to transform the team?

>> So I'm obviously a data Qlik nerd. I'm also a hockey nerd, and a couple of years ago Malmo Redhawks came to me because I had all this data, all this data generated from sensors on the bodies, wearables, AI generated data from the games, and they had so little time and so many different people looking at this data and they wanted to ... "Can we make sense of this? Can we see this together? Can we draw conclusions on how do we practice, how do we play, how do we feel just to get better?" Of course, we got Qlik cloud so we brought all that data into our platform and together with Malmo Redhawks developed this product and they've been using this for a couple of years now.

>> How long have they been instrumenting the players and give us a scope of the data ingestion. How was it being measured? What's some of the details?

>> The game related metrics are obviously ... everyone is looking at that. So passes, shots and so on. What they wanted to do was to combine that with the physical data to see how much the body is responding to the play. Also, mostly important I think during practice to make sure that everyone is ready for the game, practice individually and prevent injuries. Hockey players are quite expensive and you want them on the ice and not on the bench. So prevent injuries and be better at rehab as well to measure that you're ready to come back or not.

>> How quickly were you able to ramp up this program?

>> How good they were?

>> How quickly were you able to implement this?

>> Oh, pretty fast actually. So the hockey season is right now, the season is over. In September, the season starts, and it was at this time, three, four years ago we started and we were ready when the season started, so like two, three months from the idea to they were using it before the next season.

>> One of the big things in hockey, and we see that in NFL, our football in America, is that injury prevention has been a big part of the data. Has that come up on the data sets? Is that streaming off the player's body? Do you look at the physicality of it?

>> Yeah, absolutely, and they look at ... if something happens during the game, you get a shot or an injury, you might not notice but your body notice. So you might compensate, your body will compensate and therefore they can see that live real time and take that player off before he gets overstressed injuries and stuff like that.

>> Do you guys rank the players?

>> Yeah, we do.

>> How does that work?

>> It's almost like you play NHL on Xbox or PlayStation, so they like that. It's quite .

>> And they know they're being ranked, right? So they kind of-

>> Yeah, that's the nature of their life.

>> Take us through the ... one of the things, not to get into the weeds and nerd out on the data, but what jumps out as the keynote is the structured and unstructured data handling is really phenomenal with Qlik and that's a major benefit you're starting to see with generative AI as well. You got to stream the data, you got unstructured data. Take us through the data side of it in hockey and then also cycling and you mentioned football, soccer or football. You get a lot of unstructured data, multiple data sources. Is that an issue anymore or is that going to go away with GenAI's? What's the angle on the unstructured versus structured data?

>> I'll answer quickly and then hand over to you. I think like on the keynote, data quality is absolutely key, right? Everywhere. In normal business in sports analytics. So you got to get your data quality right. If you do that then yes, GenAI will absolutely help you also with sports. I got a question from GM a couple of weeks ago, "When can we get you GenAI to do my work?" So we'll be there, but ...

>> Thank you, Martin. I'm going to start with cycling. Now, Qlik sponsor a cycling team, a pro cycling team called Q36.5, and they've got an ambition to get into the Tour de France and eventually win the Tour de France. Now, analytics helps them in various ways. There is a logistics angle to this and that logistics being there have 1100 flights, 26 riders from all over the world. They've got 26 vehicles that have got to be shipped everywhere and follow those teams. So data coming from those systems, there's obviously the performance stuff, but every sports person has a contract, a PDF document with millions and millions of words in that. With the unstructured meeting structured, I can then get a complete picture of maybe they have riders, maybe they have different kind of aspects they want in that contract compared to their performance data. When you're comparing hockey players, we also compare cyclists and football players. So if you can imagine a dart ball and a radar chart, we look at how fast they can run, how their sprints are, their accelerations and things like that there. There's a new side to sport which has come on, which is the mental side. How do people react when put under pressure? How do people react when they get probably bumped in hockey a bit more than in football because we just go down. Also, if you are in cycling, there's a breakaway. Couple of riders go up the road, whether there be men or women up the road, and then it's can you chase them down? Are they good enough to get away? Having that information on your fingertips as a Doug Ryder that runs the team, when they're in that car following that pack around, having that information to you to make it, "Okay, go for it" or "Okay, you go catch them", because how many times do you see them catch when the finishing line is just in front of that , the team that's just got away is quite incredible. So it is very, very small margins that we're dealing here and analytics is absolutely a must for that.

