Innate talent is adaptable – comment on Baker & Wattie

An the recent article by Baker and Wattie (2018), they provided an update on the widely cited review of “Innate Talent” by Howe, Davidson and Sloboda (1998). The article summarizes that the defined criteria for “Innate Talent” are still valid, standing the test of time. However, new findings in epigenetics should be considered. The epigenome interacts with environmental factors, such as physical exercise, contributing to phenotypical and performance differences of the same gene. In this context, researchers in sport science face the task of defining ethical standards that are accepted by society. From an epigenetic perspective, one should refrain from thinking that genetics have a fixed performance outcome, since the epigenome is adaptable. Instead, research and practice should consider how created environments support athlete development

Relative Age Effects in Athletic Sprinting and Corrective Adjustments as a Solution for Their Removal

Relative Age Effects (RAEs) refer to the selection and performance differentials between children and youth who are categorized in annual-age groups. In the context of Swiss 60m athletic sprinting, 7761 male athletes aged 8 – 15 years were analysed, with this study examining whether: (i) RAE prevalence changed across annual age groups and according to performance level (i.e., all athletes, Top 50%, 25% & 10%); (ii) whether the relationship between relative age and performance could be quantified, and corrective adjustments applied to test if RAEs could be removed. Part one identified that when all athletes were included, typical RAEs were evident, with smaller comparative effect sizes, and progressively reduced with older age groups. However, RAE effect sizes increased linearly according to performance level (i.e., all athletes – Top 10%) regardless of age group. In part two, all athletes born in each quartile, and within each annual age group, were entered into linear regression analyses. Results identified that an almost one year relative age difference resulted in mean expected performance differences of 10.1% at age 8, 8.4% at 9, 6.8% at 10, 6.4% at 11, 6.0% at 12, 6.3% at 13, 6.7% at 14, and 5.3% at 15. Correction adjustments were then calculated according to day, month, quarter, and year, and used to demonstrate that RAEs can be effectively removed from all performance levels, and from Swiss junior sprinting more broadly. Such procedures could hold significant implications for sport participation as well as for performance assessment, evaluation, and selection during athlete development

Variation in competition performance, number of races, and age: Long-term athlete development in elite female swimmers

While talent development and the contributing factors to success are hardly discussed among the experts in the field, the aim of the study was to investigate annual variation in competition performance (AVCP), number of races per year, and age, as potential success factors for international swimming competitions. Data from 40'277 long-course races, performed by all individual female starters (n = 253) at the 2018 European Swimming Championships (2018EC) for all 10 years prior to these championships, were analyzed. Relationships between 2018EC ranking and potential success factors, i.e., AVCP, number of races per year, and age, were determined using Pearson's correlation coefficient and multiple linear regression analysis. While AVCP was not related to ranking, higher ranked swimmers at the 2018EC swam more races during each of the ten years prior to the championships (P < 0.001). Additionally, older athletes were more successful (r = -0.42, P < 0.001). The regression model explained highly significant proportions (P < 0.001) and 43%, 34%, 35%, 49% of total variance in the 2018EC ranking for 50m, 100m, 200m, and 400m races, respectively. As number of races per year (β = -0.29 --0.40) had a significant effect on ranking of 50-400m races, and age (β = -0.40 --0.61) showed a significant effect on ranking over all race distances, number of races per year and age may serve as success factors for international swimming competitions. The larger number of races swum by higher ranked female swimmers may have aided long-term athlete development regarding technical, physiological, and mental skill acquisitions. As older athletes were more successful, female swimmers under the age of peak performance, who did not reach semi-finals or finals, may increase their chances of success in following championships with increased experience

Competition age: does it matter for swimmers?

