Modeling the Badminton Stroke Pattern Through the Sequential Pattern Discovery Using Equivalent Classes (SPADE) Algorithm

Jefita Resti Sari; Atina Ahdika

doi:10.13164/mendel.2023.1.037

Jefita Resti Sari Universitas Islam Indonesia
Atina Ahdika Universitas Islam Indonesia https://orcid.org/0000-0001-9161-227X

DOI: https://doi.org/10.13164/mendel.2023.1.037

Keywords: Badminton, Kento Momota, Rules, SPADE, Stroke Pattern, Viktor Axelsen

Abstract

Badminton is one of the most popular sports in the world, especially in Asia. It has a parent organization called Badminton World Federation (BWF). Discussion about player strategies in winning various championships held by BWF is an interesting topic to discuss. This paper aims to analyze the hitting patterns of badminton players by paying attention to the sequence of types of strokes made by the players, including lobs, netting, smashes, drives, and dropshots. Sequential pattern discovery using the equivalent class algorithm (SPADE) is the appropriate method to identify these problems because it can determine the rules and probabilities of player's hitting patterns based on the order of the types of strokes. In this paper, we analyze the stroke pattern of the two top-ranked badminton players in the men's singles sector at the Malaysia Open 2022 championship, where Viktor Axelsen and Kento Momota met in the final. Based on the results of these research, we analyze the strategies and recommended hitting patterns from the information on the two players' patterns. The results of this study, in general, can be used as information for players to understand and analyze the opponent's performance or strategy before competing.

References

Agrawal, R., and Ramakrishnan, S. Mining Sequential Patterns. In Proceedings of the Eleventh International Conference on Data Engineering (1995), pp. 3–14.

Alcock, A., and Cable, N. T. A comparison of singles and doubles badminton: heart rate response, player profiles and game characteristics. International Journal of Performance Analysis in Sport 9, 2 (2009), 228–237.

Ardiantoro, L., and Sunarmi, N. Badminton player scouting analysis using Frequent Pattern growth (FP-growth) algorithm. Journal of Physics: Conference Series 1456, 1 (2020).

Aydogmus, M., Arslanoglu, E., and Senel, O. Analysis of badminton competitions in 2012 London Olympics. Turkish Journal of Sport and Exercise 16, 3 (2014), 55–55.

Badminton World Federation. Players Profile, 2023.

Bishop, C. M., and Nasrabadi, N. M. Pattern recognition and machine learning, vol. 4 no. ed. Springer, New York, 2006.

BWF TV. Petronas Malaysia Open 2022, 2022.

Fomby, T. Association Rules (Aka Affinity Analysis or Market Basket Analysis). PhD thesis, Southern Methodist University, Dallas, Texas, 2011.

Gomez, M. A., Rivas, F., Leicht, A. S., and Buldu, J. M. Using network science to unveil badminton performance patterns. Chaos, Solitons and Fractals 135 (2020).

Han, J., Pei, J., and Kamber, M. Data Mining: Concepts and Techniques. Elsevier, 2011.

Huang, P., Fu, L., Zhang, Y., Fekete, G., Ren, F., and Gu, Y. Biomechanical analysis methods to assess professional badminton players’ lunge performance. Journal of Visualized Experiments 2019, 148 (2019), 1–8.

Juliastio, R., and Gunawan, D. Sequential Pattern Mining Dengan Spade Untuk Prediksi Pembelian Spare Part Dan Aksesoris Komputer Pada Kedatangan Kembali Konsumen. In Seminar Nasional “Inovasi dalam Desain dan Teknologi” - IDeaTech 2015 (2015), pp. 314–325.

Malwanage, K. T., Senadheera, V. V., and Dassanayake, T. L. Effect of balance training on footwork performance in badminton: An interventional study. PLoS ONE 17, 11 November (2022), 1–14.

Rahmad, N. A., As’Ari, M. A., and As’ari, M. A. The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data. Journal of Physics: Conference Series 1529, 2 (2020).

Rahmad, N. A., As’Ari, M. A., Soeed, K., and Zulkapri, I. Automated badminton smash recognition using convolutional neural network on the vision based data. IOP Conference Series: Materials Science and Engineering 884, 1 (2019).

Rahmad, N. A., Sufri, N. A. J., Muzamil, N. H., and As’ari, M. A. Badminton player detection using faster region convolutional neural network. Indonesian Journal of Electrical Engineering and Computer Science 14, 3 (2019), 1330–1335.

Ramli, A. S. S., Kamalden, T. F. T., Sharir, R., Harith, H. H., Hanafi, M., Gasibat, Q., and Samsudin, S. Mechanical interaction within badminton forehand shot technique: A review paper. International Journal of Kinesiology and Sports Science 9, 3 (2021), 28–44.

Sakurai, S., and Ohtsuki, T. Muscle activity and accuracy of performance of the smash stroke in badminton with reference to skill and practice. Journal of Sports Sciences 18, 11 (2000), 901–914.

Torres-Luque, G., Fernandez-Garcıa, A. I., Blanca-Torres, J. C., Kondric, M., and Cabello-Manrique, D. Statistical differences in set analysis in badminton at the RIO 2016 olympic games. Frontiers in Psychology 10, APR (2019).

Valldecabres, R., Casal, C. A., Chiminazzo, J. G. C., and de Benito, A. M. Players’On-Court Movements and Contextual Variables in Badminton World Championship. Frontiers in Psychology 11, July (2020), 1–9.

Valldecabres, R., De Benito, A. M., Losada, J. L., and Casal, C. A. Badminton world championship stress zones and performance factors: The key to success through log-linear analysis. Journal of Human Sport and Exercise 17, 1 (2020), 1–12.

Yotenka, R., Dini, S. K., Fauzan, A., and Ahdika, A. Exploring the relationship between hadith narrators in Book of Bukhari through SPADE algorithm. MethodsX 9, September (2022), 101850.

Zaki, M. J. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 42, 1 (2001), 31–60.

Zhao, Y. R and Data Mining: Examples and Case Studies. Academic Press, 2012.