>> It's a great example of the data driven bumper sticker. We talk about, hey, data driven performance, fine-tuning that piece of it becomes really the big part of this.

>> The one that I like, and some people find it a little bit boring, but it's the time trial. So when you have a time trial, it's you against the clock on that bike and there's a mental picture here where you have ... and it's a great quote from a pro cyclist from many years ago, is that when you're cycling and when you're trying to get to the end, if you think "I'm going to make it", you have not gone on hard enough. If you think you're not going to make it, you've kind of blown up. The mental picture you've got to have is "Maybe I can get there, maybe I can get there." They are running off a computer that is giving their FTP value, so that's their functional threshold power that they can do for an hour and sustain that, and they're constantly looking at that and thinking about it. Actually when you watch them closely, the ones that are starting to get unstressed, they pull their mics out, the ear plugs out because they don't want to hear what people are telling them and they kind of throw their computers off the side. But that is minute margins. It's called marginal gains in cycling. I just want to come back to something that we kind of talked about earlier with data, and that's recovery. So we've got the Euros, the Euros 2024. Sweden didn't quite make it there unfortunately.

>> What sport is it?

>> Football/soccer and the recovery of players is phenomenally important. You see managers come out and go, "Okay, my player's going to be out for six weeks." There is at least four football clubs in the UK that we got sensors all over them, you time their sprints, you monitor their recovery. That six weeks estimate comes from a predictive outcome from our software looking on a football player. That's a very, very predictive ... we haven't covered that. We covered unstructured, unstructured, but that is a predictive look of what is going to happen to a footballer at that point.

>> And that's based on footballer data.

>> Oh, yes, absolutely. The data.

>> From the whole team. So do you think they will ... and actually, I'm curious because you've been working with the Redhawks for a number of years. Do you think that this will be a real differentiator between teams that are all in on analytics and quantified self for their athletes versus those who aren't? Do you think we'll start to see a delineation there? Yeah, sure.

>> Yeah, absolutely. I used to compare when I talked to the teams, you have your equipment and that always evolves and if you go back, you were playing with wooden sticks in ice hockey, now we have the composite sticks and if someone come with the composite stick, of course you use it because it's newest. Everyone wants to have the new thing because otherwise you will lose, and they have to look at the analytics of data and hockey at the same way. If you don't do it, you will lose. So I think they're going to adapt to that as much as they're interested in all the new technology on the skates and gear as in data and analytics.

>> I would imagine it's got to be a recruitment tool as well for the clubs or the teams, whether it be cycling or anything. Have you noticed that?

>> Absolutely. Oh, yes. So looking certainly for football players, they start as young as four, five and football teams come and pick them at the age of seven and eight and bring them into their family and nurture them through. Imagine being able to predict that because they have hundreds and thousands of boys and girls that go through this program and only 1% of the 1% make it through on those kind of programs. Across a European sport into the NFL, we've got Lewis Rees-Zammit, who's coming from rugby into the NFL. They have looked at his stats, his height, his speed, his power, because they won't make a chance on a human being like that if they're not going to make it in the NFL, and he's made a team. So over this side of the pond, we're very interested to see how he's actually going to perform because he was a phenomenal rugby player, very fast human being.

>> You guys are hitting on a use case in sports, obviously data's been around for sports for a while, but not to the level of penetration of everything being measured and instrumenting all aspects of the operation, players, supply chain, logistics, performance. What have you learned and how would you share folks watching that are wanting to go the next level with data? What are some of the learnings that you guys have seen folks want to take the next step, building that foundation or getting with the data? What's your advice?

