In this episode, we explore how AI in High Performance Sports is being used today. From computer vision and player tracking to adaptive coaching tools, we break down what is actually creating real impact on the ground through emerging AI performance trends. We also cover privacy safe workplace tools like Microsoft Copilot and NotebookLM, and how performance teams are using them to streamline reporting and daily workflows.
Finally, we look at Agentic AI and what it could mean for sports scientists. Think automated reports, load and wellness alerts, and deeper movement insights such as gait, contact time, and stride metrics to improve decision-making across the Australian Football League and National Rugby League.
🔑Highlights from the episode:
– How AI is already being used in high-performance sport
– The privacy problem coaches can’t ignore with athlete data
– Why Copilot is becoming a game-changer for staff workflow
– The rise of Agentic AI and your future performance assistant
– Why total distance misses the mark in AFL and rugby league
AI in High Performance Sports
The Future of AI in Pro Sports
AI is already improving how high-performance teams’ work. While many coaches first think of ChatGPT, Copilot, or Gemini, the biggest impact in sport is often coming from computer vision and player tracking. For example, systems like Hawkeye have been used for years in broadcast sport, and now similar ideas are influencing performance environments. As a result, practitioners can collect better movement and game-related information with less manual work.
Practical AI Tools for Coaches
Importantly, Rich highlights that AI is becoming more practical and accessible. Coaches can now use tools such as an iPhone camera and pose-estimation software to capture movement patterns and joint positions. This helps staff assess gait characteristics and technique without always needing expensive lab-based setups. Therefore, AI is not just for analysts or data scientists, it can support everyday coaching decisions on the field.
Privacy, Ownership, and Better Decisions
At the same time, Rich raises an important caution around privacy and data ownership. Questions remain about whether athlete data belongs to the player, the club, or the sport. However, he also explains that broader shared datasets can improve player welfare by helping identify injury risk factors more accurately. In other words, the challenge is creating strong guardrails while still using data in a way that benefits athletes.
Agentic AI and the Next Step
Looking ahead, the major shift is agentic AI. Instead of simply answering questions, these systems can complete tasks such as drafting emails, creating reports, and pulling data from multiple sources. Consequently, sports scientists and medical teams can spend less time on admin and more time making decisions. Rich also stresses that monitoring should move beyond total distance and toward more meaningful gait and IMU metrics that better reflect real sport demands.
🙌People Mentioned:
– Tim Gabbett
– Grant Duffy
– Ben Horsley
– Neil Gibson
– Neil Potts
– Reece Walsh
– Laura Dunn
🎥 Watch full episode on YouTube
🔗 https://www.youtube.com/@preparelikeapro
📅 Book a FREE discovery call for coaches
👉 https://calendly.com/plp2
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.
Book a FREE coaches discovery call
https://calendly.com/plp2/15min
