Longitudinal Master Track and Field Performance Decline Rates Are Lower and Performance Is Better Compared to Athletes Competing Only Once

Figure skating: Increasing numbers of revolutions in jumps at the European and World Championships

Figure skating is associated with a high prevalence of sport-specific injuries and overuse symptoms. Impacts are of greater magnitude in jumps with more revolutions that are thus connected to a greater risk of injury. While figure skating programs seem to have recently increased in difficulty, performance trends have not yet been reported in the literature. We hypothesized increasing performance and decreasing age trends of the best athletes who competed at international level in recent years. Furthermore, we aimed to identify and analyse objective performance parameters and to assess a potential link between age and the risk of injury. The development of the number of double, triple and quadruple jumps in the singles figure skating events at the World and European Championships since the introduction of the new ISU Judging System in the 2004/2005 season was analysed using regression statistics and Student's T-Tests. In all groups, the numbers of jumps with fewer rotations significantly decreased in the favour of jumps with more rotations. Women only started to perform jumps with four rotations in 2019. In the men, the number of quadruple jumps increased from an average of less than one to more than three in recent years (European and World Championships, both p < 0.001). In the European, but not in the World Championships, the average age increased in the men and decreased in the women (European Championships, men and women p = 0.006). Our study was the first to assess performance trends in elite figure skating. The incidence of injuries and overuse syndromes in figure skating needs to be monitored cautiously, as increases can be expected following recent gains in performance and jump complexity.

Performance Increases in Pair Skating and Ice Dance at International Championships and Olympic Games

In pair skating and ice dance, performance seems to have increased at international competitions, which is potentially associated with changes in athlete age. We hypothesized increasing age, numbers of total points and more complex jumps of the best elite couples at international championships in recent years. Corresponding data were assessed via the results databases of the European and World Championships, as well as the Winter Olympics since 2005. Linear regression statistics were conducted, and significance was assessed via one-way ANOVAs. There were no significant changes in age. Increases in total points were found in both disciplines (World and European Championships both p < 0.001 for both disciplines, Olympics pair skating p = 0.003, ice dance n/a). Significant increases were found in the number of double and triple twist jumps at the European Championships (Double p = 0.046, triple p = 0.041), but not at the World Championships or the Olympics. At the World Championships, single solo jumps decreased (p = 0.031) in favor of triple jumps, which increased (p = 0.020), without a similar effect at the European Championships or Olympics. In conclusion, increases in total points and more complex jumps were observed at international championships without associated changes in age. Attention should be given to possible changes in the incidence of acute and overuse injuries following this development.

Learning from machine learning: prediction of age-related athletic performance decline trajectories

Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline.