Individualization of Intensity Thresholds on External Workload Demands in Women’s Basketball by K-Means Clustering: Differences Based on the Competitive Level

Abstract

In previous studies found in the literature speed (SP), acceleration (ACC), deceleration (DEC), and impact (IMP) zones have been created according to arbitrary thresholds without considering the specific workload profile of the players (e.g., sex, competitive level, sport discipline). The use of statistical methods based on raw data could be considered as an alternative to be able to individualize these thresholds. The study purposes were to: (a) individualize SP, ACC, DEC, and IMP zones in two female professional basketball teams; (b) characterize the external workload profile of 5 vs. 5 during training sessions; and (c) compare the external workload according to the competitive level (first vs. second division). Two basketball teams were recorded during a 15-day preseason microcycle using inertial devices with ultra-wideband indoor tracking technology and microsensors. The zones of external workload variables (speed, acceleration, deceleration, impacts) were categorized through k-means clusters. Competitive level differences were analyzed with Mann–Whitney’s U test and with Cohen’s d effect size. Five zones were categorized in speed (<2.31, 2.31–5.33, 5.34–9.32, 9.33–13.12, 13.13–17.08 km/h), acceleration (<0.50, 0.50–1.60, 1.61–2.87, 2.88–4.25, 4.26–6.71 m/s2), deceleration (<0.37, 0.37–1.13, 1.14–2.07, 2.08–3.23, 3.24–4.77 m/s2), and impacts (<1, 1–2.99, 3–4.99, 5–6.99, 7–10 g). The women’s basketball players covered 60–51 m/min, performed 27–25 ACC-DEC/min, and experienced 134–120 IMP/min. Differences were found between the first and second division teams, with higher values in SP, ACC, DEC, and IMP in the first division team (p < 0.03; d = 0.21–0.56). In conclusion, k-means clustering can be considered as an optimal tool to categorize intensity zones in team sports. The individualization of external workload demands according to the competitive level is fundamental for designing training plans that optimize sports performance and reduce injury risk in sport.

Physical Fitness as a Predictor of Performance during Competition in Professional Women’s Basketball Players

“…These results show the relationship between the indicators of play by playing position and the physical abilities on which they are based. Coaches should adequately prepare their players, attending to a playing position so that they develop the best playing actions, individualizing the training [34].…”

“…The importance of knowing the state of PF of the athlete allows individualizing loads, confronting the athlete with planned stimuli, and designing a plan where the desired characteristics are improved [32,33]. Among the main differences, it is differentiated by sex, since the load supported both in the PF test and in competition between men and women is different [34]. In addition, the assimilation and absorption of the load also varies, which may affect aspects of training or injury prevention [35].…”

Kinematic and Neuromuscular Ranges of External Loading in Professional Basketball Players during Competition

“…With each of the five variables selected in this study, two cluster analyses were performed: two-step clustering to identify the load zones automatically, and five-rank K-means clustering as previous research has employed [8,30,31]. These investigations have employed expert judgment to establish load thresholds.…”

“…Gabbett and Domrow [7] highlighted that excessive training load and inadequate recovery can increase the risk of injuries in basketball players. To mitigate these effects, it is necessary to implement proper load monitoring by scientifically establishing the ranges of internal and external load to which players will be exposed during training processes [8].…”

“…The five speed ranges defined in the SPro software of the company RealTrack Systems (Almeria, Spain) are as follows: Very Low/Standing, Low/Walking, Moderate/Jogging, High/Running, and Very High/Sprinting. However, it is necessary to have a critical position with respect Reina et al [30] Female U'18 ≤3.6 3.6-6.5 6.5-10.2 6.5-10.2 >14.4 Vázquez-Guerrero et al [31] Male U'18 ≤6.0 6.0-12.0 12.1-18.0 18.1-24.0 >24.1 Sosa et al [32] Male U'18 <7.0 7.0-14.0 14.0-18.0 ≥18.0 Puente et al [33] Male Senior non-professional ≤6.0 In addition to these studies that identify ranges for the kinematic variable of speed, in the literature, there are other studies that establish ranges for distances traveled [34], accelerations and decelerations [8], impacts [8], or Player Load [35], which show reference values for individualization of training and adaptation to the demands of competition.…”