Sprint and Jump Mechanical Profiles in Academy Rugby League Players: Positional Differences and the Associations between Profiles and Sprint Performance

Maturity Offset, Anthropometric Characteristics and Vertical Force-Velocity Profile in Youth Basketball Players

This work aimed to analyze the relationships between maturity offset, anthropometric variables and the vertical force-velocity profile in youth (12-18 years old) male basketball players. The vertical force-velocity profile was measured in 49 basketball players, grouped in competitive-age categories, i.e., under 14, 16 and 18 years of age (U-14, U-16 and U-18, respectively). A bivariate correlational analysis was carried out between maturity offset, anthropometric variables (height, body mass, % fat, muscle mass, bone mass and body mass index (BMI)) and vertical force-velocity profile (theoretical maximal force [F0], theoretical maximal velocity [V0], theoretical maximal power [Pmax], force-velocity imbalance [Fvimb] and force-velocity profile orientation). The results showed significant correlations (p < 0.05) between Fvimb and maturity offset at early ages of training (12-15 years). The anthropometric profile was correlated (p < 0.05) with F0 in U-14, V0 in U-16, and Pmax in U-18 basketball players. The current findings suggest a relationship between the vertical force-velocity imbalance and maturity offset and the main vertical force-velocity profile variables. The vertical force-velocity profile is hypothesized as a useful index to correct vertical force-velocity deficits according to the maturity offset of male basketball players.

What Tests are Used to Assess the Physical Qualities of Male, Adolescent Rugby League Players? A Systematic Review of Testing Protocols and Reported Data Across Adolescent Age Groups

BACKGROUND

Understanding the physical qualities of male, adolescent rugby league players across age groups is essential for practitioners to manage long-term player development. However, there are many testing options available to assess these qualities, and differences in tests and testing protocols can profoundly influence the data obtained.

OBJECTIVES

The aims of this systematic review were to: (1) identify the most frequently used tests to assess key physical qualities in male, adolescent rugby league players (12-19 years of age); (2) examine the testing protocols adopted in studies using these tests; and (3) synthesise the available data from studies using the most frequently used tests according to age group.

METHODS

A systematic search of five databases was conducted. For inclusion, studies were required to: (1) be original research that contained original data published in a peer-reviewed journal; (2) report data specifically for male, adolescent rugby league players; (3) report the age for the recruited participants to be between 12 and 19 years; (4) report data for any anthropometric quality and one other physical quality and identify the test(s) used to assess these qualities; and (5) be published in English with full-text availability. Weighted means and standard deviations were calculated for each physical quality for each age group arranged in 1-year intervals (i.e., 12, 13, 14, 15, 16, 17 and 18 years) across studies.

RESULTS

37 studies were included in this systematic review. The most frequently used tests to assess anthropometric qualities were body mass, standing height, and sum of four skinfold sites. The most frequently used tests to assess other physical qualities were the 10-m sprint (linear speed), 505 Agility Test (change-of-direction speed), Multistage Fitness Test (aerobic capacity), bench press and back squat one-repetition maximum tests (muscular strength), and medicine ball throw (muscular power). Weighted means calculated across studies generally demonstrated improvements in player qualities across subsequent age groups, except for skinfold thickness and aerobic capacity. However, weighted means could not be calculated for the countermovement jump.

CONCLUSION

Our review identifies the most frequently used tests, but highlights variability in the testing protocols adopted. If these tests are used in future practice, we provide recommended protocols in accordance with industry standards for most tests. Finally, we provide age-specific references for frequently used tests that were implemented with consistent protocols. Clinical Trial Registration This study was conducted in accordance with the Preferred Reporting Items of Systematic Review and Meta-analysis guidelines and was registered with PROSPERO (ID: CRD42021267795).

Factors influencing the jump momentum - sprint momentum correlation: a data simulation

Jump take-off momentum has previously been proposed as an alternative test to predict sprint momentum. This study used a data simulation to replicate and systematically investigate relationships reported in previous studies between body mass, vertical jump performance, and sprint performance. Results were averaged for 1000 simulated data sets in each condition, and the effects of various parameters on correlations between jump momentum and sprint momentum were determined. The ability of jump take-off momentum to predict sprint momentum is greatest under relatively high inter-individual variation in body mass and relatively low inter-individual variation in jump height. This is largely due to the increased emphasis on body mass in these situations. Even under zero or a small negative (r = -0.30) correlation between jump height and sprint velocity, the correlation between the two momenta remained very large (r ≥ 0.76) on average. There were no investigated conditions under which jump momentum was most frequently a significantly (p < 0.05) greater predictor of sprint momentum compared to simply using body mass alone. Furthermore, between-individual correlations should not be used to make inferences or predictions for within-individual applications (e.g. predicting or evaluating the effects of a longitudinal training intervention). It is recommended that any rationale for calculating and/or monitoring jump take-off momentum should be separate from its ability to predict sprint momentum. Indeed, body mass alone may be a better predictor of sprint momentum.