[Retracted] Application of Tactics in Technical and Tactical Analysis of Table Tennis Mixed Doubles Based on Artificial Intelligence Graph Theory Model

Cao, Yecheng; Yan, Ping; Shen, Zhaoxin; Chen, Hui; Peng, Bo; Yan, Xuanfei

doi:10.1155/2022/6543953

Cited by 5 publications

(7 citation statements)

References 18 publications

(2 reference statements)

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“…is study introduces the application of multiple regression analysis in table tennis mixed doubles. In subsequent studies, deep analysis and discussion of singles or doubles [20][21][22][23][24]. Besides, in practice, in commercial, construction, or other sports games, it can solve many practical problems.…”

Section: Study Conclusionmentioning

confidence: 99%

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

Wang

et al. 2022

Journal of Environmental and Public Health

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Since the table tennis mixed doubles competition was officially listed as the Olympic Games, the players around the world paid more attention to the project. In this background, the 20 mixed doubles finals were used in the literature, video observation, and analysis of multiple regression. From the receiving point of view, the score difference between men and women is not very great, but female players may be more consistent. The contribution of male and female scores to the game is more effective than model 1 and model 2 for different rounds. Therefore, model 2 is more efficient in the analysis of high-level table tennis competitions. Multiple regression model can be used to analyze and predict table tennis singles, doubles, and mixed doubles games, which we will see more and more in future research results.

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Section: Study Conclusionmentioning

confidence: 99%

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

Wang

et al. 2022

Journal of Environmental and Public Health

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“…In order to find the best algorithm for prediction accuracy, this study compares the performance of LSTM-BPNN with six classical artificial intelligence algorithms, which are BPNN, XGB, RF, ET, DT and KNN, in terms of the mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( R 2 ), as shown in Eqs. (23)(24)(25)(26). Table 4 shows the testing results of LSTM-BPNN, BPNN, DTR, XGBR, RFR, KNN and ETR.…”

Section: Resultsmentioning

confidence: 99%

“…The most usual method or theory is the traditional three-phase evaluation theory 1 . Based on that, phase-based theories have been proposed to improve it, such as the four-phase evaluation theory 3 and the double three-phase evaluation theory 7 in the descriptive analysis range 14 .With the rapid development of deep learning and machine learning, many models and algorithms have been applied to study table tennis matches [15][16][17][18][19][20][21][22][23][24][25] . Researchers have built models and adjusted input values through simulations to achieve technical-tactical diagnosis 15,16,18,19,21 based on sports training and competition methods or theories.…”

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confidence: 99%

“…With the rapid development of deep learning and machine learning, many models and algorithms have been applied to study table tennis matches [15][16][17][18][19][20][21][22][23][24][25] . Researchers have built models and adjusted input values through simulations to achieve technical-tactical diagnosis 15,16,18,19,21 based on sports training and competition methods or theories.…”

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confidence: 99%

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

Song

Xiao-feng

et al. 2023

Sci Rep

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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 $${\mathrm{R}}^{2}$$ R 2 = 0.988311) compared with six typical artificial intelligence algorithms. Using LSTM–BPNN to calculate the SHAP value of each feature, the global results find that the receive-attack and serve-attack phases of the ending match have essential impacts on the mutual winning probabilities. Finally, case applications show that the SHAP can directly obtain each feature importance on one or more matches, which is more objective and reliable than the traditional simulation method. This research explores an innovative way to understand and analyze matches, and these results have implications for the performance analysis of table tennis and related racket sports.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

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confidence: 99%