AI providers in sports science need to intentionally craft their data visualisation designs to provide explainable & actionable outputs.

Sports scientists want to unpack the black box in sports technology but how can this work with artificial intelligence (AI) data science?

In this video, we discuss athlete load monitoring & specifically, player tracking technologies, such as GPS, IMU, LPS, & Optical tracking.

Why context is key in sports science data, and therefore any AI system too. But we should beware when our so-called context becomes bias.

Is the 'Big Data' collected in sports science big enough to train Artificial Intelligence models? Is the data quality clean enough?

Terms like AI & Machine Learning are poorly understood. With Zone7, we have put together an AI dictionary for sport science practitioners.

When a measure becomes a target, it ceases to be a good measure. As sport is overflowing with measures and targets, this requires exploring.

Thanks to everyone who responded to my appeal to canvas industry opinion on artificial intelligence (AI) in high performance sport. I...

This post provides three action steps to help practitioners feel less overwhelmed by big data when monitoring athlete training load.

Data visualisation is an important skill for all sports scientists. Here, we consider how we best display data in graphs.

This post discusses the book 'The End of Average' and what the talking points mean in the context of sports science. When was the last...

This posts explains important considerations for how to organise data in spreadsheets for sports science.

This guest post from Chris Bishop discusses everything you need to know about calculating athlete asymmetries!

Last month I was invited to write a guest blog on Plinths and Platforms about load monitoring and rehabilitation.  It included an...