This post introduces Action Apps Virtual Assistant (AAVA) and includes a video demonstration of the AI-powered chatbot.

This post explains how to calculate and use the Eccentric Utilization Ratio (EUR) from squat jump and countermovement jump data.

This video with Sport Horizon discusses their new Power BI for S&C Coaches course and includes a data viz demo.

This post delve into the significance of RFD, explore how it is calculated, and discuss its practical applications.

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.

How do moderators and mediators shed light on the complex interplay between workload and injury in sports science?

Sports Scientist Ashmeet Anand demonstrates how interactive data viz, built using Tableau, enhances insights from sports science data.

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

In this video, I discuss calculating athlete asymmetries, including all the 'Calculation Conundrum' equations.

This blog post discusses the importance of proactive training load management in times of fixture congestion in professional team sports.

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.

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.

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?