Main Image: USL

By Joe Lemire

Olympic rower Meghan Musnicki has competed in four Summer Games, winning gold in the women’s eight in 2012 and 2016. When she rowed in Paris at age 41, Musnicki became the oldest American woman to row in the Olympics.

Musnicki, a graduate of Ithaca College, retired in 2024 and then sought what she called only her second “real job” in the last 15 years. Through a friend of a friend, she was introduced to Biograph, a high-tech preventative health clinic with locations in New York and San Francisco. Musnicki joined as an executive in membership development and partnerships — and also learned some critical insights about her own health. She applied those findings, improved her condition and is now training or a new challenge: Musnicki recently bought a Garmin smartwatch to train for her first marathon.

On the appeal of Biograph:

“The premise behind all of it is something that obviously really resonated with me given the commitment to living the best life you can live for as long as possible. They gave me a shot given my very limited résumé.”

On the growth of data and tech in sport:

“As you can imagine, it’s become more tech- and data-driven in the last four to six years. When I initially started — I made my first team in 2010 — compared to now, there’s all sorts of different metrics and measurements and telemetry that they hook up to the boat that analyze every specific thing. When I started, it was very much your heart rate and your perceived level of exertion.”

On what she applied to her training:

“You learn quickly as an athlete how your body operates best, and you find its limits because it’s a very specific extraction mindset [prioritizing shorter-term gain]. You pick low-hanging fruit at first. We always joke, ‘Show up every day.’ That’s the easiest way, but ‘show up every day’ stops being effective when you’re in it for five, 10, 15 years. Then you have to get more nuanced and look at more detailed, more nitty-gritty things like how’s my nutrition? How’s my recovery? As an aging woman, am I focusing on my muscle mass, my body composition? What am I optimizing in order to ensure that I have longevity in this sport?”

On the surprise results in her Biograph diagnostic:

“You would think that I would be like a textbook picture of health, and I thought that it wasn’t going to be terrible. And don’t get me wrong, it wasn’t terrible, but it flagged a genetic risk for cardiovascular disease, which isn’t terribly surprising: My father passed away from a sudden heart attack when he was 49 when I was a freshman in college. And lo and behold, I have high Lp(a), a genetic predisposition to developing cardiovascular disease.”

“It just makes you pause because no one would look at me and be like, ‘Oh, she for sure has high cholesterol.’ It just goes to show you that if you dig a little bit deeper than surface level, there’s all sorts of things that could be hiding or lurking underneath that you don’t know about. It could be a little scary, but I think of it as more empowering. I empower myself with the knowledge of like, okay, this is my reality. And now what choices am I going to make to address and deal with that?”

On how she applied the findings:

“When I think about Biograph, the value for me wasn’t just discovering that I had the issue. It was having people that could interpret the information and give me tangible things to do to begin to move the needle. More data isn’t always better. It’s what you do with it. I lowered in six months my Lp(a) by 25, 30 points. I lowered my cholesterol by 30. And this was just lifestyle changes. Obviously I could have gone on a statin, that’s one of the options, but they work with you. And I was like, ‘I don’t really want to go on a statin right away. I want to see how I can do it otherwise with more lifestyle interventions.’ And so I worked with the dietician, the exercise physiologist, and it worked for me.”

On how she’s transitioned out of rowing:

“A lot of it is shifting away from more specific training, again, in thinking of that extraction and thinking more long-term. So as an aging woman, I really focus on making sure I’m doing heavy lifting to preserve my muscle mass and my bone density. I keep up my Zone 2 cardio, which has never been an issue for me given that I’m an endurance athlete. More importantly for me, I have to do a lot of sprint work. I do a lot of jump and plyometric training for my bone density and my explosive power. And then, probably not as much as I should, but I’m still a work in progress mobility and making it so when I get up in the morning, it’s not the Tin Man for too long.”

This article was brought to you by SBJ Tech, a Leaders Group company. As a Leaders Performance Institute member, you are able to enjoy exclusive access to SBJ Tech content in the field of athletic performance.

By David Clancy and Ivan Peric

The morning

At 8:20 on a Monday morning, the sports medicine and performance team gathers around a table. The physio updates the group on a player. The club doctor adds context from the recent scan. The lead S&C coach describe yesterday’s exposure. The sports scientist flags a change in high-speed decelerations. The performance nutritionist has noticed a fuelling issue. The Manager wants a simple answer: Will he train? And when will he be ready to play again?

There is no shortage of expertise in the room. There is no shortage of data either. What is missing is memory. What did the department do the last time this player had this problem? What was expected? What happened? Which decision changed the trajectory? And, with the benefit of hindsight, was it the right one?

Some of that sits in the digital records. Some sits in disconnected files. Much of it lives in the physiotherapist’s memory, or in the conversation that happened beside the treatment table three months ago. Eleven minutes later, a decision is made. By the afternoon, parts of the rationale have already begun to disappear.

That is the real opportunity for AI in elite sport. Not to replace the people around the table. Not to produce an all-knowing injury-risk score. Not to make the return-to-play decision. It is to help the team behind the team see more clearly, remember more accurately – and learn more quickly.

The bill is €3.45 billion, and technology has not moved it

Howden’s Men’s European Football Injury Index counted 22,596 injuries across Europe’s top five leagues over five seasons. The estimated wage cost of players being unavailable was €3.45 billion. The injury rate has barely shifted. One injury every 625 mins. The financial bill has not stood still; it increased by 29% across that five-year period (Exhibit 1).

The injuries are not distributed evenly. Clubs carrying the heaviest fixture demands tend to record more of them. Four clubs sat above the Premier League injury average in every one of the five seasons. Yet the cost of those injuries varies significantly. Manchester City and Liverpool operate with comparable wage commitments, but their estimated injury costs in 2024/25 were €41 million and €15 million respectively (Exhibit 2).

Injury count alone does not explain the difference. Player wage profiles and the type, timing and severity of injuries all matter. But so does the way a department coordinates decisions, progresses rehabilitation phases, manages exposure – and learns from previous cases.

The arithmetic is simple: Cost = days lost × the daily wage of the unavailable player.

The useful question, then, is not whether every injury can be prevented. It is whether a department can reduce avoidable days lost, limit recurrence and prevent a short absence from becoming a long one. Aston Villa halved its injury bill in a year despite recording three more injuries. Average absence fell from 37 days to 15. This is where the conversation becomes uncomfortable.

Elite sport has invested heavily in technology, but that alone has not solved the problem. Teamworks, whose platform sits beneath many elite performance departments, estimates that 75–90% of collected performance data never influences a decision.

More feeds. More dashboards. More alerts. But not necessarily more clarity. And certainly not more organisational memory than the room had a decade ago.

Where AI belongs in the medical / performance department

If injury cost is shaped by human decisions, the strategic question is not simply whether AI can predict an injury. It is where, across the work of the department, AI can improve the conditions in which better decisions are made.

There appear to be approximately seven core stages in a player’s lifecycle, from pre-signing assessment through preparation, availability, rehabilitation, return to play, selection and week-to-week performance (Exhibit 3).

Across that chain, five technology capabilities matter:

1. Data integration
2. Machine learning
3. Language models
4. AI agents
5. Computer vision

They are not interchangeable. They do different jobs, require different controls and carry different risks (Exhibit 4).

Placed against the seven stages, their value is uneven. That unevenness is the point (Exhibit 5).

Research into AI’s ‘jagged technological frontier’ shows that the same system can improve performance on one task and degrade it on another that appears remarkably similar. In a large field experiment, consultants working with AI completed suitable tasks faster and to a higher standard. Just outside the technology’s capability boundary, however, they were nine percentage points more likely to reach the wrong answer.

In sports medicine, confident error carries a human cost. Summarising a meeting is not interpreting pain. Drafting a case chronology is not diagnosing pathology. Identifying a missing piece of information is not deciding that a player is match ready.

The matrix makes this boundary visible. Data integration, language models and agents have potential around return to play, selection, documentation and organisational memory. Machine-learning injury prediction, the most heavily marketed capability, earns a more cautious place. A first-team squad may experience roughly 50 injuries in a season. That is a small, shifting and highly contextual sample from which to make high-confidence predictions.

AI is often strong in complicated environments where the rules and relationships can be modelled. Injury is complex. The same load does not create the same response in every player, on every day, in every context, at any point of the season. A model can surface a signal and even prompt a better question. It should not make the call. Judgement should never be passed off.

No column in the matrix is empty of people. The technology changes what arrives on the desk – not who carries the decision. Read together, the lifecycle, the capabilities and the matrix point to three opportunities, in ascending order of ambition.

Opportunity one: connect the player’s data to the practitioners

Here is a precondition for everything else. Most clubs have connected parts of the player record: training load, testing, wellness scores, medical events and match exposure. The practitioner record is less connected. Clinical reasoning, return-to-play criteria, changes of plan, and contextual observations are written up in SOAP notes or similar, filed somewhere and maybe surfaced again.

Join both records around one player identity, one timeline and one agreed definition of availability. Then make what has already been written searchable and usable. The challenge is not simply technical. The system needs appropriate consent and permission to read practitioner notes… not merely permission to store them.

Opportunity two: execute the workflows

Once the record is connected, AI agents can complete or prepare routine work. Our task analysis of the rehabilitation stage within a Premier League backroom department identified 22 recurring tasks, consuming roughly 40 hours each week.

Eleven could be produced automatically overnight. Seven could be drafted for a practitioner to review and correct. Two, diagnosis and the return-to-play decisions, should not move from the human. That’s complex terrain.

The prize is not a smaller department. It is approximately a day each week returned to the practitioner. More time with players, more time coaching, more time thinking – and more time discussing the difficult cases.

Opportunity three: reinvent how the department learns

The first two opportunities improve today’s work. The third changes tomorrow’s department.

What if the team recorded what it expected at every major decision point? What if it compared expected against actual at the end of each case? What if similar cases, changes of plan and second opinions could be retrieved in seconds? What if the knowledge of an experienced sports med physician, for example, became part of the department’s memory rather than leaving when that person did?

This is the move from digitising activity to building organisational intelligence and retaining and managing knowledge. It requires more than a new tool or workflow. It requires the will to redesign the week around better systems, not bolt another platform onto an already crowded one.

The importance of strategy

The question most departments ask is, “Which AI tools should we buy?”. What about, “How should our department evolve because AI exists?” That mindset shift matters.

Strategy should begin with a performance problem worth solving. Avoidable games lost to injury, inconsistent return-to-play decisions, excessive admin, or lessons that disappear when a case closes.

It should not begin with a product demonstration. A performance department is a human environment. A wrong call is felt by a player and carried by a practitioner. Adoption must therefore be intentional, governed and connected to the realities of player care and performance.

We recommend five steps, in order, based on recent use case experiences across elite football environments:

1. Audit

Understand how the department works today. Recurring processes, decision points, systems, people, information flows and data gaps.

2. Benchmark

Compare the department stage by stage against the player lifecycle. Compare its outcomes against an appropriate league and reference set, within the league, and even outside the league/sport.

3. Roadmap

Decide what to improve first, and which capability is required. Consider the pilot case and how the system will expand once the initial workflow proves useful and gains traction.

4. Boundaries and governance

Define what AI may produce, what it may only assist and what must remain human-led. Establish the controls around access, consent, review, retention, accountability and escalation. This step is what separates an AI strategy from a technology project.

5. Monitoring and operating model

Track whether the change improves the work. Decide what measures matter, which capabilities the department needs, and how responsibilities, meetings and incentives must evolve.

The jagged frontier must be mapped at task level for each department:

• Inside the frontier: AI can execute within defined controls.
• At the boundary: AI can assist, but a qualified person must review.
• Outside the frontier: The task remains human-led.

The principle is simple.  Automate the task. Augment the practitioner. Never outsource accountability and discernment. That map is not permanent. It should be redrawn frequently, as the tech changes and the department builds its own evidence and relevant use cases.

Five workflows to start with

A credible AI strategy does not need to begin with a moonshot. It can begin with five useful workflows that sit largely inside the frontier, use information the sports med/performance department already holds – and can be piloted within a season.

1. A daily availability brief

Produce a concise account of what changed overnight, where each fact came from and what remains unknown. No invented risk score. No automated decision about who trains. Think of this as a shared, sourced starting point for the morning conversation.

2. A return-to-play decision memory

Build a living case chronology containing the diagnosis, key milestones, response to loading, changes of plan, criteria, and the rationale behind major decisions.

If the timeline changes, people can see why. If a second opinion is required, the external expert receives a coherent case, not a collection of disconnected files.

3. Meetings that become action

Capture decisions, owners, deadlines, unresolved questions and genuine disagreements as the meeting happens. Require the team to review the record before it becomes final. This closes the gap between what the group believes it agreed and what individuals subsequently do.

4. Documentation that returns time

Draft clinical notes, referrals, rehabilitation updates and other forms of communications at the point of care, with the responsible practitioner reviewing and signing off every record. A 2025 randomised trial found that an ambient AI scribe returned nearly a tenth of documentation time. Across a season, those minutes become hours returned to player-facing care.

5. A learning management system that compounds

Complete an after-action review when a case closes. What did we expect? What happened? Where did the plan change? Which signals mattered? What would we repeat? What would we do differently?

Link similar cases so that a new practitioner can ask, “How have we handled this before?” and receive a sourced answer that will help. That is how knowledge and team IP stops leaking away and starts compounding.

Start small and keep a score

None of this involves pasting player information into a public chatbot. Health data requires approved platforms, appropriate consent, role-based access, retention rules, audit trails and named accountability. Any output capable of affecting care must be traceable to its source and reviewed by a qualified professional.

