Background: Three commonly used non-invasive protocols are implemented to estimate the timing at which PHV most likely occurs. Accurate estimation of circumpubertal years can aid in managing training load of adolescent athletes. Aim: Three protocols were compared against observed age at PHV: an estimate of 13.8 ±1.0 year -generic age at PHV (from longitudinal measures); an estimate based on the maturity offset equation, predicted age at PHV ±1.0 year; a window of PHV based on 85 -96% of predicted adult height at time of observation. Subjects and Methods: A final sample of 23 (from 28) adolescent male participants were selected from the academy of an English Premier League club. Anthropometric measures were collected across five playing seasons; age at PHV was estimated with Super-Imposition by Translation and Rotation (SITAR). The three protocols were compared based on measures at 13.0 years. Results and Conclusions: An age window based on predicted maturity offset did not improve estimation of PHV compared to generic age method; however, the percentage of predicted adult height window showed improvement in performance shown by the following results. Predicted age at PHV correctly assigned 15 participants (65%) as experiencing PHV, while the percentage height correctly assigned 17 participants (74%). Generic age and predicted age at PHV correctly predicted observed age at PHV for 14 participants (61%), percentage of adult height window correctly predicted 22 participants (96%).

The main and interactive effect of biological maturity and relative age upon physical performance in adolescent male soccer players was considered. Consistent with previous research, it was hypothesised that participants of greater maturity or born earlier in the selection year would perform better in terms of physical performance tests. This cross-sectional study consisted of 84 male participants aged between 11.3 and 16.2 years from a professional soccer academy in the English Premier League. Date of birth, height, weight, and parental height were collected. Sprint, change of direction, countermovement jump, and reactive strength index were considered for physical performance. Relative age was based on the birth quarter for the selection year. Maturity status was based upon the percentage of predicted adult height attained. Linear regression models highlighted that maturation was associated with performance on all but one of the physical performance tests, the reactive strength index. In contrast, relative age only served as a significant predictor of performance on the countermovement jump. This study indicated that physical performance (in the tests studied) seems to be related to the biological maturity status of a player but not their relative age. This finding is important because it suggests that early-maturing players perform better in the majority of physical performance tests, and the commonly held belief that relative age effect influences performance may be overstated.

Both maturity and relative age selection biases are entrenched within professional academy soccer programmes. Lay opinion, and that of some scholars, holds that relative age effects exist as a product of advanced biological maturity. That is relatively older players succeed as a consequence of the physical and athletic advantages afforded by earlier maturation There is, however, a growing body of evidence to suggests that this is not the case, and that relative age and maturation should be considered and treated as independent constructs. To avoid a disconnect between contemporary academic evidence and practitioner practice, the aim of this commentary is to provide discussion of pre-existing and new evidence relating to maturity and relative age selection biases in soccer. It is hoped that this commentary will provide an overview of new insight regarding the differences between the two selection phenomena and enable practitioners who are responsible for the (de)selection of academy soccer players for talent development programmes to make more informed decisions regarding their retention/selection strategies.

Multidirectional speed (MDS) can be defined as ‟the competency and capacity to accelerate, decelerate, change direction, and maintain speed in multiple directions and movements, within the context of sport-specific scenarios.” The components of MDS are linear speed, change of direction speed, curvilinear speed, contextual speed, and agility. A MDS development framework is provided for the practitioner who considers the complexities of the growing athlete within a progressive sequence of skill learning and adaptation. Practical examples for each MDS component are provided and discussed within weekly microcycle examples that represent different stages of development for the youth athlete.

Purpose: To investigate the influence of maturation on match running performance in elite male youth soccer players. Methods: A total of 37 elite male youth soccer participants from an English professional soccer academy from the U14s, U15s, and U16s age groups were assessed over the course of 1 competitive playing season (2018–2019). Relative biological maturity was assessed using percentage of predicted adult height. A global positioning system device was used between 2 and 30 (mean = 8 [5]) times on each outfield player. The position of each player in each game was defined as defender, midfielder, or attacker and spine or lateral. A total of 5 match-running metrics were collected total distance covered, high-speed running distance, very high-speed running distance, maximum speed attained, and number of accelerations. Results: Relative biological maturity was positively associated with all global positioning system running metrics for U14s. The U15/16s showed variation in the associations among the global positioning system running metrics against maturity status. A multilevel model which allowed slopes to vary was the best model for all parameters for both age groups. In the U14 age group, advanced maturation was associated with greater high-speed running distance. However, maturation did not contribute toward variance in any of the indices of running performance in the U15/16s. In the U15/16 age group, significance was observed in the spine/lateral playing positions when undertaking actions that required covering distance at high speeds. Conclusions: Maturation appeared to have an impact on match-running metrics within the U14s cohort. However, within the U15/16s, the influence of maturation on match-running metrics appeared to have less of an impact.

Background: A novel bi-exponential method has emerged to estimate critical speed (CS) and D-prime (D′) from a 3-min all- out test (3MT). Objectives: To compare CS analysis methods to determine whether parameter estimations were interchangeable. Reference values and relationships with key soccer match- play variables were explored. Methods: Thirteen elite male youth (14-15 years old) players completed a 30 m shuttle run 3MT to estimate CS, D′, rate of speed decline time constant, maximal speed (Smax), time to Smax (tmax), and fatigue index (FI), using the traditional method and bi-exponential model on average (Bi-ExpAverage) and max speed settings (Bi-ExpMax-Speed). High-speed running (HSR) and sprinting distances and counts, and the number of accelerations were collected from two matches. Magnitude- based inferences (p < 0.05) with smallest worthwhile change of 0.2 effect sizes were used to analyse differences. Pearson’s and Spearman’s correlation coefficients were used to measure associations between CS model variables and match-play parameters. Results: There were significant differences between the traditional method and both bi-exponential models for CS and D′, as well as between the bi-exponential models for all variables except tmax. Using the Bi-ExpAverage model, strong correlations (r = 0.70-0.73; p < 0.05) were observed for D′ and FI with the number of standardised and individualised HSRs, respectively. With the Bi-ExpMax-Speed model, there were strong correlations (r/ρ = 0.64-0.68; p < 0.05) between D′ and the number of standardised HSRs and sprints, and the number of individualised sprints. Conclusion: There is a lack of interchangeability between analysis methods. It appears that D′ and FI from the bi- exponential models could be associated with high-intensity actions in soccer match-play.

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