## 2 Materials and Methods
### Data Processing, Parameter Selection, and Statistical Analysis  
The assessment comprised 9 trials per participant, resulting in a total of 108 trials. The acquired raw data underwent processing using a five-point smoothing filter (supplied by VICON software). This processed dataset provided critical three-dimensional coordinates of the 18 markers. Subsequently, the processed 3D coordinates were used to construct the 7-segment biomechanical model [3; 26] for kinematic quantification of the three skills. The data from the synchronized ground reaction force (GRF) measurement were used for kinetic quantification, including performing the inverse dynamic analysis of the 7-segment model by using VICON provided software.  
Proficiency in the three specified basketball skills relies heavily on the agility, speed, and leg-power-generation capacity of players. Agility refers to the ability to quickly and accurately change body motion direction in response to competitive situations [27; 28]. Because agility movements involve rapid acceleration, there is an increased risk of joint and soft tissue injuries [28]. It is important to note that lateral movements, like defensive slides and crossover dribbling, elevate foot inversion/eversion torque, thus heightening the risk of injury [7; 29; 30]. Therefore, injury prevention is relevant in sport performance and belongs to part of performance analysis [4]. Considering the unique control requirements of these skills and aiming to quantitatively assess these abilities without overloading (i.e., injury prevention), the following parameters were selected from data collection: For defensive slide (lateral movement only)--performing/contact time and lateral impulse acquired throughground reaction force measurement; lateral stride length during defensive sliding derived from 3D motion data; average inversion/eversion torque obtained through biomechanical modeling (inverse dynamic analysis). For crossover diribbling--performing/contact time; anterior-posterior impulse associated with sudden braking movements; medio-lateral impulse relevant to cutting tasks; motion direction change (aiming to be as close to a 90-degree angle as possible) determined based on 3D motion data; average inversion/eversion torque. For full approach jump--performing/contact time; anterior-posterior impulse related to sudden break movement; vertical impulse related to jumping; jumping height determined by 3D motion data. To mitigate the confounding effect of body weight on the quantification of skills, the research findings regarding impulse and torque were normalized by the subjects' body weight, i.e., impulse/body weight and torque/body weight.  
For statistical analysis, the mean and standard deviations (SD) were calculated for the selected parameters. Descriptive statistics (mean \(\pm\) SD) were used to illustrate the characteristics of these parameters. Due to the small sample size, the Shapiro-Wilk test was first performed to test the normality of the collected data. If normality was found, one-way ANOVA was conducted to detect changes in control patterns induced by the shoe friction coefficient. All statistical analyses were performed using IBM SPSS Statistics 23 (IBM Japan, Tokyo, Japan), and the significance level was set at \(p\) = 0.05.