Start with one workflow. Map the tasks inside it. Define the boundary. Pilot it with the people who do the work. Track time returned, errors caught, and decisions improved. Ask do you trust this. Run an after-action review. Adapt before scaling. This is slow enough to be safe but fast enough to learn.

Remember… AI will not palpate a hamstring. It will not notice the hesitation in a player’s voice. It cannot carry the moral weight of a return-to-play decision. It cannot rebuild trust after a setback. But… it can place the right information in front of the right people. It can preserve why a decision was made. It can return hours to practitioners… give some white space back into a crammed calendar. Free up headspace and offset cognitive load. It can help a department learn from every case rather than simply move on to the next one. Knowledge management.

Several seasons of data suggest the injury count may not change quickly but the days can. The departments that move them will not be those with the most technology and apps. They will be those with the clearest strategy, the strongest judgement, and those constantly monitoring what they are doing.

David Clancy is the CEO of The Nxt Level Group | LinkedIn

Ivan Peric is a Founding Partner of Peric & Partners | LinkedIn

References

Dell’Acqua F, et al. Navigating the Jagged Technological Frontier. Harvard Business School / Organization Science.

Ekstrand J, et al. Hamstring injury rates have increased and now constitute 24% of all injuries: the UEFA Elite Club Injury Study, 2001/02 to 2021/22. British Journal of Sports Medicine, 2022.

Extraction of structured information from clinical notes: International Journal of Medical Informatics, 2025; JMIR Medical Informatics, 2024.

Howden, Men’s European Football Injury Index 2024/25 – five-season review, 2020/21 to 2024/25, including appendix tables by club.

Leckey C, et al. Machine learning approaches to injury risk prediction in sport. British Journal of Sports Medicine, 2025.

Lukac P J, et al. Ambient AI Scribes in Clinical Practice: A Randomized Trial. NEJM AI, 2025.

Task census, chain, matrix and operating-model figures: Peric & Partners analysis of a Premier League performance department, 2026 – estimates, to be corrected against club data.

Teamworks, “The Future of AI in Sports,” citing Dr Robin Thorpe’s survey of elite performance directors.

By John Portch

A few years ago, the US Air Force invested a year of time and substantial resources to better understand why nearly a third of its pararescue candidates were self-eliminating within the first 72 hours of entering the training pipeline.

“It would be like you’re already through the NFL Draft, you show up to training camp, and 30% of your draftees say: ‘I thought I wanted to be an NFL player, but now that I’m actually doing it, not so much’ and they just walk,” says Andrea Tullos.

It led to a period of introspection, she tells the Leaders Performance Institute. “We realized we didn’t believe we were bringing the candidates to the camp that had the greatest potential to be exceptional performers across a career versus just successfully getting them through the training,” she continues. “And those can be two very different things.”

Tullos, a retired Air Force lieutenant general now leading strategic advisory firm Outperform IQ, felt a moral obligation at the time to provide the Air Force with the best possible candidates. They decided to “go down that road less traveled,” which meant reweighting assessments towards attributes harder to measure such as will or imperviousness to discomfort rather than versus counting pushups or swim times over pool lengths.

It made some Air Force people nervous, but they pressed on. “We learned a ton through the process too,” says Tullos. “That’s what I believe is an attribute of a learning organization; when you’re not afraid to go down these rabbit holes that might not end up where you think they were going to end up.” It could not have happened by accident. “Being a learning organization is a choice,” she adds.

It was also a triumph of imagination, a point raised by Dr Wendy Walsh, the Chief Learning Officer at the US Air Force, who has joined Tullos in the conversation to discuss their six years working together. Walsh made a similar point in part one with regards to the 9/11 Commission’s warning about a “failure of imagination” and, here, commends the Air Force for confronting the possibility that their system was solving the wrong problem.

“So don’t get stuck in a data story that is not taking you to a possibility that you want to imagine,” Walsh tells the Leaders Performance Institute. “Is there something else that we can notice, that we can investigate, that we can try?”

How organizations learn and help their people to learn is at the heart of learning engineering, a topic introduced by Walsh when the duo spoke onstage at June’s Leaders Sport Performance Summit in New York. “It’s a new one for a lot of people,” says Walsh. “It’s been emerging over the last decade.”

Learning engineering asks how information moves between the learner and the teacher within an environment. Walsh uses AI as an example of what is risked when learning is not engineered. “You can give a teacher AI, but if you don’t teach them how to utilize that and give them that psychologically safe space to investigate and to actually learn it, when they bring it into a classroom, especially a classroom today where people have been utilizing it, the technology risks becoming a source of anxiety rather than learning.”

In part one, Walsh and Tullos explored the threats that can cause leaders to lose sight of their people’s development needs. In part two, we turn our attention to what happens when we have the person in our sights: how do we identify and develop their potential?

Learning engineering explained

As Walsh says, learning engineering provides “a rigorous rubric to understanding the variables of learning so that you can optimize.” It asks: do we understand this person well enough to help them become who they could be?

She simplified it to three tenets:

1. Human-centered design: understand the learner

“We really want to start with the learner,” says Walsh. Understand the person before designing the intervention. Coaches, for example, can ask:

• How does the athlete learn?
• What support to they need?
• What environments help them succeed?

2. Interdisciplinarity: no single discipline sees the whole picture

Interdisciplinary collaboration broadens that understanding of the learner by bringing together multiple perspectives. “Different disciplines can have different theories, different scholarship, that are valuable to achieving the outcomes that we want.”

3. Evidence: test your assumptions against reality

The findings then feed back into the learner’s experience to create a cycle of improvement.

“Learning engineering is an empowerment structure for the learner too,” says Walsh.

“Using the evidence not just to have somebody tell me what I need to do or to assess me, but for me to know how I’m going to be assessed, how I can gain those reps and sets or learning that needs to happen for me to move forward.”

Tullos, for example, says she is “not good at being the first person in line if I hadn’t seen it” but, once she’d seen it, “I was on.” She echoes Walsh in emphasizing the importance of understanding how someone learns and, if you have a firmer grasp of the required learning methodology, “you can prepare before the player even shows up.”

She adds: “You don’t want to structure practices and then realize that for 90% of the team it just went in one ear and out the other because it was delivered in the wrong format.”

Choosing to learn

Tullos is keen to address the elephant in the room. “In certain sports today, the incentive structure is not to be a learning organization, because your coaching turnover is too high, your player turnover is too high, and you’re designed to be a stepping stone,” she says. “You’re trying to get the talent that you have optimized to win once.”

The fact is, she adds, “you can win without being a learning organization”. However, “the dynasties, the ones that never get relegated? My hypothesis is they are learning organizations.” They have more stability and are “continuously feeding that family tree of great coaches to the outside because they’re investing in them. Everybody wants to go to that organization.” By contrast, where it’s obvious that athlete and coach development aren’t priorities, “you want to go there, but God, you want to get out of there too. You want to take what you can get, win a championship, and move on.”

