Personalise Player Data to Make It Meaningful
It’s best achieved by visualising data so athletes can see what they do well and how they’re progressing
By Ben Smith & Rapha Brandon
We recently assembled a roundtable of sports brains at our London office to discuss and debate the current landscape of athlete data and what the future holds.
The most prominent theme was individualisation in European football and how data truly resonates with players when the context creates a clear connection to their performance.
Context is often presented by comparing a player’s individual metric against metrics for the rest of the team. But a better use case seems to be engaging players in discussions to see which metrics are most relevant to them and giving them the ability to follow those metrics—the context being their lived experience and what they feel in their bodies.
Yaw Amankwah shared a story from his playing days about the time he had been handed a feedback report on his training session heart rate data, without any explanation or discussion about its “poor” rating. Needless to say, Yaw didn’t really pay much heed to heart rate training data after that experience.
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In contrast, Scott Drawer shared a success story from professional cycling about athletes being put at the centre of their development with data. Through an ongoing trial and error process, they became immersed in their performance metrics and developed high levels of engagement and understanding. It often takes more effort and time up front to educate and involve athletes, but such an approach can have the greatest impact in applied sports science and in the arena of competition.
The discussion extended into the area of player development and how data has to have a purpose for the athletes. One example of data’s purpose—an important one—is to enhance decision-making.
If data can be visualised to show athletes what they do well, and if that data can also show how they’re progressing, then meaningful purpose has been injected into a personalised process. James Moore spoke about keeping athletes in their “sweet spots” and how that’s best achieved by engaging athletes to understand how their own physical and mental needs are represented in the data. As one athlete he works with says, it allows athletes to “take care of their business.” (An example in the U.S. of an athlete falling out of his sweet spot would be MLB’s Matt Carpenter, who only embraced the power of data after he’d lost his swing and potentially his career. Data, it turns out, helped him fix his issues and he went on to land at the New York Yankees.)
The thing to be wary of, as both James and Geir Jordet pointed out, is being dogmatically fixed upon metrics to make decisions without considering what those decisions might lead to. This oversimplification results in being blindly data-driven rather than intelligently data-informed.
Using performance data to capture a player’s performance journey is another innovative way of combining metrics and purpose.
For a senior 1st Team player, this might be giving him a picture of his high-performance week and a breakdown of what it takes to consistently deliver those outcomes and stay on track. For the up-and-coming athlete, they could compare their data to where their role models were at the same age.
BreakAway Data wants to contribute to these intelligent data-informed athlete development approaches by making data meaningful and purposeful. We’ll keep picking the brains of the best minds around to explore player ownership of data, personalising athlete development, and enhancing communication around data in coaching.
If you have an example you’d like to share or a specific question, connect with Ben and Rapha. We’d love to hear how data is making your team and players better, and we’d love to talk about how we can help you further develop your strategies around athlete data.