March 17, 2026 | Podcast
The Active Share Podcast
The Active Share Podcast
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What happens when elite sport meets elite data? In this episode of SuiteTalk, Hugo sits down with Will Lopes, CEO of Catapult Sports, to explore how technology is reshaping the way athletes train, perform, and stay healthy. From wearable devices and real-time analytics to AI-powered insights, Lopes explains how teams are moving beyond intuition to make smarter, more precise decisions. Lopes also offers candid insights on leadership and the role AI will play in enhancing—not disrupting—the future of sports.
Comments are edited excerpts from our podcast, which you can listen to in full below.
Will Lopes: We’re a sports analytics platform that operates across two main verticals. The first is athlete performance and health. Athletes wear our pioneering technology in training and games, and it helps teams prevent injuries, manage workloads, and keep players in peak condition. We’ve been building and refining this product for more than a decade, supporting teams from the National Football League (NFL), English Premier League (EPL), and Australian Football League (AFL).
The second vertical is tactics and coaching. Our video analytics platform helps teams identify key moments in games, adjust strategy, and design future plays. It also gives coaches a clearer view of what’s really happening on the field.
What makes us different is how we bring those two together. For example, if you’re deciding when to substitute a player, you can pair performance data with video data to see when that athlete was at peak condition and when performance started to drop.
Will: When we measure athletes, we’re trying to understand how hard they’re working in specific moments, and how that workload affects their overall capacity, health, and performance.
We also analyze performance through the lens of sport and position because those differences matter. Take ice hockey: the load on a goalie is completely different from the load on a forward. And in soccer, what matters for a goalkeeper isn’t what matters for a midfielder.
The sports scientists on our team then refine our algorithms to understand, in a micro sense, how workload varies across players and positions.
And we’ve gone even further. Today, we’re factoring in sport, position, body type, and in some cases, where athletes are in their careers. All those variables matter when you’re trying to build an accurate picture of performance and risk.
Will: We like to think of ourselves as part of the sports team because we want to help it win.
Last year in the EPL, around £300 million in salaries went to players who were injured and sitting on the bench.
That’s a massive loss of value. And the correlation between having your best players on the field and winning championships is incredibly high.
So, when we work with teams, we talk less about the technology and more about outcomes. They’re spending huge amounts of money to acquire top players, and even more to develop them. Our technology helps protect that investment.
We like to think of ourselves as part of the sports team because we want to help it win.
Will: It’s about balancing standardization and customization. On one hand, you want to show benchmarks for what works and what doesn’t. On the other, you need to give teams the freedom to adapt those insights to their own style.
For us, the real challenge is having people who truly understand the sport. We typically hire industry veterans and pair them with strong engineers. The engineers then focus on turning data into meaningful patterns and predictive outcomes. But we always give control back to the customer so they can adjust and refine those insights.
Across every sport, we capture as much athlete and game data as possible, combine it with video and external inputs, and give teams the ability to run scenarios. A good example is Formula 1. On a race weekend, we capture about 1,000 telemetry points per millisecond from each car. We combine that data with 150 to 200 video feeds and external data such as wind speed, air temperature, and track conditions. From there, we generate 2 million to 4 million outcome predictions.
Whether it’s pregame, in-game, or post-match, the goal is to help teams make better decisions and make them faster.
Will: I’ve always believed there’s still an advantage. Technology, in many ways, democratizes access. It gives smaller teams tools they never had before.
Take Brentford Football Club. Today, it can build recruiting systems that would have been impossible 15 or 20 years ago. Technology gives the club access to information it simply didn’t have in the past, and a lot of what we do helps enable that.
But there will always be teams that are smarter, faster, or better resourced, and that use technology in more advanced ways. Teams also use technology differently depending on their goals. Size, strategy, and objectives all matter. The priorities of Brentford and Chelsea Football Clubs may be very different. One might focus on developing players and generating capital through transfers. The other might be focused primarily on winning the Champions League.
So yes, I still think technology creates differentiation. It creates speed. It creates smarter decision-making. And in many cases, our platform is the backbone of both.
