Statistics - Complementary Training

• # bmbstats – Bootstrap Magnitude-Based Statistics for Sports Scientists

The aim of this book is to provide an overview of the three classes of tasks in statistical modeling: description, prediction, and causal inference.

• # Extending the Classical Test Theory with Circular Performance Model

In this video, I will go more into these details and also provide an extension of this model, which I termed Circular Performance Model (CPM). CPM, in my opinion, better represent performance phenomenology we experience as coaches and athletes.

• # Statistical 101: Gender Salary Gap

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 subjective decisions and cannot be claimed to be purely objective, but it is still way better than ignorance or ideology.

• # Predicting Non-contact Hamstring Injuries by Using Training Load Data and Machine Learning Models

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 using two seasons day-to-day training load data.

• # Fat Tails and Inequality

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 on how it is important to be statistically educated when comparing groups and discussing inequality.

• # Smallest Worthwhile Change: Individual vs Group

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 is defining smallest practically meaningful change, or smallest worthwhile change (SWC). Together with Typical Error (TE) of a test, SWC is very needed...

• # Simple Sensitivity Analysis with R

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 boundaries that depend on one or more input variables, such as the effect that changes in interest rates have on bond prices.

• # Data Preparation for Injury Prediction

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.