Older or Wiser? Age and Experience Trends in 20 Years of Olympic and World Swimming Championships Open Water 10-km Races

The aims of the present research were to estimate the age of peak performance (APP) and to examine the role of previous experience at the world-level open water race performances. Finishing positions and age of swimmers (639 females and 738 males) in the 10-km events of World Championship (WCH) and Olympic Games (OG) from 2000 to 2019 were obtained from the official results websites. Years of previous experience were computed using the number of previous participations in WCH or OG. APP was estimated using quadratic models of the 10th percentile top race positions and resulted in 28.94 years old for males (R2 = 0.551) and 27.40 years old for females (R2 = 0.613). Regression analysis revealed an improvement of 1.36 or 8.19 finishing positions for each additional year of age or experience, respectively (R2 = 0.157). However, significant differences (p < 0.001) between age and experience showed that the swimmer’s age became less relevant for performance as years of experience increased. These results, in terms of age, are in line with other mass-start disciplines of similar duration (≈2 h) and, in terms of experience, confirm the importance of previous participation in improving tactical decision making during open water races.

Changes in Sex Difference in Time-Limited Ultra-Cycling Races from 6 Hours to 24 Hours

Background and objective: Existing research shows that the sex differences in distance-limited ultra-cycling races decreased with both increasing race distance and increasing age. It is unknown, however, whether the sex differences in time-limited ultra-cycling races will equally decrease with increasing race distance and age. This study aimed to examine the sex differences regarding performance for time-limited ultra-cycling races (6, 12, and 24 h). Methods: Data were obtained from the online database of the Ultra-Cycling Marathon Association (UMCA) of time-limited ultra-cycling races (6, 12, and 24 h) from the years 1983–2019. A total of 18,241 race results were analyzed to compare cycling speed between men and women by calendar year, age group (<29; 30–39; 40–49; 50–59; 60–69; >70 years), and race duration. Results: The participation of both men (85.1%) and women (14.9%) increased between 1983 and 2019. The age of peak performance was between 40 and 59 years for men and between 30 and 59 years for women. Between 2000 and 2019, more men (63.1% of male participants and 52.2% of female participants) competed in 24 h races. In the 24 h races, the sex difference decreased significantly in all age groups. Men cycled 9.6% faster than women in the 12 h races and 4% faster in the 24 h races. Both women and men improved their performance significantly across the decades. Between 2000 and 2019, the improvement in the 24 h races were 15.6% for men and 21.9% for women. Conclusion: The sex differences in cycling speed decreased between men and women with increasing duration of ultra-cycling races and with increasing age. Women showed a greater performance improvement than men in the last 20 years. The average cycling speed of men and women started to converge in the 24 h races.

Age at Nomination Among Soccer Players Nominated for Major International Individual Awards: A Better Proxy for the Age of Peak Individual Soccer Performance?

Individual soccer performance is notoriously difficult to measure due to the many contributing sub-variables and the variety of contexts within which skills must be utilised. Furthermore, performance differs across rather specialised playing positions. In research, soccer performance is often measured using combinations of, or even single, sub-variables. All too often these variables have not been validated against actual performance. Another approach is the use of proxies. In sports research, the age of athletes when winning championship medals has been used as a proxy for determining their age of peak performance. In soccer, studies have used the average age of players in top European leagues or in the Champions League to determine the age of individual peak performance. Such approaches have methodological shortcomings and may underestimate the peak. We explore the use of a new proxy, the age at nomination for major individual awards, to determine the average age at peak individual soccer performance. A total of 1,981 players nominated for major awards from 1956 to 2019 were included, and a subset of 653 retired players was extracted, thus including players’ complete careers. Players’ average ages at nomination, at their first nomination, and at their last ever nomination were calculated, and differences across playing positions were calculated together with changes over time in the average age at peak. Based on our proxy, the age of individual peak soccer performance occurs around 27–28 years, varying across playing positions from 26 to 31 years. A player’s first peak, on average, seems to coincide with known peaks of physiological variables; their last-ever peak occurs long after physiological performance has started to decline, indicating that the decline can be compensated for by other variables. The peak age is higher than previously reported for soccer; however, it is similar to those in other team ball sports. The average age at peak performance has increased over time, especially in the last decade. Our approach of using proxies for unearthing information about hidden features of otherwise immeasurable complex performance appears to be viable, and such proxies may be used to validate sub-variables that measure complex behaviour.