Objective: To establish reference data on required competition age regarding performance levels for both sexes, all swimming strokes, and race distances and to determine the effect of competition age on swimming performance in the context of other common age metrics. In total, 36,687,573 race times of 588,938 swimmers (age 14.2 ± 6.3 years) were analyzed. FINA (Fédération Internationale de Natation) points were calculated to compare race times between swimming strokes and race distances. The sum of all years of race participation determined competition age. Results: Across all events, swimmers reach top-elite level, i.e. > 900 FINA points, after approximately 8 years of competition participation. Multiple-linear regression analysis explained up to 40% of variance in the performance level and competition age showed a stable effect on all race distances for both sexes (β = 0.19 to 0.33). Increased race distance from 50 to 1500 m, decreased effects of chronological age (β = 0.48 to - 0.13) and increased relative age effects (β = 0.02 to 0.11). Reference data from the present study should be used to establish guidelines and set realistic goals for years of competition participation required to reach certain performance levels. Future studies need to analyze effects of transitions between various swimming strokes and race distances on peak performance

Strategies to Support Developing Talent

The high performance unit within the Swiss Federal Institute of Sports Magglingen (SFISM) is chartered with supporting talented athletes via its collective inputs to students, athletes, coaches and national sporting federations. This is achieved by drawing upon the multi-disciplinary expertise of practitioners in the areas of sports medicine, recovery and rehabilitation, training science, sports psychology, nutrition, endurance and power physiology, strength and conditioning, and data management. This critical mass of specialists provides opportunities to collaborate “broadly” across a specific talent theme (e.g. on what basis should we select future sporting talent?), as well as the provision of sufficient content expertise to provide “deeper” knowledge and insights related to these interdisciplinary discussions (e.g. how can we account for biological maturity?). Therefore, this paper presents an example of the “broad” interdisciplinary work undertaken by SFISM to improve talent selection, and the complementary “deep” work used to investigate biological maturation as one component of this process. New and ongoing projects will continue to harness the collective potential of the multidisciplinary experts to better understand the processes of talent identification, selection, and development at the broadest and deepest levels. Our collective ability to support Switzerland’s best and brightest talent will require us to maximise the considerable expertise of the many stakeholders which influence and impact on development

Effect of bio-banding on physiological and technical-tactical key performance indicators in youth elite soccer

Bio-banding has been introduced to reduce the impact of inter-individual differences due to biological maturation among youth athletes. Existing studies in youth soccer have generally examined the pilot-testing application of bio-banding. This is the first study that investigated whether bio-banded (BB) versus chronological age (CA) competition affects reliable physiological and technical-tactical in-game key performance indicators (KPIs) using a randomized cross-over repeated measures design. Sixty-five youth elite soccer players from the under-13 (U13) and under-14 (U14) age category and with maturity offsets (MO) between −2.5 and 0.5 years, competed in both a BB and CA game. For statistical analysis, players were divided into four sub-groups according to CA and MO: U13MOlow (CA ≤ 12.7, MO ≤ −1.4), U13MOhigh (CA ≤ 12.7, MO > −1.4), U14MOlow (CA > 12.7, MO ≤ −1.4), U14MOhigh (CA > 12.7, MO > −1.4). The two-factor mixed ANOVA revealed significant (p < .05) interactions between competition format and sub-group for the KPIs high accelerations (h2 p = .176), conquered balls (h2 p = .227) and attack balls (h2 p = .146). Especially, 13MOhigh (i.e. early maturing players) faced a higher physiological challenge by having more high accelerations (|d| = 0.6) in BB games. Notably, U14MOlow (i.e. late maturing players) had more opportunities to show their technical-tactical abilities during BB games with more conquered balls (|d| = 1.1) and attack balls (|d| = 1.6). Affected KPIs indicate new hallenges and learning opportunities during BB competition depending on a player’s individual maturity status. Bio-banding can beneficially be applied to enhance the talent development of youth lite soccer players

Start Fast, Swim Faster, Turn Fastest: Section Analyses and Normative Data for Individual Medley.

