Elite male table tennis matches diagnosis using SHAP and a hybrid LSTM-BPNN algorithm.

ABSTRACT: This study adopts a new approach, SHapley Additive exPlanation (SHAP), to diagnose the table tennis matches based on a hybrid algorithm, namely Long Short-Term Memory-Back Propagation Neural Network (LSTM-BPNN). 100 male singles competitions (8535 rallies) from 2019 to 2022 are analyzed by a hybrid technical-tactical analysis theory, which hybridizes the double three-phase and four-phase evaluation theories. A k-means cluster analysis is conducted to classify 59 players' winning rates into three levels (high, medium, and low). The results show that LSTM-BPNN has excellent performance (MSE = 0.000355, MAE = 0.014237, RMSE = 0.018853, and [Formula: see text] = 0.988311) compared with six typical artificial intelligence algorithms. Using LSTM-BPNN to calculate the SHAP value of each feature,

SUBMITTER: Song H 

PROVIDER: S-EPMC10352295 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature