With this short article I wanted to showcase how easy is to jump to conclusions with simplistic statistical analysis using fake salary data. It is important to recognize that statistical analysis involve a lot of subject…
Research has shown that there is an association between training load and likelihood of suffering non-contact injuries. But can we predict the injury? In this paper I have tried to predict non-contact hamstring injury by…
The following R workbook is meant to show different effects (or should I call them inequalities or differences) between groups when different thresholds for selection are applied. Hopefully, this will be a good example o…
Smallest Worthwhile Change: Individual vs Group If you haven’t been living under a rock over past few years, you must be familiar with Will Hopkins work on magnitude-based inferences (MBI). One of the basis behind MBI …
A sensitivity analysis is a technique used to determine how different values of an independent variable impact a particular dependent variable under a given set of assumptions. This technique is used within specific boun…
I have recently wrote a technical note (actually a video) for Sport Performance and Science Reports journal regarding Data Preparation for Injury Prediction. Both data and R core are available on GitHub repository.
I believe that the following video and the accompanying R code and data set will be very useful to sport scientists out there and will teach them extremely pragmatic technique in data mining.
I have recently stumbled on a few great papers that outline very useful statistical techniques, that are VERY applicable to sport and training analytics. If you are interested in analytics, this is a gold mine.
Most of us are collecting and analyzing training load data and readiness metrics (i.e. GPS training load, sRPE and wellness). The most common analysis method is to use two rolling averages windows – acute (around 7 day…
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