# Audio-based performance evaluation of squash players

**Authors:** Katalin Hajdu-Szucs, Nora Fenyvesi, Jozsef Steger, Gabor Vattay

arXiv: 1704.08765 · 2018-07-04

## TL;DR

This paper presents a novel audio-based framework for precisely classifying, localizing, and analyzing squash shots, providing detailed performance metrics to enhance player evaluation and coaching strategies.

## Contribution

It introduces an innovative audio analysis system that captures and interprets sound patterns to assess squash performance objectively, a novel approach in sports analytics.

## Key findings

- Framework accurately classifies shot types
- Localizes shot positions with high precision
- Provides detailed shot statistics and performance metrics

## Abstract

In competitive sports it is often very hard to quantify the performance. A player to score or overtake may depend on only millesimal of seconds or millimeters. In racquet sports like tennis, table tennis and squash many events will occur in a short time duration, whose recording and analysis can help reveal the differences in performance. In this paper we show that it is possible to architect a framework that utilizes the characteristic sound patterns to precisely classify the types of and localize the positions of these events. From these basic information the shot types and the ball speed along the trajectories can be estimated. Comparing these estimates with the optimal speed and target the precision of the shot can be defined. The detailed shot statistics and precision information significantly enriches and improves data available today. Feeding them back to the players and the coaches facilitates to describe playing performance objectively and to improve strategy skills. The framework is implemented, its hardware and software components are installed and tested in a squash court.

## Full text

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## Figures

19 figures with captions in the complete paper: https://tomesphere.com/paper/1704.08765/full.md

## References

14 references — full list in the complete paper: https://tomesphere.com/paper/1704.08765/full.md

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Source: https://tomesphere.com/paper/1704.08765