Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country, Leioa,Basque Country.
Exercise Science Laboratory, School of Kinesiology, Faculty of Medicine, Universidad Finis Terrae, Santiago,Chile.
Int J Sports Physiol Perform. 2023 Jul 24;18(11):1269-1274. doi: 10.1123/ijspp.2023-0174. Print 2023 Nov 1.
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.
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.
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).
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.
评估为国际游泳赛事预测获奖表现而开发的统计模型，并为巴黎 2024 年奥运会生成更新的表现预测。
评估了 2 个用于预测国际游泳表现的统计模型的性能。第一个模型采用线性回归和预测，考察了 8 年来奖牌获得者、决赛选手和半决赛选手的表现趋势。使用机器学习算法为巴黎 2024 年奥运会的每个个人项目生成时间预测。第二个模型是一个贝叶斯框架，包括自回归项（前一次获胜时间）、移动平均（过去 3 项赛事）以及泳姿、性别、距离和赛事类型（世界锦标赛与奥运会）的协变量。为了检验两个模型预测的准确性，通过比较布达佩斯 2022 年世界锦标赛的预测时间和实际结果，确定了平均绝对误差。
线性回归机器学习模型和贝叶斯模型预测游泳表现的平均绝对误差分别为 0.80%和 0.85%。布达佩斯 2022 年世界锦标赛的预测时间和实际时间高度相关（两种方法的 r 值均为.99）。
这些模型以及巴黎 2024 年奥运会游泳项目的相关预测，为教练、运动科学支持人员和运动员提供了一个基于证据的表现框架，以便根据他们在巴黎 2024 年奥运会上的潜力，制定和评估训练计划、战略和策略，并为运动员分配资源。
