>> Announcer: Live from Las Vegas, it's theCUBE covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with its ecosystem partners. >> Okay, welcome back to theCUBE, everyone. We're live in Las Vegas for AWS exclusive coverage of Amazon Web Services re:Invent 2019. I'm John Furrier with Stuart Miniman. Want to thank Intel for sponsoring our two sets. Shout-out to them for the sponsorship bringing great content to you from SiliconANGLE. Our next guest is with the NFL, and Andy Jassy just consummated a deal here in Las Vegas with Roger Goodell, the commissioner of the National Football League, on a new strategic initiative to use next gen stats, Amazon cloud, that whole data infrastructure with the NFL to change the profile and posture for safety and for all the athletes. And the guest here, we have Dr. Jeff Crandall, who's the chairman of the NFL Engineering Committee. Thanks for coming on. >> Thanks for having me. >> So I saw your guys' speech up there as part of the announcement with Dr. Matt Wood and a fellow NFL executive. This is a really cool initiative, because the NFL, you guys have a lot of data geeks there. You have an enormous amount of data. We see stat tasks and next gen stats from Amazon on TV. There's been a lot of advertising dollars doing that. Pretty cool. You're taking it to a next level. Explain the program you're doing. It's got $300 million in funding behind it. You started three years ago. Take a minute to explain. >> Sure, I think one of the things, it's $100 million, but-- >> Okay. >> Not quite $300 million yet. But if you look at it, it was part of an initiative the league developed to say what could they do about safety. I think part of the thing that not everyone recognizes is what the NFL does for safety and innovation, how much effort they put into that. So I'm part of an engineering effort called the engineering road map, and really what we want to do there is we thought there was an opportunity to transform the space for head protection by us putting our understanding in, creating tools, we could help those in the market develop better equipment and better protect our players. >> And so one of the things I'm learning is that you guys have tons of data, and I learned a fun stat that the fastest runner this year was running at, what, 22 miles an hour. >> Jeff: Yeah. >> But you guys are collecting a lot of data from the equipment, surface, everything. Can you explain some of the insight into the data collection, specifically the amount and diverse types. >> Yeah, that's one of the things that we've learned is in order to make effective interventions, you really have to have a handle on the breadth of what's going on on field, and in order to do that, it's a very fast, dynamic game, so you'd have to have a number of data sources coming in. You need to know about the game itself, the plays, the position, the particular play types. You need to know about the players. What speed, what's their position, what orientations, what routes. And then their environment, the surfaces, the equipment. What helmets are they wearing, what shoes. So we're trying to say what we have for extrinsic factors, what's in the environment, and then the intrinsic factors, what do the players themselves experience for factors. >> We know that data can be a differentiator, but it sounds like data like this will help the entire ecosystem. Can you speak a little bit to the medical community, the equipment manufacturers, the teams that have to build new stadiums. We've interviewed some of the architects that put a lot of technology into the stadiums themselves. So how does that data flow happen? >> Yeah, that's one of the things. We have so much data that we're able to create sort of an evaluation of what's happening to the players and what they're experiencing. And I think very few other sports, even, or very few other applications have that level of quantification. And so what we're looking at is how do each of those factors contribute to how players train, how players perform, how players are injured. And so by having that, we can come up with something we've called the digital athlete, which is essentially a virtual representation. And through that virtual representation, we start to understand how any of these factors influence the dimensions of performance and injury. It scales broadly to anything where the body would be stressed or loaded or trained. Any of those applications could benefit from what we're doing. >> So your simulation, that's a digital twin in parlance of IT nerds here. >> Sure. >> But this is a really killer idea because you can do many simulations that the cloud will provide, right? >> Jeff: Sure. >> I mean, and you got video to match it. So talk about that dynamic. 'Cause you got video and you got data points. >> Jeff: Yeah. >> They're kind of working together. >> Exactly. And so I think if you want to take the digital twin analog, I mean, one of the unique things about that is that you can have this virtual representation and you get this continuous input of feeds from sensors, from video, and you start to refine what that digital twin looks like, whether it's a player or whether it's a mechanical device. And the more feeds and the more data you have and the more time goes on, the better represented that is. And so that's really what we're gearing towards. >> Yeah, one of the things I abstracted out of the presentation was honestly, the head injuries, helmet, that's clear. That's got to get done. You're working hard around that. But there was a mention of lower body injuries, as well. So it's not just head. There's other things you guys are thinking about. Can you expand on what that might look like and how you guys are thinking about it? >> Sure, I mean, obviously we want to make sure whole body, head to toe, we're protecting the players the best we can. I think if you look from an injury