>> I can go first, I was going to pick on a different sport here now. Formula 1 for me just simply rocks stats, numbers, data. They have the speed, the power going round corners and that is telemetry data, realtime data that is streamed immediately to a terminal so they can see what is going on and it's a team sport then. It suddenly becomes a team sport even though there's an individual in a very, very fast car. What I kind of see is the data has changed the way that a manager and certainly in a pro team would look at things. They can look at the competition in far more detail than they ever could and sometimes they pick ... why did they pick that player, why have they put that person there? That is either to negate the strength of someone or to dominate the strength of something. That is analytics and to me, those fine margins are going to win you or lose you that game and losing in sport certainly gets you relegated our side. But it just simply is so, so important and to me it's always been marginal gains. Coming back to the AI piece, if they don't get starting to look at predictive, they're going to get left behind and the next thing you know they're going to be relegating, they're going to be playing .

>> So your advice is get in the data game to implement impact strategy and the aspect of the sport, whatever that sport is in real time?

>> Absolutely. I mean, humans are athletes. Now most sport, they are peak, peak athletes and there's also strategy that has come into it as well. The way we play the games are very, very different from they were 20, 30 years ago when I might have put on a pair of boots at that point. It's changed completely and the way that , data is the thing that keeps pushing these things forward. The people that run faster ... when I was young, you do the hundred meters run, I think it's the 40 yard dash in the NFL. The faster you are at that, the faster you're going to make it into a team. You've got an asset to be able to do that. But now we look at you as a whole human rounded individual, your mental state, your performance, physique and power.

>> It's amazing. I mean, the holistic picture of an athlete, I'm just thinking of in the US we have baseball cards where you used to trade them and collect them when I was a little kid. I'm wondering if your baseball card now would have all your biometrics essentially, and your analytics. I mean, it's kind of cool. You could just scan a QR code and then here's my updated, here's my rest, here's my whatever. Have you found, and I'm going to bring this to you, nearest Martin, have you found that athlete wellness has gone up as a result of this? We're talking about injury prevention, we're talking about a lot of things. Rest is obviously huge in recovery.

>> Yeah, I'd like to say so. I do have too little data because I think we need to do this for many, many teams for many, many years to actually make sure that we see that. But we've seen that, yes. A lot about recovery, as you said, measuring sleep and how you recover after away games and stuff like that. Sweden is a very tall country, so you spend a lot of time in buses between games and sleep bad, and then you have games every second day or so, and absolutely, we can see a measure that that's been improved a lot already now.

>> I mean, I can imagine you look at all little pieces of data, like you change suppliers on transportation, maybe the wins go up, hotel selection, better beds, better sleep. I mean, little things might jump out.

>> Right. It's true. That's a good point.

>> I mean, all data could open up.

>> Absolutely. Food.

>> What's the biggest thing that you found? Share with us the coolest thing that you've discovered in hockey with data that surprised you or might surprise someone?

>> Oh, good question. I think what's interesting is that you cannot replace ... I mean, we're here, we love data, we love analytics, but you can never replace the coach or other people behind. You help them, because they have skills and they have gut feelings and I think that's the extra percentage that will make you win is always going to be up to you being skilled and have the gut feeling to combine with the data you have. I guess also understanding the need of the personal skills and the addition with the data. So data's not going to do everything for you because then everyone ...

>> I could imagine that the data will help the general managers figure out the makeup of a team. If the strategy to be faster, maybe the different makeup on body type personality or if you want to go stronger and, say, play bigger, some coaches might prefer size and durability. This all comes into play.

>> Oh, absolutely. The physical side, you can play skill sides, you can play physical sides and it depends who you are, where you are in what position in the league. You may come against a team that's struggling for their life at the bottom of a table and they will fight for everything, and maybe it is fighting fire with fire at that point is the way to get through those things.

>> The one thing that I think ... because hockey is a little bit different from football, so there's a lot of players on and playing different forwards together and the possibility for the GM and the coach to predict, "What if we put these three players? Based on all their data, how would they play together against the other team's best players?", to try to predict that is really, really cool because that's a deal breaker for them to be able to not just chance, but actually build it up on .

>> Situational awareness becomes ... like power plays versus man advantage, these all come into situational decision-making.

>> Yeah.

>> Yep.

>> All right. This has been really thrilling. I have one question for you, because this is obviously moving fast, to your point. You're looking forward to having more data points about, say, the Redhawks depending on what happens next. When we have you on the show next year, what do you hope to be able to say then that you can't say today?

