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Basketball is one of the three ball games, loved by all sports enthusiasts, and shooting is the most basic means of scoring. In order to improve the scoring rate of basketball shooting, the teaching objective program and method program are combined to form an efficient teaching algorithm with comprehensive characteristics, which can improve the effect of basketball shooting training. In this paper, we design reasonable auxiliary teaching methods for basketball shooting, so that students can improve their scoring rate in daily basketball training and reduce injury when improving shooting skills. Our evaluation suggests that increasing the emphasis on the training of core muscle group is beneficial to enhance the control of core muscle group of athletes and promote the improvement of their shooting percentage. For people, without the basis of foundation and has a certain movement intermittent time, longer training time while shorter core training time were observed. Moreover, for physical quality, excellent and experienced player should be aimed at training projects, which strictly controls the intermittent time, while fully stimulates the core muscles, in order to achieve the training targets and complete the training results. We conclude that it is feasible and effective to design an auxiliary teaching algorithm for basketball shooting on the basis of object decomposition program. Furthermore, the proposed algorithm is approximately 11.23% better than the other methods in terms of higher scores. [ABSTRACT FROM AUTHOR]

Basketball is one of the students' favorite ball games, and it is also one of the most popular sports for college students to carry out after class. Especially in recent years, with the spread of NBA culture around the world and the extensive development of CUBA in domestic colleges and universities, Yao Ming has appeared in China to compete in the NBA and achieve brilliant achievements. With the increasing investment in the number of basketball venues, basketball itself, as a sport with low dependence on venues, equipment, and people, has the characteristics of economy, convenience, and remarkable sports effect compared with other sports. College students' basketball skills, basketball awareness, and love for basketball are increasing day by day. Attribute reduction algorithm is one of the core contents of knowledge discovery, which describes whether every attribute in the attribute set of information system is necessary and how to delete unnecessary knowledge. Based on the attribute reduction algorithm, this paper studies the early warning of basketball injury risk. The basketball injury can not only make athletes unable to participate in training or competition, but even cripple or lose their lives, which hinders the normal development of sports. Therefore, we should make a comprehensive and objective analysis of sports training to find out the causes of sports injuries, so as to prevent sports injuries. This algorithm takes the attribute frequency as the heuristic information and solves the attribute selection problem when the attribute frequency is the same. [ABSTRACT FROM AUTHOR]

Introduction: Object detection methods for team ball games players often struggle due to their reliance on dataset scale statistics, resulting in missed detections for players with smaller bounding boxes and reduced accuracy for larger bounding boxes. Methods: This study introduces a two-fold approach to address these challenges. Firstly, a novel multi-scale attention mechanism is proposed, aiming to reduce reliance on scale statistics by utilizing a specially created SIoU (Similar to Intersection over Union) label that explicitly represents multi-scale features. This label guides the training of multi-scale attention network modules at two granularity levels. Secondly, an integrated scale equalization algorithmwithin SIoU labels enhances the detection ability of multi-scale targets in imbalanced samples. Results and discussion: Comparative experiments conducted on basketball, volleyball, and ice hockey datasets validate the proposed method. The relative optimal approach demonstrated improvements in the detection accuracy of players with smaller and larger scale bounding boxes by 11%, 7%, 15%, 8%, 9%, and 4%, respectively. [ABSTRACT FROM AUTHOR]

In soccer, the world's most popular ball game, there are no systems currently in use capable of measuring soccer ball speed and spin. This paper describes a novel image recognition system capable of obtaining accurate measurements of ball velocity, elevation, spin rate and spin axis. Two sequential images of a soccer ball, a fixed time interval apart, are obtained and a series of image-processing techniques are employed to identify the spatial position of the ball within the image frame and the specific ball orientation. The soccer ball is marked with a total surface-marking scheme using coloured panels, such that a single 2D view of the ball gives a unique pattern for any orientation. A pattern matching algorithm is used to determine the specific ball orientation, and comparison of the two ball images allows the determination of ball velocity, launch elevation, spin rate and spin axis orientation to a high degree of accuracy. [ABSTRACT FROM AUTHOR]

The purpose of this study was to capture the lower limb kinematics before during and after ball impact of soccer kicking by examining the influence of both sampling rate and smoothing procedures. Nine male soccer players performed maximal instep kicks and the three-dimensional leg movements were captured at 1000 Hz. Angular and linear velocities and accelerations were determined using four different processing approaches: processed using a modified version of a time- frequency filtering algorithm (WGN), smoothed by a second-order low-pass Butterworth filter at 200 Hz cut-off (BWF), re- sampled at 250 Hz without smoothing (RSR) and re-sampled at 250 Hz but filtered by the same Butterworth filter at 10 Hz cut-off (RSF). The WGN approach appeared to establish representative kinematics, whereas the other procedures failed to remove noisy oscillation from the baseline of signal (BWF), lost the peaks of rapid changes (RSR) or produced totally distorted movement patterns (RSF). The results indicate that the procedures used by some previous studies may have been insufficient to adequately capture the lower limb motion near ball impact. We propose a new time-frequency filtering technique as a better way to smooth data whose frequency content varies dramatically. [ABSTRACT FROM AUTHOR]