Abstract
In the past, the fans used to evaluate the strength of the team according to the victory and defeat ranking or according to their own intuition and preferences, however, the strength of the team is difficult to measure in analytical figures. The team's winning rate is not the only factor to be considered to determine the strength of the team. There are many factors to be considered for determining the strength of the team. According to the variation coefficient of basketball scoring frequency, the paper designs the principal model of basketball players' pitching target system. The data is captured by IoT devices and smart devices. The algorithm sets the number of the frequency of Gabor filter transformation features, controls the error accumulation, extracts the cascade features of basketball score video, constructs the video conversion discrimination rules, detects the basketball target, and obtains the tracking target contour to frame information. Finally, it realizes the target tracking detection of the team based on the team strength using an evaluation algorithm. The aim of this research work is to determine the strength of the team based on the healthcare data, team cohesiveness, and variance coefficient of basketball score frequency. The study on the coefficient of variation for basketball score frequency in teams can provide a theoretical research direction for team strength evaluation and meet the real-time needs of the coefficient of variation of basketball score frequency in teams. The empirical results show that the designed algorithm has the optimal execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team.
Highlights
Because of the popularity and substantial commercial value of competitive sports, the internet has carried out data statistics on all kinds of basketball matches, given the performance of each team, and ranked the teams. e game followers often evaluate the strength of the group according to the victory and defeat ranking or divide the unity strength according to their own intuition and preferences [1,2,3]
This study develops a team strength evaluation method based on the variation coefficient and draws a valid conclusion. e data is collected from the sportspersons from the Chinese sports academy. e data is based on the questionnaire, and the records are recorded using smart devices. e IoT-based smart devices are used to sense the person’s opinion in a better manner and know whether the person is giving a true statement
(v) e evaluation of the team strength is made on the basis of the variation coefficient of basketball score frequency. e empirical results show that the designed algorithm has the shortest execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team
Summary
Because of the popularity and substantial commercial value of competitive sports, the internet has carried out data statistics on all kinds of basketball matches, given the performance of each team, and ranked the teams. e game followers often evaluate the strength of the group according to the victory and defeat ranking or divide the unity strength according to their own intuition and preferences [1,2,3]. Contributions of the Paper (i) is paper proposes a team strength evaluation algorithm based on the variation coefficient of basketball scoring frequency. (v) e evaluation of the team strength is made on the basis of the variation coefficient of basketball score frequency. 2. Basketball Score Frequency Mutation Coefficient Team Strength Evaluation Algorithms. Before ascertaining the basketball score frequency and before determining the team strength between the players, it is important to collect the healthcare-related data of the players. The study of complex health plays an important role in applying the algorithms to determine and evaluate the team strength in basketball players. It is found that some players with weak scoring ability and less playing time rely on the extremely low number of trial shots to get a high hit rate. The traditional method completes the training by analyzing the biggest influencing factors of basketball players’ goal skill training by different boundary conditions [15,16,17], but it cannot accurately analyze the stress and strain variation characteristics of the basketball players in the process of pitching. e result is that the training of pitching skills cannot achieve the effect [13, 18]
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