
Race Dynamics in Triathlon Mixed-Team-Relay Meaningfully Changes with The New Regulation Towards Paris 2024.
Journal of sports science & medicine
Mixed-Team-Relay (MTR) triathlon is a novel Olympic discipline whose performance determinants and tactical behaviors have barely been studied. Additionally, a regulatory change has been made to the male and female relay order for the Paris 2024 Olympics. Therefore, this study aimed to determine the performance determinants and race dynamics as a function of competitive level on the new regulated MTR triathlon. Results from 129 national teams, (516 elite triathletes) across five MTR World Triathlon Series and two MTR European Championships in 2022 and 2023, were analyzed. Split times, average speeds, time behind the race leader (gap), partial and finishing positions, pack position as well as the rank positions of every segment, relay leg, and overall race were computed. Decision tree analyses were conducted as a predictive method for the overall results, and correspondence analyses were conducted to examine the relationship between the different relay legs and segments and the finishing positions. The performance of the fourth leg was the most relevant for overall result (30%), as well as the fourth running leg (16%) and the female legs performance (7%). Medallist relay teams were characterized by displaying a differential speed lower than 0.5 and 0.83 km/h, respectively, from the best-ranking athletes in the Legs 1 and 4. Furthermore, staying in the front pack after the second swimming leg showed a great relationship with achieving a medal position. New MTR triathlon rules shift race dynamics, emphasizing individual efforts in cycling and swimming, while maintaining the crucial importance of running.
10.52082/jssm.2024.358
Relationship between world-ranking and Olympic performance of swimmers.
Trewin Cassie B,Hopkins William G,Pyne David B
Journal of sports sciences
Coaches believe world-ranking lists are a reliable tool for predicting international swimming performance. To examine the relationship between world-ranking and Olympic performance, we modelled world-ranking time and best time from the 2000 Olympic Games for 407 top-50 world-ranked swimmers. Analysis of log-transformed times yielded within-athlete and between-athlete coefficients of variation (CV) and percent changes in performance from world-rankings to Olympics. Variations and performance progressions were compared across sex, stroke, distance, nation and medal status. The within-athlete coefficient of variation of performance for all swimmers was 0.8% (95% confidence limits: 0.7 to 0.9%). Females were slightly less consistent, although not substantially different to males (ratio of female/male within-athlete CV: 1.1; 95% confidence limits: 1.0 to 1.2) and had a wider range of talent (ratio of female/male between-athlete CV: 1.2; 95% confidence limits: 1.1 to 1.4). Swimmers from Australia (AUS) were more consistent than those from the United States (USA) and other nations (OTHER) (ratio of within-athlete CV, USA/AUS: 1.5; 95% confidence limits: 1.0 to 2.2; OTHER/ AUS: 1.6; 95% confidence limits: 1.2 to 2.1). Most Olympic medallists (87%) had a top-10 world-ranking. Overall performance time at the Olympics was slower than world-ranking time by 0.3% (95% confidence limits: 0.2 to 0.4%), medallists improved by 0.6% (95% confidence limits: 0.4 to 0.9%) and non-medallists swam 0.6% slower (95% confidence limits: 0.5 to 0.7%). We conclude that a top-10 ranked swimmer who can improve performance time by 0.6%, equivalent to 0.13 s in the men's 50-m freestyle, will substantially increase their chance of an Olympic medal (the difference between first and fourth place).
10.1080/02640410310001641610
Universality, limits and predictability of gold-medal performances at the olympic games.
Radicchi Filippo
PloS one
Inspired by the Games held in ancient Greece, modern Olympics represent the world's largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics-including running, jumping, and throwing-and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games.
10.1371/journal.pone.0040335
Next-Generation Models for Predicting Winning Times in Elite Swimming Events: Updated Predictions for the Paris 2024 Olympic Games.
International journal of sports physiology and performance
PURPOSE:To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games. METHODS:The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships. RESULTS:The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r = .99 for both approaches). CONCLUSIONS:These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games.
10.1123/ijspp.2023-0174