Maturity-related developmental inequalities in age-group swimming: The testing of 'Mat-CAPs' for their removal

Is Maturational Growth More Influential than Training Engagement in Longitudinal Adolescent Physical Performance Development?

Athlete development is considered a multi-factorial and dynamic overtime process. Thus, understanding what factors and behavioural activities contribute most to development is important. The present study examined longitudinal relationships between training time and maturity status with physical performance indices in youth volleyball players. Prospective mixed longitudinal tracking over 3 years. Participants were N = 78 selected male volleyball players, aged 11-15 years (M = 13.81, SD = 1.20 years) from N = 41 competitive Swiss clubs. Alongside coach-reported weekly training time, participants completed standardised anthropometric and physical performance tests (e.g., standing long jump [SLJ]; 9-3-6-3-9 agility sprint). Linear mixed models (LMM) examined longitudinal independent and interacting relationships between training time and maturity status (years from peak height velocity; PHV) with physical performance. For SLJ, both training time and maturity status explained curvilinear longitudinal performance development, but nonlinear maturation status interactions were most influential, moderating relationships. In agility sprint, similar trends were apparent, with training time influences diminishing when maturity status was added in LMM analyses. Across time points of maturational growth, increased training engagement was not associated with enhanced physical performance benefits, whereas maturational status progression better accounted for performance differences and development. For coaches and sports-science practitioners, findings question the rationale for heightened training engagement at circa-PHV directed towards physical performance development purposes, a recommendation aligned with the need for maturational-growth associated injury prevention. Instead, due to musculoskeletal and proprioceptive disturbance during maturational growth, training emphasis on technical/biomechanical skill acquisition remains important.

Talent Identification: Time to Move Forward on Estimation of Potentials? Proposed Explanations and Promising Methods

The scientific literature on talent identification is extensive, with significant advancements made over the past 30 years. However, as with any field, the translation of research into practice and its impact on the field have been slower than anticipated. Indeed, recent findings highlight a pervasive relative age effect, the effects of maturation being often overlooked, disparate populations between young and senior performers, and a necessity to embrace a holistic approach. However, simple elements can be implemented to avoid some of these known biases. This review synthesizes recent advancements, critically examines key elements to consider in talent identification, and proposes promising avenues for enhancement. These include (1) understanding and illustrating early performance influences, (2) implementing individualized performance trajectory models, (3) applying corrective adjustments based on relative, biological, and training age, and (4) improving estimation of potentials through a holistic approach that integrates "life pathways". In light of these necessary improvements, it seems opportune to propose a semantic shift to generate a change in both conceptual and operational paradigms. The proposed terminology of "estimation of potential" is based on a semantic and mathematical definition, which operationalizes its conceptualization. This study suggests that understanding the trajectories of transitions from youth to senior categories in each sport, coupled with a holistic approach centered on individual performance progressions across the three ages, would be a possible path toward estimating potential.

Assessment of Biological Age With Conventional Ultrasound Imaging as an Alternative to X-Ray-A Pilot Study in Youth Soccer

The aim of the study was to evaluate conventional ultrasound (US) as a radiation-free alternative to X-ray for determining biological age (BA; indicated by skeletal age). BA, was determined in 24 healthy, male, elite youth soccer goalkeepers around peak height velocity (11-16 years of age) using both X-ray and conventional US scans of the left hand. X-ray scans were evaluated using the Tanner-Whitehouse 2 method. Conventional US scans served to determine BA via ossification ratios of 13 hand and wrist bones. The new conventional US method showed very strong correlation with X-ray r = 0.90 (p < 0.05). However, the agreement for the difference in BA and CA, which accounts for age-related variance, was classified poor (ICC = 0.48, p < 0.05). Additionally, linear regression analysis and the Bland-Altman plot suggested the presence of a systematic and proportional overestimation of BA in younger players and an underestimation of BA in older players. Furthermore, Cohen's kappa showed a moderate agreement between players' classification into maturity groups for the two assessment methods. In conclusion, our study has shown that using US-derived ossification ratios did not deliver valid results compared to X-ray when determining BA in youth soccer goalkeepers.

Specializing When It Counts: Comparing the Dose-Time Effect of Distance Variety between Swimming and Track Running

OBJECTIVE

To conduct a longitudinal retrospective analysis, explore the relationship between success at peak performance age and the number of different race distances athletes competed in each year (within-sport distance variety), and compare the dose-time effect of this distance variety throughout the development process between male swimmers and track runners.

