# Construction and Simulation of a Multiattribute Training Data Mining Model for Basketball Players Based on Big Data

Authors: Yunbin Li; Jinyan Ge; Wei Hao
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

This paper provides an in-depth analysis and research on the construction and simulation of a big data model for multiattribute training of basketball players. To get a more accurate and three-dimensional information, the training can use a multitraining target robot, i.e., to detect feedback on multiple indicators at the same time and correct the player’s errors in time; the other is an auxiliary robot, which can actively correct technical movements and train the player to form muscle memory, compared with general training. The analysis results show that by either constructing a human model or designing an active assistive robot, the player’s technical movements can be regulated accordingly, protecting the player’s body laterally and improving the player’s ability. An assisted training system with an accurate model of physiological indicators is constructed based on the data of the player throughout the season. The Warriors, who have applied this system, not only have the best record in recent years but also have the lowest injury rate in the league, indicating that this method has indeed reduced the injury rate of players.


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## Body

## 1. Introduction

Shooting is a general term for the various methods of action used by a player with the ball to put the ball into the basket from the top of the rim using various correct techniques, and the cumulative number of shooting hits determines the winner of the game. Therefore, shooting becomes the focus of competition between offense and defense and is the core technology of basketball. With the development of basketball, the athletes’ morphology and functional quality improve, prompting the continuous development of shooting technology, from low to high shooting parts, from slow to fast shooting speed [1]. This evolution and development of the shooting technique are marking the leap from one stage to another in basketball. An athlete’s daily training includes physical training, shooting, rebounding, defense, and fast break, of which shooting training is the longest process of basketball skills. Shooting training, in turn, contains free throw shooting, jump shooting, three-point shooting, and other training [2]. The free throw drill, as the basic shooting drill, is the beginning of that skill training. Once any irregular technical movements of players in this stage are developed, it will directly affect their inconsistent shooting percentage during the game and prevent them from further improving their shooting percentage [3]. Therefore, in the free throw training stage, it is especially important to develop the shooting technique and rhythm habit of free throw shooting. There are an increasing number of shooting methods, and the hit rate continues to increase. This evolution and development of shooting technology is marking the leap of basketball from one stage to another. The daily training of athletes includes physical training, shooting, rebounding, defense, and fast break. Among them, shooting training is the longest running-in process in basketball skills. At the same time, in the game, due to the high intensity and high confrontation, a foul that brings free throws is the most simple and effective means of scoring, often in the late stages of the game, affecting the trend of the whole game and even the game’s victory or defeat.The fundamentals of competitive basketball are in developing and creating the style of basketball and in the process of the continuous development of the style to enhance their space of understanding. As the times evolve, the style presented by the game of basketball will become the brand of basketball of that era. Style is the research crystallization and core of the winning elements of the game [4]. The gradual improvement in the understanding of the laws of basketball, the understanding of the game-winning factors from shallow to deep, and the emergence of ways to defeat the opponent have driven the display of various styles. Sports style is the core content of basketball culture, more to show a spiritual culture connotation. It is the driving force that keeps basketball alive. Quantitative research usually uses numbers to measure the object of the study and find the connection between variables with the help of mathematical and statistical means. Quantitative research can represent some nonspecific, vague factors with specific data to conclude the overall trend or regularity. In turn, the patterns can be generalized [5]. Basketball is a competitive sport in which points are scored by putting the ball in the basket. To win the game, you must hit as many baskets as possible in as much an effective time as possible. Therefore, shooting is the most important training subject in daily training, and it is also the most skillful and longest training program in terms of practice accumulation. A good shot requires not only to put the ball in but also to be able to put the ball in the basket again in the face of defensive interference or fast movement. This requires the athlete to make full use of body coordination, stretch the body reasonably, and finally throw the ball in the most suitable state to throw the ball. Therefore, there are many research results on the analysis of shooting techniques.For sports, young people are the future and are the backbone of accelerating the construction of a strong sports nation. The new physical education ecosystem is built based on new-generation information technology such as big data and artificial intelligence. Its core is to help students personalize their physical education learning and training, to help teachers provide scientific guidance on the technical and tactical aspects and special qualities of sports, to assist administrators in efficient and orderly school governance, and to achieve intelligent and coordinated communication among schools, families, and society. The main teaching objects facing school sports are primary and secondary school students, the future force of AI development. By imparting intelligent knowledge and using intelligent products in daily sports teaching, information literacy is enhanced to achieve the purpose of improving the AI science education system and providing talent reserves for the development of AI in China. The current application of intelligent technology in the field of sports, especially basketball, is still in the exploration stage, the systems and technologies are not yet mature, the purpose and scope of application are not yet clear, so it becomes especially important to find the combination of intelligent technology and the field of basketball and to develop and optimize the method of the intelligent application system for sports basketball. This paper focuses on the theme of the application of artificial intelligence in the field of basketball, using literature research, video analysis, comparative research, mathematical statistics, and other research methods to explore and analyze the implementation of artificial intelligence in basketball big data, basketball robotics, and basketball teaching and training methods, to provide a theoretical basis for promoting the application of artificial intelligence in the field of basketball, and also to provide a broader range of artificial intelligence in the field of sports. It also provides a theoretical reference for the wider application of AI in sports.


