Sink or Swim? A survival analysis of sport dropout in Australian youth swimmers

Make It or Break It? Injury Impacts More Than Half of Deselected Athletes Who Dropped Out of the High-Performance Pathway System. A 4-Year Retrospective Cohort Study

To investigate: (1) the injury epidemiology in an Australian academy; (2) how athletes transition through the high-performance sport (HPS) pathway; and (3) why athletes leave this HPS program. This retrospective cohort study was conducted at an Australian HPS academy over a 4-year period. Medical attention injuries were prospectively recorded. Injury incidence rates (IIR) and burden were calculated per 365 athlete-days, according to sport, sex, and pathway level. Athlete pathway levels were mapped to the Foundations, Talent, Elite, and Mastery (FTEM) framework. Reasons for athletes transitioning out were reported. Four hundred and eighty-one injuries were reported across 124 athletes at an IIR of 2.09 injuries per 365 athlete-days (95% CI = 1.91-2.29). Most athletes (103, 83.1%) were injured at least once over the 4-year period. IIRs increased (IRR = 1.17, 95% CI = 1.06-1.29, p = 0.001) as athletes progressed through the pathway. The most common reason for athlete attrition was deselection due to performance (n = 18), with 55.6% of these athletes sustaining an injury in the season prior to deselection. Injury burden was highest at E2 (119.5 days absence per 365 athlete-days (95% CI = 62.18-229.67)) and lowest at T4 (30.47 days absence per 365 athlete-days (95% CI = 21.98-42.24)). Injury occurrence is common across HPS, with IIRs increasing as athletes progressed to higher talent levels. Deselection due to poor performance was the main attrition factor, with more than half the deselected athletes impacted by injury prior to deselection. This study highlights an increased risk of deselection, following injury, and indicates the need for further development of prevention strategies targeting pathway athletes.

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.

Sports-related factors predicting maintained participation and dropout in organized sports in emerging adulthood: A four-year follow-up study

This study explored sports-related factors predicting organized sports participation in emerging adulthood. In the Finnish Health Promoting Sports Club (FHPSC) study, 354 sports club participants aged 15 at baseline reported their main sport, onset age, training volume, current competitive level, and future competitive orientation and participated in the follow-up study at age 19. There were differences in the proportions of maintainers and dropouts in a few sports: football (maintainers 58.6%), and among females, skating (maintainers 60.7%), and swimming (dropouts 80.0%). A binary logistic regression analysis showed that those who had started their main sport by school age (females OR 3.05/95% CI 1.34, 6.98; males OR 3.97/95% CI 1.48, 10.64) and had competed at national top level plus had aimed at success at the adult level competitions in future (females OR 3.42/95% CI 1.16, 10.09; males OR 3.58/95% CI 1.12, 11.41; among females, also had competed at national top level plus had aimed at success at junior level competitions [OR 3.42/95% CI 1.20, 9.78]) were more likely maintainers than dropouts. Early onset in the main sport and competitiveness were related to maintained sports participation while the opposites were related to dropouts in the current organized sports system.

A machine learning approach for the classification of sports based on a coaches' perspective of environmental, individual and task requirements: A sports profile analysis

Well-designed talent programmes in sports with a focus on talent identification, orientation, development, and transfer support the engagement of young individuals and the pursuit of elite performance. To facilitate these processes, an analysis of task, environmental and individual characteristics per sport is much needed. The aims of this study were to 1) analyse whether unique profiles per sport could be established by generic characteristics and 2) to discuss similarities and differences for the potential application in talent development and transfer. By means of a validated survey, 1247 coaches from 34 sports ranked 18 characteristics on importance to their sports (0 = not important - 10 = very important). To discriminate the responses per sport a Discriminant Analysis (DA) was carried out. To refine the DA-classification, Uniform Manifold Approximation and Projection (UMAP) with CatBoost classifier was performed. To test the performance of the CatBoost classifier-algorithm, a confusion-matrix was generated. The cross-validated DA showed that 70.2% of the coaches were correctly classified to their sport. The UMAP/CatBoost technique revealed 75.1% accuracy with correctly predicted responses per sport ranging from 18.2% (sailing) to 98.2% (soccer). With varying precision, the algorithm was able to differentiate sports by importance of its characteristics indicating similarities and differences per sport.

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.

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.

Analysis of fatal unintentional drowning in Australia 2008-2020: implications for the Australian Water Safety Strategy

OBJECTIVE

To examine temporal trends in drowning in Australia against targets set in the Australian Water Safety Strategy (AWSS) 2008-2020 and to inform the development of the next iteration of the Strategy.

METHODS

A national analysis of unintentional fatal drowning rates per 100,000 population over 16 years (2004/05-2019/20) was conducted. Baseline rates (three-year average 2004/05-2006/07) were compared to the current three-year average (2017/18-2019/20) by sex, age group, drowning location and jurisdiction.

RESULTS

The overall rate of unintentional fatal drowning during the period decreased by 28%. Substantial reductions were observed in children 0-4 years (-63%) and 5-14 years (-56%). Progress has been less pronounced among people aged 75 years and over (-11%) and 15-24-year-olds (-14%). All locations and jurisdictions recorded reductions, aside from rocks (+46%).

CONCLUSIONS

Although the strategy fell short of its aspirational target of a 50% reduction in drowning by the year 2020, this target was exceeded in key age groups, including children. Implications for public health: The AWSS is a powerful tool to align drowning prevention sector actions to agreed objectives. Forthcoming strategies must take into consideration demographic and social change, areas where limited progress has been made and the latest evidence to guide future priorities.