It is heavily discussed whether larger variety or specialization benefit elite performance at peak age. Therefore, this study aimed to determine technical (number of different swimming strokes) and physiological (number of different race distances) variety required to become an international-class swimmer (> 750 swimming points) based on 1'522'803 race results.; Correlation analyses showed lower technical variety in higher ranked swimmers (P
To investigate performance progression from early-junior to peak performance age and compare variety in race distances and swimming strokes between swimmers of various performance levels.; Using a longitudinal data analysis and between-groups comparisons 306,165 annual best times of male swimmers (N = 3897) were used to establish a ranking based on annual best times at peak performance age. Individual performance trajectories were retrospectively analyzed to compare distance and stroke variety. Performances of world-class finalists and international- and national-class swimmers (swimming points: 886 [30], 793 [28], and 698 [28], respectively) were compared across 5 age groups-13-14, 15-16, 17-18, 19-20, and 21+ years-using a 2-way analysis of variance with repeated measures.; World-class finalists are not significantly faster than international-class swimmers up to the 17- to 18-year age group (F2|774 = 65, P < .001, ηp2=.14) but specialize in short- or long-distance races at a younger age. World-class breaststroke finalists show faster breaststroke times compared to their performance in other swimming strokes from an early age (P < .05), while world-class freestyle and individual medley finalists show less significant differences to their performance in other swimming strokes.; While federation officials should aim for late talent selection, that is, not before the 17- to 18-year age group, coaches should aim to identify swimmers' preferred race distances early on. However, the required stroke variety seems to be specific for each swimming stroke. Breaststroke swimmers could aim for early and strong specialization, while freestyle and individual medley swimmers could maintain large and very large stroke variety, respectively.
This study aimed to identify Key Performance Indicators (KPIs) for men's swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men's individual 100 m races of the 2019 European Short-Course Swimming Championships.; Duration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs - 2.3% to + 2.3%), Breaststroke (0% bias, LOAs - 0.9% to + 0.9%), Butterfly (0% bias, LOAs - 1.2% to + 1.2%), and Freestyle (0% bias, LOAs - 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.
Scanning is an important perceptual skill that enables football players to gather information about opponents, teammates and the environment in real-time. This study investigated scanning before and during ball possession and its effect on the success of the subsequent action in U19 female footballers. Sixty-one elite and grassroots players (age: 16.7 ± 1.4) were recruited and analysed during 4v4 small-sided games. A total of 2010 game situations were video recorded for subsequent manual tagging. Multilevel logistic regression models revealed that elite players performed significantly more scans prior to first ball contact than their grassroots counterparts, but the number of scans performed during ball possession did not differ between competition levels. Furthermore, scans before and during ball possession positively influenced player's performance, whereas opponent pressure negatively influenced the success of subsequent actions, regardless of competition level. Differentiating between various subsequent actions revealed that scans before ball possession had a positive effect on the success of dribblings and passes, whereas scans during ball possession only had a positive effect on dribblings. Our results underline the importance of scanning in youth female football. These findings should be considered by coaches in the long-term development of players to increase the level of performance at elite age.
This study aimed to determine kinematic and kinetic key performance indicators (KPI) of swimming turn performance using principal component analysis (PCA) and multiple linear regression analysis and provide reference values using percentiles. Touch and tumble turn performances of male (; n; = 68) and female (; n; = 48) Swiss national team members from three age categories-adult (20.2 ± 2.7 yrs, 790 ± 57 points), junior (16.2 ± 0.8 yrs, 729 ± 53 points) and youth swimmers (14.4 ± 1.0 years of age, 667 ± 53 World Aquatics swimming points, respectively)-were assessed with a motion analysis system equipped with a force plate on the pool wall, one over- and four underwater cameras sampling forces at 500 Hz and footages at 100 Hz. The PCA reduced the 27 original variables by up to 15% depending on turn type and age category using Varimax component loading of >0.6 and explained up to 91% of the total variance. The highest Varimax component loadings for each principal component were used to determine KPI for each turn type and age category using multiple-regression analysis with total turn time as dependent variable. These KPI should be used to interpret turn performances and identify individual swimmers' strengths, weaknesses and future potentials with the help of the percentiles as reference values.