## 2. Current Status of Research

We humans call ourselves Homo sapiens because we have recognized that intelligence is an extremely important trait for us. We can perceive, understand, predict, and manipulate a world that is far larger and more complex than ourselves by relying on our brains alone [6]. This is the obvious and powerful role of intelligence. But for the field of artificial intelligence, what is needed is not just to try to understand intelligence but to try to create and design intelligence based on our understanding of intelligence [7]. Today, the field of artificial intelligence as we know it covers a wide variety of subfields and scopes, ranging from the treatment of persistent diseases to chess, to the proof of a mathematical theorem, to intelligent driving, and to our usual learning and perception [8]. Nowadays, the novel general artificial intelligence is relatively high-end in the field of intelligence, because not only is general artificial intelligence limited to a certain aspect of intelligence, but also nowadays, it is aimed at creating a system that is almost as intelligent as a human being; in other words, it is aimed at achieving full intelligence [9]. To win the game, you must hit the basket as many times as possible within the effective time. Therefore, shooting training is the most important training subject in daily training. It is also the training project with the strongest skills and the longest process of accumulation of practice. The coterm analysis is a research method that indicates the relationship between topics by the number of common occurrences of condensed words that represent a piece of literature, and if the number of common occurrences is higher, the closer the relationship between the two topics. The cluster analysis method is a mathematical operation of the distance dimension on the relationship between objects in the complex network formed by the research objects, and the objects that are closer together are clustered into a major group, which is more like the objects within the group and more different from other taxa [10].Along with the improvement of computer performance and image computing power, it is becoming increasingly common to apply techniques related to video images to sports analysis scenarios, such as when [11] used multiple cameras to cover the whole field of a soccer match, seeking to capture the on-field movements and trajectories of each player [11]. A tracking algorithm is proposed to obtain the trajectories of soccer players during a match. The algorithm can identify targets well by building individual models based on features and athletes’ motion trends and can help coaches statistically analyze the tactical runs and movements of athletes during the game [12]. In a study by Li et al., a human body fitting model based on morphological techniques in a simple context was proposed to locate the four key joints of the body and extract the angular information of the body and the angle of water entry to achieve the purpose of assisting divers in correcting their aerial movements and water entry posture in their daily training [13]. The method proposes an algorithm that automatically acquires lead shot exit frames [14]. The algorithm performs lead shot frame prediction, filters out images that do not contain shot frames in the image frame sequence, and then obtains the position of the lead ball by Hough transformation, further verifies and filters the shot frames, and eliminates the pseudo-shot frames before the shot frames. Goes et al. used a monocular camera to extract the 3D motion pose of the human body and applied it to identify and detect golfers’ swings [15]. By comparing the detected posture information of the human skeleton with that of a professional athlete, appropriate guidance is given.In general, there are few research articles on the application of knowledge graphs in sports science research, but they have shown rapid growth in recent years and have been applied to many fields in sports, achieving rich research results and demonstrating their applicability and effectiveness in sports science research. Many of the articles have a high gold content and are published in core journals or become research directions for experts and doctoral theses. This is both a recognition of the application of scientific knowledge mapping to sports research and a side indication that this research method still has great potential. They hope to realize a new way of interaction between gestures and eye-following movements.


