Experimental Research with Combination of Whole Brain Teaching and Basketball Techniques

This paper adopts multi-level fuzzy comprehensive evaluation to divide the set of college students’ basketball athletic ability and performance factors into several subsets, and determines the weight coefficients according to the importance of each single factor accordingly to determine the evaluation indexes of college students’ basketball athletic ability and performance. Starting from college students’ basketball ability and performance, the basketball ability and performance assessment model based on fuzzy evaluation is constructed according to the fuzzy set of factor importance and judgment matrix. The college students’ basketball athletic ability and performance were measured by using statistical analysis to select and analyze experimental test subjects. The results show that the correlation coefficient between the sophomore men’s group and the coaches’ scores is 0.72 (P<0.01), and the correlation coefficient between the sophomore women’s group and the coaches’ scores is 0.78 (P<0.01), which indicates that the fuzzy evaluation model has a high validity scale validity for the clinical performances of the youth basketball players. Peking University, Zhejiang University, and Guangdong University of Technology’s field scores did not differ significantly, with Tsinghua University’s shooting ability having an RSR value of 0.812. By studying college students’ basketball abilities and performance measurements, this study holds great significance for enhancing college basketball.

Section: Introductionmentioning

confidence: 99%

Research on the Measurement of College Students’ Basketball Ability and Performance Based on Fuzzy Evaluation Model

2024

“…In addition, for first-time spectators or people with visual and hearing disabilities, due to the confusion of the referee's gestures, it is easy to lose interest in watching the game and unable to feel the passion of basketball [7][8]. In professional leagues, such as the U.S. Men's Basketball League and the Chinese Men's Basketball League, clubs equip their teams with video analysts, who analyze the technical and tactical characteristics of players and referee's decisions in the game to improve the team's winning rate [9][10].…”

Section: Introductionmentioning

confidence: 99%

A real-time basketball referee gesture recognition system based on multi-scale spatio-temporal features

Wang,

HoKun,

Zhou

2024

In this paper, the multi-scale spatio-temporal convolution module and the channel attention mechanism of global information synchronization have excellent performance in the field of sports gesture action recognition; therefore, combining the multi-scale spatio-temporal convolution and the channel attention mechanism to construct the dynamic gesture recognition model. The trained, dynamic gesture recognition model is deployed to cell phones and web pages through Java computational programming language, and the application scenarios of the basketball referee gesture recognition system are explained in detail. The real-time basketball referee gesture recognition system processes are analyzed using simulation analysis after determining the dataset. The basketball referee gesture recognition system’s video cropping frame analysis has an accuracy of 87.64% and 71.25%, with a loss value of 6.24 and 5.59, as shown in the results. When compared to the single-resolution features on the basketball referee gesture recognition system, the fused features exhibit some improvement, and the average recognition rate is 92.20%. This study is applicable to the referee gesture tracking method in complex basketball sports scenarios and lays the foundation for higher semantic level research in the field of sports video analytics, such as action recognition, event detection, and content understanding.

“…Basketball training is about ways and means to increase tactical awareness training while improving the level of training, and basketball tactical training can improve the will to fight tenaciously and improve the tactical level and tactical awareness of basketball players [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on basketball game tactics based on multivariate statistical methods

Liu

2023

This paper analyzes and studies the tactics in basketball games using the main branches of cluster analysis using multivariate statistical methods. A brief overview of multivariate statistical analysis is summarized by analyzing the application of multivariate statistical methods. The main method for classifying basketball tactics is cluster analysis, and nonlinear programming functions with constraints are constructed using mean square approximation theory. The classification results are reasonable when combined with the least square error sum. According to the results, man-to-man defensive tactics were most frequently used in basketball, with 72 instances accounting for 75.19% of all defensive strategies. The most frequent offensive tactic in the basketball game was dribbling with a fast break, and 15 times succeeded in scoring during 20 dribbling with a fast break in the first half, with a success rate of 75%.