Can AI really replace the human judgment behind elite sport performance? In this panel discussion on AI in Sport Science, practitioners unpack the non-negotiable rule before sending any AI-generated report to a coach, why being upfront about using AI can still cost you trust, and how the best organisations approach AI in Sport Science and AI agents. They also explore how a football club is tackling membership churn, the two data skills every graduate needs first, and where a CEO should really invest: people or technology.

AI in Sport Science

Artificial intelligence is changing how sport science teams work, but its greatest value may be helping practitioners make better decisions. In high performance, speed matters. However, speed without accuracy can create poor decisions, so AI should always sit alongside judgement and context.

Human Judgement Still Matters

For practitioners using AI to create reports, summaries, or insights, human review is essential. Before anything reaches a coach or athlete, check the facts, calculations, and language. AI can confidently produce something that sounds right while still being wrong. Therefore, the practitioner remains responsible for the final message and the decision that follows.

Start With the Problem

Start with the problem, not the technology. The strongest applications begin with a clear operational challenge, such as reducing repetitive work. For example, membership churn modelling can identify members who may not renew. From there, teams can segment those members and create targeted interventions.

Build Better Data Foundations

None of this works without data foundations. Consistent collection, valid testing methods, and understood metrics must come first. If the underlying information is unreliable, AI will only process that uncertainty faster. Consequently, sport scientists need to understand how their data is collected, cleaned, manipulated, and interpreted before trusting automated outputs.

Develop Your Data Skills

For emerging practitioners, this creates a valuable opportunity. Learn the fundamentals of data visualization and data manipulation, then use AI to accelerate the work. Turning raw information into a clear story remains a critical skill. Moreover, understanding the calculations behind an insight helps you challenge AI when its answer does not make sense.

People First, Technology Second

Ultimately, the future of sport science is not people versus AI. It is people using AI intelligently. Great practitioners ask better questions, protect data quality, understand performance context, and turn information into action. In the right culture, AI becomes a performance multiplier. Build the people, skills, and culture first, then let technology amplify what your team can achieve.

🔑Highlights from the episode:

– Why sending AI reports straight to coaches will get you in trouble
– Are elite sports teams really using AI agents?
– How a football club uses AI to predict which members won’t renew
– The two data skills every graduate needs before using AI
– Human vs. AI: where should a CEO invest first?

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What if the reason your athletes struggle to change direction has nothing to do with their final step? Jesse Green, Director of Performance Science at 1080 Motion, joins us to unpack how 1080 sprint athlete development and motorized resistance are changing the way practitioners test and train speed. We explore how 1080 sprint athlete development can reveal the phase of deceleration most coaches overlook, why handing everyone the same load quietly fails half the room, and how to keep driving real adaptation when a packed 82-game season leaves almost no window to train.

1080 Sprint Athlete Development: A Smarter Coaching Approach

Seeing More Than a Stopwatch

High-performance coaching today is moving beyond stopwatch results. In Jesse Green’s discussion, the 1080 Sprint is presented as a practical way to see how athletes create, absorb, and redirect force. Its motorized cable applies precise resistance while the encoder records movement, giving coaches insight into both performance and strategy.

One Tool, Many Applications

The device supports sprinting, change of direction, deceleration, and rehabilitation. That range matters because athletes rarely fail for one simple reason. Two players can post the same sprint time while using different force patterns. Therefore, the coach can use the data to decide whether an athlete needs more force, more speed, or a better movement solution.

Teaching Better Deceleration

Deceleration is one of the clearest examples. Green describes early and late braking, separated at a 50 percent velocity decrement. Early braking reduces speed before the final plant, so the last contact can redirect force instead of carrying the full braking demand. This approach may improve control and reduce stress on the planting limb during sharp changes of direction.

Using Resistance to Shape Technique

Resistance can also become a coaching constraint. When the 1080 pulls an athlete forward into a deceleration, the athlete naturally learns to place the center of mass behind the base of support and find more useful shin angles. Consequently, technique changes become something the athlete feels, not just another list of cues. Velocity decrement profiling then individualizes the load: a target such as 50 percent speed loss challenges each athlete appropriately, unlike a fixed ten kilogram prescription.

