{"type":"ScholarlyArticle","title":"Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset","authors":[{"name":"Wei-Yao Wang","slug":"wei-yao-wang","given":"Wei-Yao","family":"Wang","affiliation":"Department of Computer Science , National Yang Ming Chiao Tung University"},{"name":"Wei-Wei Du","slug":"wei-wei-du","given":"Wei-Wei","family":"Du","affiliation":"Department of Computer Science , National Yang Ming Chiao Tung University"},{"name":"Wen-Chih Peng","slug":"wen-chih-peng","given":"Wen-Chih","family":"Peng","affiliation":"Department of Computer Science , National Yang Ming Chiao Tung University"},{"name":"Tsi-Ui Ik","slug":"tsi-ui-ik","given":"Tsi-Ui","family":"Ik","affiliation":"Department of Computer Science , National Yang Ming Chiao Tung University"}],"abstract":"In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used by researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set, with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023, to encourage researchers to tackle this real-world problem through innovative approaches and to summarize insights between the state-of-the-art baseline and improved techniques, exchanging inspiring ideas. The baseline codes and the dataset are made available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.","doi":"10.24963/ijcai.2024/1042","url":"https://ijcai.org/proceedings/2024/1042","pdf":"https://ijcai.org/proceedings/2024/1042.pdf","markdown":"https://ijcai.org/proceedings/2024/1042.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1042","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1042,"pages":{"first":8829,"last":8832},"keywords":["Multidisciplinary Topics and Applications: MDA: Other","Multidisciplinary Topics and Applications: MDA: Sports"],"videos":[],"references":[{"position":1,"raw":"[Ban et al., 2022] Kar-Weng Ban, John See, Junaidi Abdul- lah, and Yuen Peng Loh. Badmintondb: A badminton dataset for player-specific match analysis and prediction. In MMSports@MM, pages 47-54. ACM, 2022.","title":"Badmintondb: A badminton dataset for player-specific match analysis and prediction","authors":"Kar-Weng Ban, John See, Junaidi Abdullah, Yuen Peng, Loh","year":"2022","venue":"MMSports@MM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cartron, 2022] Mathieu Cartron. Shuttlecock dataset. https://universe.roboflow.com/mathieu-cartron/ shuttlecock-cqzy3, mar 2022. visited on 2023-05-06.","title":"Shuttlecock dataset","authors":"Cartron","year":"2022","venue":null,"doi":null,"url":"https://universe.roboflow.com/mathieu-cartron/shuttlecock-cqzy3","internal_url":null},{"position":3,"raw":"[Chang et al., 2023] Kai-Shiang Chang, Wei-Yao Wang, and Wen-Chih Peng. Where will players move next? dynamic graphs and hierarchical fusion for movement forecasting in badminton. In AAAI, pages 6998-7005. AAAI Press, 2023.","title":"Where will players move next? dynamic graphs and hierarchical fusion for movement forecasting in badminton","authors":"Kai-Shiang Chang, Wei-Yao Wang, Wen-Chih Peng","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chu and Situmeang, 2017] Wei-Ta Chu and Samuel Si- tumeang. Badminton video analysis based on spatiotem- poral and stroke features. In ICMR, pages 448-451. ACM, 2017.","title":"Badminton video analysis based on spatiotemporal and stroke features","authors":"Wei-Ta Chu, Samuel Situmeang","year":"2017","venue":"ICMR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Decroos et al., 2019] Tom Decroos, Lotte Bransen, Jan Van Haaren, and Jesse Davis. Actions speak louder than goals: Valuing player actions in soccer. In KDD, pages 1851- 1861. ACM, 2019.","title":"Actions speak louder than goals: Valuing player actions in soccer","authors":"Decroos","year":"2019","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2022] Yu-Hsien Huang, Yung-Chang Huang, Hao Syuan Lee, Tsì-Uí Ik, and Chih-Chuan Wang. S 2 - labeling: Shot-by-shot microscopic