>> Definitely I'm looking forward to bring GenAI into our product to help the teams be even faster and better, save time for them and do what GenAI can do for you. Assist the people to see that that has added another percentage to the quality. Yeah, GenAI.

>> I love it. We didn't even say AI in this whole discussion actually. I was just thinking that. Wow, good job. Nice shopping, not too buzzy. We actually talked about the real stuff. What about you, Martin?

>> I'm going to go with GenAI too. I mean, we did talk a little bit about predicting things moving forward, but GenAI is going to make a huge difference because what it's going to do, it's going to make ... Coaches have skills of managing teams, they have skills of bringing players on and bringing forward. They don't necessarily have the time to look at data to get hold of all the information that they've got. They want to simply ask something a question, get an accurate response back that they can trust, use that data, and then help that player and help their team and hopefully win.

>> I love it. Well, we hope that your cycling team makes it to the Tour de France.

>> Thank you.

>> We'll be rooting for them and rooting for the Swedes in hockey for sure. Don't worry about that. Martin and Martin, thank you so much for being here. This has been really fantastic.

>> Thank you.

>> Thank you very much.

>> Thank you for having us.

>> John, always a pleasure, and thank all of you for tuning in to our fantastic and insightful coverage here at Qlik Connect in Orlando, Florida. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.

Martin Tombs | Qlik & Martin Sahlin | Stretch Qonnect

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>> Good morning, Qlik community, and welcome back to beautiful Orlando, Florida. We are here steaming through the day at Qlik Connect. Very excited for our live coverage all day long. My name's Savannah Peterson. You're watching theCUBE. I'm joined by my co-host and CUBE co-founder John Furrier. John, lovely to spend the day with you.

>> I love the data culture here at the event, data value. This next segment's going to be fun.

>> I am personally really excited because I know we both like to play sports. I'm an athlete. We're going to be talking about analytics and sports, and we have a pair of Martins here to tell us all about it. Martin and Martin, thank you so much for being here. We appreciate you. You've got big smiles. You look like you came off the keynote feeling real excited.

>> Yep. Super cool. Really interesting as always, but this was good.

>> I feel like the energy in here, I mean totally packed house, good vibes, even that happy hour last night, you must be feeling great. I want to learn a little bit more about this relationship. So Martin, you're at Qlik, GTM side, why did you bring this Martin on stage with us today?

>> So Martin has one of the best stories we've got in sport today.

>> Can't wait to hear it.

>> I can't wait to hear it. It's about ice hockey. Now, I've got to bring him because I know literally nothing about ice hockey. I'm from the UK. It doesn't kind of translate over to me. So he's going to be the expert walking through that, whereas I'm kind of like a cycling background and football, that's a football, a round ball football for us in the other side of the pond.

>> Really everywhere else in the world.

>> Yeah, it's really everywhere else in the world. That's absolutely correct. But he's here to just go through how analytics has transformed the Malmo team. I think I probably should let you do a little bit of a .

>> Let's hear it. Now I want to know. The Malmo Redhawks. Shout out to the Malmo Redhawks. What have you done to transform the team?

>> So I'm obviously a data Qlik nerd. I'm also a hockey nerd, and a couple of years ago Malmo Redhawks came to me because I had all this data, all this data generated from sensors on the bodies, wearables, AI generated data from the games, and they had so little time and so many different people looking at this data and they wanted to ... "Can we make sense of this? Can we see this together? Can we draw conclusions on how do we practice, how do we play, how do we feel just to get better?" Of course, we got Qlik cloud so we brought all that data into our platform and together with Malmo Redhawks developed this product and they've been using this for a couple of years now.

>> How long have they been instrumenting the players and give us a scope of the data ingestion. How was it being measured? What's some of the details?

>> The game related metrics are obviously ... everyone is looking at that. So passes, shots and so on. What they wanted to do was to combine that with the physical data to see how much the body is responding to the play. Also, mostly important I think during practice to make sure that everyone is ready for the game, practice individually and prevent injuries. Hockey players are quite expensive and you want them on the ice and not on the bench. So prevent injuries and be better at rehab as well to measure that you're ready to come back or not.

>> How quickly were you able to ramp up this program?

>> How good they were?

>> How quickly were you able to implement this?

