Application of multivariate statistical methods for football player analysis - Kalashnikov, Igor (2024)
Bachelor thesis is aimed to give an overview, how multivariate statistical methods could be used in football player analysis and decision making by team management departments while considering a player replacement. Firstly, Principal Component Analysis is presented as a method for dimension reduction and highlighting most common patterns in player statistics. Then, different similarity/dissimilarity metrics applied to transformed data in order to measure, how much players differ from each other. Cluster analysis is presented and applied for unionizing players into groups of similar ones. For the better presentation of results, there is a basic Shiny app developed and deployed, that has functionality useful as for specialists in analytics in statistics, as for stakeholders in order to get a quick user-friendly overview of possible candidates for player replacement and different trends visualization.