Walsh has been listening intently. “It takes practice to be present, how we respond to uncertainty takes practice,” she says, adding that no leader can assume that learning just happens naturally.

As a practical tool for tracking learning, Tullos recommends “disciplined debriefs” that happen win or lose. She suggests three questions as a starting point:

• Were we as prepared as we could have been?
• What happened that we did not expect?
• What can we do better tomorrow?

At the heart of these is the notion that success is not proof that your preparation was perfect and failure not proof that everything was wrong.

“It’s not about assigning blame. It’s about understanding how to get up tomorrow to do it all over again as a team and get better,” she says. “It’s a two-way conversation between coaches and performers that happens after every single competitive event, whether it’s the best performance you’ve ever seen or the worst.”

Andrea Tullos is a retired Air Force lieutenant general and founder and CEO of Outperform IQ. Her views are her own and do not represent those of the Department of War or the Department of the Air Force.

Read part one

Training for the Known, Learning for the Unknown: Why Imagination Matters in High Performance

By John Portch

“I think innovation-to-action is the most important pain point for us,” said David Howarth.

The Vice President of Performance at New York City FC was speaking at June’s Leaders Sport Performance Summit in New York, where he addressed the gap between the emergence of a promising tool and its performance value.

One reason that gap persists is a lack of understanding on the part of the practitioner. “There’s a depth of education and understanding that needs to happen first for the practitioner,” he continued. Then, the practitioner can act as what he called a “bridgehead”: someone who can bring ideas to coaches, performance staff and senior leaders alike.

Howarth was joined onstage by Damiano Bernacchini, the Sport & Performance Cluster Manager at Technogym, who explained how practitioners inform the company’s product development.

“We organise workshops or focus groups to understand where we are heading, what are the needs for the market from a specific category and how we can actually create priorities and implement the solution,” he said. Feedback from those sessions helps Technogym decide what to develop and where its priorities should lie.

David Howarth (left) speaks alongside Damiano Bernacchini (centre) and session moderator Joe Lemire (right) at the Leaders Sport Performance Summit at the Barclays Center in New York.

For Howarth, the primary question is: “Does it change what the athlete or coach is doing?”

The duo raised five considerations.

1. Establish whether the measure is reliable and meaningful

This comes back to Howarth’s innovation-to-action conundrum and it means identifying a smaller number of measures that practitioners can depend on. He spoke of the “signal-to-noise ratio”: distinguishing genuine athlete change from normal biological fluctuation, equipment error or inconsistencies in the testing process.

That is where practitioner education becomes important. “The simplest answer is probably the right answer,” he said. “However,” he continued, “to get that simplicity, there’s an element of comprehension and a need to understand it” before a practitioner can explain the findings to athletes and coaches.

Howarth also pushed back against what he called a “black box approach”, where practitioners are expected to accept an output without understanding how it was produced.

2. Introduce the tool gradually and seek a quick win

“You’re really looking for an early, small winner,” said Howarth. Athletes and coaches need to see why a tool is useful before they are asked to give it more time or change how they work. He called for a “slow but accurate introduction of that tech” because “if it takes me 25 minutes to explain the technology and they switch off after eight seconds, I’ve probably lost”. He argued that organisations need to make room for practitioners to learn. Professional sport offers too few opportunities to test and refine a new tool before it is expected to deliver.

For his part, Bernacchini argued that implementation should not end when a product reaches the market. “Every three months there is a product review, understanding what are the priorities to develop and how we can implement it in the process,” he said of Technogym. These reviews allow them to reassess its development priorities as it receives feedback from clients.

3. Do not discard subjective or human input

As Howarth said: “Subjective measures are still incredibly valid data points.” He said that any measure is shaped by the quality of the equipment, the way it is collected and the judgement of the person interpreting it.

Bernacchini also stressed the need for human oversight when discussing AI-enabled prescription. He argued that technology should help practitioners process information rather than replace their judgement, because the tech may reveal patterns that an athlete or coach has not noticed, but subjective information can supply the context that the device cannot capture.

4. Be brave enough to discard the tool if it adds no value

Practitioners must also be prepared to drop a tool that is not working. “Removing it can also help build trust,” said Howarth, who made the point that stopping an ineffective intervention shows that the performance team is judging it on results rather than trying to justify its continued use. He imagined a performance team openly saying “we’ve been doing this for six months and it’s shown us nothing”. In his view, that honesty preserves the performance team’s credibility when it next proposes an innovation. The test, he argued, is whether the technology produces a “clear, actionable output”.

5. Treat AI as a ‘digital twin’

“We want to be able to provide a support, not to substitute,” said Bernacchini. Technogym’s aim is to combine functional assessments, wearables and subjective data to create “a living programme that adapts also based on the previous session”. The programme would update a prescription as new information became available, with the practitioner remaining responsible for interpreting its recommendations.

Howarth also pointed to AI’s ability to sift through large amounts of information. In scouting, for example, it can find patterns across dozens of reports. He likened AI to a “digital twin” that can help practitioners “interrogate our own typical answers”.

What to read next

How to Maximise Research and Innovation within your Team

By John Portch

“One of my favorite lines is: imagine a future you want.”

Dr Wendy Walsh, the Chief Learning Officer at the US Air Force, believes that people all too often get trapped in existing narratives and assumptions, which can come at the expense of their imagination.

“Imagination is an incredibly powerful tool that is totally underdeveloped once you get past the sixth grade,” she continues. “Then you’re told ‘this is how you do things’. And so we start to learn in different ways and some of our imagination can atrophy.”

She cites the 9/11 Commission Report as a grave example of missed possibilities. In making its conclusion, the Report stated that ‘the most important failure was one of imagination;’ that US policymakers, intelligence agencies and other institutions did not sufficiently imagine that al-Qaeda would use commercial airliners as weapons in a coordinated attack.

“That really stuck with me,” says Walsh, who entered federal service not long after the attacks. “How do we continue to develop and strengthen our imagination?”

The practical answer is to build people capable of responding when those possibilities arrive, as suggested by Andrea Tullos, who is the Founder and CEO of strategic advisory firm Outperform IQ and a former lieutenant general in the US Air Force. She also served as the Commander and President of the Air University.

“We train people for the circumstances we know they’re going to face and we educate them for the things that are the unknowns,” says Tullos, who has worked with Walsh for six years and joins her in conversation with the Leaders Performance Institute. “When an unknown happens, they’re going to default to that well of knowledge, experience and wisdom.”

The duo stepped onto the stage at June’s Leaders Sport Performance Summit in New York to discuss how organizations can become better at learning.

One route is through a renewed focus on individual learning and development but, as Walsh and Tullos explain in the first of two articles, there is a series of threats that can cause leaders to lose sight of their people’s development needs.

Below, we outline four threats in particular that may stifle an individual’s potential.

