This week Arsenal ended a 22-year wait for a Premier League title. The Red Roses claimed their eighth consecutive Women's Six Nations and their fifth successive Grand Slam. And Ireland quietly continued its run as Europe's number one nation for sports tech investment per capita - a position it has held for over a decade.

Three different contexts. One pattern underneath all of them.

Seven years - three times runner-up

Arteta took charge in 2019. He finished runner-up in the Premier League three years running - twice watching Manchester City chase down Arsenal’s lead in the final weeks, and once finishing ten points behind Liverpool.

Every season, the same question got louder: is the process actually working?

The story of Arsenal is not the nerve-shredding 2025-26 season. It’s the seven-year journey that got them back to where their manager always knew they belonged.

That’s a strategy story. A clear vision, executed with discipline, held under sustained pressure, until the results arrived. The trophy was the outcome of the strategy.

Investment before the commercial case was obvious

This week, England’s Red Roses claimed their eighth consecutive Women’s Six Nations title and their fifth successive Grand Slam - something no team in the history of either the men’s or women’s game has ever done.

Since the launch of the RFU’s Every Rose action plan in 2021, annual funding in the women’s and girls’ game has tripled, female participation has risen by 38%, and match attendances have more than doubled.

The RFU committed that capital before the Red Roses were world champions and the sell-out crowds started. They made a strategic decision that the women’s game was worth sustained, structured investment - and they built the professional pathway, the infrastructure, and the competitive environment that the results are now reflecting.

A national strategy for sports tech

Ireland has 1% of Europe’s population and accounts for 10% of its sports tech deals. Since 2014, Irish sports tech startups have raised over €224 million across 181 transactions. In 2024, their deal count grew 47% while the rest of Europe declined by 15%.

This happened because they made a deliberate strategic decision about where to place a long-term bet. Enterprise Ireland co-invested early, de-risked innovation before private capital was willing to follow, activated the IRFU as a genuine customer - giving companies a real validation pathway, and built an export mindset, opening international markets.

What these three have in common

In each case, the organisations decided what they were building, made the bet before the outcome was guaranteed, and protected the strategy.

Strategic vision + Patient Capital + A global outlook = Performance

It’s why the UK Ambassador role for GSIC, that I have recently received, matters to me. Not as a credential, but as a vantage point globally where these strategic bets are being made at different stages, at different scales, with different levels of conviction. The patterns are visible when you look across enough of them.

Takeaway

Winning in sport and business requires a clear strategic bet, committed capital with discipline, the patience to stay the course when it would have been easier not to, alongside an externally facing global mindset.

What other good examples of strategy are you seeing?

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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I attended two events recently.

One was focused on sport innovation - KTPs, translating research into elite practice, building the connective tissue between science and sport organisations. The other was a broader sports forum where the same word, “sport,” was being used to describe fan engagement platforms, betting infrastructure, athlete health monitoring, and performance science tools, often in the same sentence.

Both rooms were full of smart people, using similar language but discussing very different problems.

That’s the thing nobody says clearly enough about sports tech: sport is not a category. It’s a collection of entirely distinct ecosystems.

And if that’s true - and I think it is - then product-market fit, as a concept, is doing a lot of work it isn’t equipped to do.

The Four-Fits

Chris Peploe put out a framework recently that I think gets closer to the real question than most PMF models do. Before market fit matters, he argues, a sports tech product needs four things: problem fit, proof fit, workflow fit, and ecosystem fit.

It’s a better map than the standard PMF framing, and it names something most founders underestimate: that solving a real problem is the entry condition, not the destination.

But when I sat with it, the brain juices flowed and a few thoughts emerged.

The first is the buyer-user split. Workflow fit addresses how practitioners work but in sports tech, the person experiencing the problem almost never controls the budget. A product can pass all four fits and still stall because the decision-maker has completely different success criteria from the end user.

The second is commercial fit. Ecosystem fit gestures at it, but pricing model versus how sports organisations actually procure is a distinct problem. Annual SaaS, project-based, outcome-linked, equipment-bundled - the wrong commercial structure kills adoption even when everything else works. Sports organisations are notoriously illiquid outside pre-season windows.

The third is timing fit. Sport operates against hard seasonal rhythms. A product can be right on all four dimensions and land at the wrong point in the organisation’s calendar. In other sectors, timing is a sales problem. In sport, it’s closer to a structural one.

The deeper issue: most of these fits are evaluated at the point of sale. But proof fit and ecosystem fit in particular are only testable through sustained deployment. There’s a sequencing problem.

And then there’s Women’s Sport

This is where it stops being a product question and becomes a structural one.

I came across an infographic this week posted my Michelle Colson that made the point concisely.

The gap it maps isn’t a funding gap or an awareness gap. It’s a structural conditions gap. Physiology overlooked in the research base. Financial strain that shapes how and when athletes can engage with technology. Multiple roles - athlete, student, part-time worker - that determine when, where, and how any product actually has to work.

Women’s sport is not a sub-segment of sport. It operates as a different ecosystem with different structural conditions at every level of the four-fit framework.

Workflow fit looks different when an athlete has three hours between shifts, not a dedicated training environment. Proof fit requires an evidence base that, in many cases, doesn’t exist because so little published sports science research focuses exclusively on female athletes. Ecosystem fit involves institutions that are still forming, governance structures that were never designed for this growth stage, and commercial infrastructure that’s being built in real time.

The most dangerous assumption in sports tech right now is that a product which achieves fit in men’s elite sport scales naturally into women’s sport with different marketing. The conditions are different. The problems are different. The buyers and users are often even more separated.

The Question underneath the Question

When sport innovation events talk about ecosystem development and research translation, they’re usually describing one ecosystem. When sports tech forums talk about the market opportunity, they’re usually describing another. And when investors talk about the growth story in women’s sport, they’re often describing a third - the commercial and media opportunity - while the performance infrastructure question goes largely unaddressed.

None of those conversations are wrong. They’re just not talking to each other.

Which means the most precise question a sports tech founder can ask isn’t “does this solve a real problem?”

It’s: for whom, in which conditions, with what evidence, and through which ecosystem?

The gap between those two questions is vast. And in women’s sport specifically, that gap has a structural cause - not a motivational one.

The Takeaway

The next time you hear ‘sports tech’ used as a single category, push back on it - even just in your own head. Ask which sport, which ecosystem, which athlete, and which structural conditions. Because sport isn't one market. It's a set of ecosystems with different physics that needs unpacking.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The signal

Ernest Shackleton said that leadership’s greatest penalty is loneliness.

Last night I attended a Warwick Business School webinar on this topic, lead by Rachael Urwin and it highlighted something that every leader has experienced but few discuss openly: the different types of isolation that come with strategic responsibility.

The session distinguished between relational isolation (”I don’t have someone I can say this to”), role isolation (”I can’t say this because I’m the leader”), cognitive isolation (”I’m the only one seeing or holding this”), and emotional isolation (”I’m holding a lot, but I don’t show it”).

What the webinar reminded me is that cognitive isolation - carrying the strategic thinking alone - isn’t a failure of leadership. It’s a structural feature of it.

The question is whether that isolation becomes destructive or whether it can be channeled into something useful.

The strategic lens

There’s a distinction that matters here: loneliness versus solitude.

Loneliness is unwanted isolation. It’s carrying complexity without the ability to externalise thinking. It’s the erosion that happens when you’re making strategic decisions in a vacuum, with no mechanism to test whether your thinking is sound or distorted by your own pattern recognition.

Solitude is chosen space for thinking. It’s the capacity to hold complexity long enough to see patterns others don’t, to make connections that require sustained attention, to think strategically without the noise of immediate tactical concerns.

