Alley‐oop! Basketball analytics in R

Interest in sport statistics has grown rapidly in recent years. In the scientific literature, increasing numbers of papers are published dealing with statistical methods and analyses applied in a wide range of sports, including American football, soccer, basketball, volleyball, baseball and ice hockey. Teams and individual athletes also increasingly base their decisions about game strategies or training on the analysis of performance data, while magazines and newspapers make use of statistics and data visualisations in response to their readership’s growing fascination with the topic. In response to this, the University of Brescia, Italy, created an international project, Big Data Analytics in Sports (BDsports; bdsports.unibs.it), with the aim of establishing links between scholars and professionals who have a shared interest in sport statistics. BDsports has given rise to a number of activities and applications, including a new tool for basketball analytics: an R package called BasketballAnalyzeR. It contains a number of functions aimed at developing both basic and complex statistical analyses of basketball data, and in this article we show some analyses that can be carried out, focusing – by way of illustration – on a single player: Luka Dončić, whose performance in the 2018/2019 National Basketball Association (NBA) regular season earned him the NBA Rookie of the Year Award