To compare performance progression and variety in race distances of comparable lengths (timewise) between pool swimming and track running. Quality of within-sport variety was determined as the performance differences between individual athletes' main and secondary race distances across (top-) elite and (highly-) trained swimmers and runners.; A total of 3,827,947 race times were used to calculate performance points (race times relative to the world record) for freestyle swimmers (; n; = 12,588 males and; n; = 7,561 females) and track runners (; n; = 9,230 males and; n; = 5,841 females). Athletes were ranked based on their personal best at peak performance age, then annual best times were retrospectively traced throughout adolescence.; Performance of world-class swimmers differentiates at an earlier age from their lower ranked peers (15-16 vs. 17-20 year age categories,; P;
This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA -7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs -0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training.
During puberty, the biological maturity of children of the same chronological age differs. To generate equal opportunities for talent selection in youth sports, the athlete's biological maturity should be considered. This is often assessed with a left hand and wrist radiography. Alternatively, ultrasound (US) could be advantageous, especially by avoiding ionizing radiation. This pilot study aimed to assess intrarater and interrater reliability of an experienced and a non-experienced examiner in an US-based examination of the knee in 20 healthy females (10-17 years). Epiphyseal closure at five anatomical landmarks was staged (stages 1-3) and its interrater and intrarater reliabilities were analyzed using Cohen's kappa (; k; ). Interrater reliability of the calculation of the ossification ratio (OssR) was analyzed using the Bland-Altman method and intraclass correlation coefficients (ICCs). Interrater reliability for the stages was almost perfect for four landmarks. Interrater reliability ranged from; k; = 0.69 to; k; = 0.90. Intrarater reliability for the stages was almost perfect for four landmarks. Intrarater reliability ranged from; k; = 0.70 to; k; = 1.0. For the OssR, ICC was 0.930 and a minimal detectable change of 0.030 was determined. To conclude, experienced and non-experienced examiners can reliably assign individuals to different ossification stages and calculate an OssR using US-based imaging of the knee.
This study aimed to identify Key Performance Indicators (KPIs) for men's swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men's individual 100 m races of the 2019 European Short-Course Swimming Championships.; Duration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs - 2.3% to + 2.3%), Breaststroke (0% bias, LOAs - 0.9% to + 0.9%), Butterfly (0% bias, LOAs - 1.2% to + 1.2%), and Freestyle (0% bias, LOAs - 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.
The importance of considering information related to athletes’ biological maturation within talent identification and development processes is frequently emphasized by both sport scientists and practitioners. Although there is evidence for the use of objective diagnostics for assessing biological maturation, little is known about its subjective determinations by coaches. Such approaches are particularly relevant when scientific support is limited. Therefore, the current study aimed to compare a practical subjective approach (coaches’ eye) to assess biological maturity timing (BMT) with objective reference diagnostics (MRI). For this purpose, data were collected from 63 male elite soccer players of the U12 and U14 age group who were part of the German talent promotion program. Players’ BMT (i.e., skeletal – chronological age) was assessed by MRI and a subjective rating of two coaches. Data analyses revealed high-rank correlations (rs = .55; p
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.
OBJECTIVES: To provide normative data and establish percentile curves for long-course (50m 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 (21-26years) 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 (15-17years). However, the longer the race distance, the later the race times plateaued (p
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 (Federation 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 (beta = 0.19 to 0.33). Increased race distance from 50 to 1500 m, decreased effects of chronological age (beta = 0.48 to - 0.13) and increased relative age effects (beta = 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.
