Show Notes
- Getting athlete data right before using AI
The quality of any analysis depends on the information going into it. From correctly setting up wearables to keeping information consistent across multiple devices and platforms, Mollie explains why seemingly small errors can create much bigger problems further down the data pipeline.
- Why athletes and coaches need data literacy
Collecting data is one thing. Knowing when to trust it is another. Understanding realistic ranges, identifying faulty readings, and questioning unusual results are becoming increasingly important skills as athletes rely on more technology to guide training.
- When tracking becomes chasing
Badges, streaks, scores, and training load metrics can encourage engagement, but they can also push athletes toward behaviours that conflict with good training principles. Mollie explores why athletes should focus on the metrics that actually support adaptation rather than trying to keep every number moving in the right direction.
- Designing technology that actually supports athletes
The same metric can be helpful one day and distracting the next. From HRV before competition to leaderboard notifications encouraging unnecessary training, the conversation explores how interface design can influence behaviour and why technology needs to know when to provide information and when to stay out of the way.
- Keeping human expertise at the centre of AI
AI can process enormous amounts of athlete data, but domain expertise remains essential for judging whether an output actually makes sense. Mollie explains why the future is not simply about replacing coaches and practitioners with technology, but combining computer science with real world sports science expertise to produce better decisions.