1. Technology

In sports, there is a firm focus on athlete management systems, wearables and, more recently, AI as the route to a competitive advantage. This trend is also visible in other industries and, as Tullos says, “it’s consuming an extraordinary amount of both time and resources compared to the human focus.” She likens a leader’s time to a pie chart; and “the time sports leaders would normally spend in direct contact with humans is being consumed by the technology, figuring out how to use it, apply it, figuring out what works and what doesn’t.” The result is that the “art is being drowned out by the science right now.” That balance needs to be restored. “I’m a big believer in leveraging technology, but I think that slice of the pie still needs to be relatively small compared to the amount of time I’m spending on the humans.” She sums it up with a striking metaphor: “We don’t want leaders so connected to the technology that the human is just withering over here. They still need sunlight and water.”

2. Data and analysis

Data analytics can only ever be part of a holistic understanding of an athlete. “Don’t get stuck in a data story that is not taking you to a possibility that you want to imagine,” says Walsh, who cautions against seeking new datasets without good reason. “Rather than thinking ‘okay, what is missing from this?’ Actually do some critical thinking,” she continues. When seeking to make decisions on an athlete, a leader should be ready to ask questions such as: “Why are these numbers so much different than they were before? Are there any variables that I might be able to find?”

Clearly, some menial tasks can be automated by what Tullos calls a “digital wingman” or a “coach in your pocket” but critical thinking cannot be lost. When this is your mindset, a new tool such as generative AI does not lead to a much-feared cognitive offload, as Walsh puts it, but to the posing of better questions. The key for Walsh is to learn “how to be a good human in the loop with new technology”; and neither she nor anyone else has cracked it. She says it is “going to take patience, grace, an open mind and forgiveness.”

3. Excessive planning

If leaders exert too much control over an athlete’s performance, it can undermine their development. “If you’re so rigorously planned, you don’t have the ability to adapt on the spot,” says Walsh. She points to a discussion she heard among announcers covering San Antonio Spurs star Victor Wembanyama, who they felt often performed at his best when he would “get out of his head and just go.” Whether or not that was the explanation, preparation should build an athlete’s confidence to adapt.

“A leader should understand that while they can’t control complexity, they can manage it,” says Tullos, who returns to her idea of training people for the knowns and, in doing so, educating them for the unknowns.

It is critical to build an athlete’s familiarity with uncertainty. “Maybe you haven’t hit that specific situation, but you’ve done enough to know how to successfully adapt,” adds Walsh. “It doesn’t mean your strategy will be successful, but I’m a believer that the more routinized you become, the more adaptable you become to the craziness that happens.”

It is also a question of teammates establishing a shared understanding of risk, which is not always the case. “It’s even more powerful if you have a shared rubric across teammates so other people can anticipate how you might respond as well, rather than adding in another variable of surprise with your adaptation.”

4. Blind spots created by expertise

Experience and expertise are ideal traits for filtering out noise and executing the task at hand, but, as Tullos says, it can be a “double-edged sword”. She explains that the brain develops expectations based on previous experience and, most of the time, this is a helpful trait, but “after a while, it’s not that it’s lying to you, but it’s going to tell you what it has seen the last 300 times.” An athlete in the heat of competition, for example, needs to be prepared for when the pattern changes. “That’s the value of the iPad on the sideline or even an experienced quarterback,” she continues. It ties into the threat posed by excessive planning. “They’re going to have to get their brain to rethink that and calibrate what it is that they’re selectively looking at.”

None of these threats undermine the value of technology, data, planning or expertise in performance, but there is a case for keeping them all in proportion. The next question, discussed in part two, is how leaders can create learning environments that support athlete and coach development.

Andrea Tullos is a retired Air Force lieutenant general and founder and CEO of Outperform IQ. Her views are her own and do not represent those of the Department of War or the Department of the Air Force.

Main Image: USL

By Ethan Joyce

Brett Luy realized it was odd to imply that he wanted to see an officiating call questioned. As the United Soccer League’s president of competition and administration, the ideal scenario is a perfectly called game by referees, letting the players provide the impact moments in matches.

But that changes when deploying a new support system for said officials, like the USL will be doing with its Football Video Support (FVS) pilot for the final seven games of its interleague competition between Championship and League One teams, the Prinx Tires USL Cup.

“This is a weird thing to say, but I’m hoping we get the opportunity to utilize the technology,” said Luy, chuckling.

While Video Assistant Referee (VAR) is now a household abbreviation, FVS is more a recent addition to soccer. It’s so new that FIFA is still evaluating it as a potential alternative support method for referees (this pilot is part of that larger FIFA effort).

Here are a few key takeaways from my conversation with Luy about FVS and the USL.

How it works, and how the USL will use it

FVS usage is scaled down and clear-cut compared to VAR. It can only be used to review four situations:

• Goal/no goal
• Penalty/no penalty
• Direct red cards
• Mistaken identity (the wrong player is cautioned/sent off)

A goal scored, Luy explained, will be automatically checked by the fourth official’s review on a field-side tablet. Other situations will require the match’s head coaches to request a review through the fourth official. Coaches will receive two requests per match during this pilot, which starts with quarterfinal knockout matches next month and continues through the Prinx Tires USL Cup title game.

There will be no challenge flags thrown or challenge balls punted on the field, sadly. “A little less ostentatious, I guess, for this footballing world,” Luy said.

FVS’s biggest benefit? Cost savings

Part of FIFA’s drive to test FVS is the desire for more affordable officiating support systems outside of VAR. For the smaller leagues around the world, VAR’s price (which can be in the mid- to high-seven figures for leagues) is a significant investment to make on tighter budgets. FVS is also supportive of venues set up with fewer cameras. Both cases reflect the USL’s needs, which support nearly 300 teams throughout its various levels of men’s and women’s competition.

“Even if we wanted to go that [VAR] route, we wouldn’t have the requisite bandwidth within the ecosystem to be able to accommodate that just from a strictly numbers perspective,” Huy said. “So when you create something like this that eliminates that mitigating factor, I think it’s very positive for the game and something that can be very positive for us as a league.”

An added benefit to this FVS deployment? The USL will get to rely on existing partners Spiideo and NEP Group, which it uses for its camera system and broadcast production.

Why it makes sense for the USL

This move comes as the USL prepares for an impending promotion-relegation setup in 2028, when the USL Premier debuts as the top of a three-tiered pyramid featuring the USL Championship (second division) and USL League One (division three).

Luy sees this as a natural evolution for the USL as officiating decisions could eventually impact clubs moving up or dropping down at the end of the year.

“With promotion-relegation conversations happening and talking about matches of consequence increasing throughout the regular season here and in the not-so-distant future for us, we feel like the time is now,” Luy said. “We’re going to create a dynamic and a paradigm where … you really up the stakes when you’re talking about people going up and down and the fluidity of a pro-rel system.

“Getting as many of those calls right and getting on the front foot here — it felt like the right time to accomplish that task.”

By John Portch

While often grouped together, research and innovation are distinct but interconnected activities.

