The National Football League is a great example of how organizations are embracing artificial intelligence and data analytics in a variety of ways to increase fan engagement, improve player health, and drive the business of the game.
A tool called the Digital Athlete allows the league to use AI and machine learning (ML) to predict injuries. It uses AWS software to run millions of simulations to forecast injuries that may occur during a match. Next Generation Statistics (NGS) allows leagues to visualize the on-field action, collect data and increase fan engagement.
Big Data Bowlis a crowdsourced competition hosted by Amazon Web Services that challenges users to create data analytics that can improve fan experience and player safety. Participants developed ML models that could identify blockers and pass rushers for every pass play, study pressure dynamics, and pinpoint specific blocker-rusher combinations.
Although all teams are advanced in using AI and data analytics, San Francisco 49ers It stands out for its ability to consolidate various data silos and future-proof services such as Qumulo hybrid cloud data storage.
in AWS Summit On July 10 in New York City, InformationWeek sat down with NFL Chief Data and Analytics Officer Paul Balleu to learn more about how the NFL gleans insights from real-world data and how CIOs should approach AI and data analytics.
(Editor's note: This interview has been edited for clarity and brevity.)
How is the NFL transforming fan engagement through real-time insights and data to make the fan experience more personalized?
This is the heart of the journey we committed to several years ago: the ability to see, know and engage with our fans in a meaningful way. It's a systematic approach. It's not easy. We work with all 32 clubs and were one of the first sports leagues to bring all fan data together in one place. We spend a lot of time with our fans to drive transparency and permitted use. [of] It's their data. And then, for the first time, we can bring it all together and properly connect it to who you are and what's important to you. And then we deploy that in a number of different ways. We give it to the clubs so they can intelligently engage with their fans. The league also does a fair bit of marketing and outreach, so we use it at the league level and then for our direct-to-consumer business. In terms of technology, it spans the entire ecosystem of data and analytics. We've invested heavily in the data side because if you can't bring the data together and understand it and address traditional challenges, you're not going to be able to scale.
And our ability to engage with our fans is enhanced by all the work we're doing on the analytics side: creating audiences, creating offers, learning how they respond to those offers and interactions. Sometimes it's information about major events like the draft, other times it's actual offers: selling tickets, encouraging participation, selling jerseys, selling shops. So it's been such an interesting journey. The fact that sports is jumping into this now really shows the power of the world we live in. The direct-to-consumer world has come to the forefront during COVID-19, and now it's relevant to any industry too. If you're not connecting with your customer base, in our case the fans, they're disintermediating.
You previously served as executive vice president and global chief data and analytics officer for Ford Motor Co. What data analytics strategies from your time at Ford are you carrying over to the NFL?
When you think about the job description, if you're the chief data and analytics officer for an automotive company, there are a lot of similarities in that you support quality, you support product development, manufacturing, purchasing, safety. We have an ecosystem that supports all of those functions. Similarly, with the league, our responsibility is to help the league with all of those aspects: player health and safety, marketing, media optimization, officiating analysis, evaluating the impact of the rules. The fundamental principles are very similar.
You need to be able to bring that data together, understand it, and distribute it into a decision-making environment that people can use. In manufacturing, they were very mature in terms of the factory itself, and actual power data analytics was baked into the daily operations of the factory. In the NFL, they're not quite there yet because they're not necessarily running the lines every day. But similar methods, techniques, data integrity, and analytical tools are driving efforts to help optimize schedules and gain insights on player health and safety on the impact of equipment changes or rule changes. So the mindset and thought process of analytics is very similar. The data challenges that you face are very similar. The workflow is different in terms of how it's ultimately distributed. In a manufacturing plant or a retail store, there's a constant whirlwind of activity, whereas in the NFL, there's games every weekend. You have to run a schedule, you have the draft coming up so you have to optimize your marketing. So, it's not the same frequency, but there are a lot of similarities in terms of methods and capabilities.
How will generative AI transform sports analytics, especially in the NFL?