Training Through a Busy Season

Finally, slow isokinetic work offers a valuable option during congested seasons. Controlled concentric actions can provide a strong stimulus without excessive fatigue or movement speed. For example, a belt squat can combine light resistance with an ascent around 0.25 to 0.35 meters per second. Ultimately, the 1080 connects measurement, coaching, and context, helping teams develop athletes with greater precision.

🔑Highlights from the episode:

– What is 1080 Motion
– Early vs late phase deceleration
– When to change a deceleration strategy
– What is Velocity Decrement (VDec)
– How to use isokinetics effectively

🎥 Watch full episode on YouTube
🔗 https://www.youtube.com/@preparelikeapro
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Practical sports science decisions are difficult to make when a performance team collects everything and still cannot act on it. Chris Barnes has spent a career inside professional football, rugby league and horse racing, and was one of the first full-time sports scientists in the English Premier League. He unpacks why more data does not automatically lead to better practical sports science decisions, what changes the moment a new head coach walks in, the mistake he sees young practitioners repeat, and where he thinks AI could quietly go wrong.

Practical Sports Science Decisions

When more data creates more noise

High-performance sport now produces more information than teams can use. GPS units, wellness surveys, recovery scores, and training loads promise clarity. Yet, when a session unfolds as planned, the numbers tell us only that everything is fine. Practical sports science decisions therefore begin by separating useful signals from confirmation.

When a new coach changes the picture

A new coach can make a squad look unprepared. Training may carry an external load, but drills, faster decisions, and tactical demands can raise the players’ internal response. That does not automatically mean fitness has disappeared. Instead, the group may need time to adapt to a way of working.

Start with the environment

That is why sports scientists should resist arriving with a prebuilt package of academic research, tests, and terminology. First, they need to understand the sport, club culture, and the coach’s priorities. Only then can they choose measures that answer questions and communicate findings in language people can use.

Flexibility builds trust

Every head coach brings a different personality, philosophy, and training rhythm. Barnes describes working with 32 coaches and resetting his approach each time. This flexibility is not a soft skill added to the science. It allows science to earn trust and influence decisions.

AI still needs human judgment

AI may help practitioners organize data sets and spot patterns quickly. However, speed is not the same as truth. If a model produces an impressive but unvalidated output, it can create confidence without understanding. Teams must ask whether information is reliable, relevant, and actionable.

Better decisions remain the goal

Ultimately, the best performance environments use data as a conversation starter, not a substitute for professional judgment. Coaches, athletes, and support staff still understand context, emotion, fatigue, and intent. Therefore, the practical goal is not to collect more numbers. It is to make better decisions, at the right time, for the people in front of you. That principle keeps technology connected to preparation, competition, and recovery.

🔑Highlights from the episode:

– Is data overload quietly overwhelming performance teams?
– Why saying nothing is sometimes the smartest call
– Your players are fit, just not fit yet
– The big mistake most sports scientists still make
– The hidden danger of AI in high-performance

👉Connect with Chris

Email: chrisbarnes60@gmail.com

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Why did the 10-year-old athlete who became world number one test as completely ordinary? Grant Jenkins has coached Ash Barty, Nick Kyrgios, and hundreds of athletes, and he holds an unpopular view on what separates the great from the good. Grant gets into why testing data keeps selecting the wrong players, what the best athletes do after losing, why he deliberately filled his academy with less-talented athletes, and the $120-an-hour job he now hands to AI first.

What Makes Great Athletes

Talent Is Only the Beginning

At the highest level of sport, talent matters. Some athletes move better, react faster, or process information more quickly. However, talent alone does not create greatness. The best performers combine their natural abilities with disciplined training, awareness, and consistency. Therefore, coaches should look beyond physical testing and understand the complete athlete.

Great Athletes See More

Ashleigh Barty is a powerful example. As a young player, she was not extraordinary in every physical test. Still, she could read the game, anticipate situations, and move efficiently around the court. In other words, her advantage was not always easy to measure. Great athletes often notice patterns earlier and make better decisions under pressure.

