Main Image: Getty Images

By Rob Schaefer

Ryan Sweeney first came to Timothy Gallwey’s The Inner Game of Tennis when a coach slipped a copy of the foundational sports psychology book into his tennis bag as a youth player.

Lessons learned from the text, which was first published in 1974, followed Sweeney through a Division I college career and later into the business world, where since hanging up his racket he has been an employee at automated speech recognition company Cogi and AI-powered transcription platform Rev.

Author Timothy Gallwey is finding a new audience for the book he published in 1974 thanks to a recently launched app. (Photo: Premium Content Agency)

“It changed my perception of performance and ability to trust myself,” Sweeney said of Inner Game. “My story isn’t all that unique when it comes to the impact that book has had from sports into business.”

Sweeney is now working on what you could call a magnum opus, given his experience as a tennis player turned tech entrepreneur: collaborating with Gallwey to bring the book’s teachings into the digital age, with a new mental performance app branded The Inner Game.

Sweeney’s journey to the project began in 2022, when a mutual friend and former colleague of Gallwey’s introduced them. Sweeney said he and Gallwey convened at a storage unit in Thousand Oaks, Calif., that houses Gallwey’s work, including published books, unpublished notes, audio recordings and training manuals, much of which had never been seen.

“He [Gallwey] had this feeling that the Inner Game in book form was limited in its ability to be impactful around the world,” Sweeney said. “One night, I woke up at 2 a.m. and was like, ‘I know what needs to happen.’”

Sweeney built a custom AI model using ChatGPT that could interact with someone nervous about an upcoming match or meeting, using the philosophies of “The Inner Game.” Gallwey was receptive.

Sweeney and Gallwey’s estate have a licensing deal that allows Sweeney and his co-founders to leverage the book’s intellectual property and compensates Gallwey’s family with a licensing fee and equity in the app’s parent company, Peak Performance AI. Sweeney and his team also created an Instagram page with Inner Game branding that is approaching 300,000 followers and plans to launch a podcast later this year.

“The main focus of the company is a subscription-based software product,” Sweeney said, referring to the mobile app. “[The social media and podcast channels are] a modern-day marketing engine. We’re storytelling our way to building upon this already passionate and huge Inner Game community, but we’re adding to it every day.”

The Inner Game app, released in August, is infused with layers of AI models to build personalized mental training programs. Sweeney described it as “like a MasterClass for performance psychology, but with a practical tool to help you apply what you’re learning.”

A morning in the life of Inner Game app users, he continued, could start by telling their “AI coach” how they are feeling, then listening to one of hundreds of three-to-seven-minute audio clips “specific to [their] performance journey,” created using information from the book. The app has a free tier, a three-month membership that prices at $60 and an annual subscription option for $192.

Sweeney said the app already has “thousands” of subscribers. An area of focus moving forward will be partnering with member-based tennis organizations (e.g., Universal Tennis Rating, or UTR) to distribute discounted subscriptions.

He clarified, however, that it is not a meditation app.

“A lot of apps, like Calm or Headspace, are designed to help take you away from the pressure,” he said, referencing two of the more recognizable direct-to-consumer meditation apps. “We built this to help you step into [the pressure].”

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

Too many of the systems used to support female athletes were not designed with women in mind, according to Dr Kate Ackerman.

“We already know important things about female athletes, but our systems are not effectively translating that knowledge into practice,” she told the audience at June’s Leaders Sport Performance Summit in New York.

Ackerman is the Co-Founder and President of the Women’s Health, Sports and Performance  [WHSP] Institute, which was founded to address that very issue. “I would argue we cannot fully understand human performance without fully understanding female performance,” she added.

Dr Kate Ackerman of the Women’s Health, Sports and Performance Institute talks at the 2026 Leaders Sport Performance Summit in New York. (Photo: Leaders Performance Institute / Evan Bernstein)

The problem

Women’s sport is outpacing the infrastructure built to support it.

The annual revenue for women’s sport is set to hit $3 billion in 2026 and there are more than 160 professional women’s sports leagues across the globe. “My 10-year-old son didn’t even know that men played basketball,” said Ackerman.

Yet the science still lags behind. As Ackerman explained, less than 10% of sports and exercise research is focused only on women, while only 6% of healthcare investment goes to women’s health. “If only 6% of research is focused solely on women, then you start to tease out which athletes are being studied, which women are being studied, we’re talking mostly about well-off white women. So this is a problem. We’ve got to figure out how to invest more.”

Why this matters

Sports science and performance have designed an environment based on male norms.

