Attentional control and its relationship to the accuracy of some types of basketball shooting for Iraqi youth club’s players

The importance of this study is reflected in the fact that it is an attempt to identify attentional control and its relationship to the accuracy of some types of basketball shooting for youth players for Iraqi Premier League clubs for youth for the season (2020-2021). The problem of the research is to identify the level of some mental abilities, including attentional control, represented by attentional focus, attentional shift and attentional flexibility and their relationship to the accuracy of some types of basketball shooting for the Iraqi players of youth clubs. The study aimed to identify the relationship between attentional control and the accuracy of the performance of some types of basketball shooting for youth basketball players for the 2020-2021 season. The two researchers adopted the descriptive curriculum in a survey style and correlational relationships. The research community was determined by the Iraqi youth basketball players, who numbered (160) out of (16) clubs and these clubs are (Al-Amarah, Dijlah, Dhi Qar, Samawah, Al-Kut, Al-Sharqiya, Nasiriyah, Baghdad, Al-Mina, Al-Karkh, Al-Ittihad, Al-Naft, Adhamiya, Al-Shurta, Al-Tadamon, Naft Al-Janoub) and then a sample of (50) players selected in a random way and they are club players of (Al-Amarah, Baghdad, Al-Karkh, Al-Naft, Al-Adhamiya) and they were tested on the attentional control scale and after processing the results, it was found that some of the young basketball players have a high level of attentional control and the accuracy of performing some types of shooting in youth basketball. Therefore, the two researchers recommend the necessity of paying attention to mental processes, including (attentional focus, attentional shift, attentional flexibility), as well as adopting the attentional control scale as tools to identify the level of some of the mental abilities that players possess

Implementation of Shooting Method BEEF Concept to Basketball Shooting Skill on Foundation Magic Kids of Lubuklinggau

This research was conducted because it was not known that shooting practice with the BEEF concept was to improve basketball shooting skills at the Magic Kid Foundation Club Lubuklinggau, therefore this study aimed to determine the application of shooting practice with the BEEF concept to basketball shooting skills at the Magic Kid Foundation Club Lubuklinggau. This research is an experimental study in the form of one group pretest-posttest design, a sample of 14 athletes was then given treatment in the form of training with the BEEF concept. The instrument used was a shooting skill test for the Lubuklinggau Magic Kid Foundation athletes. The data analysis technique used is Paired Sample t-test. The results of this study have a significant effect on the t value for basketball shooting results of 15.960 with a probability (sig.) of 0.000. sign value. (2-tailed) is 0.000 < 0.05, then H0 is rejected, Ha is accepted. This means that there is a significant effect of the beef concept on Basketball Shooting Skills at Magic Kid Foundation Lubuklinggau Club. Based on the results of this processing, it can be concluded that the beef concept has an effect on basketball shooting skills at the Magic Kid Foundation Lubuklinggau Club.Keywords: Basketball BEEF Concept, and Shooting

Standardized Judgment Method of Shooting Training Action Based on Digital Video Technology

Aiming at the difficulty of standardizing the action of basketball shooting training, a new method of standardizing the action of basketball shooting training is proposed based on digital video technology. The digital video signal representation, video sequence coding data structure, and video sequence compression coding method are analyzed, and the pixels of basketball shooting training action position space are sampled to collect basketball shooting training images. The time difference method is used to extract the movement target of basketball shooting training from a digital video sequence. Based on digital video technology, the initial background image is estimated, and the update rate is introduced to update the background estimation image. According to the pixel value sequence of the basketball shooting training image, the pixel model of the basketball shooting training image is defined and modified. By judging whether the defined pixel value matches the background parameter model, the standardization of shooting training can be realized. The experimental results show that the proposed method has good stability, high precision, and short time in determining the standardization of shooting movement, can correct the wrong shooting movement in real time, and can effectively guide basketball shooting training.

Study on the Automatic Basketball Shooting System Based on the Background Subtraction Method

There are many drawbacks such as clustering, background updating, inaccurate testing results, and low anti-interference performance in traditional moving target detection theory. In our study, a background subtraction method to automatically capture the basketball shooting trajectory was used to eliminate the drawbacks of the fixed-point shooting system such as cumbersome installation and time and manpower consumption. It also can improve the accuracy and efficiency of moving target detection. We also synthetically compared to common methods including the optical flow method and interframe difference method. Results showed that the background subtraction method has better accuracy with an accuracy rate over about 90% than the interframe subtraction method (88%) and the optimal flow method (85%) and presents excellent robustness with considering variable speed and nonrigid objects. Meanwhile, the automatic detection system for basketball shooting based on background subtraction is built by coupling background subtraction with detection characteristics. The system detection speed built is further accelerated, and the image denoising is improved. The trajectory error rate is about 0.3, 0.4, and 0.5 for the background subtraction method, interframe subtraction method, and optimal flow method, respectively.

The Relationship of Intra-Individual Release Variability with Distance and Shooting Performance in Basketball

The aim of this study was to investigate the role of release parameter changes within individuals (intra-individual) on basketball shooting performance across both free throws and three-point shots, and identify whether any velocity dependence exists. Twelve male basketball players were recorded shooting seventy-five three-point shots (6.75 m) and fifty free throws (4.19 m). Ball release parameters were estimated by combining an analytic trajectory model including drag, a least squares estimator, and gradient-based release distance compensation. Intra-individual release velocity standard deviations (SD) were found to be significantly smaller across all distances ([0.05-0.13 m/s] when compared to statistics reported by other studies [0.2-0.8 m/s]). Despite an increase in lower body motion and a 24% increase in release velocity (p < 0.001) as shooting distance increased, no increases in intra-individual release velocity or angle SD were observed indicating velocity-dependent changes in release parameters were absent. Shooting performance was found to be strongly correlated to the release velocity SD (r = -0.96, p < 0.001, for three-point shots, and r = -0.88, p < 0.001, for free throws). Release angle SD (1.2 ± 0.24 deg, for three-point shots, and 1.3 ± 0.26 deg, for free throws) showed no increase with distance and unrelated to performance. These findings suggest that velocity-dependent factors have minimal contribution to shooting strategies and an individual’s ability to control release velocity at any distance is a primary factor in determining their shooting performance.

Basketball shooting technology based on acceleration sensor fusion motion capture technology

Computer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.