Practical prelude to machine learning for sport - Peterson, K. D. (Sport Performance & Science Reports, 2018)
Practitioners in today's sport science scene are often becoming awash in high-dimensional data sets. From an athlete monitoring perspective, teams may exploit several mediums concurrently; from force platforms and hydration to wellness, GPS and more - each contributing numerous, if not hundreds, of variables. How are practitioners supposed to select markers that meaningfully influence their objective? The purpose of this document is to showcase a realistic methodology for extracting predictive variables from a soup full of options by employing elementary machine learning principles.