Visual Analysis and Prediction of Teen Hammer Throw Athletes’ Performance Using Data Mining Techniques
Jie Li · The 2nd International Conference on Computing and Data Science · 2021
Hammer throw is a difficult yet important event in track and field. However, in China, there exist some problems in improving teenagers’ hammer throw performance. In this paper, we conducted research to explore the factors related to hammer throw in order to tackle this problem. By utilizing athletes’ physical test data and FMS (Functional movement screen) data, we first visualized to explore and interpret factors related to hammer throw. Then several state-of-the-art regression models were performed to predict athletes’ scores. The experimental results indicate that hammer throw has high correlation with function movement screen and also has connection with weight and height. For hammer throw prediction, ELM model outperforms the other two models. The promising results will undoubtedly promote athletes’ performance and assist their coaches for better training programs.