BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction

Kar Weng Ban*, John See, Junaidi Abdullah, Yuen Peng Loh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper introduces BadmintonDB, a new badminton dataset for training models for player-specific match analysis and prediction tasks, which are interesting challenges. The dataset features rally, strokes, and outcome annotations of 9 real-world badminton matches between two top players. We discussed our methodologies and processes behind selecting and annotating the matches. We also proposed player-independent and player-dependent Naive Bayes baselines for rally outcome prediction. The paper concludes with the analysis performed on the experiments to study the effects of player-dependent model on the prediction performances. We released our dataset at https://github.com/kwban/badminton-db.

Original languageEnglish
Title of host publicationMMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
PublisherAssociation for Computing Machinery
Pages47-54
Number of pages8
ISBN (Electronic)9781450394888
DOIs
Publication statusPublished - 10 Oct 2022
Event5th ACM International Workshop on Multimedia Content Analysis in Sports 2022 - Lisboa, Portugal
Duration: 14 Oct 2022 → …

Conference

Conference5th ACM International Workshop on Multimedia Content Analysis in Sports 2022
Abbreviated titleMMSports 2022
Country/TerritoryPortugal
CityLisboa
Period14/10/22 → …

Keywords

  • badminton
  • dataset
  • match analysis
  • match outcome prediction
  • match prediction
  • naive bayes

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software
  • Media Technology

Fingerprint

Dive into the research topics of 'BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction'. Together they form a unique fingerprint.

Cite this