Strategic leadership requires both. The challenge is knowing which one you’re experiencing at any given moment.

The cognitive load problem

The webinar framed cognitive isolation as: “I’m the only one seeing or holding this.” Strategy feels lonely. Complexity sits with you. Others don’t get it.

This resonates with something I wrote last week about strategic capability: most organisations have a strategy, but few have people who think strategically. When you’re the person who thinks strategically, the cognitive load sits with you in ways it doesn’t sit with others.

That’s not a complaint. It’s the role. Senior leaders see context that others don’t. They hold complexity that can’t be fully delegated. They make connections across domains that require years of pattern recognition to identify.

But here’s the risk: when cognitive complexity sits with you alone, how do you calibrate whether your thinking is reliable?

Last week I wrote about the expertise paradox - that experience creates deep pattern libraries but can also create blind spots. The more experienced you become, the more vulnerable you are to overconfidence in your pattern recognition.

Cognitive isolation amplifies that risk. If you’re the only one holding the complexity, you’re also the only one testing the thinking. And isolated thinking, uncalibrated, drifts.

Externalised thinking

The webinar’s solution to cognitive isolation was “externalised thinking” - discussing and being challenged before decisions are made.

This isn’t about consensus. It’s about calibration.

When you externalise strategic thinking with someone who understands the context but isn’t embedded in your same pattern recognition, you create a testing mechanism. Not to validate what you already think, but to surface where your thinking might be distorted by recency bias, overconfidence, or outdated mental models.

In my opinion, this is why safe connection matters more as you become more senior. Not because leadership is emotionally difficult - though it is - but because the quality of your strategic thinking depends on having people who can challenge it before you commit to it.

Role isolation (”I can’t say this because I’m the leader”) compounds this. When you filter yourself because of power distance, you lose the externalisation mechanism that keeps strategic thinking calibrated.

The leaders I’ve seen navigate this well don’t try to eliminate isolation. They build infrastructure around it. Regular decompression rituals. Permissioned spaces for appropriate honesty. Small numbers of people they can speak to openly who understand the context and can challenge the thinking.

Strategic solitude

Solitude - the capacity to think alone - is also strategic capability.

Strategic decisions require sustained attention that can’t happen in collaborative environments. You sometimes need uninterrupted time to hold complexity, to see patterns, to make connections that require deep focus.

The challenge is distinguishing between productive solitude and destructive isolation.

The difference isn’t the amount of time spent alone with strategic thinking. It’s whether that thinking gets tested against reality and challenged by people who can see your blind spots.

Leaders who treat solitude as thinking space but build mechanisms to externalise and calibrate that thinking before committing to decisions navigate this well. Leaders who mistake isolation for strategic independence often don’t.

The takeaway

Leadership isolation isn't optional. It comes with the role.

The question isn’t how to eliminate isolation. It’s how to distinguish between productive solitude and destructive loneliness, and how to build infrastructure that keeps strategic thinking calibrated even when complexity sits with you alone.

Solitude produces clarity when it’s paired with mechanisms to externalise and test thinking. Isolation produces drift when it isn’t.

Not being aware of isolation, not addressing it, and experiencing it for too long without calibration - those are the factors that matter. The isolation itself is structural.

Strategic leadership requires both the capacity to think alone and the humility to recognise when that thinking needs to be challenged. The gap between the two is where strategic decisions either compound or collapse.

As Shackleton understood, loneliness is the penalty of leadership. But solitude, properly calibrated, can be its greatest asset.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The signal

There’s a finding from decision science that I keep returning to this week.

A study published in the Journal of Judgment and Decision Making, by researchers at Cornell, Central Washington University and Sogang University, identified something that should make leaders stop and pause on.

The act of making a decision can itself create overconfidence.

They called it the confidence-distortion-confidence cycle. And it works like this.

When we evaluate information sequentially we form an early preference. From that point, we unconsciously distort subsequent information to support it. New evidence that confirms our emerging view gets weighted more heavily than it deserves. Evidence that challenges it gets minimised. The more confident we become, the stronger the distortion. The stronger the distortion, the more confident we become.

By the time we commit to a decision, we are significantly more certain than the evidence actually warrants.

This isn’t a novice problem

The research was conducted with a range of professionals and entrepreneurs and it showed a consistent message.

Expertise doesn’t protect you. In some respects it makes things worse. The more pattern recognition you’ve accumulated, the faster you form an early preference, and the faster the distortion cycle begins.

In high-performance environments this dynamic is particularly live. What feels like considered deliberation may sometimes be sophisticated rationalisation.

The question worth sitting with

Think about the last significant decision you made. Not the outcome - the process.

At what point did you form your initial preference? How did your evaluation of subsequent information change after that point? Were there voices or evidence that challenged your emerging view that received less attention than they deserved?

The research suggests the answer to that last question is almost certainly yes, and that you almost certainly didn’t notice.

This isn’t a character flaw. It is the predictable consequence of how human cognition operates under uncertainty. The cycle is most powerful precisely when decisions are most consequential - when the stakes are high, the information is ambiguous, and the pressure to commit is real.

What actually helps

The researchers identified four countermeasures worth taking seriously.

The first is structural: gather information before forming a preference where possible. Once a leading option emerges, distortion begins. Delaying that emergence and evaluating all available information before committing to a direction reduces the cycle’s power.

The second is procedural: commit to what a piece of evidence means before you know which option it supports. Pre-valuing information in isolation, before it enters a decision context, significantly reduces distortion.

The third is cognitive: develop a counterargument mindset. Actively attempting to refute your own emerging preference - not as performance, but as genuine intellectual practice - disrupts the cycle.

The fourth, and most practically powerful for organisational leaders: maintain genuine dissent in your decision process. The research found that groups preserving real advocates for competing options showed significantly lower distortion than those moving toward premature alignment.

This last point deserves particular attention. High-performance cultures are often alignment cultures. Decisive leadership is valued. Dissent can feel disruptive. But the research is clear: the most effective protection against confidence-distortion-confidence is a room that maintains competing preferences for as long as possible.

Not devil’s advocate as theatre. Real disagreement, sustained.

The takeaway

Jeffrey Yan, founder of Hyperliquid, one of the most profitable companies per employee on earth, described his approach to leadership in terms that connect directly to this research:

“Be very confident you’re going in the right direction, and execute well on the step you’re taking right now, without knowing exactly where you’re going.”

What he’s describing is a separation that the confidence-distortion-confidence cycle collapses. Conviction about direction. Genuine humility about the specific path. Confidence isn’t wrong but it’s dangerous when confidence in direction becomes false certainty about every subsequent step.

A genuine way forward it to be more precise about what we are and aren’t confident about. Hold direction firmly and path lightly and build dissent into the process.

Confidence that makes leaders look decisive may sometimes be the confidence that the decision process itself manufactured.

Sitting with that distinction feels like the right place to start.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

“Strategic” is one of the most overused words. Strategic partnership. Strategic plan. Strategic investment. But what I believe we are missing is Strategic Leadership.

Here’s the reality: most sports and performance organisations have a strategy. Few have people who think strategically.

That gap is where the real work lives.

The Strategic Lens

At its core, strategy is clarifying how you create value. Not a document. Not a presentation. A clear answer to one hard question: how do we create value and for whom?

This matters because you can risk conflating two different things: having a strategic document and having strategic capability.

Strategic documents are everywhere. Five-year plans. Roadmaps. Vision statements. These get produced, presented to boards, filed away, and rarely revisited. They’re treated as finished products rather than working hypotheses.

Strategic capability is different. It’s the ability of people throughout an organisation to think and act strategically - to understand how value is created, to recognise when context shifts require different approaches, and to make decisions that compound rather than contradict.

Most organisations invest heavily in the former. They invest rarely in the latter and at its’s heart I believe it comes down to communication.

The Identity Problem

Strategy has an identity side. It clarifies who you are and just as importantly, who you are not. Without that clarity, every opportunity looks like the right one.

Otherwise the risk is chasing partnerships, technologies, and initiatives because they seem strategically important, without first clarifying whether they align with how the organisation actually creates value. The risk is strategic drift disguised as activity.

The challenge is that strategic clarity requires saying no to things that look valuable in isolation. It requires leadership that understand the difference between what could create value and what actually will create value for this specific organisation.

Most organisations don’t have enough of those leaders.

The Hypothesis Challenge

Strategy is a hypothesis. Nobody knows the future. Every strategy is a bet on where the world is going.

But organisations have historically treated strategic plans as fixed answers. The more time spent on the plan, the more concrete it becomes. The more concrete it becomes, the less dynamic the organisation’s response when reality doesn’t match the plan.

This creates a particular failure mode: organisations that execute their strategic plan perfectly but fail strategically because the underlying hypothesis was wrong and nobody had the capability to recognise it.

The organisations that navigate this well treat strategy as continuous learning. They make a hypothesis about how to create value, test it against reality, adjust based on what they learn, and repeat.

That’s not a planning capability. That’s a thinking capability.

Strategic documents don’t adapt. Strategic leaders do.

The Human Side

Strategy has a human side. And those who understand this, understand strategy.

Strategy isn’t about the perfect document. It’s about people knowing how to think and act strategically. That’s a different skill. And it’s rarely taught.

Investing in developing the strategic thinking capability of people throughout an organisation is a good ROI. It increases the chance of those who are implementing the strategy to understand how it created value or why certain decisions mattered more than others.

Leadership programmes that focus on strategic thinking capability - helping people understand how to clarify value creation, how to test hypotheses against reality, how to balance logic and intuition - produce different returns than programmes that just teach planning frameworks.

Without leaders who can think strategically, teams don’t execute coherently, and innovation doesn’t compound.

The Takeaway

Most organisations have a strategy. Few have people who think strategically.

That gap won’t be closed by better planning processes or more comprehensive documents. It will be closed by investing in strategic leadership capability - helping people understand how value is created, how to test hypotheses, how to balance logic and intuition, and how to make decisions that compound.

Strategy is clarifying how you create value. Strategic leadership is building the capability throughout an organisation to think and act on that clarity.

The question I have - are you investing in developing strategic leaders as deliberately as you’re investing in strategic plans?

Without strategic leaders, the plans sit in drawers, the technology doesn’t get implemented coherently, and the organisation drifts despite having a clear strategy on paper.

Strategic documents are necessary. Strategic leaders are sufficient.

The gap between the two is where organisational capability either compounds or collapses.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

Everyone assumes AI will commoditise expertise. The opposite is true. As AI gets better at optimisation, unique lived experience becomes more valuable, not less.

The below should be a really easy question for humans to answer:

“If AI can process all the data faster and more accurately than we can, what exactly are we adding?”

This question sits at the centre of all high-performance environments because AI and human intelligence are not the same thing.

The Strategic Lens

Something I read this week clarified what’s been bothering me about how we talk about AI in sport. I believe that true strategy doesn’t come from more optimisation - it comes from “unpredictable connections” grounded in unique lived experience.

What does this look like in reality?

You’re born in particular circumstances, you live your life, you have specific education, unique things happen to you, you develop particular interests, you build a mental map of the world, then you apply that map to problems.

That’s where genuine insight comes from.

Not from processing more data. From living more experience.

Applied to sports and performance, this distinction matters. AI can tell you what the data says. It can optimise within known parameters. But it can’t make the low-probability connections that come from years inside high-performance environments where decisions have immediate physical consequence.

The pattern recognition that emerges from that embodied experience - from repeatedly making decisions under uncertainty where you see the outcome in real time - creates a form of intelligence that can’t be replicated by processing solely historical data.

The Pattern Recognition Argument

Last week I wrote about the Warwick-Aston Villa partnership professionalising sports education. The implicit question: can academic programs accelerate the learning curve for sports leadership?

This week I’m arguing something that seems contradictory but isn’t: the most valuable insights in sports can’t be taught. They have to be lived.

Consider medical staff deciding if an athlete is ready to return from injury. Data says 95% recovered. Movement screens clear. But the practitioner sees something - a hesitation, a compensation pattern, a psychological guardedness - that isn’t captured in a metric.

That judgement doesn’t come solely from better data analysis. It comes from years of hard graft too.

AI can process the large volumes of quantifiable markers that exist, and that will literally save teams hours a week. But it can’t replicate the somatic intelligence built through repeated exposure to high-stakes decisions where you see the consequence play out in real time.

The coach making the substitution decision faces the same dynamic. Analytics suggest one player. Pattern recognition from years on the touchline suggests another. Not because the data is wrong, but because the decision requires weighing variables that can’t easily be quantified: psychological state, team dynamics, how a player responds to specific types of pressure.

These decisions require what can be referred to as “a colossal iceberg of rounded experience.” You need the depth, not just the surface data.

The Calibration Problem

But here’s where it gets more complex: pattern recognition can also become a hindrance.

Research on expert decision-making reveals an uncomfortable truth - the more experienced someone becomes, the more vulnerable they are to overconfidence in their pattern recognition. Kahneman and Klein’s work on intuitive expertise shows that experts can be remarkably accurate in stable, predictable environments, but remarkably poor in uncertain or rapidly changing ones.

The practitioner who’s seen hundreds of athletes return from injury can start seeing patterns that aren’t there. The coach with twenty years on the touchline can become anchored to tactical approaches that worked in previous contexts but don’t apply to current conditions. Experience creates deep pattern libraries, but it can also create blind spots.

This is the expertise paradox: the same embodied intelligence that produces breakthrough insights can also produce overconfident errors when the environment shifts in ways the expert doesn’t recognise.

The challenge isn’t choosing between human expertise and AI. It’s ensuring human pattern recognition remains properly calibrated. This requires systems that help practitioners identify when their intuition is reliable versus when it’s being distorted by overconfidence, recency bias, or outdated mental models.

AI has a role here - not replacing human judgement, but providing the counter-signal that helps calibrate it. The data that says 95% recovered doesn’t override the practitioner’s observation of compensatory movement. But it should prompt the question: am I seeing something real, or am I seeing what I expect to see?

Trajectory Creates Insight

There are gaps that exist - between what markets build and what environments require. These are only visible though when someone has the experience to see the performance problems. If you also have commercial experience you can see the market opportunity too. The intersection of both creates a different set of connections.

Pattern recognition built across different working domains and trajectories creates insights that someone with only one dimension of that experience can’t replicate.

Different trajectories recognise that genuinely novel insights come from genuinely novel combinations of experience.

But - and this is critical - those insights only retain value if they’re tested against reality, not just assumed to be correct because of accumulated experience.

The Competitive Advantage

As AI gets better at optimisation, unique lived experience becomes more valuable, not less.

AI can tell you the optimal decision based on historical patterns. But sport doesn’t operate at the optimal. It operates at the edge - where context shifts, where psychological factors dominate, where the decision that worked last time won’t work this time because the situation has subtly changed in ways that aren’t captured in the data.

Operating at the edge requires pattern recognition that can only be built through embodied experience in high-stakes environments. You need to have been there, made the decision, seen it play out, adjusted, failed, learned, repeated.

But the sustainable competitive advantage comes from pattern recognition that remains properly calibrated - where the practitioner knows when to trust their intuition and when to question it.

In my opinion, this is why the human-AI question in sport is being framed incorrectly. The question isn’t “what are humans adding that AI can’t do?” The question is “how do we optimise the human in the system as much as we’re optimising the technology?”

This means building environments where:

• Practitioners develop deep pattern recognition through lived experience

• That pattern recognition is continuously calibrated against data and outcomes

• There’s infrastructure to surface when intuition and evidence diverge

• Decision-makers can distinguish between reliable intuition and overconfident pattern-matching

We don’t need less practitioner expertise. We need better-calibrated practitioner expertise operating in well-designed systems.

The Takeaway

The most valuable expertise can’t be credentialed because it can’t actually be taught - it has to be lived.

You can teach frameworks. You can provide exposure. You can compress learning curves. But you can’t replicate the pattern recognition that comes from years inside environments where decisions have immediate consequence.

What you can do - and must do - is help practitioners calibrate that pattern recognition. Experience alone isn’t enough. Experience combined with continuous reality-testing is what produces reliable decision-making.

This creates a different question for the sector: not how do we formalise what practitioners know, but how do we create pathways for people to build the embodied experience that produces genuine insight while maintaining the humility to question it?

We’re not adding better data processing. We’re adding the pattern recognition that comes from living inside the complexity, making decisions under uncertainty, seeing outcomes play out, and building the somatic intelligence that can’t be replicated.

But we’re only adding value if that pattern recognition remains properly calibrated.

In the AI era, that experience doesn’t become less valuable. It becomes the only sustainable competitive advantage - provided we’re optimising the human in the system as deliberately as we’re optimising the technology.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

On Tuesday this week I sat through three interlinked conversations at Warwick Business School at The Shard. Separately, each would be unremarkable but together, they revealed a shift in how sports is being built, funded, and taught.

In the morning, I was on a panel for the Warwick Enterprise Start Up Programme and the conversations with the organisers highlighted a deep interest in sports tech.

In the afternoon, Angels Den were on site at The Shard for an event and the conversation drifted to the desire to explore deeper investment in sports tech.

Between those two sessions, it was discussed that Warwick Business School and Aston Villa Football Club had announced a partnership to launch MBA and MSc specialisms in Elite Sports Leadership and Management.

Different stakeholder groups all converging around the desire to build a sports ecosystem.

The Strategic Lens

The Warwick-Aston Villa partnership is significant not because it’s novel - universities have been adding sports modules for years - but because of what it signals about market maturity.

Aston Villa’s President of Business Operations framed it clearly: “From operations and analytics to technology, leadership and athlete development.” This isn’t about adding the odd sports case studies to existing MBA curriculum (which happened in my Physiotherapy MSc!). It’s about recognising that sports operates with the complexity of any other professional services business, and the education needs to reflect that.

The partnership model makes commercial sense. The university provides rigorous curriculum, research capability, and talent development. The club provides real-world application, performance validation, and industry access. Students get academic credentialing combined with practical experience in an environment where decisions have consequence.

What’s shifting is the recognition that the broader sports ecosystem requires specific expertise that sits at the intersection of technology, performance science, commercial strategy, and stakeholder management. You can’t just drop a general MBA graduate into a Premier League operations role or a sports tech startup and expect them to navigate the complexity without context.

The sector has professionalised. The education infrastructure is catching up.

The Funding and Investment Angle

Conversations with members of Warwick Enterprise and Angels Den reinforced this. Sports tech businesses need to articulate clear value propositions, defensible IP, scalable business models, and paths to profitability. The passion for sport needs to be balanced with strategic and commercial rigour.

However, Sports Tech as an investment thesis is also wide and broad - from media and fan engagement to athletic and human performance. Advisors and due diligence can assist with the challenges of translating practitioner insight into investable businesses.

Sports tech has outgrown the “build it and they will come” phase. Capital is available, but it’s conditional on professional execution. Universities creating dedicated pathways suggest they see this gap and believe they can fill it. The question is whether academic credentialing can keep pace with how quickly the sector is moving. An MBA takes 12-24 months. The sports tech landscape changes every quarter.

The Credibility Check

I built and exited a business but the majority of my journey, until I undertook an Executive MBA, was learned through trial, failure, iteration, and eventually, execution.

The Warwick programme is attempting to formalise what I learned empirically. There’s value in that - compressed learning curves, access to mentorship, exposure to case studies, connection to industry. But there’s also risk. Academic environments tend towards structured thinking. Sports innovation requires unstructured problem-solving.

The best sports tech founders I know came from practitioner backgrounds. They understood the performance problem intimately because they’d lived it. The challenge wasn’t understanding the problem - it was building a business around the solution.

Where I see value in the Warwick model: helping practitioners translate their expertise into commercial strategy. Where I’m sceptical: whether a programme can replicate the pattern recognition that comes from years inside high-performance environments.

The Personal Perspective

If this specialism had existed when I did my MBA, I would have jumped at it.

Sports is a viable sector and I would have valued the structured thinking around how to address leadership and commercial challenges, the exposure to different commercial models, and the network of people tackling similar challenges.

The MBA gave me frameworks for strategic thinking, decision-making under uncertainty, and stakeholder management that I’ve applied constantly since. Adding sports-specific context would have accelerated that application significantly.

But I also recognise that my MBA came after I’d already operated a business. I brought context to the curriculum. The frameworks made sense because I had real-world examples to map them onto.

For someone coming straight from undergraduate or early career into an MBA with sports specialism, the challenge will be building that context while learning the frameworks. That’s where the Aston Villa partnership becomes critical as it provides the applied environment where theory meets consequence.

The Takeaway

The ecosystem pieces are connecting. Universities are creating pathways. Investors are allocating capital. Clubs are formalising partnerships. Sports is being treated as a professional discipline, not a passion project.

But a question remains: does credentialing lead or follow practitioner expertise?

In my opinion, the best leaders in sport come from practitioners who have experienced the challenges and see the problems that needed solving. They didn’t wait for universities to create specialisms or investors to validate the sector.

As the infrastructure professionalises, Sports needs people who understand performance environments at a cellular level. Academic credentialing should amplify that capability, not replace it.

The Warwick-Aston Villa partnership gets this right by combining both. The test will be whether it produces leaders who can navigate the messy reality sports, or just graduates who can talk about it in boardrooms.

Yesterday’s three conversations suggested the appetite is there. The question is whether the infrastructure can move fast enough to match it.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

Leave a comment

We discussed customer service and delighting customers, the benefit of downtime, benefit of relationships, development and business architecture, book recommendations and IWD.

Feel free to have a listen and we would love to know your feedback and what you have been seeing, hearing and reading about recently.

Nic x

The Signal

A British Journal of Sports Medicine editorial drops an uncomfortable truth: sport suffers from a “collect it all ideology” - capturing data because we can, not because we need to.

This isn't just about data protection compliance though. The fundamental strategic question that risks getting missed in sports tech development - what are we actually trying to achieve?

How Have We Got Here

Sports technology has created unprecedented access to athlete data - more types of data, on more athletes, captured more frequently than ever before. The authors of this editorial call this the “Three Vs”: variety, volume, and velocity.

But here’s where it gets strategic: competitive advantage no longer comes from capturing data competitors can’t access. It comes from the ability to transform data into actionable insights.

Translation: the differentiator isn’t sensor technology. It’s decision architecture.

Most sports tech companies are still competing on data collection capability - better sensors, more metrics, real-time capture. However, I believe they are solving the wrong problem.

The Data Minimisation Violation

GDPR established a core principle in 2018: data minimisation - collecting only the minimum data necessary to deliver a specific service.

The “Collect it all” approach in Sports violates this. We justify comprehensive monitoring under “load management” - a clinical necessity that’s become blanket authorisation for surveillance-level collection.