>> Oh, pretty fast actually. So the hockey season is right now, the season is over. In September, the season starts, and it was at this time, three, four years ago we started and we were ready when the season started, so like two, three months from the idea to they were using it before the next season.

>> One of the big things in hockey, and we see that in NFL, our football in America, is that injury prevention has been a big part of the data. Has that come up on the data sets? Is that streaming off the player's body? Do you look at the physicality of it?

>> Yeah, absolutely, and they look at ... if something happens during the game, you get a shot or an injury, you might not notice but your body notice. So you might compensate, your body will compensate and therefore they can see that live real time and take that player off before he gets overstressed injuries and stuff like that.

>> Do you guys rank the players?

>> Yeah, we do.

>> How does that work?

>> It's almost like you play NHL on Xbox or PlayStation, so they like that. It's quite .

>> And they know they're being ranked, right? So they kind of-

>> Yeah, that's the nature of their life.

>> Take us through the ... one of the things, not to get into the weeds and nerd out on the data, but what jumps out as the keynote is the structured and unstructured data handling is really phenomenal with Qlik and that's a major benefit you're starting to see with generative AI as well. You got to stream the data, you got unstructured data. Take us through the data side of it in hockey and then also cycling and you mentioned football, soccer or football. You get a lot of unstructured data, multiple data sources. Is that an issue anymore or is that going to go away with GenAI's? What's the angle on the unstructured versus structured data?

>> I'll answer quickly and then hand over to you. I think like on the keynote, data quality is absolutely key, right? Everywhere. In normal business in sports analytics. So you got to get your data quality right. If you do that then yes, GenAI will absolutely help you also with sports. I got a question from GM a couple of weeks ago, "When can we get you GenAI to do my work?" So we'll be there, but ...

>> Thank you, Martin. I'm going to start with cycling. Now, Qlik sponsor a cycling team, a pro cycling team called Q36.5, and they've got an ambition to get into the Tour de France and eventually win the Tour de France. Now, analytics helps them in various ways. There is a logistics angle to this and that logistics being there have 1100 flights, 26 riders from all over the world. They've got 26 vehicles that have got to be shipped everywhere and follow those teams. So data coming from those systems, there's obviously the performance stuff, but every sports person has a contract, a PDF document with millions and millions of words in that. With the unstructured meeting structured, I can then get a complete picture of maybe they have riders, maybe they have different kind of aspects they want in that contract compared to their performance data. When you're comparing hockey players, we also compare cyclists and football players. So if you can imagine a dart ball and a radar chart, we look at how fast they can run, how their sprints are, their accelerations and things like that there. There's a new side to sport which has come on, which is the mental side. How do people react when put under pressure? How do people react when they get probably bumped in hockey a bit more than in football because we just go down. Also, if you are in cycling, there's a breakaway. Couple of riders go up the road, whether there be men or women up the road, and then it's can you chase them down? Are they good enough to get away? Having that information on your fingertips as a Doug Ryder that runs the team, when they're in that car following that pack around, having that information to you to make it, "Okay, go for it" or "Okay, you go catch them", because how many times do you see them catch when the finishing line is just in front of that , the team that's just got away is quite incredible. So it is very, very small margins that we're dealing here and analytics is absolutely a must for that.

>> It's a great example of the data driven bumper sticker. We talk about, hey, data driven performance, fine-tuning that piece of it becomes really the big part of this.

>> The one that I like, and some people find it a little bit boring, but it's the time trial. So when you have a time trial, it's you against the clock on that bike and there's a mental picture here where you have ... and it's a great quote from a pro cyclist from many years ago, is that when you're cycling and when you're trying to get to the end, if you think "I'm going to make it", you have not gone on hard enough. If you think you're not going to make it, you've kind of blown up. The mental picture you've got to have is "Maybe I can get there, maybe I can get there." They are running off a computer that is giving their FTP value, so that's their functional threshold power that they can do for an hour and sustain that, and they're constantly looking at that and thinking about it. Actually when you watch them closely, the ones that are starting to get unstressed, they pull their mics out, the ear plugs out because they don't want to hear what people are telling them and they kind of throw their computers off the side. But that is minute margins. It's called marginal gains in cycling. I just want to come back to something that we kind of talked about earlier with data, and that's recovery. So we've got the Euros, the Euros 2024. Sweden didn't quite make it there unfortunately.

