The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Abstract

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

Automatic Scoring Model of English Interpretation Based on Semantic Scoring

“…Te forgetting gate is used to screen old cell information and update the current memory cells according to the memory cell candidate information generated by the input gate. Te sigmoid activation function in the forget gate processes the input information and outputs a value between [0, 1] [17]. Te output value indicates that the old storage unit information is stored, the output value 0 indicates that all the information in the old storage unit is forgotten, and the output value 1 indicates that all the information in the old storage unit is stored.…”

Artificial Intelligence Technology in Basketball Training Action Recognition

“…In the stage of function extraction, the analysis of element function is basically completed, and the extraction of associated attributes is taken as the sample data of calculation. Selected samples are formed according to the different classification principles, which will lead to the separation of unknown samples (Sannino et al, 2019 ; Xu and Yi, 2021 ).…”

Application and Implications of Artificial Intelligence Technology in Basketball Coaching Decision Making

“…At this stage, artificial intelligence is the most cutting-edge technology in the field of modern information science and technology, providing more intelligent products and services for society [8]. The introduction of artificial intelligence in the field of basketball is not only able to monitor and record the relevant data of athletes' training and competition, understand the athletes' physical condition and technical and tactical deficiencies, and provide them with scientific and reasonable training methods, which not only improves the training effect but also effectively reduces the probability of injury in training [9][10][11]. At the same time, make full use of artificial intelligence technology to promote the basketball coaching decision-making level to the direction of specialization and intelligence, which helps to cultivate more excellent talents for the development of the basketball sports program and promotes the growth of basketball to a wider and stronger direction [12][13].…”