The question practitioners should ask: What specific decision does this data point inform?

Not “might inform with future AI development.” What decision, right now, that improves athlete welfare or performance?

Why This Matters

Regulatory risk is concentrating and GDPR isn’t going away. Other jurisdictions are adopting similar frameworks. Sports tech companies built on comprehensive data collection without clear decision utility are building regulatory liability into their business models.

For M&A and exits: data governance practices are becoming due diligence items. Clean data policies, clear consent frameworks, demonstrable decision utility - this creates enterprise value protection.

The Practitioner Stewardship Gap

The editorial makes an observation that captures the core issue:

“Sports science and medicine professionals are de facto athlete safety, health and data stewards.”

Practitioners therefore have responsibility. They obtain consent. They manage access. They are accountable for athlete welfare.

But they may not always be in the room where data governance decisions get made.

Real data stewardship requires authority to ask: Should we collect this? What decision does it inform? Have we minimised to what’s necessary? Are athletes genuinely informed about what they’re consenting to?

Those are strategic decision architecture questions that don’t happen in treatment and rehab rooms. This is why practitioners need seats at the decision making tables too - to ensure conversations serve athlete welfare alongside commercial objectives.

The Takeaway

Sports tech's data collection practices are facing scrutiny. The companies that will thrive as Tech and AI advance, are those that demonstrate:

• Clear decision utility for every data point collected

• Practitioner governance in data strategy

• Genuine minimisation practices

• Transparent athlete consent

For practitioners: if you’re responsible for athlete data, you should have a voice in data governance. Not as a validator after decisions are made - as a contributor when strategy is set.

For sports tech companies: “collect it all” might work until it doesn’t. Regulatory enforcement, practitioner adoption barriers, and athlete rights advocacy are all moving in the same direction.

The question is whether practitioners will lead a transition or be forced into it.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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• Chris’ journey from strength & conditioning into performance psychology

• Lessons from working within elite sport systems

• Translating sport psychology frameworks into business

• Purpose, burnout, and sustaining high performance

• Adaptability and decision-making under pressure

• Building environments that support long-term success

The Signal

You can’t influence people you haven’t identified and don’t understand. This keeps surfacing in conversations across high-performance environments - practitioners, founders, executives all struggling with the same challenge: they’re trying to execute without mapping who actually matters.

Stakeholder mapping seems straightforward. Four questions:

• Who has POWER?

• Who has URGENCY?

• Who has LEGITIMACY?

• Are they INTERNAL or EXTERNAL to the organisation?

These questions identify stakeholders and categorise them.

Power determines who can impose their will - control resources, block decisions, grant access. Legitimacy determines whose claim is appropriate - formal authority, expertise, or affected status gives them right to be involved. Urgency determines whose need is time-critical - who faces immediate consequences if this isn’t resolved.

The intersection of these attributes determines stakeholder priority:

• Definitive stakeholders (all three) demand immediate attention

• Dominant stakeholders (power + legitimacy) shape strategic decisions

• Dependent stakeholders (urgency + legitimacy) need advocacy

• Dangerous stakeholders (power + urgency) can force short-term action

• Demanding stakeholders (urgency only) are loud but limited influence

• Discretionary stakeholders (legitimacy only) receive attention as resources allow

• Dormant stakeholders (power only) monitor but don’t currently act

This categorisation helps practitioners prioritise who to engage and how.

But it doesn’t clarify who decides when these stakeholders disagree.

Where Stakeholder Mapping Stops

Stakeholder mapping identifies who matters, categorises their importance, and develops engagement strategies to secure support. The implicit assumption is: if we map stakeholders correctly and influence them effectively, execution will follow.

However, if a trade-off emerges - priorities conflict, resources are constrained, time pressure forces choices - who decides? It is important to clarify who has final authority, as well as who owns outcomes when things go wrong. Most organisations have mapped their stakeholders. Few have designed their decision architecture.

What Decision Architecture Adds

Stakeholder influence and decision architecture both matter but stakeholder influence without decision architecture creates meetings where everyone talks and nothing gets decided.

In high performance environments this distinction is critical.

For example, Medical team says athlete isn't ready. Coach says team needs them. Performance data is borderline. Athlete wants to play. Who decides? Without decision architecture, this becomes a 90-minute meeting where everyone talks, nothing gets decided, and the athlete remains in limbo while stakeholders negotiate.

On a commercial level, a product team may want feature expansion. Sales team wants faster delivery. Finance team worries about cost. Customer has specific requirements. Who decides? Without decision architecture, product team gets conflicting directives, every sprint and execution velocity drops because nobody knows whose priority actually wins.

In each case, stakeholder identification and influence are necessary. But decision architecture determines whether execution happens when trade-offs emerge.

This process can feel uncomfortable. Making authority explicit reveals power structures. Specifying accountability creates exposure. Acknowledging that some stakeholders’ priorities trump others challenges consensus culture.

So organisations avoid designing decision architecture explicitly. They map stakeholders, develop engagement strategies, build alignment - then discover when trade-offs emerge that nobody knows who actually decides.

Making It Operational

Translating stakeholder mapping into decision architecture requires explicit frameworks.

RACI Framework: Responsible, Accountable, Consulted, Informed

• Responsible: Who does the work to make it happen

• Accountable: Who has final authority and owns the outcome

• Consulted: Who provides input that must be considered

• Informed: Who needs to know after the decision is made

The critical element: only one person is Accountable. Everyone else has defined role but not final authority.

Stakeholder mapping creates the conditions for alignment. Decision architecture creates the structure for execution when alignment breaks down.

The Takeaway

Stakeholder mapping is necessary. Decision architecture makes it actionable.

Organisations that map stakeholders without designing decision architecture create the appearance of sophistication while leaving execution capability ambiguous.

Individual contributors and practitioners can start to map who has power, urgency, legitimacy. Then map decision architecture: whose authority is final in different contexts? Understanding this lets you navigate effectively even without formal authority.

For founders and executives, using for example, RACI framework allows you to clarify who’s Accountable vs Consulted. This provides operational clarity and enables execution under real-world constraint, not just alignment under ideal conditions.

Stakeholder mapping asks: Who matters and what do they want?

Decision architecture asks: Who decides when what they want conflicts?

Both questions matter. But execution depends on the second question being answered explicitly, not left to emerge under pressure.

You can’t influence people you haven’t identified. But you also can’t execute effectively if you haven’t clarified who carries consequence when trade-offs force choices and alignment breaks down.

We need to stop assuming stakeholder alignment means execution will follow. Start designing decision architecture that works when alignment doesn’t.

Stakeholder mapping tells you who's in the room. Decision architecture tells you who makes the call.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

High-performance organisations invest heavily in practitioner technical development - from the latest training courses to buying sophisticated equipment and decision support platforms. But what happens when these highly-credentialed practitioners can’t articulate value, lack business acumen, and don’t understand stakeholder management, resource allocation or systems thinking? They are ineffective when it comes to strategic discussions despite their technical excellence.

This pattern repeats across the majority of high-performance environments which results in organisations having technical depth but lacking strategic breadth.

Whether it is at an individual or organisational level, a repetitive cycle occurs: investment in what is measurable and comfortable (technical credentials) while systematically neglecting what’s actually required for organisational effectiveness (strategic thinking).

The Strategic Lens

Organisations fund domain-specific technical skills development systematically.

As an example, in elite sport that may include clinical certifications (manual therapy, return-to-sport protocols, movement screening), technical proficiency (GPS analysis, force plate interpretation, athlete monitoring) or sport-specific knowledge (injury epidemiology, periodisation, recovery modalities).

