Abstract
The study aimed at establishing the inertial sensors-containing device (ISCD)-based match characteristics that act as prognosticators of elite, male badminton players’ performance levels. Twenty-two (22) male single players (aged: 23.39 ± 3.92 years), who represented 10 African countries participated in the study. Players were categorized as successful and less-successful players according to the results of five championships during two seasons. ISCD units (Catapult MinimaxV4), Polar Heart Rate Transmitter Belts and digital video cameras were used to collect match data. ISCD-based variables were corrected for match duration and independent t-tests, a cluster analysis and a binary forward stepwise logistic regression were used for statistical analyses. A Receiver Operating Characteristic Curve (ROCC) indicated the validity of the group categorization model. High-intensity accelerations per second was identified as the only ISCD-based variable that showed a significant difference (p = 0.05) between groups. Furthermore, only high-intensity accelerations per second (p = 0.03) and low-intensity efforts per second (p = 0.04) were identified as significant prognosticators of group categorization with 76.88% of players that could be classified back into their original groups by making use of the ISCD-based logistic regression formula. The ROCC showed a value of 0.87. The identification of the last-mentioned ISCD-based variables for the attainment of badminton performances emphasizes the importance of using badminton drills and conditioning techniques not only to improve the physical fitness levels of players, but also their ability to accelerate at high-intensities.
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Abdullahi, Y., Coetzee, B. (2022). Inertial Sensors Containing Device-Based Match Characteristics that Act as Prognosticators of Elite, Male Badminton Players’ Performance Levels. In: Baca, A., Exel, J., Lames, M., James, N., Parmar, N. (eds) Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference. PACSS 2021. Advances in Intelligent Systems and Computing, vol 1426. Springer, Cham. https://doi.org/10.1007/978-3-030-99333-7_6
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