## 3. Basketball Players’ Multiattribute Training Big Data Mining Model Construction and Simulation Analysis

### 3.1. Big Data Mining Design for Multiattribute Training of Basketball Players

The process of coming up with hidden, potentially valuable, and informative information from a massive database is called data mining. We can define data mining from different standpoints, and despite the differences in these expressions, there is no contradiction in its essence. In defining data mining, the authors focus on the technical perspective and the business perspective [16]. It provides a theoretical basis for promoting the application of artificial intelligence in the field of basketball sports and provides a theoretical reference for the wider application of artificial intelligence in the field of sports. From the technical point of view, data mining is the process of extracting and collecting hidden, but valuable, information and knowledge from a large amount of data with interference and randomness with the help of computer technology and other means. It is believed that knowledge takes data as an important source. The raw data existing in relational databases are structured, while the raw data existing in text, graphics, and images are semistructured. We can acquire knowledge by mathematical and nonmathematical means, and we can also acquire knowledge by deductive and inductive means. The knowledge we obtain through data mining can be used both for optimization and management of information and at the same time for supporting decision-making. In summary, data mining is a multidisciplinary technology that brings together researchers from different fields. In the future, data mining will be the focus and hot spot for technology research and development.Data mining makes use of statistics and computer technology to find patterns that fit market dynamics and customer psychology, and currently, data mining technology has been fully developed to automate mining techniques, while combining data mining and business data warehousing to show the results of mining to the decision-makers of the company. We can acquire knowledge through mathematical and nonmathematical means, and we can also acquire knowledge through deduction and induction. The knowledge we obtain through data mining can be used to optimize and manage information and, at the same time, can also be used to support decision-making. In the process of applying data mining techniques, it is not only necessary to make use of scientific algorithms and build data mining models. At the same time, it is necessary to ensure that data mining technology does not conflict with the complex information technology application environment. On the other hand, data mining technology requires the participation of data mining analysts [17]. In contrast, data mining technology is not intuitive and cannot judge the meaning of the mining model in real life, so the participation of data mining analysts should be valued. Since the amount of data to be acquired exceeds 10 GB, the load balancing capability and stability of the system are highly required, and the crawler module involved needs to be implemented using a distributed architecture, and the data is stored uniformly. In addition, the problem of rescheduling tasks due to the short-term failure of crawler nodes and the problem of how to replace crawler nodes with long-term failure are also issues that the system needs to focus on.High-efficiency requirements, due to a large amount of data and heavy crawling task, will require the system to take into account the efficiency problem under the premise of a stable operation, which is the need to use the agent node technology to solve and complete continuous data acquisition, but because the agent node technology will be with the help of a third party, while the service has limited thread work capacity, so the technology used by the agent node will also need to consider the load balancing problem; in addition, the overall data calibration project will also consume larger problems and put higher requirements on the accuracy of system crawling, as shown in Figure1.Figure 1
Multiattribute basketball training big data design framework.A foul against sportsmanship by a player is awarded to the fouled pair two free throws and one serve; if the fouled player does not make the shot, but the shot is missed, two or three free throws and one server are awarded. Technical foul: a technical foul by a player awards the opposing team two free throws and a right to serve; the same result is awarded for technical fouls or unsportsmanlike fouls by coaches, substitutes, or accompanying personnel. A camera is set up on the extension of the free throw line to record the player’s free throw shooting action. For the convenience of the analysis and the stability of the free throw technique, 20 free throws were captured at a time as a basic video analysis unit