Resetting Quickly Builds Consistency

Mistakes happen to everyone, even champions. The difference is how long athletes carry them. Elite performers such as Roger Federer and Rafael Nadal became known for recovering quickly after frustration or a lost point. Instead of staying trapped in the previous moment, they focused on the next opportunity. As a result, one mistake was less likely to become several.

Environment Shapes Performance

Talent develops faster in the right environment. Interestingly, a disciplined athlete with less natural ability can influence a younger, more gifted teammate. Mature peers model responsibility, independence, and self-control. Moreover, feedback from a respected teammate can sometimes have more impact than another instruction from a coach. Strong cultures make good habits normal.

Technology Should Support Judgment

Finally, high performance is also about using the right tools. AI can organize information, speed up research, and reduce repetitive work. However, it should support judgment rather than replace it. In performance settings, technology can create a strong starting point, while qualified people make the final decisions. Ultimately, greatness comes from combining talent, awareness, emotional control, strong environments, and smart support systems together.

🔑Highlights from the episode:

– What actually separates the best at the highest level
– The quality he spotted in Ash Barty at ten
– Is the 10,000 hours theory getting talent wrong
– Why he recruited the less talented athletes on purpose
– The $120-an-hour job he now hands to AI

🎥 Watch full episode on YouTube
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Are you ready to enhance your coaching? Uncover the strategies to build a thriving online platform, liberating yourself from the time-for-money constraints while pursuing a career as an AFL S&C coach.

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Today we sat down with world-class performance coach Nic Gill to explore Simple High-Performance Coaching. With over 20 years in the All-Blacks environment and a recent transition into the NFL with the Baltimore Ravens, Nic shares what it really takes to deliver Simple High-Performance Coaching and build and sustain elite performance over time.

This conversation dives into culture, leadership, testing, and the realities of working across different sporting codes, highlighting that while environments change, the core challenges remain the same.

If you’re an AFL athlete, coach, or practitioner looking to simplify your approach and focus on what truly matters, this episode is packed with actionable insights.

Simple High-Performance Coaching

High performance is not always about doing more. Sometimes, it is about doing less, doing it better, and creating an environment where athletes can consistently perform at their best. In his conversation about the Baltimore Ravens High-Performance Program, Nic Gill shares several lessons that apply far beyond professional sport.

Safety Creates Sustainable Performance

First, Gill highlights the importance of managing training exposure and protecting athletes. The NFL’s preseason restrictions are designed to gradually build workload rather than immediately placing athletes under excessive stress. Ultimately, good high-performance coaching is about finding the right balance between challenge, adaptation, and recovery.

Technology Must Improve Decisions

Furthermore, having access to more technology does not automatically create better performance. Gill explains that the Ravens evaluate whether technology actually provides value or changes decision making. In other words, data should serve the coach and athlete, not create more noise. The best systems turn useful information into clear action.

Collaboration Strengthens Performance

Gill also emphasizes purposeful meetings and collaboration across departments. Instead of allowing staff to work in isolation, bringing different perspectives together can create better solutions. As a result, performance becomes an organizational responsibility rather than something owned by one department.

Elite Athletes Master Their Craft

Another powerful lesson is that sustained excellence requires consistency. The best athletes are not simply talented. They continue developing their skills when nobody is watching. Moreover, they understand that becoming great for a decade requires daily commitment to the fundamentals.

Simplicity Creates Clarity

Finally, Gill’s belief that simplicity is better may be the most valuable lesson. Remove unnecessary complexity, focus on the basics, and eliminate distractions. Therefore, coaches should constantly ask whether a process adds value or simply adds drag. When performance becomes clearer, athletes can focus their energy on what matters most.

🔑Highlights from the episode:

– Why the NFL puts strict limits on preseason training
– What a bigger budget actually buys in sports science
– How he structures (or doesn’t structure) team meetings
– The one skill every elite athlete never stops working on
– Why he believes simple beats complex in performance

🎥 Watch full episode on YouTube
🔗 https://www.youtube.com/@preparelikeapro
📅 Book a FREE discovery call for coaches
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Are you ready to enhance your coaching? Uncover the strategies to build a thriving online platform, liberating yourself from the time-for-money constraints while pursuing a career as an AFL S&C coach.

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https://calendly.com/plp2/15min