The Age of Knowledge is Ending. Welcome to The Decision Renaissance

I’ve been telling friends in this industry that the age of knowledge is coming to an end. Not because knowledge stopped mattering, but because access to information stopped being the limiting factor. There are no secrets anymore. The research, the methods, the technology: it’s all at your fingertips.

So if information isn’t the advantage, what is?

I think we’re entering a decision renaissance. The question that matters is no longer “what do we know?” It’s “how do we orient ourselves to make a decision, and how do we learn from it?”

Where this started

This piece came out of a conversation I had with Alex Morrow and Drew Hammond, who host the MOPs & MOEs podcast on military human performance. They opened with a scale problem that’s even larger than my role at IMG Academy. For context, we have an entire campus, fully equipped with staff from every discipline, that’s responsible for roughly 1,700 student-athletes. However, in many military units, one performance practitioner may be responsible for an entire battalion, several hundred to roughly a thousand service members, and sometimes more.

Then they asked the question I haven’t stopped thinking about: how do you make data useful at that ratio?

Over the course of our conversation, my answers kept coming back to the same few things: people and process, the decision the data interpretation informs, and the person who has to act on it. Their world and mine look different, yet fundamentally they’re the same. How do you help people make choices that raise the odds of a good outcome, and judge the process rather than the result? Even at the highest levels, nobody has all of this figured out.

The tech was never the program

When Catapult took off in American football in the mid-2010s, I watched teams rush to put bras (tracking vests) on players without a reason or plan in place. When ForceDecks made it far easier to collect kinetics, people forgot the calculus and assumptions behind the tests. Everyone was tracking numbers, but almost no one had decided what they would do differently because of them. We skipped the behavior change and the decision science.

I see the same pattern in how people talk about injury prediction and "readiness." We don't have 24/7 monitoring. We don't see the stress an athlete carries from home or what they do in their free time. There's frankly more we don't know than we do know. Even force-plate readiness metrics have limits the research keeps exposing: an athlete can change how they jump and still produce the same output. That doesn't make force plates useless. It means one number rarely settles the question. I'd rather be honest about that, and bring in other signals that raise our confidence, than promise a dashboard that turns green and red and pretends to.

Orientation is the hard part

I want to home in on the second step of John Boyd's OODA loop: observe, orient, decide, act. Orientation is where you make sense of what you're seeing, filtered through your experience, your biases, and your expectations. It shapes everything downstream, and it's the step that's easiest to rush.

John Kiely and Allan MacDonald, through their work at the University of Limerick, study how performance staff actually make decisions, and why having information is different from knowing how to weigh it. Along with complex systems theory and decision science, their work shapes how I think about this step.

Dave Snowden's Cynefin framework (kuh-NEV-in; thanks to Trent Salo for confirming the pronunciation on that one!) helps you make sense of a problem before you pick a tool to fix it. It sorts situations by how cause and effect actually behave: clear, complicated, complex, and chaotic, plus a confused space for when you don't yet know which one you're in. Two of those matter most here.

In a clear situation, the right response is already known. If an athlete is medically flagged as unavailable, they don't train today. You apply the rule.

In a complex situation, many things interact and the cause isn't obvious. Say an athlete's strength score is down this month. It could be pain, fatigue, stress outside the gym, a change in testing conditions, or a day they didn't give their best effort. No rule tells you which. You ask questions, try something small, and watch what happens.

That's harder when the answers live in different systems. Strength numbers sit in one, wearable data in another, wellness surveys and medical notes in others. Each shows a slice of the athlete. Bringing them together doesn't give you the whole athlete. That picture doesn't exist, and the context we can't see never goes away. But it gives coaches, clinicians, performance staff, and leadership the same starting point, so they spend less time hunting for context and more time arguing about what it means.

Most of the damage I've seen comes from treating a complex situation like a clear one: putting a tracker on every athlete and expecting the number to tell you what to do.

A playbook: how I think about data and systems

Here is how I’d approach it, roughly in order.

1. Start with the decision, not the data. The best monitoring systems I’ve seen agree on the action before they agree on the test. A common version in field sports: if an athlete’s adductor squeeze strength drops by a set amount, their high-speed running volume comes down by a set amount, and coaches sign off on that before the season starts. Everyone knows it, so nobody argues about it in the moment. For routine monitoring, if you can’t say what changes when the number moves, don’t collect the number. When you’re still learning what matters, write down the question you’re trying to answer. 

2. Know the person who will use it. Does the coach want graphs or tables? Why did they pick one player over another? Do my numbers match their gut feel? The data should make their judgment better, not replace it. Keep a human in the loop, and use the information to challenge what you believe rather than confirm it.

3. Earn trust before you measure. In the NBA, wearables in practice are voluntary under the CBA. No one has to wear a device because you think it's useful. That forces the right conversation: why are you asking, and what's in it for them? Even where measurement is required, the principle holds. A technically excellent system people won't use or trust is a dust collector.

4. Let one question lead, then add the rest. Our volleyball coaches had a simple question: how high can the athlete touch? So one of my analysts is building a camera-based way to capture that during training. Build what they ask for first. Once they trust it, you can add what you think they need.

5. Keep the collection natural. If testing means a specialized machine, a separate location, and a lost practice, count the opportunity cost. Our athletes set goals and complete their mental performance assessments through surveys in Teamworks AMS at check-in, so it sits inside the day they're already having. A good system should make the work easier, not create more of it.

6. Individualize the path to a common standard. A shared standard doesn't mean a shared workout. The 30-15 Intermittent Fitness Test gives you each athlete's final intermittent velocity (VIFT), which integrates aerobic capacity, anaerobic reserve, recovery between efforts, and change-of-direction ability. Prescribe interval distances as a percentage of VIFT, and everyone does the same work-rest structure at a load that fits them. It isn't a perfect prescription, but it's a far better starting point than the same distance for everyone. It's one example among many. I just happen to like it.

7. Align perceptions. At the USOPC, we had wrestlers and coaches rate how hard practice was over a stretch of weeks. Days the coaches rated easy, athletes often rated very hard, sometimes the hardest of the week. We didn't need a better chart of each side's opinion. We needed to see the gap and talk about it.

8. Audit and prune. When I arrived at IMG Academy, we ran the same tests across every sport to build a normative dataset. Over time we started asking what each sport actually needs and what we'd actually change. Now the question is what will drive change in a program. We don’t change the battery mid-year, but we’re not afraid to assess how it went and pivot. 

9. Match the question to the problem. Before you reach for a tool, ask what kind of situation you're in (remember the Cynefin framework?). Is the answer already known? Does it need expert analysis? Does it need experimentation? Or is it urgent enough that you stabilize first and make sense of it later? Different problems need different responses, and treating them all the same is expensive.

Where I land

I won't promise to predict every injury or declare with certainty who's "ready." I'd rather build something that helps athletes, coaches, clinicians, and leaders understand together what's happening, be honest about what they can't see, act, and learn from it.

Information used to be the advantage. Now everyone has it. The edge belongs to the teams that turn it into a shared picture and a better decision, and then judge whether the process was sound, not just whether it worked out.

That's the decision renaissance.

Thanks to Alex and Drew at MOPs & MOEs for the chance to articulate what's been circling my head for the last several years, and to Teamworks, a longtime partner of IMG Academy, for the encouragement to put these thoughts on paper.

“Clues live in pieces, clarity lives in the picture” -BryceGPT