“‘Research’ is providing and creating new knowledge in a specific area, which is relevant to the theme in hand,” said Dr Robin Thorpe. “Where ‘innovation’ comes in is the implementation and how that applied new knowledge creates value for the environment.”

Thorpe, who has worked across multiple elite high-performance environments, was speaking and presenting at a virtual roundtable for members of the Leaders Performance Institute entitled ‘Where does research and innovation sit within a high-performance strategy’.

Knowledge alone, he continued, does not lead to innovation. “How can we actually embed processes and human behaviour aligned with some of this new knowledge and value it may bring?”

Building on that question, he explained that confusion around research and innovation often stems from a misunderstanding of what he sees as four connected but distinct stages:

1. Research: Creates knowledge
2. Innovation: Applies knowledge to create value
3. Implementation: Embeds change into everyday practice
4. Performance impact: Improves outcomes on and off the field

Organisations can go wrong by jumping straight to technology and bypassing the critical stages that connect knowledge to performance.

As he brought the rest of the table into the conversation, Thorpe posed a series of provocations:

• Most organisations invest more in technology than they do in innovation.
• Research should solve operational problems, not academic ones.
• Innovation is rarely limited by ideas; it is usually limited by implementation.
• Buying technology is one of the least innovative things an organisation can do.
• The success of innovation is determined less by the quality of the idea and more by how effectively its value is communicated.

In reflecting on where organisations succeed and where they become stuck, the table identified four challenges facing research and innovation across elite sport:

1. The tendency to start with the answer instead of the question

There is a tendency to jump straight to solutions before properly understanding a problem. Participants argued that research should be driven by performance questions and strategic priorities rather than new technologies or fashionable ideas. One attendee warned that without clarity around the questions an organisation is trying to answer, it becomes easy to pursue “a shiny new tool” without aim or purpose.

The table highlighted the value of establishing external research partnerships (e.g. with universities) when seeking to answer performance questions and close knowledge gaps. Such collaborations are relatively common in the UK, Europe and Australasia but less so in North America. The key, Thorpe noted, is ensuring these partnerships are understood for the value they bring and not just as academic exercises.

Several contributors also described internal governance structures such as review panels, technology trials and internal pitch processes to help identify those knowledge gaps. As an example, one participant described a process whereby prospective research questions are challenged by specialists from across their organisation in a Dragons’ Den/Shark Tank-style scenario. The aim is to test whether the issue being raised is genuinely a question worth investigating or simply a preconceived answer in search of justification. The exercise occasionally reveals the latter. As the member reflected, “it’s not even a question, it’s just we’ve come up with the answer in the room.”

2. Mistaking technology for innovation

Competitive advantage rarely comes from owning technology; it comes from using knowledge in ways that improve performance. As Thorpe said, “we tend to think of innovation as technology and technology as innovation”; it is one of the main industry challenges in this space.

While the table acknowledged that technology is an important enabler, attendees agreed with Thorpe’s idea that innovation is better understood as the creation of value through the application of knowledge. As one contributor explained: “The innovation piece isn’t necessarily the technology itself but the process change.”

Another noted that “technology can accelerate innovation in some respects” but that genuine innovation comes from “the redesign and the way that people think about that”. “Innovation,” one participant suggested, is ultimately about “creating value from a problem that matters.”

3. Turning ideas into practice

Thorpe observed that when innovation fails it is usually due to poor implementation rather than poor ideas. This resonated with attendees who described the realities of introducing change in elite sport. Professional environments often demand immediate answers, which creates pressure to pursue quick solutions rather than carefully embedding new approaches.

One participant noted “the perceived need of the organisation for the speed of an answer” while another spoke of “the need for speed, the need to be first” creating pressure to solve today’s problems at the expense of investing in initiatives whose value may only emerge over a longer timeframe.

Additionally, the table recognised that new ideas alter workflows, responsibilities and behaviours, which means successful implementation depends as much on people as it does on tools.

4. The ability to prove performance impact

The table agreed that neither research nor innovation carries inherent value; their worth depends entirely on whether they improve outcomes. Research priorities, they argued, should be linked to strategic goals and organisational performance. Projects should ultimately be judged by their contribution to better decision-making, healthier athletes, improved availability, operational efficiencies or competitive advantage.

Just as importantly, organisations must be able to communicate value to key stakeholders. Participants noted that strong ideas frequently fail because decision-makers do not understand their relevance. As one attendee observed, “the success of innovation is determined less by the quality of the idea and more by how effectively its value is communicated.”

Thorpe wrapped up the session with a set of questions designed to encourage the table to further explore the practical realities of research and innovation within their own organisations:

• Who owns research and innovation within your organisation?
• How do you decide which questions are worth investigating?
• How do you communicate the value of innovation to coaches, executives and owners?
• How do you measure whether innovation has genuinely improved performance?
• How do you balance short-term delivery with long-term innovation?

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Main Image: Fifa via Getty Images

By Joe Lemire

EAST RUTHERFORD, N.J. — The introduction of mandatory hydration breaks in this year’s FIFA men’s World Cup was, depending on one’s perspective, an intrusion into the sport’s natural pace, a naked cash grab for advertising dollars or a welcome reprieve from the onslaught of swampy summer heat.

After France defeated Sweden 3-0 in the Round of 16, several of the Swedish players indicated that they found the mid-half intermissions beneficial on a day with a game-time temperature of 90 degrees and a heat index of 93 at N.Y. N.J. Stadium.

“It was hot out there, and it’s always nice to get a little bit of a break to fuel up,” Sweden defender Victor Lindelöf said. “Obviously against a team like France, it can be good as well to get a break and talk a little bit.”

Just days later, an apocalyptic-sounding Heat Dome was forecasted through the long holiday [July 4] weekend. Only three of the 16 World Cup venues — Atlanta, Dallas and Houston — were air-conditioned, making this “the World Cup of the sports scientist” for the outsized impact support staff can have, said Douglas Casa, the CEO of the Korey Stringer Institute and a kinesiology professor at UConn. Casa said proper heat mitigation strategies can lead to a 10% gain in performance.

The available modalities on site were personalized sports drinks, high-tech cooling devices that can be worn or held, and sweat-tracking patches, not to mention halftime cold plunges that can be 2-3 times more effective than air conditioning alone, according to Casa. He added that 90% of the benefits can be achieved with targeted heat acclimation in 10 days. The U.S. women’s national team spent that amount of time at the KSI prior to the Tokyo Olympics.

The Powerade-sponsored hydration breaks have been multipurpose for the athletes: consuming sports drinks, applying cooling towels and discussing tactics (and allowing time for fans to refill their beers). But relying on those three-minute pauses alone is not enough.

“Hydration is one of those things that, if you’re in the moment, you’re too late,” said Darcy Norman, the Director of Performance at Chicago Fire FC who previously held similar roles at three World Cups with Germany in 2014 and 2018 and the U.S. in 2022. “It takes time for your body to absorb the water. Typically, you would have more sodium, more salt, and that would draw in more water into the muscle itself. So you’re filling up the stores.”

