Citation

Ban, Kar Weng and See, John Su Yang and Abdullah, Junaidi and Loh, Yuen Peng (2022) BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction. In: MM '22: The 30th ACM International Conference on Multimedia, 14 October 2022, Lisboa, Portugal.

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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.

Item Type:	Conference or Workshop Item (Other)
Uncontrolled Keywords:	Sport analysis, Analysis, dataset
Subjects:	C Auxiliary Sciences of History > CJ Numismatics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	30 Nov 2022 04:23
Last Modified:	30 Nov 2022 04:24
URII:	http://shdl.mmu.edu.my/id/eprint/10746

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