METHODOLOGY

Male swimmers (n = 6033) and track runners (n = 19,278) still competing at peak performance age were ranked, and the number of different race distances was extracted retrospectively for each year until early junior age (13-14-year-old category) from the databases of the European Aquatics and World Athletics federations. Firstly, correlation analysis determined the relationship between ranking at peak performance age and distance variety. Secondly, Poisson distribution provided the probability and dose-time effect of distance variety for becoming an international-class athlete at peak performance age.

RESULTS

Generally, correlation analysis revealed low coefficients (r ≤ 0.22) but significant effects (p < 0.001) for larger distance variety and success at peak performance age. Poisson distribution revealed the highest probability of becoming an international-class swimmer when competing in 2-4 race distances at junior age, depending on the primary race distance. The dose-time effect indicated a gradual reduction in the number of race distances as athletes approached peak performance age, narrowing down to 1-2, 2-3, and 3-4 distances for sprint, middle-, and long-distance races, respectively. Track runners exhibited a lower distance variety than swimmers, with a consistent optimum of 1-2 race distances across the age groups.

CONCLUSIONS

The present findings including data of the most combined race distances for each primary race distance and a comparison between swimming and track running provide new background information to challenge traditional training regimes and help establish new strategies for long-term athlete development.

Advancing athlete development: How Percentile Comparison Methods (PCMs) can identify youth developmental profiles

OBJECTIVES

Inter-individual developmental differences confound the capability to accurately evaluate youth athletic performance, highlighting the need for considerate methodology and analytical approaches. The present study demonstrated how Percentile Comparison Methods (PCMs) were developed, tested, and applied to identify athlete developmental profiles in Australian youth swimming.

DESIGN

Cross-sectional.

METHODS

Participants were N = 866 female 100-metre (m) Front-Crawl swimmers, aged 9-15 years, competing at 36 Australian regional-national level long course events. At respective events, swim performance time was collated alongside, age, date of birth, and anthropometric measures to identify age group, relative age, and maturity status. Quadratic relative age and maturity status with 100-m performance regression trendlines were generated. Then, individual swim performances at a given relative age or maturity status were converted into percentile rank distributions and compared with raw (unadjusted) annual age-group performance percentile ranks.

RESULTS

At a cohort level, initial testing confirmed relative age and maturity-adjusted percentile rankings were associated with general rank improvements for relatively younger and later maturing swimmers compared to raw ranks (and vice versa). When assessing individual swimmer plots, where three percentile rank scores were compared and rank change threshold criteria applied, five Percentile Comparison Method profile types were identified, namely: 'Early Developing' (19 %); 'Later Developing' (18 %); 'Consistent' (15 %); 'Mixed' (38 %) and 'Counteracting' (10 %). Percentile Comparison Method plots helped identify developmentally (dis-)advantaged swimmers; specific factors leading to (dis-)advantage, and likely onward development trajectories.

CONCLUSIONS

Overall and with practical considerations, Percentile Comparison Methods can improve the validity of youth athletic performance evaluation as well as inform athlete development programming.

Predicting future stars: Probability and performance corridors for elite swimmers

OBJECTIVES

To evaluate the new age groups of the World Junior Championships in swimming from a scientific perspective, establish benchmarks and performance corridors that predict success at peak performance age and compare performance corridors between men and women and short-, middle-, and long-distance freestyle races.

DESIGN

Longitudinal big data analysis.

METHODS

In total, 347,186 annual best times of male (n = 3360, 561 ± 177 Swimming Points) and female freestyle swimmers (n = 2570, 553 ± 183 Swimming Points) were collected across all race distances at peak performance age and retrospectively analyzed throughout adolescence. Cumulative Poisson distribution was used to calculate probabilities of becoming world-class finalist, international-class, or national-class swimmer for each age group. Performance corridors were expressed relative to the World Record and compared between performance levels, sex, race distances, and age groups with a 2-way analysis of variance.

RESULTS

Females are required to swim faster relative to the World Record at a younger age and show earlier performance plateaus than males at national and international levels. Additionally, world-class long-distance finalists show higher Swimming Points earlier in their career compared to short-distance swimmers. This effect is more distinctive in females than males.

CONCLUSIONS

Based on the sex-specific performance corridors and developments, the newly aligned age groups for the World Junior Championships are questionable regarding long-term athlete development. Based on race times from 131 nations, the present benchmarks provide valid international normative values to predict success chances at peak performance age and guide young swimmers along their talent pathway.

