# Detecting and classifying moments in basketball matches using sensor tracked data

## TL;DR

This paper presents a method to automatically identify and classify active moments in basketball games using sensor-tracked player data, enhancing sports analytics by distinguishing offensive and defensive phases.

## Contribution

It introduces a novel threshold-based approach with tuning strategies to detect active periods and classify them, leveraging video-annotated ground truth for accuracy.

## Key findings

- Effective identification of active game periods
- Accurate classification of offensive and defensive phases
- Improved data-driven sports performance analysis

## Abstract

Data analytics in sports is crucial to evaluate the performance of single players and the whole team. The literature proposes a number of tools for both offence and defence scenarios. Data coming from tracking location of players, in this respect, may be used to enrich the amount of useful information. In basketball, however, actions are interleaved with inactive periods. This paper describes a methodological approach to automatically identify active periods during a game and to classify them as offensive or defensive. The method is based on the application of thresholds to players kinematic parameters, whose values undergo a tuning strategy similar to Receiver Operating Characteristic curves, using a ground truth extracted from the video of the games.