Industry Takeaways at the Intersection of Sport, Data, and Business Innovation

Observations from the United Soccer Coaches Convention on data adoption, understanding gaps, and the real opportunity ahead.

Over the past decade, I've attended the United Soccer Coaches Convention almost every year. This year felt different in a way that's worth documenting.

The shift wasn't subtle. Over the last three years, there has been an explosion of data and video companies entering the soccer ecosystem, largely centered around hardware, GPS, and tracking technologies. Three years ago, there were effectively zero sessions focused on data analytics. This year, analytics-focused presentations ran multiple times per day.

That change matters. It signals that data adoption in soccer has crossed an inflection point.

But adoption alone isn't the story. How data is actually being used — and misunderstood — is where the real insight lives.

Data Is Being Collected Faster Than It's Being Understood

Despite the rapid growth in tools and platforms, most coaches are still interpreting data through context, instinct, and feel. That isn't a criticism. Human judgment will always be a necessary part of sport.

What it does reveal, however, is the current ceiling of data usage. Data is often treated as supporting evidence for decisions already made, rather than as an input that meaningfully shapes those decisions.

In practice, this means teams are collecting more information than ever, but extracting very little incremental advantage from it.

The Rise of the "Game Model" — and the Metrics Gap

Among more advanced staffs, a different pattern is emerging.

These coaches define a clear game model: a deliberate identity for how they want their team to play. From that foundation, they attempt to design KPIs that align with their tactical and strategic intent.

The problem is that many of the metrics these coaches actually care about don't exist in standard datasets. Off-the-shelf platforms and league-provided data rarely map cleanly to a team's internal definition of success.

This gap — between how teams want to play and what data is readily available — is one of the most underappreciated challenges in modern sports analytics. It's also where meaningful innovation has to happen.

Visualization Is Being Mistaken for Insight

Even among teams and platforms that describe themselves as "data-driven," there is very little true modeling taking place.

In most cases, data is visualized rather than interpreted. Dashboards display numbers, charts, and trends, but rarely translate those inputs into signal. The result is a landscape that is noise-heavy and insight-light.

Visualization has value, but it's not the end goal. Without modeling, normalization, and context, data becomes another cognitive burden rather than a decision-making aid.

Turning noise into clarity is the real unlock.

The Hardware Misconception

One of the most common misconceptions I encountered repeatedly was the belief that hardware alone creates insight.

The interaction often goes something like this:

"Great — so I just set up a camera or strap on a sensor?"

The reality:

"No — someone still has to decide what matters, capture it intentionally, and interpret it correctly."

Technology does not remove the need for structure, intent, or understanding. Sensors and cameras generate inputs, not answers. Treating hardware as a shortcut to insight leads to frustration, underutilization, and poor ROI.

The Business Opportunity Beneath the Surface

Stepping back, the broader takeaway is clear.

The market is moving decisively toward data collection, but not yet toward data understanding. Coaches, teams, and organizations are swimming in information, while still relying primarily on instinct to make decisions.

That gap — between collection and insight — is the opportunity.

The next phase of sports analytics will not be won by companies that collect the most data. It will be won by those that reduce cognitive load, apply real modeling, and translate complexity into decisions that align with how teams actually operate.

At DSA Labs, much of our work sits at that intersection of sport, data, and business innovation — including how market behavior, performance, and valuation intersect in areas like NIL. Rather than asking what data exists, the more important question is what decisions teams are actually trying to make.

The industry has largely answered whether data matters.

The question now is whether we're ready to understand it.