>> What sport is it?

>> Football/soccer and the recovery of players is phenomenally important. You see managers come out and go, "Okay, my player's going to be out for six weeks." There is at least four football clubs in the UK that we got sensors all over them, you time their sprints, you monitor their recovery. That six weeks estimate comes from a predictive outcome from our software looking on a football player. That's a very, very predictive ... we haven't covered that. We covered unstructured, unstructured, but that is a predictive look of what is going to happen to a footballer at that point.

>> And that's based on footballer data.

>> Oh, yes, absolutely. The data.

>> From the whole team. So do you think they will ... and actually, I'm curious because you've been working with the Redhawks for a number of years. Do you think that this will be a real differentiator between teams that are all in on analytics and quantified self for their athletes versus those who aren't? Do you think we'll start to see a delineation there? Yeah, sure.

>> Yeah, absolutely. I used to compare when I talked to the teams, you have your equipment and that always evolves and if you go back, you were playing with wooden sticks in ice hockey, now we have the composite sticks and if someone come with the composite stick, of course you use it because it's newest. Everyone wants to have the new thing because otherwise you will lose, and they have to look at the analytics of data and hockey at the same way. If you don't do it, you will lose. So I think they're going to adapt to that as much as they're interested in all the new technology on the skates and gear as in data and analytics.

>> I would imagine it's got to be a recruitment tool as well for the clubs or the teams, whether it be cycling or anything. Have you noticed that?

>> Absolutely. Oh, yes. So looking certainly for football players, they start as young as four, five and football teams come and pick them at the age of seven and eight and bring them into their family and nurture them through. Imagine being able to predict that because they have hundreds and thousands of boys and girls that go through this program and only 1% of the 1% make it through on those kind of programs. Across a European sport into the NFL, we've got Lewis Rees-Zammit, who's coming from rugby into the NFL. They have looked at his stats, his height, his speed, his power, because they won't make a chance on a human being like that if they're not going to make it in the NFL, and he's made a team. So over this side of the pond, we're very interested to see how he's actually going to perform because he was a phenomenal rugby player, very fast human being.

>> You guys are hitting on a use case in sports, obviously data's been around for sports for a while, but not to the level of penetration of everything being measured and instrumenting all aspects of the operation, players, supply chain, logistics, performance. What have you learned and how would you share folks watching that are wanting to go the next level with data? What are some of the learnings that you guys have seen folks want to take the next step, building that foundation or getting with the data? What's your advice?

>> I can go first, I was going to pick on a different sport here now. Formula 1 for me just simply rocks stats, numbers, data. They have the speed, the power going round corners and that is telemetry data, realtime data that is streamed immediately to a terminal so they can see what is going on and it's a team sport then. It suddenly becomes a team sport even though there's an individual in a very, very fast car. What I kind of see is the data has changed the way that a manager and certainly in a pro team would look at things. They can look at the competition in far more detail than they ever could and sometimes they pick ... why did they pick that player, why have they put that person there? That is either to negate the strength of someone or to dominate the strength of something. That is analytics and to me, those fine margins are going to win you or lose you that game and losing in sport certainly gets you relegated our side. But it just simply is so, so important and to me it's always been marginal gains. Coming back to the AI piece, if they don't get starting to look at predictive, they're going to get left behind and the next thing you know they're going to be relegating, they're going to be playing .

>> So your advice is get in the data game to implement impact strategy and the aspect of the sport, whatever that sport is in real time?

>> Absolutely. I mean, humans are athletes. Now most sport, they are peak, peak athletes and there's also strategy that has come into it as well. The way we play the games are very, very different from they were 20, 30 years ago when I might have put on a pair of boots at that point. It's changed completely and the way that , data is the thing that keeps pushing these things forward. The people that run faster ... when I was young, you do the hundred meters run, I think it's the 40 yard dash in the NFL. The faster you are at that, the faster you're going to make it into a team. You've got an asset to be able to do that. But now we look at you as a whole human rounded individual, your mental state, your performance, physique and power.

