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.
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