Exploring Physical Fitness Profile of Male and Female Semiprofessional Basketball Players through Principal Component Analysis-A Case Study

Longitudinal Monitoring of Biomechanical and Psychological State in Collegiate Female Basketball Athletes Using Principal Component Analysis

Background

The growth in participation in collegiate athletics has been accompanied by increased sport-related injuries. The complex and multifactorial nature of sports injuries highlights the importance of monitoring athletes prospectively using a novel and integrated biopsychosocial approach, as opposed to contemporary practices that silo these facets of health.

Methods

Data collected over two competitive basketball seasons were used in a principal component analysis (PCA) model with the following objectives: (i) investigate whether biomechanical PCs (i.e., on-court and countermovement jump (CMJ) metrics) were correlated with psychological state across a season and (ii) explore whether subject-specific significant fluctuations could be detected using minimum detectable change statistics. Weekly CMJ (force plates) and on-court data (inertial measurement units), as well as psychological state (questionnaire) data, were collected on the female collegiate basketball team for two seasons.

Results

While some relationships (n = 2) were identified between biomechanical PCs and psychological state metrics, the magnitude of these associations was weak (r = |0.18-0.19|, p < 0.05), and no other overarching associations were identified at the group level. However, post-hoc case study analysis showed subject-specific relationships that highlight the potential utility of red-flagging meaningful fluctuations from normative biomechanical and psychological patterns.

Conclusion

Overall, this work demonstrates the potential of advanced analytical modeling to characterize components of and detect statistically and clinically relevant fluctuations in student-athlete performance, health, and well-being and the need for more tailored and athlete-centered monitoring practices.

Intensity Thresholds for External Workload Demands in Basketball: Is Individualization Based on Playing Positions Necessary?

Currently, basketball teams use inertial devices for monitoring external and internal workload demands during training and competitions. However, the intensity thresholds preset by device manufacturers are generic and not adapted for specific sports (e.g., basketball) and players' positions (e.g., guards, forwards, and centers). Using universal intensity thresholds may lead to failure in accurately capturing the true external load faced by players in different positions. Therefore, the present study aimed to identify external load demands based on playing positions and establish different intensity thresholds based on match demands in order to have specific reference values for teams belonging to the highest competitive level of Spanish basketball. Professional male players (n = 68) from the Spanish ACB league were monitored during preseason official games. Three specific positions were used to group the players: guards, forwards, and centers. Speed, accelerations, decelerations, impacts/min, and player load/min were collected via inertial devices. Two-step clustering and k-means clustering categorized load metrics into intensity zones for guards, forwards, and centers. Guards covered more distance at high speeds (12.72-17.50 km/h) than forwards and centers (p < 0.001). Centers experienced the most impacts/min (p < 0.001). Guards exhibited greater accelerations/decelerations, albeit mostly low magnitude (p < 0.001). K-means clustering allowed the setting of five zones revealing additional thresholds. All positions showed differences in threshold values (p < 0.001). The findings provide insights into potential disparities in the external load during competition and help establish position-specific intensity thresholds for optimal monitoring in basketball. These data are highly applicable to the design of training tasks at the highest competitive level.

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

The evaluation of physical fitness in team sports is enjoying greater importance in the training of professional teams. The objectives of this research were to characterize physical fitness and game indicators based on the game position. This is an empirical study, with a quantitative, descriptive and cross-sectional methodology. In addition, different relationships between the level of physical fitness and the game indicators during the competition were determined. Finally, a predictive analysis of the selected variables was carried out in order to know the importance of the variables in the performance and at what time of the season they had the greatest impact. For this, a professional female basketball team (n = 12) with a mean age of 25.25 ± 7.617 years, height 178.25 ± 9.206 cm and a body mass of 72.33 ± 11.657 kg was analyzed. Each player was equipped with a WIMUPRO inertial device, and all competition statistics were analyzed. The results obtained show that there were no differences among all the skills evaluated and game indicators depending on the game position. Likewise, a relationship was determined between the level of physical fitness and the technical-tactical contribution, being different between different times of the season. Moreover, physical fitness only predicted the player's final performance in the competition for female player centers in the first and second rounds of the championship, and for forwards in the first round. Four physical-physiological profiles with contributions to the different ones during the competition were also determined. Finally, three groups of female players by playing position were identified according to their sport performance, namely PIR, high, medium and low ratings, associated with physical performances. In all groups, there were significant differences between playing positions, PIR and physical performances.

Exercise Evaluation and Prescription-Second Edition

In the first volume of "Exercise Evaluation and Prescription" in the Journal of Functional Morphology and Kinesiology [...].

Specific Physical Ability Prediction in Youth Basketball Players According to Playing Position

This study investigated the hierarchical structure of physical characteristics in elite young (i.e., U17-U19) basketball players according to playing positions. In addition, their predictive value of physical characteristics was determined for the evaluation of players’ physical preparedness. Sixty elite male basketball players performed 13 standardized specific field tests in order to assess the explosive power of lower limbs, speed, and change-of-direction speed. They were divided into three groups according to playing positions (guard [n = 28], forward [n = 22], center [n = 10]). The basic characteristics of the tested sample were: age = 17.36 ± 1.04 years, body height = 192.80 ± 4.49 cm, body mass = 79.83 ± 6.94 kg, and basketball experience = 9.38 ± 2.10 years for guards; age = 18.00 ± 1.00 years, body height = 201.48 ± 3.14 cm, body mass = 90.93 ± 9.85 kg, and basketball experience = 9.93 ± 2.28 years for forwards; and age = 17.60 ± 1.43 years; body height = 207.20 ± 3.29 cm, body mass = 104.00 ± 9.64 kg, and basketball experience = 9.20 ± 1.62 years for centers. For all playing positions factor analysis extracted three factors, which cumulatively explained 76.87, 88.12 and 87.63% of variance, respectively. The assessed performance measures were defined as significant (p < 0.001), with regression models of physical performance index (PP_INDEX). PP_INDEX of guards = −6.860 + (0.932 × t-test) − (1.656 × Acceleration 15 m) − (0.020 × Countermovement jump); PP_INDEX of forwards = −3.436 − (0.046 × Countermovement jump with arm swing) − (1.295 × Acceleration 15 m) + (0.582 × Control of dribbling); PP_INDEX of centers = −4.126 + (0.604 × Control of dribbling) − (1.315 × Acceleration 15 m) − (0.037 × Sargent jump). A model for the evaluation of physical performance of young basketball players has been defined. In addition, this model could be used as a reference model for selection procedures, as well as to monitor the efficacy of applied training programmes within the short, medium and long-term periodization.