Assessing the scoring probability of teams and players in different areas of a court map is an important topic in basketball analytics, in order to define both game strategies and training programmes. In this contribution we propose a spatial statistical method based on classification trees, aimed to define a partition of the court in rectangles with maximally different shooting performances. Each analyzed team/player is characterized by its/his own partition, so comparisons can be made among different teams/players. In addition, shooting efficiency measures computed within the rectangles can be used to define spatial shooting performance indicators.
Spatial Performance Indicators and Graphs in Basketball
Zuccolotto, Paola;Sandri, Marco;Manisera, Marica
2021-01-01
Abstract
Assessing the scoring probability of teams and players in different areas of a court map is an important topic in basketball analytics, in order to define both game strategies and training programmes. In this contribution we propose a spatial statistical method based on classification trees, aimed to define a partition of the court in rectangles with maximally different shooting performances. Each analyzed team/player is characterized by its/his own partition, so comparisons can be made among different teams/players. In addition, shooting efficiency measures computed within the rectangles can be used to define spatial shooting performance indicators.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
