Filtering Procedures for Sensor Data in Basketball

Abstract

Big Data Analytics help team sports' managers in their decisions by processing a number of different kind of data. With the advent of Information Technologies, collecting, processing and storing big amounts of sport data in different form became possible. A problem that often arises when using sport data regards the need for automatic data cleaning procedures. In this paper we develop a data cleaning procedure for basketball which is based on players' trajectories. Starting from a data matrix that tracks the movements of the players on the court at different moments in the game, we propose an algorithm to automatically drop inactive moments making use of available sensor data. The algorithm also divides the game into sorted actions and labels them as offensive or defensive. The algorithm's parameters are validated using proper robustness checks.

A Dockerized big data architecture for sports analytics

“…Metulini [37] concerned with basketball data processing, and aimed to suggest an ad-hoc procedure to automatically filter a data matrix containing players' movement information to the moments in which the game is active, and by dividing the game into sorted and labelled actions as offensive or defensive. Knobbe et al [29] worked on professional speed skating and devised a number of features that capture various aspects of sports events by aggregating discrete sequences of such events.…”