Filtering Procedures for Sensor Data in Basketball

TL;DR

This paper presents an automatic data cleaning algorithm for basketball sensor data that identifies inactive moments, segments game actions, and labels them as offensive or defensive, enhancing data analysis accuracy.

Contribution

The paper introduces a novel algorithm for automatic cleaning and segmentation of basketball sensor data based on players' trajectories, validated through robustness checks.

Findings

Effective removal of inactive moments from sensor data

Successful segmentation of game into actions

Accurate labeling of offensive and defensive plays

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.