GenAI is the key engine that powers what we do with respect to our 1:1 program, Unified View; that is, the various applications. I don't know if we limit it to GenAI. When you think about artificial intelligence, GenAI is certainly a category. But machine learning is also within the AI family. Machine learning models are the oxygen that an analytics organization uses. It's our primary methodological approach to any business problem.
You mentioned player health and safety, how are you using data analytics and AI to glean insights in that area?
This is a big area for us, and a big part of it is about good data, data collection, making sense of the data. This includes systems like our electronic health record system. So it's a very systemic approach. To me, this is one of the best examples of the role of data analytics, because a big part of this approach is how you collect the ingredients, the data, ensuring the integrity of the data and being able to understand it, and it's not just about focusing on advanced technology. To make a good soufflé, you have to have the ingredients. So in terms of player health and safety, we've spent a lot of time focusing on that. It's impacting a lot of what we do. KICKOFF [rules] The changes planned for the 2024 season are a good example of this, often involving equipment changes. Guardian Cap “It's the technology that we use in practice to reduce concussions, head impacts, head trauma, the very significant work we're doing with footwear, and then the work we're doing with surfaces and climate to see what we can control to improve the overall health and safety of our players. Two of those things are frequency and severity. We want to reduce the frequency and reduce the severity. All of that fits into a holistic approach to finding ways to continue to improve the overall safety of the game while still preserving the essence of what everyone loves about the game: it's action-packed, it's intense, it's physical.”
So it's always about trying to find the right balance. Kickoff is a good example. We do a lot of work on the impact, how does it affect the game, how does it affect scoring, how does it affect offense, how does it affect injuries, and then we bring it all together to drive the rule change. And then after we implement it, we focus on measuring it and seeing if it's actually achieving the results that we were planning to achieve or hoped to achieve.
What types of advanced metrics are you capturing to improve the in-stadium experience?
There's a lot going on in terms of clubs experimenting with how to use that very limited latency data to optimize concessions, traffic flow, and so on. We have some clubs that are on the front lines. My local Lions have an amazing war room that they run themselves during the game. And it's real-time for security purposes, concessions, traffic flow, game flow. They're using it almost instantly to change how they run their stadium before, during, and after the game. It's amazing to see how far they've progressed and their ability to understand the data that's coming in and act on it very quickly. We're seeing more and more clubs doing that. And one of the things that's very satisfying for us, because we're making them more efficient and giving them the tools and so forth, is the clubs are doing this on their own. They're taking what we're doing and going down a path that we like. It's not like we directly told them to do this. They just went in there and built it themselves. The Lions are a great example of that. San Francisco 49ers They're doing some fantastic things with the match day experience, other clubs are doing the same and it's very impressive.
What is Big Data Bowl and how does it generate insights into the game?
Every year, we do something like an open source competition. Think of it like a Kaggle competition where you have a specific problem and people come together to solve that problem. And as part of that, we open up our NGS data so that people can find their own insights. We live in a world where we can harness the intellectual power of large groups of people. The Big Data Bowl is part of that, as well as other activities we do with universities. My organization currently has partnerships with universities that are just as strong.
What advice do you have for CIOs and other C-suite executives on how to use AI to gather real-time data?
Understand that this is a set of enabling capabilities that you need to weave into what you do, how you do it, and where you do it. My advice to you is to think in this regard. Don't think of it as an alien race. Don't think of it as something so unique that it has to be postponed on its own planet. After 15 years, nearly every company that ran it has asked itself how to reintegrate it into the mothership and experienced the organizational turmoil, pain, and suffering associated with that. We really need to think like that.
And always go back to your mission as a company and what you're trying to do. Yes, this will generate new business opportunities, but at the end of the day, it's you. There are two functions associated with this: first, it increases the efficiency of what you do, and second, it gives you the opportunity to better engage with your customers. Embracing these two will make it possible and increase your chances of success.
There's also an element of change management in this. You can generate insights and do great analytics, but ultimately it's your business partners who need to leverage your efforts to make better decisions to change their processes and workflows. So we spend a lot of time with our business partners in this time to help them, understand what problems they're struggling with, get context, and then we can help them leverage our efforts.