Ackerman points to 2020 as the first year that female-specific soccer cleats came on the market. “We’re jamming our feet into men’s shoes – and we know our feet are different,” she said. “Female athletes have unique considerations. Sex shapes injury, recovery, fuelling and adaptation.”

She then explained that female athletes face two to eight times the risk of ACL injury compared to their male peers. Similarly, “women have three times the incidence of bone stress injury compared to men” and “female athletes have a greater risk of developing relative energy deficiency in sport [REDs]”.

British former long distance runner, Pippa Woolven, experienced the symptoms of REDs and its consequences long before she was diagnosed.

“I think a lot of athletes aren’t well informed enough, especially females, about the energy demands that their sport might place on them,” Woolven told the Leaders Performance Podcast in 2022. “And when that’s paired with an active lifestyle and travelling around and commuting via active transport, these things can easily add up into a low energy availability.” At the time, the International Olympic Committee had not even named REDs and, as Woolven observed, “nobody really knew what was wrong with me.” It took her several years to recover with the right support.

Woolven went on to found Project REDs in an effort to educate and inform the industry. “There are so many myths and misconceptions out there about how to as somebody who is active or somebody who participates in sport, especially for women whose nutritional information is often based on that of males, whereas now we know it’s incredibly different.”

The research and practice gap

Women’s sport is already at a disadvantage, with coaches, practitioners and medical staff often making decisions with incomplete evidence. Female athletes themselves must navigate support structures originally designed around male norms. The result is not necessarily poor care, but fragmented care.

In reflecting on an athlete who was seeing multiple specialists for interconnected problems, Ackerman recalled asking: “What is happening? Why do you know all these different doctors? Why are you being sent all over town for your treatment?” Female athletes do not experience injury, fuelling, hormonal health, recovery and performance as separate issues but support structures often treat them that way. “The system seems like it’s kind of broken,” she added.

Progress, she argued, lies in building environments where research, medicine and performance operate side by side.

What’s next

It led to Ackerman establishing the Female Athlete Program, the Female Athlete Conference and ultimately the WHSP Institute, which combines clinical care, research and performance training within a single environment.

Of the Institute, she said: “It’s a 5,000 sq ft clinic, a 5,000 sq ft research space, and a 5,000 sq ft gym training facility all connected – it actually doesn’t sound that novel, does it? It’s the first one in the world for women. That’s kind of sad, but we’re excited to be the first ones.”

For Ackerman, the challenge is ensuring that knowledge reaches athletes, clinicians and coaches quickly enough to make a difference. She shared a striking stat: it takes an average of 17 years for scientific discovery to go fully into real-world practice. “I can figure out something in my lab, and it’s not going to get implemented with routines for quite a while.”

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Why the WNBA Is Ready to Enter a New Era of Personnel Decisions, Game Strategy and Sports Science

Main Image: Kraft Sports & Entertainment

By Ethan Joyce

Michael Israel likes to say, “You’re only as good as yesterday.” Spoken like a true IT lifer who has turned big problems into quick fixes.

As the CIO for the Kraft Group, Israel just saw a major tech project reach fruition in the New Balance Athletics Center. The 160,000-square-foot building, which officially opened last month, will be the home for everything Patriots players need.

And with that, Israel has overseen a connective behemoth in that three-story space, spanning facial authentication, biometrics, player-tracking data and a slew of other needs that support coaches, analysts, talent evaluators and the like.

A benefit for the Patriots? A longtime relationship between the Kraft Group and tech integrator NWN, a fellow Massachusetts company. The pair stepped forward with a five-year sponsorship deal last year, with NWN becoming the official technology infrastructure partner with Kraft Group. NWN also works with the Orioles and a handful of NBA teams, and it is the official technology provider for Boston College athletics.

Here were a few things that stood out to me about the project from a conversation with Israel and NWN CMO Andrew Gilman:

Two major pride points

Two of the venue’s greatest assets are the NFL Draft room and the virtual reality room. The draft room, which features 100-inch LED boards from Daktronics, is the “pride and joy” of ownership, Israel said.

The team’s new virtual reality room is a digitally infused next-gen walkthrough space that first originated under former coach Bill Belichick. This version is the width of an NFL field and features 20 yards of hashed turf so the team can run through plays and red-zone drills. Israel said it is a convergence of practice, game tape and player data.

Added Gilman: “To see the players going through drills and having all this running over Michael’s technology innovation — being able to stream and get video, low latency, high throughput — it’s where the technology becomes a real competitive differentiator for the organization.”

