Statistics & Data Analysis - Complementary Training

• # 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.

• # Profiling Issues – Mind Your Target Variable

This is VERY IMPORTANT concept and if you are doing any type of profiling, I urge you to watch this video.

• # Uncertainty, Heuristics and Injury Prediction

I was asked by Rod Whiteley and Nicol van Dyk to contribute to the Aspetar Journal targeted topic issue that just got released off the press. I tried to combine my knowledge of predictive analytics, machine learning, philosophy of science, heuristics and practical experience as coach & sport scientist into one article. Hopefully I managed to create readable narrative.

• # What Is Propensity Matching and How Can We Improve Validity of Causality Claims?

Researchers usually try to randomize correctly, but sometimes the effect is not in the treatment, but maybe in the difference between group (pre-treatment) or during the treatment (for example volume of training) which should be similar/same so we can judge the effects of something else of interest (for example novel periodization). The solutions are to involve covariates in the…

• # Effect of Typical Variation of a Test on Confidence Interval

Confidence intervals gives us the range of a given statistic when generalizing from a sample to a population. The simplest example could be mean of a sample (e.g. average height) – what we are interested in are the generalizations (or inferences) from this sample to a population (e.g. average height in population). Due the sampling error we are not…

• # Making Sense Out of the Session GPS Data

We collect more and more data and it is becoming increasingly difficult to make meaning out of it. What I would like to do is to present one simple way to make the meaning out of session GPS data using LOF and Clustering. Most GPS units produce multiple features p compared to number of observations n, so we are…

• # Importance of Context in Evaluating Wellness Questionnaires

In the previous post I’ve shared the novel idea on how to ‘aggregate’ wellness categories into positive/negative wellness score.