PURPOSE: To explore reasonable application purposes and potential confounders of the Swiss-Ski Power Test (SSPT) that is, since 2004, annually performed by all youth competitive alpine skiers of the under-16-years age category in Switzerland. METHODS: Preseason SSPT results (8 individual tests on anaerobic and aerobic capacity, muscle strength, and speed and coordination) of 144 skiers (57 female and 87 male) age 14.5 (0.7) years were analyzed along with anthropometry and biological age. Skiing performance was quantified as the actual performance points according to the Swiss national ranking. After the SSPT tests, skiers were prospectively monitored over 12 months using the Oslo Trauma Research Center questionnaire. Data were analyzed using multivariate analysis of variance, Pearson correlations, and multiple linear/binary logistic regression models. RESULTS: Biological maturation and SSPT results differed between sexes and age (P < .05). For males, SSPT results in the subdisciplines Swiss Cross, 1-leg 5-hop, and standing long jump were correlated to maturity offset, while for females only the obstacle run was related. High box jump and Swiss Cross scores were associated with skiing performance (P < .05). However, none of the SSPT subdisciplines was related to traumatic and overuse injuries (P < .05). CONCLUSIONS: The SSPT is a broadly implementable and cost-effective field test providing a general fitness profile of youth skiers. Around the growth spurt, differences in biological maturation should be considered. While SSPT results showed association with skiing performance, the test in its current form is limited for identifying injury-relevant physical deficiencies. Consequently, more specific tests may be required.
Bone maturity is an indicator for estimating the biological maturity of an individual. During adolescence, individuals show heterogeneous growth rates, and thus, differences in biological maturity should be considered in talent identification and development. Radiography of the left hand and wrist is considered the gold standard of biological maturity estimation. The use of ultrasound imaging (US) may be advantageous; however, its validity and reliability are under discussion. The aims of this scoping review are (1) to summarize the different methods for estimating biological maturity by US imaging in adolescents, (2) to obtain an overview of the level of validity and reliability of the methods, and (3) to point out the practicability and usefulness of ultrasound imaging in the field of youth sports. The search included articles published up to November 2022. The inclusion criteria stipulated that participants had to fall within the age range of 8 to 23 years and be free of bone disease and fractures in the region of interest. Nine body regions were investigated, while the hand and wrist were most commonly analyzed. US assessment methods were usually based on the estimation of a bone maturity stage, rather than a decimal bone age. Furthermore, 70% of the assessments were evaluated as applicable, 10% expressed restraint about implementation, and 20% were evaluated as not applicable. When tested, inter- and intra-rater reliability was high to excellent. Despite the absence of ionization, low costs, fast assessment, and accessibility, none of the US assessments could be referred to as a gold standard. If further development succeeds, its application has the potential to incorporate biological age into selection processes. This would allow for more equal opportunities in talent selection and thus make talent development fairer and more efficient.
In football, annual age-group categorization leads to relative age effects (RAEs) in talent development. Given such trends, relative age may also associate with market values. This study analyzed the relationship between RAEs and market values of youth players.; Age category, birthdate, and market values of 11,738 youth male football players were obtained from the "transfermarkt.de" database, which delivers a good proxy for real market values. RAEs were calculated using odds ratios (OR) with 95% confidence intervals (95%CI).; Significant RAEs were found across all age-groups (; p; < 0.05). The largest RAEs occurred in U18 players (Q1 [relatively older] v Q4 [relatively younger] OR = 3.1) ORs decreased with age category, i.e., U19 (2.7), U20 (2.6), U21 (2.4), U22 (2.2), and U23 (1.8). At U19s, Q1 players were associated with significantly higher market values than Q4 players. However, by U21, U22, and U23 RAEs were inversed, with correspondingly higher market values for Q4 players apparent. While large typical RAEs for all playing positions was observed in younger age categories (U18-U20), inversed RAEs were only evident for defenders (small-medium) and for strikers (medium-large) in U21-U23 (not goalkeepers and midfielders).; Assuming an equal distribution of football talent exists across annual cohorts, results indicate the selection and market value of young professional players is dynamic. Findings suggest a potential biased selection, and undervaluing of Q4 players in younger age groups, as their representation and market value increased over time. By contrast, the changing representations and market values of Q1 players suggest initial overvaluing in performance and monetary terms. Therefore, this inefficient talent selection and the accompanying waste of money should be improved.
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