Making it seamless across the entire group

The goal for a technology experience is to be standardized and even unnoticed, Israel said. A key component there is filling the space with vendors they’re already familiar with. The Patriots campus has 65 Cisco WebEx rooms, and 31 of those are now in the new facility.

The center also uses facial authentication provided by Wicket, which is the same tech used across all league stadiums for credentialing. Those are two of many examples.

“The same way we do with autonomous purchasing in the stadium — frictionless is across the board,” Israel said. “Making it so that the technology is there, but they don’t even realize that they’re using it.”

(We talk about frictionless fan experiences all the time here — hadn’t thought about it in the player experience, but it makes so much sense).

New creativity with data

Data is such a catch-all, borderline buzzy word. But it can be anything. Case in point: cafeteria food.

Instead of the buffet-style treatment of old, tablets now let players pull up their diet (the Patriots use a precious metal naming system for meal plans like gold, silver and bronze) and get a meal that fits their parameters. It also sets up a system that can monitor consumption of various options for the benefit of operations.

“We’ve also rolled out an AI application that monitors the food that’s being cooked and ties into how much we’re buying, how much we’re prepping, how much gets donated to charity at the end of the day to right-size and be more efficient,” Israel said.

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

“We’re seeing more and more examples of athletes and coaches just saying ‘enough’s enough’.”

Performance specialist Iain Brunnschweiler made that very observation when discussing ‘the future athlete’ at a recent Leaders Virtual Roundtable and the pressure on young athletes and their coaches to perform.

“Outputs are increasing, people are getting faster, people are running faster, they’re scoring faster, they’re hitting things harder, they’re running for longer, they’re lifting heavier, and there’s more scrutiny,” he continued. “So whilst those main components don’t change, the demand on young athletes and current performance athletes is they just need to keep on evolving.”

Brunnschweiler spoke of the need to strike a balance. “How do we provide that interdisciplinary support to ensure that we’re helping them stretch the edges of their capabilities whilst also not exploding?”

At the centre of the discourse were Gen Z (16-28-year-olds) and Gen Alpha (up to 16 years old) athletes who, like their peers in wider society are adaptable, informed and collaborative, but also sceptical, and anxious.

The table began by discussing day to day challenges.

A coaching lead in the Australian system questioned whether supportive youth programmes adequately prepare athletes for senior environments featuring stronger personalities, more forceful feedback and ever-changing coaches. He said:

Should we be assholes because there are assholes in the next environment? Or actually, should we be doing things that we believe are the best way to develop players?

He explained that players may struggle when exposed to a senior coach’s “real big spray” because their development environments are centred on learning rather than confrontation. With athletes potentially moving between several coaches, they need to understand how they learn, seek what they need and handle different forms of feedback. He added:

We need our players to be self-sufficient because they’re going off into 18 different environments and coach tenure can be quite short. Are we doing them a disservice if we don’t prepare them for that?

He was joined by a peer who noted a similar issue:

There are some things that, regardless of the coaching, the environment will ask of you. All we can do is prepare them to be able to understand what the actual environment or industry is going to ask of them.

Of the best athletes, he said:

They actually have got a clear picture on what their week looks like from Monday to Friday. They nearly own their career.

A psychologist working in European football highlighted a contradiction within well-resourced academies: large support teams want to help athletes, but can inadvertently remove their ownership and reduce opportunities for independent learning. He said:

You have loads of people around them and we’re taking them off the driver’s seat in a lot of ways because we want to maintain our roles.

He also warned against allowing sport to become an athlete’s entire identity:

Too often football is framed as who they are, not what they do.

As a result, setbacks can become disproportionately damaging, turning “not a problem in soccer, but an existential problem”.

Additionally, their coaches struggle to give autonomy in everyday actions:

Some of our coaches are really good at it. Others are really struggling with giving up control and power.

A talent programme lead within English cricket explained that his programme gave athletes considerable control over their individual development plans, but found that some lacked the knowledge to make productive choices:

They don’t know what they don’t know… We just lost a little direction when we really did open out for the players and it just became a bit basic. I actually think it’s stifled our development in that regard at times.

A coaching lead from within the British system spoke of engagement as an ongoing challenge. He said:

There’s still that, like, ‘what can you do for me?’ vibe versus ‘what can I do for everybody?’ At the same time, there’s a fatigue at co-creation and asking them to get involved in everything. They are starting to say ‘just tell me’… We know that first-hand experience is far better than second-hand knowledge, but how do you give them those first-hand experiences in your current environment?

More pointedly, he observed:

We say all this stuff is important, but actually let’s hold up the mirror and how much time do we spend on it?