A plan to beat the heat

Norman said a proper hydration plan starts the night before and should be built on data collected over time. The Fire use Nix Biosensors to build sweat profiles of every player in every condition, monitoring water and electrolyte loss on humid summer days, icy winter evenings and several types in between (Nix worked with one World Cup team but was unable to disclose which one). Perspiration “can vary quite substantially,” added Norman, who said some athletes are metabolically efficient even in intense heat while others sweat “like a fire hose.”

Gatorade has supported the Brazilian national team for a decade, including the use of sweat-tracking wearables from Epicore Biosystems that monitors the water and sodium lost from sweat and also skin temperature. Epicore CEO Roozbeh Ghaffari said longitudinal tracking is critical, noting, “You have to know what’s happening over time to predict forward.” Also, where a player competed in his professional season will affect his baseline.

Heat acclimatization can be objectively monitored through devices such as the Core body sensor, a wearable popular in triathlon and cycling. Core is making its way from endurance pursuits to team sports — it also has worked in and around this World Cup but isn’t at liberty to say whom it’s supporting — to gauge each athlete’s internal temperature and adaptation to the heat.

Core Chief Commercial Officer Ross McGraw said athletes face biomechanical limitations when heat overwhelms their system. “If they’re overheated, they’re not going to be able to run as fast,” McGraw said. “They’re not going to win those individual balls. And the less of those individual battles you win, the more likely you’re not going to do well as a team.”

Even teams that don’t use a sensor have prioritized heat training. Swedish defender Daniel Svensson said the team did heat training in climate-controlled rooms and saunas prior to coming to the U.S., where the Swedes held training camp in sweltering Frisco, Texas.

Defender Daniel Svensson and Sweden had to battle the pressure of France’s attack and the New Jersey heat. Photo: AFP via Getty Images

McGraw said Core measures body temperature in tandem with heart rate. When there is a “decoupling” of the metrics — that is, your heart rate continues to rise even when power or speed output remains steady — it can be a sign of heat strain. Some teams, he noticed, warm up the legs while wearing a cooling vest to keep the core temperature lower, calling the practice “counterintuitive” but one that has been effectively implemented by endurance sports.

Adidas outfitted 14 national teams with its new cooling vests and boots at the World Cup, including Argentina, Mexico and Spain. In training, England used Therabody’s new CryoTherm Palms, a handheld device that can lower core temperature. (Several pro sports teams, including MLB’s Cubs, have used a similar product from Apex Cool Labs called the Narwhal.)

Building up heat tolerance can make athletes feel more confident, too. As the Tokyo Games started, Megan Rapinoe posted on Instagram that she never felt better prepared for the conditions. McGraw, who later competed professionally in triathlons, spent childhoods attending weeks of soccer camp in central Pennsylvania where he remembers intense training in the summer sun and, afterwards, feeling “like Superman.” (The camp director, ironically, was a former pro with the aptly named Harrisburg Heat, and his surname is familiar: Mark Pulisic, the father of USMNT star Christian Pulisic.)

No matter how prepared an athlete is and how successful he may perform, the postgame objective is clear. After scoring two goals against Sweden, French superstar Kylian Mbappé told a reporter before leaving the field: “I’m looking forward to the changing room and the AC.”

Main Image: VeloU

By Joe Lemire

The SBJ Tech Sandbox series is where we share our experiences testing products, gear, solutions and more in the sports tech space.

PLEASANTVILLE, N.Y. — The critique was stinging but true.

“Your body is telling you how it wants to move, and you’re ignoring it,” said Nick Serio, the GM of Velo University, near the end of my hourlong session.

The baseball and softball training center regularly works with individual high school, college and pro players, and I had undergone a robust battery of tests: a movement evaluation, multiple force plate drills, an ArmCare assessment and a dozen pitches in front of a TrackMan radar.

This data fuels the training programs and guidance VeloU offers its players, with hybrid options available for on-location and remote training. It developed a custom app, built upon white-label provider Everfi’s foundation, and guarantees responses within 24 hours, although typically VeloU coaches respond within an hour and a half.

“Our big promise — what we feel was the biggest gap in that industry — is our ability to communicate with an athlete,” Serio said. “We were always trying to think what is missing when you’re talking about remote training, and it’s the feeling that you have this connection to a coach.”

Serio, a former college baseball player who later earned a doctorate in sport and performance, had seen me squat comfortably into a deep stance but lack hip-shoulder separation — I have limited upper thoracic spine rotation, meaning I don’t generate much force from the twist of my torso, and my hips aren’t flexible. This much I knew, dating back to a Springbok Analytics scan when I was told in no uncertain terms that my hip flexors were struggling.

This is not my first — or even my second — summer spent chucking baseballs in the pursuit of speed. What VeloU diagnosed is that I try to compensate by over-rotating my trunk to increase the whipping action of my motion in hopes of eking out a few more precious miles per hour. But, as Dir of Pitching Thomas Jankins noted, I didn’t have the pelvic control to benefit from that move.

“To be honest, you are older — you’re just not going to have the same separation as a 19-year-old,” Jankins told me in a comment that was somehow both reassuring and devastating.

The downstream effect of these mechanics is that, when I ultimately pitched the ball, I lowered my left shoulder in an exaggerated fashion, and my right hand came over the top at an extremely high arm slot.

Here’s Jankins demonstrating my exaggerated arm slot for me. Image: VeloU

“Your rotation mechanism right now is basically just pure thoracic extension and tilting,” added Jankins, who rose to Triple-A in the Brewers system, explaining that I bring my arm over the top to create energy because, despite my best efforts, I couldn’t easily whip my torso around, thereby not taking full advantage of my lower half’s ability to generate force.

As Jankins put it, pitching mechanics are a byproduct of the body’s strength and flexibility. The throwing motion is a way to feel comfortable within those limitations. Serio believed I should prioritize generating force from the ground and through my legs because it ought to be more fruitful than whipping my upper body around.

“Most of the time, we don’t worry about the end of a race,” he said, referring to the throwing motion and likening my pitch to the finish and my mobility to the starting line. “We worry about the start of a race. In my opinion, you’re not built to wrap the way you’re wrapping, and that’s throwing everything else off.”

Performance coordinator Paul Franzese chimed in with some helpful weight room tips to emphasize the proper hip position when lifting. Serio noted that my squat during the movement assessment should be better leveraged in my pitching motion.

“You have too good of a squat for somebody that’s your height to not try and work on getting into that [position],” Serio added. “You’re avoiding loading your hip.”

Throwing a med ball on force plates helped isolate my force production and sequencing. Image: VeloU

KinaTrax, the high-end motion capture system, is set to be installed later in the summer as well as a set of Bertec force plates built into a mound. In combination, that’ll give richer data on biomechanics.

Projecting velo for U

VeloU has built its own custom player reports, and Jankins led the creation of a proprietary algorithm predicting an athlete’s capacity for velocity. The end result is a total energy score analogous to the max throwing velocity a body is capable of throwing.