Growing up and reaching for the top: A longitudinal study on swim performance and its underlying characteristics in talented swimmers

The present study strived to gain a more profound understanding of the distinctions in development between swimmers who are considered to be on track to the elite level at late junior age (males aged 16; females aged 15) compared to those who are not. In this effort, swimmers were followed during their pubertal years (males aged 13-15; females aged 12-14), which marks a period when performance development aligns with maturation. Longitudinal data of 90 talented sprint and middle-distance swimmers on season best times (SBT) and underlying performance characteristics (anthropometrics, maximal swimming velocity, stroke index [SI] and countermovement jump [CMJ]) were collected over three swimming seasons. Based on their SBT at late junior age (males aged 16; females aged 15), swimmers were classified as high-performing late juniors or lower-performing late juniors. Retrospectively studying these swimmers, we found that all but two high-performing late juniors were already on track to the elite level at early junior age (males aged 13; females aged 12), evidenced with faster SBT throughout puberty compared to their lower-performing peers (p < 0.05). Independent sample t-tests revealed that high-performing late juniors significantly outscored their lower-performing peers when they were early juniors on maximal swimming velocity (males aged 13-15 and females aged 12-14), SI (males aged 13 and 14; females aged 12), CMJ (females aged 14) and height (females aged 13 and 14, p < 0.05). Additionally, multilevel models showed faster rates of development for high-performing late juniors on maximal swimming velocity (males and females) and SI (males) compared to lower-performing peers throughout puberty (p < 0.05). Higher initial levels of SBT and underlying performance characteristics at early junior age as well as the faster rates of development on SBT, maximal swimming velocity and SI (males only) during the pubertal years, may be crucial factors in maintaining the trajectory towards the elite level after puberty.

Why age categories in youth sport should be eliminated: Insights from performance development of youth female long jumpers

Long-term sports participation and performance development are major issues in popular sports and talent development programs. This study aimed to provide longitudinal trends in youth female long jump performance development, participation, and relative age effects (RAEs), as longitudinal data for female athletes are missing. 51'894 season's best results of female long jump athletes (n = 16'189) were acquired from the Swiss Athletics online database and analyzed within a range of 6-22 years of age. To examine longitudinal performance development and RAEs, data from athletes who participated in at least three seasons were selected (n = 41'253) and analyzed. Performance development was analyzed using age groups (AGs) and exact chronological age (CA) at competition. Differences between performances of birth quarters were analyzed using 83% confidence intervals (CIs) and smallest worthwhile change. Odds ratios (ORs) with 95% CI were used to quantify RAEs. With the traditional classification into age groups (AG), performances of athletes born between January and March (Q1) were significantly better than those born between October and December (Q4) from U8 to U17. Using exact CA resulted in similar performances in Q1 and Q4 until the U20 age category. The peak of participation was reached in the U12 category, and then decreased until the U23 category with a substantial drop at U17. Significant RAEs were observed from U8 to U19 and at U22. RAEs continuously decreased from U8 (large effect) to U14 (small effect). The present results show that differences in performance arise from the comparison of athletes in AGs. Thus, going beyond AGs and using exact CA, Q4 athletes could benefit from a realistic performance comparison, which promotes fair performance evaluation, un-biased talent development, realistic feedback, and long-term participation.

Relative age effects in track-and-field: Identification and performance rebalancing

Introduction: Relative Age Effect (RAE) consists of a biased distribution of the dates of birth in a same-age group. Objectives: This study aimed to investigate Relative Age Effect among French athletes in different track-and-field events, and propose a corrective adjustment method to highlight the true potential of an athlete with respect to his/her relative age. Methods: 358,610 performances from 2009 to 2019 of female and male athletes between 12 and 21 years old were collected. Relative age distributions of performances were analyzed by level of competitiveness ("All," "Top50%," "Top10%" where "all" represents all athletes, top50% and top10% represent the best 50% and 10% of athletes per age category respectively) and age category, with chi-square and odd-ratio statistics. A linear relationship between distribution of performances and age leads to a calibration coefficient allowing to rebalance the performance by considering the effect of Relative Age Effect. Validation is obtained by Wilcoxon statistical test on actual athlete data. Results: Relative Age Effect is present in all types of events. It is larger when the level of competitiveness increases. In male 100 m sprint, 1 year difference between two athletes birth date represents an average gain of 931.01 ms (6.5%) in the U13 (Under 13 years old) and 229.65 ms (1.9%) in the U17 (Under 17 years old) categories. Our validated rebalancing methods allows to compensate for the biases induced by the relative age effect. By comparing the rebalanced performance and the realised performance of each athlete, we cannot say that they are significantly different. On average, there is no significant difference between these two performances. Conclusion: This study showed that there is a relative age effect among young French athletes, with an even greater effect as the level of competition increases. Thanks to the rebalancing method that has been validated, performances can now be better appreciated according to category and event.