Will: Technology in sports is still very nascent. If you look at how technology disrupts any industry, it usually happens in three stages. The first is digitalization, where you start capturing activity and collecting data, often in overwhelming amounts. The second stage is optimization, where you begin turning that data into predictions and prescriptions. The third stage is transformation. That’s when technology starts driving fundamental changes in business models, but we’re still a long way from that in sports.
In sports, we’re just beginning to move from digitalization into optimization. A good example is something we discovered a couple of years ago. For wide receivers in American football, the most important factor in success wasn’t acceleration; it was how quickly they could decelerate. Training for deceleration is very different from training for speed. That kind of insight simply didn’t exist in the market before, and it only emerged once enough data was available.
Today, success is still mostly correlated with how much teams spend on players and coaching staff. It’s not yet strongly tied to technology, but I think it will be.
Technology in sports is still very nascent.
Will: There are really two main drivers behind our growth.
The first driver is technology adoption. Ongoing disruption continues to drive demand, but at the same time, the sports industry itself is expanding. Sports has become one of the last major forms of live entertainment. I like to say it’s the only industry where your product can be bad, and you still have loyal fans. I’m a longtime Jets fan, so I’m living proof of that.
So, we’re expanding our technology. There are roughly 20,000 professional teams worldwide, so there’s still a lot of untapped opportunity, especially around wearables and performance. We want to help teams that don’t have large in-house sports science departments train as effectively as clubs such as Brentford or Chelsea.
The second driver is how we use the data we’ve built. We now sit on about five petabytes of data. If you took every written word in human history and multiplied it by a thousand, you still wouldn’t reach that amount. But the question is how we turn that data into something more useful in a team’s daily workflow.
Today, teams spend heavily on technology, but often in fragmented ways. Our goal is to create a more cohesive system that connects training, recruitment, scouting, and game-day decisions into a single point of view.
Will: My role wasn’t about inventing a bold new strategy. It was about learning to say no.
Catapult was a collection of small companies brought together through acquisitions. When organizations are small, they tend to say yes to everything. They customize too much. They bolt on products they probably shouldn’t. They chase every opportunity because they feel they must.
But scaling is different; it’s about focus. It’s about deciding what really matters and being willing to say no to a lot of good ideas in order to stay aligned with the mission.
I thought the company had strong products and a solid strategy, but what it lacked was experience in scaling. So, my focus was on identifying what was truly mission critical and building a leadership team that had been through that transition before.
My role wasn’t about inventing a bold new strategy. It was about learning to say no.
Will: It helps that I get to work in a place where people are excited to come to work every day. Everything we do impacts products watched by billions worldwide. We’re helping athletes perform better, teams succeed, and fans get the best experience. Reminding people of that long-term impact is crucial.
Beyond that, it’s just hard work. Some days you hit it out of the park; some days you don’t. The key is staying focused on the long-term win and being relentless.
Morale also comes from ownership. I grew up working at Amazon and Audible, where everyone acted like an owner of the business. When I came to Catapult, I made sure equity was a meaningful part of compensation, so if the company succeeds, every team member shares in that success.
Will: Not all decisions are the same. I tend to separate them into what we call one-way-door and two-way-door decisions.
One-way-door decisions are irreversible. Those require a deliberate, data-driven approach. Two-way-door decisions are reversible. By the time they reach me, speed matters most. My job is to be decisive, remove roadblocks, and trust the team and experience.
Will: I see AI as a positive for sports, not a threat. It helps level the playing field, which is good for fans as you don’t want the same two or three teams winning every year.
We think about AI in three layers, like one would think about oil. The bottom layer is the oilfield: raw, first-party data, which you always need to capture. AI can’t replace that, especially when it comes to physical attributes.
The middle layer is refinement. This is where AI adds value, helping us make sense of data, uncover insights, and translate them so less sophisticated teams can use them effectively.
The top layer is consumption, where insights are applied. You still need domain expertise to know when and how to use them, and those who master that will be successful.
For us, AI is an enabler, not a disruptor, and it has helped us deliver faster, smarter insights so our customers can make better decisions.
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