Abstract
The R package BasketballAnalyzeR, described in the book “Basketball Data Science”, is designed to be a flexible tool for a great variety of aims. It is simple enough to eliminate barriers-to-entry for aspiring data scientists and sports analysts, but is also allows to perform more complex analyses for scientific research. It is appropriate for teaching, in both degree courses in Statistics and specific Masters and post-graduate courses in sports. In this article we show some of the statistical graphical tools made available by BasketballAnalyzeR: bubble plots, to assess relationships among several game variables; shot charts and heatmaps of the shots’ spatial density, to extract information about spatial performance; shot density charts, to analyze shot frequency with respect to some concurrent game variables; assist/shot networks, to highlight the relationships between teammates; nonparametric estimation of scoring probability and expected points with respect to some concurrent game variables, to investigate which are each player’s most efficient shots.
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