Predicting competitive weightlifting performance using regression and tree-based algorithms

Abstract

Athletes are rigorously trained and practice for gradually improving their fitness, sports-specific skills, and motor qualities to achieve success in various competitions. Periodic testing of physiological and physical fitness components is done to assess the progress of athlete in the training. Athletes are also selected for the upcoming competitions using these measurement results. However, there is a lack of an objective method for evaluating and selecting athletes for competitions. Machine learning predictive models are developed for important feature selection and prediction of weightlifting performances at the national or international level and how much total weight an athlete could lift in the future competitions. Predictive modeling requires historical data; hence, five-year fitness testing and actual competition performances were used for the prediction. Various fitness components are often highly correlated, and multicollinearity is a major issue in developing regression models for predicting performances. Hence, regularized ridge, lasso, and elastic net regression and tree-based random forest algorithms were developed to predict the performances along with the conventional statistical regression models. Boruta algorithm was applied to identify the most important predictors of the weightlifting performances. Back and handgrip strength, lower body average power, upper body explosive power and strength, age, and height were the most important predictors of the weightlifting performance. Thus, random forest with confirmed most important variables is the best model to predict the total weight-lifted and the national/international performances in selecting the weightlifters objectively for the upcoming competitions.