用机器学习与LSTM预测NBA球员生涯表现衰退趋势
Aging Decline in Basketball Career Trend Prediction Based on Machine Learning and LSTM Model
- 结合自编码器与K-means聚类进行生涯轨迹分类
- LSTM模型实现球员个体表现的长期趋势预测
- 方法可推广至其他体育领域，通用性强
本研究探讨了年龄对NBA球员表现的影响。采用自编码器结合K-means聚类的机器学习方法对NBA球员生涯轨迹进行分类，并运用LSTM深度学习模型预测每位球员的表现趋势。数据来自资深NBA球员的比赛记录。该方法在评估多种类型的职业生涯轨迹方面优于其他现有方法，具备良好的泛化能力，可应用于体育分析领域的各类运动场景。
原文摘要 · Abstract (English)
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.
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