frequency or an injury burden standpoint, time lost for players, lower limb is one of the major injuries in that calculation. And so what we're doing is we've been working on concussions and helmets for the last three or four years. We've been working on cleats and turf for a long time. We're starting to curate that data, and that will go into our digital twin, digital athlete platform. >> It's like they're having LIDAR. It's like when my car backs up and stops, maybe when there's a rollover coming over, an alert kicks the leg around the right spot. But this is what, the kind of thing you guys are thinking about, the rule changes and the innovation and safety is, you can actually make direct impact. So there was a rule change on kickoffs. >> Jeff: Sure. >> Talk about that dynamic, 'cause this is kind of a teaser of where things might go, right? >> Yeah, exactly. I think if you look at injury prevention, they obviously talk about where can you change? You can do it with engineering, you can do it with education, or you can do it with enforcement or rules. And what we've learned is that we can take the data we're gathering and do data-driven initiatives on any of those. We've done a kickoff rule that was informed by data. We've done a use of helmet, leading with the helmet rule. So I think the same underlying data leads to any of these application areas. >> And the results just on the numbers. You guys quoted some stats. What was the reduction in concussions last year? >> Last year there was a reduction in games of 29% for concussions. >> Awesome. >> That's great. I've been watching a lot of the 100-year football anniversary here, and it's evident how much technology has been having an impact. Gives us a little bit of how the AWS and NFL, where we'll see that going in the coming decades and beyond. >> So I think historically, we've had very strong medical, we've had very strong engineering. What we've been seeing, though, is we've been doing a lot of stuff manually. It's been very labor-intensive. We've had some wins and successes, as you just heard from last year, but now we're looking to scale and accelerate that. So building on what AWS brings to the table in terms of their data analytics, their cloud computing. We believe we can do a better job understanding what's happening on field and lead to interventions and innovations much more quickly, much more broadly than currently exist. >> For the folks watching, we're here at an IT show or cloud show. You guys are immersed in data, so you're leaning on AWS for a lot of the expertise on scale, machine learning. They got a lot of goodness in their portfolio, but you guys have the data. So for other companies that are looking at this transformation with the top-down leadership model that you guys have, what have you learned? What is some of the scar tissue you might have from the process you've been through? Any observations or learnings you could share around the order of magnitude, approaches. Is there some paths that you'd recommend? >> Well, I think one of the things we've learned is there's a hard way and there's a more efficient way. We've had as many as 17 people looking at videos, and it led us to believe, we've looked at more than 100,000 helmet impacts manually. There's got to be a better way. And so we actually spent two years talking with tech companies, exploring what was out there, before we came to this AWS partnership. So I think when we look at the future and look at the opportunities, I would say where we were bounded previously and we were looking at maybe an immediate horizon, now what we've said is let's wipe the slate clean. Let's see where we want to end up far into the future. Let's look at what we would build, something to be scalable that we could leverage. >> And this is a pretty significant announcement, 'cause Roger Goodell was here with Andy Jassy. So it's not just a tech deal. This is a bigger play here. >> Jeff: Yeah. >> Can you give some insight into the strategic impact of the AWS-NFL piece? >> So AWS has had a relationship with the league, and one of the primary things they've done is the next gen sport, the next gen stats, rather, tracking player motion on field. You know, you've seen a lot of the stats that come up in games. And so there was an idea how we could take data, leverage it. That was more for fan engagement. But that very same information, we've looked at collisions. We take next gen stats data with two players coming together. What's the closing velocity? What are the closing angles? And so I think what you've seen is how you can take this wealth of data in the NFL and by taking those that are sort of best in class and innovators with the data analytics and machine learning, what else can you extract from the data that may not have been evident without sort of a broader computing platform. >> You know, a lot of people look at the NFL. They see the big networks who cover the sport for the fan experience. There's kind of a nerd culture going on with NFL and the fan base. We've been hearing feedback all the time about theCUBE becoming a broadcaster for NFL. Has that been kicked around at Roger's level yet? (Jeff laughs) Has it gotten there? >> Well, I was thinking of doing digital twins of you guys. (John laughs) I was just sizing it up. But I'm not sure we're quite there yet. >> Dr. Crandall, thank you so much for coming on. Congratulations. What a great initiative. You guys are being transparent, forthright with your research. It's open. Congratulations. It's a good step. >> Great. My pleasure. Appreciate it. >> Thanks for coming on. I'm John Furrier, Stuart Miniman, with the NFL here as part of the big announcement on Thursday with Andy Jassy and the commissioner of the NFL, Roger Goodell, it's theCUBE getting you all the action here at re:Invent. We'll be right back with more after this short break. (techno music)