The aims of the study were to provide benchmarks and normative data for 100 m, 200 m, and 400 m short-course individual medley (IM) races, investigate differences between the various swimming strokes and turns involved in IM, and quantify the effect and contribution of various race sections on swimming performance. All IM races (n = 320) at the 2019 European Short-Course Swimming Championships were video monitored and digitized with interrater reliability described by a mean intra-class correlation coefficient of 0.968. Normative data were provided for the eight finalists of each event (FINA points = 886 ± 37) and the eight slowest swimmers from each event (FINA points = 688 ± 53). Contribution and effects of race sections on swimming performance were investigated using stepwise regression analysis based on all races of each event. Regression analysis explained 97-100% of total variance in race time and revealed turn time (β ≥ 0.53) as distinguishing factor in short-course IM races in addition to swim velocity (β ≥ -0.28). Start time only affected 100 m (β ≥ 0.14) and 200 m (β ≥ 0.04) events. Fastest turn times were found for the butterfly/backstroke turn. Breaststroke showed slowest swim velocities and no difference between fastest and slowest 100 m IM swimmers. Therefore, breaststroke may provide largest potential for future development in IM race times. Correlation analyses revealed that distance per stroke (r ≥ -0.39, P 0.05) is a performance indicator and may be used by coaches and performance analysts to evaluate stroke mechanics in male IM swimmers despite its more complex assessment. Performance analysts, coaches, and swimmers may use the present normative data to establish minimal and maximal requirements for European Championship participation and to create specific drills in practice

Turn fast and win: the importance of acyclic phases in top-elite female swimmers

The aim of the study was to investigate the effect of start and turn performances on race times in top-elite female swimmers and provide benchmarks for all performance levels, all swimming strokes, and all race distances of the European Short-Course Championships (EC). The individual races (n = 798) of all female competitors (age: 20.6 ± 3.9 years, FINA points: 792 ± 78) were video-monitored for subsequent analysis of start and turn performances. Benchmarks were established across all competitors of each event based on the 10th, 25th, 50th, 75th, and 90th percentiles. Start and turn performances contributed up to 27.43% and 56.37% to total race time, respectively. Mechanistic analysis revealed that the fastest swimmers had the lowest contribution of the acyclic phases to race time. Therefore, relative to their faster race times, these swimmers were even faster during starts and turns. Multiple linear regression analysis showed large effects of turn performance on 50, 100, 200, 400, and 800 m race times (β = 0.616, 0.813, 0.988, 1.004, and 1.011, respectively), while the effect of start performance continuously decreased the longer the race distance. As turn performance may be the distinguishing factor in modern short-course races, benchmarks should be used to set goals and establish training guidelines depending on the targeted race time

Normative data and percentile curves for long-term athlete development in swimming

Objectives: To provide normative data and establish percentile curves for long-course (50 m pool length) swimming events and to compare progression of race times longitudinally for the various swimming strokes and race distances. Design: Descriptive approach with longitudinal tracking of performance data. Methods: A total of 2,884,783 race results were collected from which 169,194 annual best times from early junior to elite age were extracted. To account for drop-outs during adolescence, only swimmers still competing at age of peak performance (21-26 years) were included and analyzed retrospectively. Percentiles were established with z-scores around the median and the Lambda-Mu-Sigma (LMS) method applied to account for potential skewness. A two-way analysis of variance (ANOVA) with repeated measure and between-subject factor was applied to compare race times across the various events and age groups. Results: Percentile curves were established based on longitudinal tracking of race times specific to sex, swimming stroke, and race distance. Comparing performance progression, race times of freestyle sprint events showed an early plateau with no further significant improvement (p > 0.05) after late junior age (15-17 years). However, the longer the race distance, the later the race times plateaued (p < 0.05). Female swimmers generally showed an earlier performance plateau than males. Backstroke and freestyle showed an earlier performance plateau compared to the other swimming strokes (p < 0.05). Conclusions: Performance progression varied between sex, swimming strokes, and race distances. Percentile curves based on longitudinal tracking may allow an objective assessment of swimming performance, help discover individual potentials, and facilitate realistic goal setting for talent development

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