Annual professional development budgets accommodate these readily but organisations systematically fail to invest in strategic capabilities across domains. Capabilities, like strategic thinking, business acumen, stakeholder management and decision frameworks, don’t routinely appear in professional development pathways. Therefore practitioners are expected to develop them through osmosis or experience.

They don’t. This creates a gap.

A gap that probably occurs because strategic skills are ambiguous and organisations can’t easily quantify ROI on strategic development. Everyone avoids the uncomfortable development work that could actually enable progression in practice.

In sport, a physiotherapist with strategic capability doesn’t just deliver excellent rehabilitation. They understand how injury rates impact organisational finances because they can articulate return-to-play programs’ value and navigate stakeholder conflicts with clear decision frameworks.

Adding strategic thinking makes technical expertise organisationally effective. It is a distinct skill set that requires deliberate development, not domain experience.

The Funding & Investment Angle

Technology companies serving high-performance environments default to selling decision support systems to practitioners who can’t articulate strategic value to decision-makers.

This means the sales conversation focuses on technical benefits - “Our system provides real-time performance metrics”. But the purchasing decision requires strategic justification - “What’s the ROI on operational improvement?”

Technology companies compensate by providing ROI calculators and business case templates. But these don’t substitute for practitioners who can think strategically about organisational value, not just technical outcomes.

This gap represents a commercial opportunity for executive development programs for high-performance practitioners that focuses on strategic thinking capabilities. As well as an opportunity for organisations to invest in strategic capability development.

The return on this investment would be substantial - practitioners who can operate strategically are dramatically more valuable organisationally than those who can only execute technically.

The Credibility Check

The premise that practitioners across high-performance domains lack strategic development opportunities is accurate from my experience. In my career, my technical development was comprehensive and well-funded - Manual therapy, Sports Performance, Pilates, Acupuncture and Pelvic Health courses.

On the other hand, strategic capability development: zero. No training on how to think beyond individual athlete cases to program-level decisions. No development on stakeholder management and communication when priorities conflicted.

I learned these capabilities by necessity when situations demanded them, not through deliberate investment. There is a false assumption that experience = strategic thinking. This isn’t the case though as strategic thinking is a distinct skill set that requires deliberate practice and development.

A Personal Perspective

I have lived the practitioner-strategist gap - from physiotherapist to Strategy Consultant and Board Advisor - by identifying the knowledge gaps and undertaking an Executive MBA. Alongside this, operating a business made me learn financial management, commercial strategy and organisational processes.

I love to see practitioners thinking strategically as their value intensifies. Being able to articulate value in terms that resonate with multiple decision-makers also means they can navigate organisational complexity, trade-offs and prioritisation.

This shift to a strategic leader, requires an identity evolution which can be uncomfortable. Professional identity is built on technical expertise and peer validation. Moving to strategic roles means validation comes from different sources and the path from one to the other requires deliberate development, not just accumulated experience.

The Takeaway

High-performance organisations across sectors invest systematically in technical skills development and almost nothing in the strategic capabilities that actually determine organisational effectiveness.

This creates practitioners with deep domain expertise who struggle when promoted to leadership positions requiring strategic thinking. Strategic capabilities are developable skills and organisations that invest in strategic development create internal pipelines of leaders who combine domain credibility with operational effectiveness.

The gap is systematic, preventable, and expensive. Closing it requires deliberate investment in capabilities that don’t come with clear certifications or credentials.

Technical excellence gets you in the room. Strategic thinking determines what you accomplish when you're there.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

Sports tech companies overwhelmingly prioritise pro club validation. Elite sport provides credibility, concentrated decision-making, and headline-worthy outcomes. This elite credibility opens doors and validates tech capabilities.

But there’s a parallel, and growing, channel that operates on different economics and creates distinct strategic advantages: performance facilities with private-pay models.

Whether private rehab clinics expanding into performance services, specialised training centres, or sports medicine facilities with hybrid revenue models, these facilities represent an underutilised strategic option for sports tech companies.

They have faster decision cycles, clearer ROI frameworks, and different market access advantages. The B2B2C model - facility as channel to athletes - creates distribution and data accumulation opportunities that pro club relationships alone can’t provide.

The strategic question is whether sports tech companies are overlooking a high-value channel that could accelerate growth, generate revenue, and ultimately strengthen their position with pro clubs. Not instead of pro sport, but as a complementary channel with unique benefits.

The Strategic Lens

Performance facilities, as well as hybrid sports medicine and performance centre amenities, with private-pay models operate on different economics that create distinct strategic opportunities for tech adoption.

In pro sport, purchasing decisions optimise for squad availability, medical-legal defensibility and long-term athlete development.

In private-pay performance facilities, purchasing decisions optimise for client acquisition and retention, revenue per service and market positioning through superior outcomes.

Neither set of criteria is more important than the other. But they create different adoption dynamics that sports tech companies can leverage strategically.

These facilities introduce tech to their athletes and have globally evolved into specialised service offerings. For example ACL return-to-sport programmes, combine/draft preparation, and pre and offseason training camps.

In markets where athletes have choice, facilities need to differentiate through their equipment, technology, data, protocols and outcome metrics. For sports tech companies, these facilities will generally have faster decision speed, aid commercial validation, data diversity and have agile adoption and integration processes.

Ultimately, elite sport provides: validation, case studies, performance credibility, systemic implementation, and long-term relationships. Whilst performance facilities provide: revenue, volume data, market penetration, commercial validation, rapid deployment feedback.

The Funding & Investment Angle

Sports tech investors historically evaluate B2B sales models primarily through pro club lens. However, performance facilities represent a complementary channel that could accelerate growth and de-risk go-to-market strategy.

1. Dual and diversified revenue streams with different characteristics

Pro club and performance facility revenue combined creates revenue diversification. Elite sport provides anchor clients and case study credibility. Facilities provide recurring revenue volume, faster growth trajectory, and a substantial TAM alongside professional club market.

2. Complementary go-to-market efficiency

Tech companies can pursue both simultaneously with different resourcing strategies. Facilities generate early revenue while pro club relationships develop. Volume facility deployment creates market presence that strengthens professional club conversations.

3. Complementary data asset development

Pro clubs provide concentrated high-quality data from elite athletes in controlled environments. Performance facilities provide volume and diversity across sports, ages, and training contexts. For companies building algorithmic capabilities, both data sources create strategic value.

Alongside this, performance facilities globally are experiencing consolidation and professionalisation creating acquisition opportunities for embedded technology companies. Tech companies with facility deployment at scale become strategically valuable in any M&A transactions.

The Credibility Check

Performance facilities and pro clubs evaluate tech through different lenses, and at different speeds and risk calculus. Some performance centres may even choose to purchase tech for marketing value that has minimal training impact. The Instagram-worthy equipment that attracts clients but doesn’t improve outcomes. This is optimising for client perception rather than performance science.

Pro clubs move slowly but when they adopt technology, it’s typically because performance staff believe it will impact outcomes. The decision is evidence-driven, even if consensus-building is frustratingly slow.

In practice this means, performance facilities can provide early market adoption and revenue based on competitive positioning. Whilst pro clubs provide systematic implementation and outcome validation, to strengthens credibility and long-term product refinement.

Rather than choosing one channel, the same technology can serve different objectives across channels without compromising either. Pro clubs validate performance impact through systematic evidence. Performance facilities validate market fit through commercial adoption. Companies benefit from both.

The Takeaway

Performance facilities globally represent an underutilised strategic channel for sports tech companies. Not instead of pro sport, but as a complementary route to market with distinct advantages.

