Mind, body, and shuttle: multidimensional benchmarks for talent identification in male youth badminton

Multidimensional and Longitudinal Approaches in Talent Identification and Development in Racket Sports: A Systematic Review

BACKGROUND

Better methods to support talent identification and development processes may contribute to more effective and efficient athlete development programs in racket sports. Both researchers and practitioners recommend multidimensional and longitudinal approaches to better understand the nature of talent (development). However, the added value of these 'innovative' approaches has not yet been clarified for racket sports. This systematic review intends to gain further insight into the outcomes of multidimensional and longitudinal approaches for talent identification and development in racket sports and to provide directions for future talent research.

METHODS

Electronic searches were conducted in PubMed, Scopus, SPORTDiscus, and Web of Science (January 2000-August 2022). Search terms covered the areas of racket sports and talent in sports. Studies using multidimensional and/or longitudinal talent approaches were included and analyzed regarding the methodology, included performance characteristics (i.e., anthropometrical, physiological, technical, tactical, psychological), and study findings.

RESULTS

A total of thirty-two studies were included using multidimensional (n = 15), unidimensional longitudinal (n = 3) or multidimensional longitudinal designs (n = 14). Most research covered physiological characteristics (n = 28), while fewer articles investigated anthropometrics (n = 21) and technical characteristics (n = 16). Only limited research investigated psychological (n = 4) and tactical characteristics (n = 1). Almost all studies measured physiological characteristics in combination with other characteristics. There was moderate to strong evidence that physiological and technical characteristics have value for athlete development programs in racket sports. Positive but limited evidence was found for psychological and tactical characteristics. Anthropometrical assessments were generally used as controlling variables for maturation. Study designs varied, and many studies used unidimensional statistical models and analyses within multidimensional study designs and datasets.

CONCLUSIONS

This review provides an overview of talent research using multidimensional and/or longitudinal approaches within racket sports and gives guidance on what characteristics to include in decision-making and monitoring processes. However, it remains difficult to draw conclusions about the added values of these approaches due to their variety in methodology. Future talent research should include more consistent study designs and conduct multidimensional and longitudinal studies using multivariate statistical approaches that benefit from the data's multidimensionality.

The relationships between body mass index, reciprocal ponderal index, waist-to-height ratio, and fitness in young adult males

Background

Anthropometric measures such as the body mass index (BMI), reciprocal ponderal index (RPI), and waist-to-height ratio (WHR) have been proposed as predictors of physical fitness. This study aimed to identify the differences in explanatory capacity and fit of BMI, RPI, and WHR on physical fitness, which involves jumping, sprinting, change of direction, and aerobic capacity, by adjusting the polynomial regression.

Methods

A sample of 297 healthy, recreationally active male university students between 18 and 20 years old was recruited for this study. Anthropometric measurements (height: 174.09 ± 6.27 cm, weight: 78.98 ± 20.27 kg, waist circumference: 93.74 ± 14.56 cm) were taken for each participant. Jumping tests (squat jump, countermovement jump), sprinting tests (20 m sprint), agility tests (agility T-test), and aerobic/endurance tests (6 min walk test, VAM-EVAL test) were performed. Nonlinear quadratic regression models were used to assess the relationship between the jump, sprint, and fitness test scores and the anthropometric indices. The models were compared based on R-squares and Bayesian Information Criterion (BIC). The significance level was set at p < 0.05.

Results

The results showed that all the indices predicted a portion of the variance because all variables and index relationships were significant. Regarding the fitted models, the Bayesian Information Criterion showed that BMI was the best indicator of performance, although the RPI was better for VO2max.

Conclusion

These findings may be of great interest to practitioners because it appears that anthropometric measures can be used to predict physical fitness in certain tests although the accuracy raises any concerns.