Corrective adjustment methods for relative age effects on French young swimmers' performances

BACKGROUND

This study aimed to identify a Relative Age Effect (RAE) among French young swimmers and apply corrective adjustment procedures to rebalance performances according to categories and events.

METHODS

5,339,351 performances of French swimmers aged 10 to 18 were collected between 2000 and 2019. Birth quarters distribution was examined according to competitiveness level ('All', 'Top50%', 'Top25%' and 'Top10%'), event and age category. A linear relationship between the distribution of performances and calendar days provides a calibration coefficient allowing to rebalance performances by considering the effect of RAE for each event. Then, adjusted performances are recalculated using this coefficient, the initial performance and the relative age.

RESULTS

Proportion of swimmers born in the first quarter was higher than the proportion of those born in the last quarter for all events and strokes (p < 0.01). RAE increases with the competitiveness level for all events. Indeed, among 'All' 12 years old 50m freestyle swimmers, the proportion born in the first quarter is 30.9% vs 19.2% in the fourth quarter, while among the "Top10%", 47.5% were born in the first quarter vs 10.3% in the last one. (p-value < 0.01). In average, each day represents a gap of 0.008 second, resulting in a difference of almost 3 seconds over a year. This tool is validated by comparing swimmers who have performed at least twice in a season. It provides a day by day rebalancing method for all swimming events and age categories.

CONCLUSIONS

Relative age effect is present among French young male and female swimmers, and is strengthened by competitiveness level. A new corrective adjustment procedure to rebalance performances considering categories and events is proposed and validated. By applying such a tool, we are able to reveal the full potential of swimmers and make it possible to compare them at the same relative age.

Maturity-based correction mechanism for talent identification: When is it needed, does it work, and does it help to better predict who will make it to the pros?

When identifying talent, the confounding influence of maturity status on motor performances is an acknowledged problem. To solve this problem, correction mechanisms have been proposed to transform maturity-biased test scores into maturity-unbiased ones. Whether or not such corrections also improve predictive validity remains unclear. To address this question, we calculated correlations between maturity indicators and motor performance variables among a sample of 121 fifteen-year-old elite youth football players in Switzerland. We corrected motor performance scores identified as maturity-biased, and we assessed correction procedure efficacy. Subsequently, we examined whether corrected scores better predicted levels of performance achievement 6 years after data collection (47 professionals vs. 74 non-professional players) compared with raw scores using point biserial correlations, binary logistic regression models, and DeLong tests. Expectedly, maturity indicators correlated with raw scores (0.16 ≤ | r | ≤ 0.72; ps < 0.05), yet not with corrected scores. Contrary to expectations, corrected scores were not associated with an additional predictive benefit (univariate: no significant r-change; multivariate: 0.02 ≤ ΔAUC ≤ 0.03, ps > 0.05). We do not interpret raw and corrected score equivalent predictions as a sign of correction mechanism futility (more work for the same output); rather we view them as an invitation to take corrected scores seriously into account (same output, one fewer problem) and to revise correction-related expectations according to initial predictive validity of motor variables, validity of maturity indicators, initial maturity-bias, and selection systems. Recommending maturity-based corrections is legitimate, yet currently based on theoretical rather than empirical (predictive) arguments.

Don't Throw the Baby Out With the Bathwater: Talent in Swimming Sprinting Events Might Be Hidden at Early Age

PURPOSE

This study aimed to describe the career performance progression of elite early- and later-success international swimmers competing in sprint events (ie, 50 and 100 m).

METHODS

The career performance trajectories of 6003 swimmers (50.9% females; 58,760 unique records) competing in the 4 swimming strokes were evaluated. Swimmers with early and later success were identified. The authors identified the top 50 all-time swimmers competing in junior career who did not reach the top 50 rankings in their senior career, and vice versa, and successful swimmers in both junior and senior career.

