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

日本における外資系企業のロビー戦略:金と圧力から知識構築へ 国際ビジネス(IB)環境における企業政治活動(CPA)

早大学位記番号:新7622早稲田大

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

The Score-Difference Flow for Implicit Generative Modeling

Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work (e.g. score-matching networks, diffusion models) has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution via dynamical perturbations or flows in the ambient space. In this direction, we present the score difference (SD) between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them while also solving the Schroedinger bridge problem. We apply the SD flow to convenient proxy distributions, which are aligned if and only if the original distributions are aligned. We demonstrate the formal equivalence of this formulation to denoising diffusion models under certain conditions. We also show that the training of generative adversarial networks includes a hidden data-optimization sub-problem, which induces the SD flow under certain choices of loss function when the discriminator is optimal. As a result, the SD flow provides a theoretical link between model classes that individually address the three challenges of the "generative modeling trilemma" -- high sample quality, mode coverage, and fast sampling -- thereby setting the stage for a unified approach.Comment: 25 pages, 5 figures, 4 tables. To appear in Transactions on Machine Learning Research (TMLR

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