badminton singles tac- tical dataset. In APNOMS, pages 1-6. IEEE, 2022.","title":"S 2labeling: Shot-by-shot microscopic badminton singles tactical dataset","authors":"Yu-Hsien Huang, Yung-Chang Huang, Tsì-Uí Hao Syuan Lee, Chih-Chuan Ik, Wang","year":"2022","venue":"APNOMS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kim et al., 2022] Hyunsung Kim, Bit Kim, Dongwook Chung, Jinsung Yoon, and Sang-Ki Ko. Soccercpd: For- mation and role change-point detection in soccer matches using spatiotemporal tracking data. In KDD, pages 3146- 3156. ACM, 2022.","title":"Soccercpd: Formation and role change-point detection in soccer matches using spatiotemporal tracking data","authors":"Kim","year":"2022","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Merhej et al., 2021] Charbel Merhej, Ryan J. Beal, Tim Matthews, and Sarvapali D. Ramchurn. What happened next? using deep learning to value defensive actions in football event-data. In KDD, pages 3394-3403. ACM, 2021.","title":"What happened next? using deep learning to value defensive actions in football event-data","authors":"Charbel Merhej, Ryan J Beal, Tim Matthews, Sarvapali D Ramchurn","year":"2021","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Wang et al., 2020] Wei-Yao Wang, Kai-Shiang Chang, Teng-Fong Chen, Chih-Chuan Wang, Wen-Chih Peng, and Chih-Wei Yi. Badminton coach ai: A badminton match data analysis platform based on deep learning. Physical Education Journal, 53(2):201-213, 2020.","title":"Badminton coach ai: A badminton match data analysis platform based on deep learning","authors":"Wei-Yao Wang, Kai-Shiang Chang, Teng-Fong Chen, Chih-Chuan Wang, Wen-Chih Peng, Chih-Wei Yi","year":"2020","venue":"Physical Education Journal","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Wang et al., 2021] Wei-Yao Wang, Teng-Fong Chan, Hui- Kuo Yang, Chih-Chuan Wang, Yao-Chung Fan, and Wen- Chih Peng. Exploring the long short-term dependencies to infer shot influence in badminton matches. In ICDM, pages 1397-1402. IEEE, 2021.","title":"Exploring the long short-term dependencies to infer shot influence in badminton matches","authors":"Wei-Yao Wang, Teng-Fong Chan, Hui-Kuo Yang, Chih-Chuan Wang, Yao-Chung Fan, Wen-Chih Peng","year":"2021","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Wang et al., 2022a] Wei-Yao Wang, Teng-Fong Chan, Wen- Chih Peng, Hui-Kuo Yang, Chih-Chuan Wang, and Yao- Chung Fan. How is the stroke? inferring shot influence in badminton matches via long short-term dependencies. 14(1), nov 2022.","title":"How is the stroke? inferring shot influence in badminton matches via long short-term dependencies","authors":"Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Wang et al., 2022b] Wei-Yao Wang, Hong-Han Shuai, Kai- Shiang Chang, and Wen-Chih Peng. Shuttlenet: Position- aware fusion of rally progress and player styles for stroke forecasting in badminton. In AAAI, pages 4219-4227. AAAI Press, 2022.","title":"Shuttlenet: Positionaware fusion of rally progress and player styles for stroke forecasting in badminton","authors":"Wang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Wang et al., 2023] Wei-Yao Wang, Yung-Chang Huang, Tsi-Ui Ik, and Wen-Chih Peng. Shuttleset: A human- annotated stroke-level singles dataset for badminton tac- tical analysis. In KDD. ACM, 2023.","title":"Shuttleset: A humanannotated stroke-level singles dataset for badminton tactical analysis","authors":"Wei-Yao Wang, Yung-Chang Huang, Tsi-Ui Ik, Wen-Chih Peng","year":"2023","venue":"KDD. ACM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Wang, 2022] Wei-Yao Wang. Modeling turn-based se- quences for player tactic applications in badminton matches. In CIKM, pages 5128-5131. ACM, 2022.","title":"Modeling turn-based sequences for player tactic applications in badminton matches","authors":"Wei-Yao Wang","year":"2022","venue":"CIKM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}