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

Kar Weng Ban; John See; Junaidi Abdullah; Yuen Peng Loh

doi:10.1145/3552437.3555696

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

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (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 language	English
Title of host publication	MMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
Publisher	Association for Computing Machinery
Pages	47-54
Number of pages	8
ISBN (Electronic)	9781450394888
DOIs	https://doi.org/10.1145/3552437.3555696
Publication status	Published - 10 Oct 2022
Event	5th ACM International Workshop on Multimedia Content Analysis in Sports 2022 - Lisboa, Portugal Duration: 14 Oct 2022 → …

Conference

Conference	5th ACM International Workshop on Multimedia Content Analysis in Sports 2022
Abbreviated title	MMSports 2022
Country/Territory	Portugal
City	Lisboa
Period	14/10/22 → …

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

Access to Document

10.1145/3552437.3555696

Cite this

@inproceedings{1001ea77200c44bfa3005094690e3a3f,

title = "BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction",

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.",

keywords = "badminton, dataset, match analysis, match outcome prediction, match prediction, naive bayes",

author = "Ban, {Kar Weng} and John See and Junaidi Abdullah and Loh, {Yuen Peng}",

note = "Funding Information: This research is supported by the Ministry of Education (MOE), Malaysia under the Fundamental Research Grant Scheme (FRGS) with Reference Code FRGS/1/2019/ICT02/MMU/03/6. Publisher Copyright: {\textcopyright} 2022 ACM.; 5th ACM International Workshop on Multimedia Content Analysis in Sports 2022, MMSports 2022 ; Conference date: 14-10-2022",

year = "2022",

month = oct,

day = "10",

doi = "10.1145/3552437.3555696",

language = "English",

pages = "47--54",

booktitle = "MMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports",

publisher = "Association for Computing Machinery",

address = "United States",

}

Ban, KW, See, J, Abdullah, J & Loh, YP 2022, BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction. in MMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, pp. 47-54, 5th ACM International Workshop on Multimedia Content Analysis in Sports 2022, Lisboa, Portugal, 14/10/22. https://doi.org/10.1145/3552437.3555696

BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction. / Ban, Kar Weng; See, John; Abdullah, Junaidi et al.
MMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, 2022. p. 47-54.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - BadmintonDB

T2 - 5th ACM International Workshop on Multimedia Content Analysis in Sports 2022

AU - Ban, Kar Weng

AU - See, John

AU - Abdullah, Junaidi

AU - Loh, Yuen Peng

N1 - Funding Information: This research is supported by the Ministry of Education (MOE), Malaysia under the Fundamental Research Grant Scheme (FRGS) with Reference Code FRGS/1/2019/ICT02/MMU/03/6. Publisher Copyright: © 2022 ACM.

PY - 2022/10/10

Y1 - 2022/10/10

N2 - 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.

AB - 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.

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KW - dataset

KW - match analysis

KW - match outcome prediction

KW - match prediction

KW - naive bayes

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M3 - Conference contribution

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BT - MMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports

PB - Association for Computing Machinery

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ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

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