in the thesis. The background can be approximated as static because the relative spatial displacement of the athlete is small during the execution of the free throws at the free throw line position.The basketball’s shot information, which determines the flight trajectory, corresponds to a shot angle and a shot speed at the time of the shot and a corresponding entry angle and entry speed at the time of the basket. The angle of entry is the angle between the tangent line and the plane of the basket at the point of the trajectory now at entry when the center of the ball passes through the plane of the basket. Data mining technology requires the participation of data mining analysts. In contrast, data mining technology is not intuitive and cannot judge the meaning of mining models in real life. Therefore, the participation of data mining analysts should be emphasized. When the center of gravity of the ball passes through the center of the circle of the basket, it is a “hollow entry.” Theoretically, the ideal angle of entry is 90°, which has the highest hitting rate and allows the largest margin of error. However, as the angle of entry decreases, the permissible error range becomes smaller. In the following, we defineE to represent the allowable error in basketball entry, R is the radius of the basketball frame (22.5 cm), and r is the radius of the basketball (12-13.5 cm), then we have
(1)E=Rcosα+r.Then, the relationship between the angle of entryα and R and r is as follows:
(2)cosα=rR.When taking the value ofr as the middle value, we can get α=32.39°. At this point, if the angle of entry is reduced again, the basketball will have to touch the front of the basketball frame and not directly into the frame. The corresponding allowable error is 0, so the angle of entry has a range: 32.39°-90°. A good angle of entry is the key to the hitting rate, but a good angle of entry is achieved by the control of the player of the angle of the ball. The following is an analysis of the basketball’s flight trajectory. The ball’s flight trajectory can be considered as a parabola by ignoring the forces other than gravity during the ball’s flight and considering that the ball is only subjected to the action of gravity in the air. Based on physical knowledge, an equation between the factors can be derived as follows:
(3)V02=gL2sin2θcotα+h/L.The speed of the shot is determined by four parameters: the shot height, the shooting distance, the angle of the shot, and the angle of the basket, and for a more detailed analysis, the paper makes the following analysis by setting the relevant parameters. Setting a certain shooting distance, the ball is subjected to the action of gravity only ignoring all other resistances, and the components of the ball’s exit velocity in the horizontal and vertical directions are as follows.(4)Vx=Vxsinθ,Vy=Vxcosθ.Obviously, the greater the angle of entryα, the better, but an increase in α requires the ball to gain greater speed and angle of exit at the time of the shot, undoubtedly increasing the difficulty of the shot. Good free throw shooting technique training is to get the ball in with the least effort, which is especially important in the late game of basketball when physical exertion is too high. The following theoretical analysis is used to derive the possibility and existence of minimum speed and angle of the shot. In the free throw process, the shooting distance and the height of the shot are determined. The Vom required to hit the center of the basket with an θ angle of the shot is minimal; this angle is called θ which is the minimum velocity angle. Correspondingly, the allowable error at this time is 0, so the basket angle has a range of 32.39°-90°. The muscles gain memory through a lot of repetitive training and can always be adjusted with a better power posture and shot angle to obtain an improvement in free throw shooting rate to obtain a guarantee of free throw shooting rate when it matters. The theoretical optimum provides the direction of improvement for the athlete’s daily training, as shown in Figure 2.Figure 2
Multiattribute training feature design.We analyze the players’ playing time, physical exertion, and change of status and then make reasonable substitutions to improve the team’s defensive or offensive capabilities [18]. As we all know, as the game match goes on, different players’ statuses and physical exertions are different. By establishing a video database, the physical condition of both players can be monitored in real-time to improve the overall defensive and offensive capabilities. In training, chips and sensors are used to accurately record players’ strength when shooting, running speed, and holding time, etc. These data are compiled and analyzed by analysts and coaching staff, from which the players’ technical characteristics and physical conditions are judged, and on this basis, training plans are developed to meet the players’ needs.


### 3.2. Mining Model Construction and Simulation Experiment Design

Holdout validation is not a kind of cross-validation in the strict sense. It is based on the principle of randomly dividing the dataset into two parts, one for training the model and the remaining part for validating the model, without forming a cross-validation process in the process. Its advantage is that it can avoid the practice of training and testing on the same dataset at the same time, thus reducing the phenomenon of model overfitting caused by the reuse of training and testing data, but because it groups the original data by a random method, the final model validation effect will depend excessively on the method of grouping data, which is not conducive to widespread application in actual prediction [19]. Take each subset of data as a validation set, and the remaining K−1 subsets of data will be used as the training set. In this way, K models will be obtained, and the average of the classification accuracy of the final validation set of these K models will be used as this K. The performance index of the classifier is under CV, which is a k-fold cross-validation basic validation. The method is as follows: the original data is divided into K groups, each subset of data is used as a validation set, and the remaining K−1 subsets are used as training sets so that K models are obtained, and the average of the classification accuracy of the final validation set of these K models is used as the performance index of the classifier under K-CV. K-CV is the most commonly used cross-validation method, which can effectively avoid the occurrence of overfitting and underfitting, and the results obtained are more convincing if K is larger, and K-CV also greatly improves the computational cost from the performance perspective, as shown in Figure 3.Figure 3
Preball possession score and time statistics.Accuracy is the most basic measure of the evaluation of the binary classification model and is calculated by the formula shown in (5), which denotes the number of TP and TN divided by the number of all predicted values, and TP, TN, FP, and FN are the number of times the predicted values of the model fall into these categories.
(5)Acc=TP−TNTP+TN+FP+FN.The error rate is the proportion of incorrect classifications and is calculated as shown in (6). The error rate can also be obtained by subtracting the accuracy rate from 1.
(6)Error=1−Acc.A useful classifier often must make a good trade-off between too conservative and too aggressive decisions. This trade-off can be achieved by two metrics: sensitivity and specificity. The sensitivity of the model is used to measure the proportion of positive samples that are correctly classified, as shown in(7)Correctly=TPTP−FN.The specificity of the model is used to measure the proportion of negative samples that are correctly classified, as shown in(8)Negative=TNTN−FP.The range of values for both sensitivity and specificity is 0 to 1, with values closer to 1 being more satisfactory. It is important to find an appropriate balance between the two, but this balance is often determined on a case-by-case basis. Two evaluation metrics, prediction accuracy, and recall accuracy were initially used in the field of information retrieval to describe the relevance of the model results or whether the predictive power of the model would be diminished by meaningless noise. Predictive accuracy is defined as the proportion of true positives among all predicted positive cases, and an accurate model will predict positives only when the category is very much like a positive, in which case the prediction is very reliable. An accurate model will only predict positive when the category is very similar to positive. In this case, the prediction is very reliable. On the other hand, retrospective accuracy is a measure of the completeness of results, which is defined as the ratio of true positives to the total number of negatives. Although this is the same formula as sensitivity, its meaning is different, and a model with high retrospective accuracy can capture many positive samples, which means it has a wide range of applications. The specific formulas for prediction accuracy and retrospective accuracy are shown in(9)PAcc=TPTP+FN,(10)GAcc=TNTN+FN.A model performance measure that combines predictive accuracy and retrospective accuracy into a single value is theF-metric, which uses a harmonic mean to integrate predictive accuracy and retrospective accuracy. The summed mean is an average value used to describe the rate of change, and because both predictive and retrospective accuracies have a range of 0 to 1, the summed mean is used instead of the more common arithmetic mean. The specific formula for calculating the F-metric is shown in equation (11).
(11)F=2TNTN−FN,(12)θm=45°−12cot−1hL,(13)V0m=gh−h2.−L22,(14)Vin−y=LgV0sinθ+V0cosθ,(15)cotα=LgV0sin2θ+V0cos2θ.In summary, the accuracy rate is used as the first evaluation index to judge the classification model of this paper, and theF-measure will be used as the second evaluation index because the classification model of this paper satisfies the premise that the prediction accuracy and the retrospective accuracy have the same weight, and the other indexes are used as auxiliary indexes.Develop team strategy using trajectory analysis method: after the database is established, the trajectory of the target is analyzed by artificial intelligence of big data to extract multiple movement trajectories in the video database of the opposing team and select tactics with higher offensive and defensive efficiencies. Players’ running position, speed, characteristics, and game habits, footwork, blocking, and other characteristics to find out the opponent’s attacking weaknesses are captured, and corresponding defensive strategies are developed [20]. Player joint analysis method to develop team tactics: basketball is a high-level team sport. On the court, we often see the wonderful cooperation between players. The cooperation between different players will produce different offensive efficiencies. In the database, the players who often cooperate on the court are identified, and the offensive or defensive efficiency of multiple players’ partnership is analyzed from different time conditions (such as playing time and physical exertion), and the best lineup of the team is identified to enrich the team tactics and establish the best team tactical system, as shown in Figure 4.Figure 4
Schematic analysis of wrist paddle action.The main purpose is to restore the core of the whole technical movement of the free throw basketball by marking the joint points of the arm and analyzing the trajectory of the marked feature points. The research objectives in the thesis can be summarized into two parts. Let the athletes maintain the shooting state as close as possible to the ideal value, combined with the analysis of technical movements; extract the free throw rhythm that is most suitable for the athlete; and abstract it into an arm trajectory curve. Using this curve as a template, repeated training can achieve muscle memory and improve the stability of free throws in the game. The first part targets the correction of the technical movements of the free throw shooter, targeting the detection of the four steps of preparation, arm lift, squat, and extension in the first stage, and whether the technical features corresponding to each step are optimal. The second part is done by detecting the angle and speed of the ball’s shot, through statistical analysis. Comparing the difference between this data and the theoretical minimum shot speed and the corresponding optimal shot angle, the athlete maintains the shot state as close to the ideal value as possible, and combined with the analysis of technical movements, the most suitable free throw rhythm for the athlete is extracted and abstracted into an arm trajectory curve. Using the curve as a template, the repeated training achieves muscle memory and improves the stability of free throw shooting in the game.The technical elements of each phase are described, and the process of wrist flicking on the ball during the shot is analyzed [21]. By approximating the trajectory of the ball as a parabolic motion subjected to gravity only, the relationship between the angle of the shot and the angle of entry can be derived based on physical knowledge, and the existence of the optimal angle of the shot and the minimum speed of the shot is argued.The variables of the system are represented as a linear combination of a small number of common factors and special factors that affect only one variable. Factor analysis refers to the study of extracting common factors from a population of variables by grouping several variables that are more closely related in the same category. Capturing these major factors can lead to a clearer analysis and interpretation of complex problems. First, the original group of variables is examined to determine whether it is suitable for factor analysis. (i.e., the original variables should have a strong correlation with each other). A common method for determining the number of factors is to determine the number of factors with the help of two criteria. The principal components with eigenvalues greater than or equal to 1 are selected as the initial factors, while those with eigenvalues less than 1 are discarded. And the number of factors is finally determined by combining with the gravel plot for validation. After that, the factors are further subjected to an effective naming interpretation.


