This post provides an intro to my new Sportsmith article, 'Integrating muscle volume analysis into athlete monitoring, training & rehab'

This post explores the potential for screening tests to predict sports injury and how they fit into an injury risk minimization framework.

This excerpt is taken from the VALD eBook 'Practitioner's Guide to Preseason', which covers the fundamentals of planning preseason.

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

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

This post explores how AI-driven analytics of MRI scans enables more detailed insights into body composition and musculoskeletal health.

Here we discuss the Premier League Festive football calendar and consider the recovery and travel demands for each club.

This post discusses how to use the VALD Performance ForceFrame to measure shoulder strength in internal and external rotation.

This video will show you how to use the VALD Performance ForceFrame to measure strength in the hip and groin area.

In this video, we're going to discuss hamstring testing, and specifically the Nordic Hamstring Exercise (Russian Leans) strength test.

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

According to the Pareto Principle, 80% of results will come from 20% of the inputs. So what does this mean in sports science & performance?

The book "Risk: A User's Guide" highlights that we cannot eliminate risk but must focus our energies on boosting our “Risk Immune System”.

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.

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

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.