The algorithm, which Serio said will soon be licensed to college programs, is built on four metrics that are all composites of the individual assessments:

• Energy potential (body composition).
• Energy production (weight room metrics).
• Energy transference (med ball, force plate).
• Arm health (ArmCare).

My total energy score was 81.8, and that’s right on the nose. A couple summers ago, I touched 82 mph. More recently, I topped out at 81 mph while throwing on a Pocket Radar. The big test, though, would be throwing on VeloU’s TrackMan. I had never thrown on an enterprise device like that; it formerly powered MLB’s Statcast and remains a leading provider in top colleges.

The VeloU reports tells me I’ve got great energy potential and subpar interference, meaning I need to strengthen my arm. Image: VeloU

As I neared the end of my throwing session, I had been maxing out in the upper 70s when Jankins gave me a final charge.

“We’re going to go just one more fastball to finish,” he said. “Give us 80.”

I rocked and fired, the ball sailing well wide of the strike zone as I put far more effort into velocity than command. TrackMan flashes a preliminary speed immediately before taking a few seconds to display the confirmed final number, initially showed 80 mph as the half-dozen VeloU trainers watching me cheered … before settling on 79.3, with the background noise descending into groans.

“One more,” I said, knowing the pressure was on.

This time, TrackMan began north of 80 and stayed there, registering 80.5, a reading met with a sustained cheer around me. Someone in the crowd equated the anticipation to one of their regular trainees trying to hit 98mph for the first time.

It was a supportive comment to show how invested the group was in my success, but it also reinforced for me how far away I was from the elites. I felt very much like a retiree auditing a course at VeloU.

This baseball may look stationary in the photo, but TrackMan told me it was traveling 80.5 mph. Image: VeloU

This article was brought to you by SBJ Tech, a Leaders Group company. As a Leaders Performance Institute member, you are able to enjoy exclusive access to SBJ Tech content in the field of athletic performance.

By John Portch

When Ben Crockett served as Director of Player Development at the Boston Red Sox, he tended to be an early adopter of technology. But it didn’t always work out for the best.

At the 2025 Leaders Sport Performance Summit in London, he shared a decade-old story about a pitch-tracking technology that had been excitedly introduced by the club’s performance team. “It was a tool to help provide the coaches with more information that would help them in their coaching efforts and provide feedback,” said Crockett, who now serves the Red Sox as a Special Assistant of Basketball Operations.

The project proved to be a flop despite the technology doing exactly what it was supposed to do. Today, Crockett understands where his team went wrong. “We didn’t teach it enough; and we actually installed somebody to help operate it, which we thought would be helpful.” However, “in some ways it actually took away the ownership of our coaches for a solution that was ultimately going to help them.”

The Red Sox pitching coaches carried on doing their own thing; the merits of the technology were irrelevant to them. They didn’t trust, understand or see how it would help. “That was a good lesson in terms of what not to,” added Crockett, who shared the stage with Troy Thomson, the Football Operations Manager at the Brisbane Broncos, who had a cautionary tale of his own.

“Quite often, someone over-promises and under-delivers,” he said. “When you finally get the hardware in, it doesn’t quite have the capabilities you need.” It is no wonder that coaches can be wary of new datasets.

Ben Crockett (centre) speaks onstage at the 2025 Leaders Sport Performance Summit in London alongside Troy Thomson (left).

Over the course of half an hour onstage at the Kia Oval, Crockett and Thomson explained why data is valuable only if it can positively influence decision-making and behaviour. Their efforts to refine their approach point to five common principles: transparency, education, alignment, ownership and storytelling.

Transparency – information only matters if people can see it

Data is most useful when athletes understand the metrics, contribute to the resulting discussion and take responsibility for acting upon the information unearthed.

Transparency at the Red Sox, as Crockett explained, is embedded in a system of “continuous feedback loops within session, after session, after games” between coaches and the players, who develop a “continued understanding of where they stand on chasing specific measurable goals that have been agreed upon between the player and the team.” He added that “the athletes today are much more open to getting that type of information”.

Thomson also noted that Broncos players actively seek feedback on their performance metrics. For example, they now “want to finish the session and come and see what their line speed metrics are,” he said.

Education – data must be understood before it can influence decisions

Data can begin to influence decision-making when people understand both the numbers themselves and the behaviours those numbers are intended to shape. As Crockett noted, “it isn’t a one-time presentation; ‘here’s what everything means’. It’s ongoing.”

At the Broncos, Thomson emphasises the importance of helping players understand what data reveals about their performance. The coaches’ role is one of showing rather than simply telling.

He said: “The coaches also work really hard from a visual point of view as well, so being able to show the players technically how they’ve been working is a really important part of the process.”

Alignment – everyone working towards the same goals

To reach a collective understanding of what success looks like and how it is achieved, teams must first establish what matters most. Data can be a useful tool in that regard.

When Crockett discussed the challenge of coordinating a baseball operation that includes Major League, Minor League and player development programmes, he stressed the importance of “clearly defining key metrics that drive value for us at the Major League level and drive value throughout the industry.” Once those metrics have been identified, the task becomes ensuring everybody is working towards the same outcomes. Crockett described how the Red Sox seek to “help align our player development staff and personnel development staff with the things that were most important to driving that value”.

It is a similar story at the Broncos. “We’ve got a whole heap of different stats that we use; so post-game, there’s some standardised stats that we know if you get those processes right, the result will look after itself,” said Thomson, whose team has put in a lot of work behind the scenes. “To be able to bring the whole collective leadership group on the journey was really important.”

Ownership – data belongs to all stakeholders

Crockett and Thomson both argue that data becomes valuable when the people making decisions feel responsible for using it. Crockett’s pitch-tracking debacle is a prime example of what happens when that ownership is absent. As he recalled: “It ended up becoming a bit more of a ‘well, that’s their information and the pitching coach’s; and I’ll do what I’ve just been doing the whole time’.”

To prevent such scenarios, as Thomson explained, teams must create environments in which practitioners feel comfortable sharing ideas, challenging assumptions and seeking new sources of information. He said: “It’s really important to have a psychologically safe environment where we’re challenging each other on a daily basis to be able to find new metrics or new data sets that can actually enhance the programme.” At the Broncos, the software eventually “became part of a routine for us”.

Storytelling – data is most useful when it explains performance

Behavioural change in a data context depends on the story your numbers tell. Thomson repeatedly stressed the importance of using statistics to tell a story because it “adds to the coaching”.

Not that every stat holds the same weight; coaches have a responsibility to “share the relevant data that tell that story to achieve your outcomes”.

The Broncos’ momentum-tracking software is a case in point, as Thomson explained. While initially unsure how useful it would be, the tool was valued because “it allowed the coaches to be able to present the feel of the game”. By helping coaches explain something they previously struggled to articulate, the data became far more powerful as a catalyst for decision-making.

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