Aging Decline in Basketball Career Trend Prediction Based on Machine Learning and LSTM Model
This addresses the problem of predicting career trends for NBA players and sports analysts, but appears incremental as it combines existing methods (autoencoder, K-means, LSTM) on a specific dataset.
This study tackled the problem of predicting aging decline in NBA players' performance by using an autoencoder with K-means clustering for career trend classification and LSTM for performance prediction, achieving better performance than other methods with generalization ability for various career trends.
The topic of aging decline on performance of NBA players has been discussed in this study. The autoencoder with K-means clustering machine learning method was adopted to career trend classification of NBA players, and the LSTM deep learning method was adopted in performance prediction of each NBA player. The dataset was collected from the basketball game data of veteran NBA players. The contribution of the work performed better than the other methods with generalization ability for evaluating various types of NBA career trend, and can be applied in different types of sports in the field of sport analytics.