Basketball players' versatility: Assessing the diversity of tactical roles

Abstract

Basketball has seen an increase in the number of players who perform multiple tactical roles. Therefore, the aim of the study was twofold: (i) to define a method to characterize basketball players as versatile or specialists, based on 13 game-related statistics; (ii) to evaluate versatile-specialist tendencies in a professional national league. A predictive model was proposed using the Automated Machine Learning (AutoML) of the H2O framework. The model was tested using data from nine seasons (2008–2017) from the Brazilian national league (NBL), encompassing 1497 players' observations, achieving an accuracy of 70.81%. We classified players as versatile or specialist and observed the following: (i) the number of versatile players has grown over the nine seasons period (from 25.16% to 47.85%), with Small Forward and Power Forward players presenting the fastest growth in versatility; (ii) NBL teams had similar proportions of versatile and specialist players; (iii) for the best players in the NBL (All-Star game players), there was a trend toward a higher number of versatile players (58.33%) compared to specialist ones. In conclusion, the method was effective in indicating the players' degree of versatility and demonstrated a tendency of increasing versatility over the analyzed seasons. In practice, it may support the assessment of player's profile and contribute for coaches' strategic decisions.