Open-source tools and code for applied sport science — built in R, focused on making performance data analysis reproducible and accessible.
A collection of R functions for processing, visualising, and analysing acceleration-to-deceleration data. Designed to make deceleration demand analysis reproducible and accessible for applied sport scientists.
Updated 5 months ago
Covers Minimal Detectable Change, Smallest Worthwhile Change, Effect Sizes, and Magnitude-Based Inference — with clear R code and visual outputs to help coaches and analysts decide when a performance shift actually matters.
Updated 3 months ago
Applies NMF dimensionality reduction to multi-metric force plate data, tracking how an athlete's overall biomechanical profile shifts relative to their healthy baseline state throughout a return-to-play programme.
Updated 3 months ago
Uses nMDS ordination on multiple force plate metrics to create a visual "fingerprint" of an athlete's movement profile, making it easy to see how close they are to their pre-injury baseline during rehabilitation.
Updated 5 months ago
Reproduces all analyses from the published paper — threshold detection, regression modelling, and visualisation — providing a fully reproducible workflow for identifying strength targets that predict sprint performance.
Updated 2 months ago
All projects are open source and freely available on GitHub .