Development of an R-Shiny-based Shooting Area Visualization Application for Use in Basketball
Ryota Bani, Yoshiro Yamamoto · 2022
In recent years, a number of methods of collecting and utilizing data such as video and statistics have been discussed in various competitive sports, including baseball and soccer. In basketball competitions, some teams utilize wearable devices such as CATAPULT and KINEXON to analyze tracking data via the use of global positioning system data. Numerical indicators to understand the game, such as four-factor parameter, are obtained via calculations based on observation data. Detailed qualitative and quantitative methods for analyzing play dynamics have been established via analyses undertaken in Hudl and Sports Code. The introduction of these tools requires payment fees to obtain a license. Therefore, it is deemed not realistic for all teams to introduce such tools. However, these tools provide a clear method for teams to provide feedback, and the information provided by such resources is of great utility. Basketball is a competitive sport in which points are scored by shooting, and the team with the most points at the end of the game wins. In, this study, we focus on the number of shots and the locations from which scoring attempts are made (shooting area); this work aims to convey quantitative information in an intuitive manner via the visualization of such data. The visualization method is via the development of an application in R-Shiny. We thus aim to characterize each team’s shooting location data and discuss how to use such data in actual games and scouting.