The B2B2C model - facility as channel to athlete - creates distribution, data, and market penetration advantages that complement pro club validation. Facilities provide volume deployment and athlete reach. Professional clubs provide performance credibility and case study depth. Together, they create comprehensive market positioning.

For founders building sports performance tech consider performance facilities as early revenue channel while professional club relationships develop.

For investors evaluating sports tech consider how dual-channel presence creates multiple adoption and exit pathways.

For performance facility operators tech is client acquisition mechanism and provides specialised service offerings.

For tech companies in sports performance consider facilities as revenue-generating channel that funds professional club pursuit, rather than waiting for elite sport validation before building commercial traction.

Tech companies that understand economics and validation create the strongest market positions.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

A recent LinkedIn poll I ran highlighted the tensions that exist in sports performance when it comes to different stakeholder perspectives. There isn’t a single “correct” answer - different stakeholders have fundamentally different objectives and competing priorities.

The reality is that athletes, coaches, physiotherapists, performance scientists, clubs, and agents want legitimately different things from the same situation. This creates misaligned decision-making. I believe sports tech would benefit from considering these different perspectives to provide clarity and not risk compounding the problem, when there isn’t clear accountability for outcomes.

The Strategic Lens

Multidisciplinary teams (MDT) operate on the belief that collaboration, information sharing, and aligned expertise lead to optimal decisions. In practice, there is also the risk that when everyone has input, nobody has final authority because stakeholder objectives fundamentally conflict:

• Athlete: Wants to play now

• Physiotherapist: Wants to prevent re-injury

• Coach: Wants to win the next match

• Performance scientist: Wants to optimise load management

• Club: Wants to protect asset value

• Agent: Wants to showcase the athlete

These objectives are all correct but aren’t reconcilable through better communication or more information as they’re structurally opposed. For example, the physiotherapist minimising injury risk reaches different conclusions than the coach maximising performance.

Therefore an important question may be “whose objective takes priority?”

In high performing organisations, someone needs to make the call. However if it is implicit who makes the call, this creates three possible failure modes:

1. Decision by committee attrition - The person who persists longest wins. This has nothing to do with decision quality and everything to do with the loudest voice or highest status determining outcomes.

2. False consensus - Stakeholders optimise for agreement rather than decision quality. Everyone signs off to avoid conflict.

3. Decision paralysis - When stakeholder objectives conflict and authority is unclear, decisions simply don’t get made.

High-functioning organisations overcome this by making decision authority explicit: who is consulted (provides input), who is accountable (has final authority), who is informed (needs to know the outcome). If we apply this same thinking to Sports tech, specifying who has decision authority is a key step. It means data can generate recommendations but also not risk creating stakeholder conflict.

The Funding & Investment Angle

Sports tech investment thesis typically centres on decision support capabilities: “Our algorithm reduces injury risk by 30%” or “Our platform optimises training load for peak performance.”

ROI through better outcomes and dashboards aggregating data streams assumes organisations will pay for decision quality improvement. But commercial value accrues differently depending on whose decision-making the product actually improves.

Positioning for all stakeholders simultaneously doesn’t truly acknowledge that these stakeholders all want different things. A key investment diligence question could be: whose decision-making does this product actually improve, and do they have authority to act on it?

Products that clarify decision rights for specific stakeholders create defensible value. For example, a platform that explicitly positions as “athlete-controlled data enabling informed conversations with coaches” has clear value proposition. The athlete owns the data and decides when to share insights. Decision authority is explicit.

The Credibility Check

The premise that poor outcomes come from thinking quality under pressure, not information availability, is clinically accurate. From my own experience, I’ve seen examples of these failure modes:

• Root cause problem analysis failures - treating symptoms, not underlying issues

• Misaligned MDT decision-making - consensus replacing accountability

• Over-reliance on protocols in complex environments - applying population guidelines to individual cases.

But there’s an unstated assumption: that exposing practitioners to better case analysis and expert thinking patterns will improve decision quality in their own environments. However, the most thoughtful clinical reasoning in the world doesn’t produce better outcomes if the practitioner doesn’t have authority to implement the recommended intervention. This is why the same practitioners can be highly effective in one environment and frustrated in another.

When it comes to sports tech, algorithmic recommendations can be scientifically valid, statistically robust, and clinically meaningful. But if the organisation using the system hasn’t clarified who has authority to act on those recommendations, the tech doesn’t improve outcomes.

Tech needs to consider commercial strategy and clinical reality. Specifically this means, aiding to resolve decision support for practitioners and whose objective takes priority.

Professional development emphasises clinical reasoning, data analysis, and training programming - the methods, protocols, evidence, and technology. But decision authority in complex stakeholder environments remains largely unaddressed and undiscussed.

The Takeaway

MDT decision-making in sports performance fundamentally conflicts, especially if decision authority is implicit. If everyone has input, nobody has final authority. Outcomes emerge from whichever stakeholder has the most persistence or institutional power.

This is a decision rights problem that risks creating poor outcomes.

For founders building decision support products, the strategic question is: whose decision-making does this actually improve, and do they have authority to act on it?

Asking this helps us to stop trying to align all stakeholders, who want fundamentally different things, equally. It allows us to start being explicit about whose objective wins, in what situations, and who has authority to make that call.

The quality of your thinking matters only as much as your authority to act on it.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

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The Signal

AI researchers are observing that the better AI agents become at generating output, the more exhausted humans feel trying to manage them. This is specific to those working as developers and engineers but I believe this also resonates with those working in human performance.

Physiotherapists, strength coaches, and performance analysts face dashboards displaying heart rate variability, GPS load, sleep quality, menstrual cycle phase, strain scores, recovery metrics, and training readiness algorithms - to name a few. The data arrives in real-time. Decisions about player selection, training modifications, and injury risk must happen immediately.

The tools promise optimisation. The reality is decision fatigue, especially if the technology generates decisions faster than human cognition can process them.

The Strategic Lens

The shift from data collection to decision-making represents a significant change in sports performance technology. The question is no longer “can we measure this?” but “who decides what the measurement means?” This evolution has happened in three phases:

Phase 1: Data scarcity (2000-2015) Practitioners had too little information. GPS units were expensive. Heart rate monitors were basic. Sleep tracking didn’t exist. Injury risk models relied on training logs filled out by hand. Decisions were made from experience and intuition because data simply wasn’t available.

Phase 2: Data abundance (2015-2022) Wearables proliferated. Every player wore multiple sensors. Cloud platforms aggregated streams. Dashboards displayed everything simultaneously. The problem inverted and we had too much data but not enough interpretation, risking practitioners spending more time looking at screens than athletes.

Phase 3: Decision interfaces (2022-present) AI algorithms can now interpret data streams and output recommendations. For example, “Player X shows elevated injury risk - reduce training load by 20%.” “Athlete Y’s recovery score suggests readiness for high-intensity session.” “Menstrual cycle phase indicates strength training optimisation window.”

This sounds like progress as the technology now does the analysis and practitioners make the final call. Human judgment augmented by machine intelligence.

But here’s a paradox that is worth exploring: the more sophisticated the decision support becomes, the more cognitively demanding the practitioner’s job gets.

Why? Because each AI recommendation requires evaluation. Is this algorithm validated for our population? Does the model account for contextual factors? What happens if I override the system and I’m wrong? Should I trust the readiness score or the athlete’s self-report? Which data stream takes priority when they contradict?

Decision-making has always been hard, and more data doesn’t automatically make it easier. Instead, it risks making practitioners responsible for validating and contextualising algorithms - all while maintaining the same decision velocity they had with less information. This highlights a bottleneck effect of human cognitive bandwidth.

The Practitioner Perspective

From my experience as a Physiotherapist, data helped demonstrate objectivity and improve engagement but on a personal level, I have experienced times when recovery metrics from wearables suggested a low score but I felt great.

