What's your why? (Don’t worry, this isn’t another Simon Sinek post)
Off on a Tangent: Data and Judgement
Sometimes, inspiration comes from funny places, and in this case, it came from the Durango-Durango post race debrief.
Over the past couple of months I’ve touched on the topics of data and confidence, and how sport-tech should be designed to spark a conversation between the athlete and their coach. At the same time I’ve seen an uptick again in conversations around AI and coaching.
In parallel, the past few weeks at my desk job have had some interesting themes, all-linked to predictive analytics (in healthcare) and the evolution of access to care. A popular question making the rounds again? “What’s your why?”
You probably noticed the title of this post. No shade toward Simon Sinek, the question, “What’s your why?” is a great starting point, but I feel like we’ve lost touch with what’s really at the root of that question, and why it is in fact an important question for health and sport-tech entrepreneurs to consider: Empathy.
Imagine asking the founder of an AI-enabled coaching company: “What’s your why?” Now ask a human coach the same thing. Who would you trust with your health, training, and performance?
"Having better prediction raises the value of judgment. Prediction machines don’t provide judgment. Only humans do because only humans can express the relative rewards from taking different actions."
- Ajay Agarwal
That quote nails it. Prediction is powerful, but it’s not everything. Machines can process data and generate outputs, but judgment, context, and empathy? That’s all human.
And not just empathy in theory, empathy built from experience. In a previous post, I talked about how sport-tech should be built to work with us, not for us. That mindset becomes even more essential as AI continues to work its way into coaching.
Yes, AI can collect data, build models, and spit out predictions, but it takes a human to interpret those outputs in a meaningful way, and judgement to decide what’s next.
The other part of the equation? As we continue down this path of efficiency, big data sets, and AI, we also need to consider how an individual’s relationship with data will evolve.
Reflecting on my work in health-tech, when it comes to predictive analytics and AI, I see opportunities for sport-tech to catch up, not just in innovation, but in how we apply those tools with empathy and context.
…so, back to that post-race analysis. Ultimately, on race day I underperformed. The data I had going in gave me confidence. But when it came to a crucial race moment, the legs weren’t there.
Why?
The answer, in part, was in the data. But it wasn’t the data alone that uncovered it, it was my coach. His experience helped spot what the numbers hinted at but didn’t explicitly say. He connected the dots, looked beyond just the data points, and helped identify the real limiting factor. More importantly, he was able to shift our original training focus to address these gaps ahead of my next race.
Data helped us guide the conversation, but it took human judgment to ask the right questions, weigh the objective vs subjective data, and interpret what really mattered.
My lingering question, how much did pre-race data influence me on race day (good and bad), and how will my relationship with data continue to evolve?
Alongside this next race block, I’m going to go into a bit of a deep dive on the topic of human-data interaction, and how it will continue to shape the evolution of sport and health tech.
Going on these deep dives is something I often do, but I rarely share what I learn (except for the lucky few people who get to listen to me ramble on at the dinner table!) This time, I may share snippets from conversations with researchers, and maybe challenge a few assumptions along the way.
From an investor standpoint, my advice to founders is pretty simple: start with empathy, experience, and real-world context. Not just better predictions, but better conversations.
But from an athlete perspective? I still have more questions than answers.
More soon.