A Basketball Player Technical Analysis Framework Based on Decision Tree Optimized Recursive Neural Network

In this study, we unveil a novel and groundbreaking methodology that utilizes a Recursive Neural Network (RNN) optimized with a Decision Tree for the analysis of basketball player performance. This approach is transformative in its integration of these two powerful models to yield more accurate predictions and more insightful analysis. The focus of our study is the ratio of two-point to three-point shots, which we identify as a pivotal determinant of a player's skill level and strategic approach to the game. This metric offers a quantifiable measure of a player's shooting efficiency and decision-making capabilities on the court, and its accurate prediction is of vital significance to player development and game strategy formulation. To this end, we present a model that employs a Decision Tree optimized RNN to predict these shooting ratios with unprecedented accuracy. This model leverages the robust decision-making capabilities of Decision Trees and the temporal pattern recognition abilities of RNNs, thereby providing a potent tool for the detailed analysis of basketball techniques. The use of an optimized RNN not only boosts the predictive accuracy of the model but also enhances its ability to generalize and adapt to new data. A detailed comparison between the Decision Tree, RNN, and Decision Tree optimized RNN models forms a significant portion of our study. Our findings indicate that the Decision Tree optimized RNN model significantly outperforms the other two models across all evaluation metrics. This not only validates the effectiveness of our proposed model but also reinforces its potential as a powerful tool for technical analysis in the realm of basketball. In conclusion, this paper provides a comprehensive exploration of a cutting-edge technique for basketball player performance analysis, offering a fresh perspective on player skill assessment and game strategy optimization.