RESULTS

Early-success swimmers mainly achieved their peak performance before the age of 20 years and approximately 5-6 years before successful senior swimmers or approximately 3-4 years before successful swimmers both in junior and senior careers. The annual performance improvements of later-success swimmers were higher (about 1%-2%) until the age of 20 to 24 years, whereas early-success swimmers showed a performance stagnation at about 16 to 18 years in females and 19 to 20 years in males.

CONCLUSIONS

Early-success swimmers who achieved peak performance at a young age were unable to maintain the same level of competitiveness in adulthood as they experienced a plateau in performance from the age of 20 years. The procedure of considering early performances solely for talent identification (and not the current rate of progression) might represent a limited approach for selecting future elite swimmers. Our results indicate that performance progression in the transition toward adult careers might be a strong indicator of performance potential.

Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers

To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare differences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers’ annual best times and a two-way analysis of variance (ANOVA) was used to determine differences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P < 0.05) compared to the cross-sectional analysis. This difference was found in the lower percentiles (1st to 20th) across all age categories (P < 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P < 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers.

Relative age effect in French alpine skiing: Problem and solution

This study aims to identify the potential bias of the relative age effect (RAE) in French alpine skiers and to propose a mathematical correction adjustment for such a bias. All performances and birthdates of skiers on the national and international circuit were collected from the 2004 up to 2019. A goodness-of-fit chi-square test and the residuals were used to study the distribution of birth trimesters in youth competitors. A linear relationship between the distribution of performances and the months obtains a calibration coefficient allowing to rebalance the performance by considering the effect of RAE. Individuals born at the beginning of the year are over-represented in the elite young selections in all disciplines for both genders. A coefficient based on the relationship between month of birth and performance adjusts individual performance and cancels out the effect of RAE. The results show that RAE is present in French alpine skiing. We present a method allowing to consider the effect of the RAE in the performances realized in each gender and discipline. This method allows coaches to have a more objective opinion on performance and to reduce selection bias.

Observed and predicted ages at peak height velocity in soccer players

The purpose of the study was to evaluate predicted maturity offset (time before age at PHV) and age at PHV (chronological age [CA] minus maturity offset) in a longitudinal sample of 58 under-13 club level soccer players in central Portugal for whom ages at PHV were estimated with the SITAR model. Two maturity offset prediction equations were applied: the original equation which requires CA sitting height, estimated leg length, height and weight, and a modified equation which requires CA and height. Predicted maturity offset increased, on average, with CA at prediction throughout the age range considered, while variation in predicted maturity offset and ages at PHV within CA groups was considerably reduced compared to variation in observed ages at offset and at PHV. Predicted maturity offset and ages at PHV were consistently later than observed maturity offset and age at PHV among early maturing players, and earlier than observed in late maturing players. Both predicted offset and ages at PHV with the two equations were, on average, later than observed among players maturing on time. Intra-individual variation in predicted ages at PHV with each equation was considerable. The results for soccer players were consistent with similar studies in the general population and two recent longitudinal studies of soccer players. The results question the utility of predicted maturity offset and age at PHV as valid indicators of maturity timing and status.

Being a top swimmer during the early career is not a prerequisite for success: A study on sprinter strokes

OBJECTIVES

To quantify the junior-to-senior successful transition rate in sprint swimming events in elite European performers.

DESIGN

Retrospective analysis of publicly available competition data collected between 2004 and 2019.

METHODS

The yearly performance of 6631 European swimmers (females = 41.8% of the sample) competing in 50 and 100 m freestyle, backstroke, breaststroke, and butterfly were included in the analysis. The junior-to-senior transition rate was determined as the number of elite junior athletes who maintained their elite status in adulthood. To investigate how the definition of elite may affect the calculation of the transition rate, we operationally defined elite athletes as those ranked in the all-time top 10, 25, 50, and 100 in their category. We also calculated the correlation between junior and senior performances.

RESULTS

The average transition rates ranged, depending on the age of reference, from 10 to 26% in males and from 23 to 33% in females. The transition rate for the top 100 junior swimmers was greater than that for the top 10 swimmers. In general, swimmers who swam 50 m showed a slightly lower transition rate compared with those that swam 100 m. Depending on the age of reference, low-to-moderate correlations were observed between junior and senior peak performances.

CONCLUSIONS

Most elite junior athletes did not maintain the elite level in adulthood. Except for athletes in the last year of the junior category (18 years for males and 17 years for females), junior performances were poorly correlated with senior performances.