The table then raised some potential solutions.

The coaching content and learning lead argued that pathways cannot prepare athletes for one particular senior environment or coaching style. Instead, they should help athletes understand how they learn, obtain the support they need and respond constructively to different forms of feedback.

You can put them under quite a bit of pressure, challenge them in the way in which they’re doing things. Putting them in situations that they’re not actually very good at.

A performance director in Australia advocated teaching athletes to plan their working week and make effective use of time outside formal training by holding them accountable. He said:

First of all, identifying why they’re here and what’s driving them. So it’s purpose-driven. Understanding why they’re doing what they’re doing, what’s driving them, and then some accountability around the actions they said they’re going to do to get there.

The psychologist in European football spoke of his team’s efforts to promote self-reflection:

One concept that we use is asking questions in the first person. So it’s actually only the player who has the answer: ‘What was difficult for you?’ This self-reflection comes before peer feedback, before coach feedback… We’re actually encouraging the players in their IDPs to think for themselves and then present their ideas and hopefully come out with at least 70 to 80% of what their IDP looked like.

The pathway lead from English cricket shared their ‘three lions’ framework to help cricketers read changing situations. Players are asked to consider whether the moment requires them to absorb pressure as a “protector”, prepare as a “stalker” or attack as a “hunter”. He added:

We’re trying to steer away from roles a little bit because the game’s so changeable and talk about which mindset do we need to be in… ‘What were we in this moment? What made us be this mindset as opposed to this mindset?’

What to read next

10 Ways in Which the Best Environments Support their Pathway Athletes

By John Portch

Ahead of each academic year, the Juilliard School wades through 2,300 applications to fill its 18 freshmen places on its drama course.

It can be arduous for the teacher and prospective student alike, with several stages of auditions designed to establish the applicants’ acting potential before the chosen few can matriculate at the New York-based performing arts conservatoire.

“We’re not necessarily looking for who’s great already, although some people do look great already,” said Evan Yionoulis, speaking in her capacity as Dean of the drama division at Juilliard at June’s Leaders Sport Performance Summit in Brooklyn. “So we’re looking for that spark. We’re looking for somebody who can connect to a character and hopefully take you on a journey when you see what they’re doing. Sometimes that’s something you have to teach them. So not everybody will have that. Somebody who has an appetite for language usually is somebody you want, too.”

To sum it up, she added, “we’re looking for things that we can’t teach.”

Yionoulis, who left Juilliard in July to become the Dean of the David Geffen School of Drama and Artistic Director of the Yale Repertory Theatre, distinguished between the qualities the school looks for in prospective actors and the capabilities that she and her colleagues can develop.

Acting is not alone in that regard. Toby Purser, Head of Conducting at the Royal College of Music [RCM] in London, will work to identify if a student candidate has the right attributes by asking probing questions that force them to dig deep.

He told an audience at Leaders Meet: the Talent Journey: “This is something you have to be psychologically sensitive about. Sometimes you need to get into somebody’s personal space to force something out of them.”

It raises an important question for academy coaches: are talent pathways selecting athletes for performance markers that good coaching could develop anyway and doing so at the expense of qualities that are harder to teach?

We explore Yionoulis’ advice on that front and see how it compares not only to the RCM, but to the Queensland Ballet and the Royal Academy of Dramatic Art [RADA].

Test their response, see how they learn

Of the audition process, Yionoulis said: “I give them adjustments on the pieces and I see how flexible they are.” She is specifically looking to see how they respond to a new direction.

That’s one phase. Another is the Juilliard’s “callback weekend” where applicants are assessed on how they work with others. “We teach them some classes and we see how they play with others. Are they interested when other people are working or only when they are in the spotlight? And then we try to put a class together that we think can help each other grow.”

It calls to mind the reflections of Iván Gil-Ortega, the Artistic Director of the Queensland Ballet, who places a similar emphasis on collegiality.

“Every single dancer is thinking of themselves. They’re not thinking of the person next to them,” he told an audience at February’s Leaders Meet: Australia in Brisbane. “Even if I have the best Romeo and the best Juliet, unless everyone else plays their part, you won’t see the performance. We’ve had great dancers where the company wasn’t really into the whole process. So it didn’t really matter how great they were because the show didn’t come up to that next level.”

Build the foundations before expanding capacity

The Juilliard curriculum, as Yionoulis explained, has three phases:

1. Foundation: “In the first year, we lay the foundation.”
2. Capacity building: “The second and third year start to do capacity building, working on Shakespeare, working on clowns, working with masks, working with things that expand the actor’s range.”
3. Ease: “Coming back into the fourth year, getting that sense of ease. So the truthful acting is just there, they don’t seem to sweat.”