Who do you believe - the algorithm or how you actually feel?

The “right” answer depends on context that the data can’t capture. Did you sleep poorly because of stress about a family issue, or because of physiological overload? Is the elevated heart rate variability a sign of parasympathetic fatigue, or did you have coffee an hour ago?

These aren’t questions tech answers. They require judgment, pattern recognition, and intuition developed through clinical experience. But the presence of algorithmic recommendations risks creating pressure to justify every deviation.

This is where decision fatigue compounds - when you are having to make contextualised judgments about algorithmic outputs.

The Commercial Reality

The sports performance tech market positioned itself on a promise: more data leads to better decisions, leads to competitive advantage.

This wasn’t wrong to believe. The measurement technology is remarkable, and improving all the time when it comes to validity and reliability.

But the value proposition assumed that practitioners had unlimited cognitive bandwidth to process information streams. It assumed that decision quality scaled linearly with data quantity. It assumed that the constraint was lack of information, not capacity to act on information.

Those assumptions are now becoming questionable.

What we’re seeing now is companies that sold “data platforms” are pivoting to “decision platforms.” The pitch shifts from “track everything” to “tell me what to do.”

This sounds like progress - take the interpretation burden off practitioners and let AI handle it. But this creates new commercial tensions for practitioners where technology threatens to replace human expertise, for organisations if practitioners don’t trust the tech to act on recommendations, and for tech companies if product roadmaps faces contradictory demands.

Female athlete tech faces an acute version of this problem due to the systematic research gap and algorithms therefore being validated on male populations.

This poses capital deployment questions: invest in research to validate algorithms properly (years, expensive, uncertain outcome), or ship products with wellness positioning and population-level correlations (fast, cheaper, commercially viable but scientifically incomplete)?

The Path Forward

The sports performance tech bottleneck becomes a human performance problem that requires an understanding of how practitioners actually make decisions under pressure.

This doesn’t mean more data or more sophisticated algorithms. It might mean though systems designed for the speed humans can actually process and contextualise recommendations, algorithms that communicate uncertainty explicitly, systems that learn from practitioner overrides rather than treating them as errors, and allowing AI to handle data processing and pattern recognition but ensure humans make contextualised judgments about what actions to take.

In essence, this means companies building decision support systems to solve for practitioner cognitive capacity rather than technological capability.

The Takeaway

Sports performance tech has followed the same trajectory as AI agent adoption: initial productivity gains, then cognitive overwhelm, then humans becoming bottlenecks in systems designed to augment them.

There is a need for “mental fitness of an athlete” to manage fleets of AI agents and the outputs. Because more data doesn’t equal better decisions. It equals more decisions, and decision quality depends on cognitive capacity to process, contextualise, and apply information.

Ultimately, the question is whether tech can be designed to work at human speed because that’s the only speed at which good decisions actually get made.

Sometimes the most advanced technology is the one that lets humans be human.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

Leave a comment

The Signal

In December 2025, UC Berkeley launched the Women’s Health and Performance Initiative, a first-of-its-kind program collecting biometric data from female student-athletes to build machine learning models specifically for female physiology. The initiative addresses the reality that less than 10% of sports medicine research involves women exclusively, leaving female athletes training under systems built from male-derived data. The data being collected requires the highest protection tier but who owns it when commercial applications begin is not clear.

The Strategic Lens

Female athlete biometric data represents the intersection of three market forces: systematic research underfunding, technological capability expansion, and regulatory uncertainty about data ownership.

The data itself is fundamentally different from male performance metrics as it includes menstrual cycle phase tracking, hormonal profiles, reproductive health indicators, and ovulation patterns. Under GDPR, this combination of health data and biometric data falls into Article 9’s special category - the highest tier of data protection.

This creates both a scientific opportunity but also governance complexity as the ownership question of this data remains unresolved and lags behind technology deployment. Meaning companies building female-specific performance technology currently operate in a legal grey area. Who owns menstrual cycle data collected during training? Can clubs use hormonal profile data in contract negotiations? May federations commercialise reproductive health patterns for research licensing?

The strategic opportunity exists because governance structures lag technology deployment. First movers establishing frameworks now will shape industry standards before regulators impose them. On the flip side, those building female athlete performance tech without addressing data ownership are accumulating structural risk.

The UC Berkeley model demonstrates one approach - academic institution as data controller, voluntary student-athlete participation, research-focused purpose limitation. This sidesteps commercial ownership questions but doesn’t resolve them.

The Funding & Investment Angle

The female athlete biometric data opportunity sits at the convergence of femtech ($50B projected market) and sports tech ($13B market).

Investment considerations in this space are complex. Traditional sports tech investors evaluate technology capability and market traction. Female athlete data requires additional due diligence on regulatory compliance, data protection impact assessments, and ownership structure clarity.

2026 is described as a defining year for femtech as AI platforms shift toward deeply personalised, biology-specific solutions. Machine learning models trained on female athlete data could generate predictive health insights impossible with male-derived research.

UC Berkeley’s initiative is academic research, but parallel commercial efforts are emerging. These commercial companies collecting menstrual cycle data from elite athletes will need ownership clarity before monetisation, as without it, they’re building on unstable legal foundations. Athletes could assert data sovereignty rights. Regulators could impose restrictions on commercial use. Competitors could challenge inadequate consent frameworks - all creating deal risk that standard sports tech due diligence doesn't capture. Patient capital deployed at the governance layer creates moats that product companies cannot replicate.

The Credibility Check

UC Berkeley’s academic institutional authority provides research legitimacy but doesn’t resolve commercial ownership questions. The initiative operates under voluntary participation with documented consent, which works for research but doesn’t translate directly to professional sports contexts where employment relationships create power asymmetries.

The technical capability for collecting and analysing female athlete biometric data exists. The regulatory frameworks for processing that data exist. What doesn’t exist is consensus on who owns data generated through athletic performance and what legitimate uses look like.

A Personal Perspective

Personally, I find this topic strategically interesting - probably because it is so complex. What is clear is that we can’t continue to ignore the clinical reality of the lack of female athlete data. A topic that I was very naive about when I worked clinically!

We are moving from data to decisions in sports tech. Therefore ensuring machine learning models are built on female data is key but the ownership rights to this data also needs resolving.

This governance vacuum is dangerous because data collection is accelerating faster than ownership structures are emerging. UC Berkeley’s initiative is research-focused, but commercial applications will follow. Companies need frameworks before regulatory intervention forces compliance, rather than enables innovation.

The Takeaway

Female athlete biometric data sits at the convergence of systematic research gaps, expanded technological capability, and regulatory uncertainty about ownership. This creates both opportunity and risk.

Organisations building female athlete performance technology need governance solutions as athletes will increasingly assert sovereignty rights over data generated through their bodies.

For founders and investors, the strategic implication is that female athlete data governance represents infrastructure-layer positioning opportunity. Companies solving ownership and consent frameworks will establish industry standards that others must meet. Those focused solely on product development without governance clarity are accumulating structural risk.

The UC Berkeley initiative demonstrates how research institutions can navigate special category data collection. Professional sports contexts though are more complex - employment relationships, commercial interests, and power asymmetries require different approaches.

Sometimes the most valuable asset isn’t the data itself, but the governance model determining who controls it.

I would love to know your thoughts on this Issue by commenting below.

Thanks for reading! Nic x

Leave a comment

We discussed quality of MRI imaging, impact of AI, professional development courses, wider perspective views and the benefit of space and switching off.

Feel free to have a listen and we would love to know your feedback and qhat you have been seeing, hearing and reading about recently.

Nic x