>> It's amazing. I mean, the holistic picture of an athlete, I'm just thinking of in the US we have baseball cards where you used to trade them and collect them when I was a little kid. I'm wondering if your baseball card now would have all your biometrics essentially, and your analytics. I mean, it's kind of cool. You could just scan a QR code and then here's my updated, here's my rest, here's my whatever. Have you found, and I'm going to bring this to you, nearest Martin, have you found that athlete wellness has gone up as a result of this? We're talking about injury prevention, we're talking about a lot of things. Rest is obviously huge in recovery.

>> Yeah, I'd like to say so. I do have too little data because I think we need to do this for many, many teams for many, many years to actually make sure that we see that. But we've seen that, yes. A lot about recovery, as you said, measuring sleep and how you recover after away games and stuff like that. Sweden is a very tall country, so you spend a lot of time in buses between games and sleep bad, and then you have games every second day or so, and absolutely, we can see a measure that that's been improved a lot already now.

>> I mean, I can imagine you look at all little pieces of data, like you change suppliers on transportation, maybe the wins go up, hotel selection, better beds, better sleep. I mean, little things might jump out.

>> Right. It's true. That's a good point.

>> I mean, all data could open up.

>> Absolutely. Food.

>> What's the biggest thing that you found? Share with us the coolest thing that you've discovered in hockey with data that surprised you or might surprise someone?

>> Oh, good question. I think what's interesting is that you cannot replace ... I mean, we're here, we love data, we love analytics, but you can never replace the coach or other people behind. You help them, because they have skills and they have gut feelings and I think that's the extra percentage that will make you win is always going to be up to you being skilled and have the gut feeling to combine with the data you have. I guess also understanding the need of the personal skills and the addition with the data. So data's not going to do everything for you because then everyone ...

>> I could imagine that the data will help the general managers figure out the makeup of a team. If the strategy to be faster, maybe the different makeup on body type personality or if you want to go stronger and, say, play bigger, some coaches might prefer size and durability. This all comes into play.

>> Oh, absolutely. The physical side, you can play skill sides, you can play physical sides and it depends who you are, where you are in what position in the league. You may come against a team that's struggling for their life at the bottom of a table and they will fight for everything, and maybe it is fighting fire with fire at that point is the way to get through those things.

>> The one thing that I think ... because hockey is a little bit different from football, so there's a lot of players on and playing different forwards together and the possibility for the GM and the coach to predict, "What if we put these three players? Based on all their data, how would they play together against the other team's best players?", to try to predict that is really, really cool because that's a deal breaker for them to be able to not just chance, but actually build it up on .

>> Situational awareness becomes ... like power plays versus man advantage, these all come into situational decision-making.

>> Yeah.

>> Yep.

>> All right. This has been really thrilling. I have one question for you, because this is obviously moving fast, to your point. You're looking forward to having more data points about, say, the Redhawks depending on what happens next. When we have you on the show next year, what do you hope to be able to say then that you can't say today?

>> Definitely I'm looking forward to bring GenAI into our product to help the teams be even faster and better, save time for them and do what GenAI can do for you. Assist the people to see that that has added another percentage to the quality. Yeah, GenAI.

>> I love it. We didn't even say AI in this whole discussion actually. I was just thinking that. Wow, good job. Nice shopping, not too buzzy. We actually talked about the real stuff. What about you, Martin?

>> I'm going to go with GenAI too. I mean, we did talk a little bit about predicting things moving forward, but GenAI is going to make a huge difference because what it's going to do, it's going to make ... Coaches have skills of managing teams, they have skills of bringing players on and bringing forward. They don't necessarily have the time to look at data to get hold of all the information that they've got. They want to simply ask something a question, get an accurate response back that they can trust, use that data, and then help that player and help their team and hopefully win.

>> I love it. Well, we hope that your cycling team makes it to the Tour de France.

>> Thank you.

>> We'll be rooting for them and rooting for the Swedes in hockey for sure. Don't worry about that. Martin and Martin, thank you so much for being here. This has been really fantastic.

>> Thank you.

>> Thank you very much.

>> Thank you for having us.

>> John, always a pleasure, and thank all of you for tuning in to our fantastic and insightful coverage here at Qlik Connect in Orlando, Florida. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.