No Trends in the Age of Peak Performance among the Best Half-Marathoners and Marathoners in the World between 1997–2020

Background and Objectives: We examined the possible trend in the age of peak performance in elite endurance athletes according to sex, continent of athletes’ national citizenship, and ranking position. Since performance is a multifactorial trait, this information can be used to guide the long-term training and to plan the strategies related to the selection process of athletes. Materials and methods: Information of 1852 professional athletes, classified as top 20 performance of each year in marathon and half-marathon events between 1997 and 2020 were considered. Analysis of variance was computed to test differences in age between sex, continent, and rank position. Results: A significant difference between groups in the mean age of peak performance was observed (F (3, 1884) = 42,31; p < 0.001). For both sexes, half-marathoners were younger than marathoners (male, 25.6 ± 3.6 years vs. 28.0 ± 3.9 years; female, 27.5 ± 4.4 years vs. 28.4 ± 4.1). Female half-marathoners in 2004 presented the highest mean age (31.1 ± 4.8 years) compared to their peers in the years 1997, 2001, 2018 and 2019; among male half-marathoners, those in 1999 presented the highest mean age when compared to 2011, 2018, and 2019. Differences between the continents of athletes’ national citizenship were observed (F (4, 1884) = 62,85,601; p < 0,001). Asian runners presented the lowest mean age (26.5 ± 3.7 years), while their European peers presented the highest (31.1 ± 3.9 years). No significant interaction between sex and ranking position was verified. Differences were observed between sexes for categories “4th–10th positions” and “11th–20th” (F (1, 1879) = 23,114; p < 0.001). Conclusions: Over the last two decades, no clear trend was observed in the changes in the age of peak performance among endurance athletes of both sexes, but, in general, female half-marathoners tended to be significantly older than their male peers.

Successful talent development in popular game sports in Switzerland: The case of ice hockey

There is continuing discussion in talent research on the best approach to developing sporting expertise through learning activities during early sport participation. Among other concepts, the specialized sampling model describes a pathway between early specialization and early sampling and yields promising results in Swiss football. As successful constellations of early sport participation might be affected by sport-specific constraints (e.g., age of peak performance, selection pressure, and physiological/psychological requirements), other popular game sports may show similar promising pathways. This study investigates whether ice hockey, another popular game sport in Switzerland, shows similar successful constellations of early sport participation. A sample of 98 former Swiss junior national team players born between 1984 and 1994 reported on early sport participation through a retrospective questionnaire. Using the person-oriented Linking of Clusters after removal of a Residue (LICUR) method, volumes of in-club practice, free play, and activities besides ice hockey until 12 years of age were analyzed, along with player’s age at initial club participation. The results indicate that ice hockey enthusiasts with the most free play and above-average in-club practice had a greater chance of reaching professional level compared to other groups. This implies that high domain specificity with varied sampling experiences is the most promising approach to developing sporting expertise in ice hockey. As similar results were previously found in Swiss football, comparable sport-specific constraints might indeed require similar constellations of learning activities during early sport participation. Therefore, in popular game sports in Switzerland, the specialized sampling model seems to be most promising.

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.

Career Performance Trajectories of Professional Australian Football Players

Purpose: To develop position-specific career performance trajectories and determine the age of peak performance of professional Australian Football players. Methods: Match performance data (Australian Football League [AFL] Player Rank) were collected for Australian Football players drafted via the AFL National Draft between 1999 and 2015 (N = 207). Players were subdivided into playing positions: forwards (n = 60; age 23 [3] y), defenders (n = 71; age 24 [4] y), midfielders (n = 58; age 24 [4] y), and ruckmen (n = 18; age 24 [3] y). Linear mixed models were fitted to the data to estimate individual career trajectories. Results: Forwards, midfielders, and defenders experienced peak match performance earlier than ruckmen (24–25 vs 27 y). Midfielders demonstrated the greatest between-subjects variability (intercept 0.580, age 0.0286) in comparison with ruckmen, who demonstrated the least variability (intercept 0.112, age 0.005) in AFL Player Rank throughout their careers. Age had the greatest influence on the career trajectory of midfielders (β [SE] = 0.226 [0.025], T = 9.10, P < .01) and the least effect on ruckmen (β [SE] = 0.114 [0.049], T = 2.30, P = .02). Conclusions: Professional Australian Football players peak in match performance between 24 and 27 years of age with age, having the greatest influence on the match performance of midfielders and the least on ruckmen.

Elite Junior Throwers Unlikely to Remain at the Top Level in the Senior Category

Purpose: This study investigated (1) the transition rate of elite world-class throwers, (2) the age of peak performance in either elite junior and/or elite senior athletes, and (3) if relative age effect (RAE) influences the chance of being considered elite in junior and/or senior category. Methods: The career performance trajectories of 5108 throwers (49.9% females) were extracted from the World Athletics database. The authors identified throwers who had reached the elite level (operationally defined as the World all-time top 50 ranked for each age category) in either junior and/or senior category and calculated the junior-to-senior transition rate. The age of peak performance and the RAE were also investigated. Results: The transition rate at 16 and 18 years of age was 6% and 12% in males and 16% and 24% in females, respectively. Furthermore, elite senior throwers reached their personal best later in life than elite junior throwers. The athletes of both genders considered elite in the junior category showed a large RAE. Interestingly, male athletes who reached the elite level in senior category also showed appreciable RAE. Conclusions: Only a few of the athletes who reach the top 50 in the world at 16 or 18 years of age manage to become elite senior athletes, underlining that success at the beginning of an athletic career does not predict success in the athlete’s senior career. Moreover, data suggest that being relatively older may confer a benefit across the whole career of male throwers.