Sometimes development means further experimentation. Yionoulis added: “I’m not judging good or bad. I’m going, ‘what’s the next step to take it further? What’s the next step to take it deeper?’” She couches her teaching interventions as “let’s try this, let’s try that” or “what if?”

This approach creates room for failure, which is also a feature at RADA. In 2023, Lucy Skilbeck, the academy’s Collaborative Projects Lead, spoke onstage at the Leaders Sport Performance Summit in London about her methods.

“This is something we talk about a lot because we’re encouraging people to not to try and get it right and to allow failure to be part of one’s creative practice,” said Skilbeck. “You can’t have a creative practice if you’re not willing to fail. You can only have a complacent practice because you’ll only try the things that you know will succeed. To have a creative practice you have to be willing to accept a degree of failure – and I think that goes for all of us – those trying to lead, run and develop courses as well as those who are participating on them.”

What to read next

Working with Young Performers: How Juilliard Adapted While Retaining their Standards

By John Portch

• Coaches must recognise how younger performers are changing without assuming they are any less committed.

• They should create environments where challenge, support and a sense of belonging give people the confidence to take developmental risks.

• They should listen to how their coaching is landing and tailor their approach to the individual, while also remaining clear about the qualities they continue to value.

Evan Yionoulis’ observations of acting students resonated with the coaches in the room at June’s Leaders Sport Performance Summit in Brooklyn.

“I think they’re coming in less prepared in a way than earlier generations in terms of experience of reading, in terms of attention span, in terms of being able to take deep dives into something,” said Yionoulis, who spoke in her capacity as Dean of the drama division at the world-renowned Juilliard School, the New York-based conservatoire.

“I read something horrifying that in film schools people are having trouble watching two hours now, keeping that sustained attention,” she continued.

Yionoulis, who has since moved on to become Dean of the David Geffen School of Drama and Artistic Director of the Yale Repertory Theatre, called it a question of stamina.

“I think we have to help them on that earlier level and get their stamina, not just their physical stamina, but their attention stamina to do this work.”

It also posed another question to the coaches in the room: what do younger athletes require of coaches and the coaching environment?

Do not put the blame on a generation

Firstly, young performers may be different but they are not deficient. Yionoulis believes teachers and coaches must be better at providing the right support. Her students, she said, remain highly motivated, while schools have had to become more responsive to issues (such as mental health concerns) that became particularly apparent after the pandemic.

“There was a lot of anxiety and I think that the school and many schools became more responsive to that; they now have more support for people who need help,” she said. That does not mean lowering expectations. Yionoulis instead focuses on developing the capabilities students still need, including that “attention stamina”.

Create the conditions for risk-taking

“If we’re worried about fear, we have to foster an environment where you can be brave,” said Yionoulis. “Part of that is to create an environment of belonging, where people are respected for what they bring, and where you posit trust in their capacity to develop.”

She will say to a student: “I believe in you, I brought you into the school. You’re going to graduate, and I’m going to take you all the way there, and I’m going to be patient, and I want you to be patient.”

Listen to understand what works

For Yionoulis, listening is one way a coach can understand whether their approach still works for the person in front of them. “I think sometimes teachers, coaches, parents, bosses think that their job is to talk and everybody else’s job is to listen,” she said. “We’re listening to see how things are landing because I can change the end of my sentence, I can harden it up or I can soften it at the end, based on what I perceive.”

She distinguishes between instruction, experimentation, coaching, problem-solving and critique. “If we’re entering into a discussion together, I don’t ask what you think and then tell you that you’re wrong,” she said. “Because then you’re not going to offer next time I ask.”

Coaches should take developmental responsibility for their athletes

Juilliard selects for “potential, not necessarily for who’s great already”, which requires patience given that development isn’t linear. The school abandoned its old system in which students, as part of a large cohort, could be cut at the end of the academic year. Today, Yionoulis said, “We take 18 and we want to graduate 18.” Unless the environment is clearly wrong for someone or they are unwilling to work, she said, “we’re committed to them, whether we think we made a good decision or we think we made a bad decision.”

Know what shouldn’t change

Though generations of students come and go, the qualities Juilliard looks for remain unchanged.

“I think a person has to come in with an appetite,” said Yionoulis. “We have conversations with them to see how curious they are. We’re looking for things that we can’t teach.”

What to read next

10 Ways in Which the Best Environments Support their Pathway Athletes

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”.

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