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

Published: 10 October 2022 Publication History

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.

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References

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Cited By

View all
  • (2024)A stroke of genius: Predicting the next move in badminton2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00342(3376-3385)Online publication date: 17-Jun-2024
  • (2024)MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton PerformanceScientific Data10.1038/s41597-024-03144-z11:1Online publication date: 5-Apr-2024
  • (2024)Badminton Shot Recognition with LSTM NetworkThe Future of Artificial Intelligence and Robotics10.1007/978-3-031-60935-0_28(307-315)Online publication date: 20-Aug-2024

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

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    cover image ACM Conferences
    MMSports '22: Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
    October 2022
    152 pages
    ISBN:9781450394888
    DOI:10.1145/3552437
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 10 October 2022

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    Author Tags

    1. badminton
    2. dataset
    3. match analysis
    4. match outcome prediction
    5. match prediction
    6. naive bayes

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    • Ministry of Education (MOE)

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    MMSports '22 Paper Acceptance Rate 17 of 26 submissions, 65%;
    Overall Acceptance Rate 29 of 49 submissions, 59%

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    View all
    • (2024)A stroke of genius: Predicting the next move in badminton2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00342(3376-3385)Online publication date: 17-Jun-2024
    • (2024)MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton PerformanceScientific Data10.1038/s41597-024-03144-z11:1Online publication date: 5-Apr-2024
    • (2024)Badminton Shot Recognition with LSTM NetworkThe Future of Artificial Intelligence and Robotics10.1007/978-3-031-60935-0_28(307-315)Online publication date: 20-Aug-2024

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