Tennis Match Trend Prediction Model Based on Bi-LSTM Model

Bi-directional Long Short-Term Memory (Bi-LSTM) is an enhanced recurrent neural network designed for sequential data processing, capable of considering relevant information from both preceding and following texts. This paper applies the model to analyze tennis matches, predicting potential turning points in match momentum. It first utilizes visualization tools to evaluate player performance from multiple dimensions, demonstrating the existence and significance of momentum through hypothesis testing. A Bi-LSTM model is then constructed to forecast tennis trends. The training and test sets are rigorously defined, and the model is applied to predict several matches while examining the influence of various factors through ablation experiments. The accuracy and robustness of the predictions are assessed using generalized performance tests and sensitivity analysis, revealing an error rate of less than 0.15 in trend predictions. This study enhances prediction accuracy and athlete performance evaluation, providing an effective tool for tennis match data analysis.