This post provides sports practitioners with an introduction to artificial intelligence and machine learning in sport.

This comprehensive guide delves into z-scores, t-scores, and STEN scores, specifically their calculations, and practical applications.

What data viz tool should sports scientists use for their analytics? In this video we discuss options and share a demo of Microsoft PowerBI.

If you monitor training load in multi-directional athletes (like in team sports), you need to watch this video on complete acceleration!

In this video, we'll discuss different ways you can utilise the standard ten (STEN) score and t-score calculations in sports science.

This post explains the Dynamic Strength Index, a calculation for sports scientists from a CMJ and IMTP that can guide training programming.

This post explores the use of the minimum effort duration in GPS technology to define high-speed, acceleration and deceleration efforts.

My comment on Zone7’s white paper: The Importance of Workload, Strength, Recovery and Environmental Context Data in Injury Risk Forecasting

In this video, I discuss how to calculate a z score and why it can be so valuable in sports science.

This video discusses how invisible monitoring can be used in sports science. Invisible monitoring can negate the need for fitness testing.

Video of my episode on the HIIT Science Training Science podcast, discussing AI, ChatGPT, sports science careers, and diversity in staff.

Here you'll find a link to my new free report with Zone7, entitled Applications of Artificial Intelligence within High Performance Sport.

Is it Man vs Machine when it comes to using artificial intelligence with athlete training load management, or can we use a hybrid approach?

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

In this video, we discuss the top trends for sports science in 2023. What will be some of the most talked about topics in sports research?

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.