## 3.1. Big Data Mining Design for Multiattribute Training of Basketball Players

The process of coming up with hidden, potentially valuable, and informative information from a massive database is called data mining. We can define data mining from different standpoints, and despite the differences in these expressions, there is no contradiction in its essence. In defining data mining, the authors focus on the technical perspective and the business perspective [16]. It provides a theoretical basis for promoting the application of artificial intelligence in the field of basketball sports and provides a theoretical reference for the wider application of artificial intelligence in the field of sports. From the technical point of view, data mining is the process of extracting and collecting hidden, but valuable, information and knowledge from a large amount of data with interference and randomness with the help of computer technology and other means. It is believed that knowledge takes data as an important source. The raw data existing in relational databases are structured, while the raw data existing in text, graphics, and images are semistructured. We can acquire knowledge by mathematical and nonmathematical means, and we can also acquire knowledge by deductive and inductive means. The knowledge we obtain through data mining can be used both for optimization and management of information and at the same time for supporting decision-making. In summary, data mining is a multidisciplinary technology that brings together researchers from different fields. In the future, data mining will be the focus and hot spot for technology research and development.Data mining makes use of statistics and computer technology to find patterns that fit market dynamics and customer psychology, and currently, data mining technology has been fully developed to automate mining techniques, while combining data mining and business data warehousing to show the results of mining to the decision-makers of the company. We can acquire knowledge through mathematical and nonmathematical means, and we can also acquire knowledge through deduction and induction. The knowledge we obtain through data mining can be used to optimize and manage information and, at the same time, can also be used to support decision-making. In the process of applying data mining techniques, it is not only necessary to make use of scientific algorithms and build data mining models. At the same time, it is necessary to ensure that data mining technology does not conflict with the complex information technology application environment. On the other hand, data mining technology requires the participation of data mining analysts [17]. In contrast, data mining technology is not intuitive and cannot judge the meaning of the mining model in real life, so the participation of data mining analysts should be valued. Since the amount of data to be acquired exceeds 10 GB, the load balancing capability and stability of the system are highly required, and the crawler module involved needs to be implemented using a distributed architecture, and the data is stored uniformly. In addition, the problem of rescheduling tasks due to the short-term failure of crawler nodes and the problem of how to replace crawler nodes with long-term failure are also issues that the system needs to focus on.High-efficiency requirements, due to a large amount of data and heavy crawling task, will require the system to take into account the efficiency problem under the premise of a stable operation, which is the need to use the agent node technology to solve and complete continuous data acquisition, but because the agent node technology will be with the help of a third party, while the service has limited thread work capacity, so the technology used by the agent node will also need to consider the load balancing problem; in addition, the overall data calibration project will also consume larger problems and put higher requirements on the accuracy of system crawling, as shown in Figure1.Figure 1
Multiattribute basketball training big data design framework.A foul against sportsmanship by a player is awarded to the fouled pair two free throws and one serve; if the fouled player does not make the shot, but the shot is missed, two or three free throws and one server are awarded. Technical foul: a technical foul by a player awards the opposing team two free throws and a right to serve; the same result is awarded for technical fouls or unsportsmanlike fouls by coaches, substitutes, or accompanying personnel. A camera is set up on the extension of the free throw line to record the player’s free throw shooting action. For the convenience of the analysis and the stability of the free throw technique, 20 free throws were captured at a time as a basic video analysis unit in the thesis. The background can be approximated as static because the relative spatial displacement of the athlete is small during the execution of the free throws at the free throw line position.The basketball’s shot information, which determines the flight trajectory, corresponds to a shot angle and a shot speed at the time of the shot and a corresponding entry angle and entry speed at the time of the basket. The angle of entry is the angle between the tangent line and the plane of the basket at the point of the trajectory now at entry when the center of the ball passes through the plane of the basket. Data mining technology requires the participation of data mining analysts. In contrast, data mining technology is not intuitive and cannot judge the meaning of mining models in real life. Therefore, the participation of data mining analysts should be emphasized. When the center of gravity of the ball passes through the center of the circle of the basket, it is a “hollow entry.” Theoretically, the ideal angle of entry is 90°, which has the highest hitting rate and allows the largest margin of error. However, as the angle of entry decreases, the permissible error range becomes smaller. In the following, we defineE to represent the allowable error in basketball entry, R is the radius of the basketball frame (22.5 cm), and r is the radius of the basketball (12-13.5 cm), then we have
(1)E=Rcosα+r.Then, the relationship between the angle of entryα and R and r is as follows:
(2)cosα=rR.When taking the value ofr as the middle value, we can get α=32.39°. At this point, if the angle of entry is reduced again, the basketball will have to touch the front of the basketball frame and not directly into the frame. The corresponding allowable error is 0, so the angle of entry has a range: 32.39°-90°. A good angle of entry is the key to the hitting rate, but a good angle of entry is achieved by the control of the player of the angle of the ball. The following is an analysis of the basketball’s flight trajectory. The ball’s flight trajectory can be considered as a parabola by ignoring the forces other than gravity during the ball’s flight and considering that the ball is only subjected to the action of gravity in the air. Based on physical knowledge, an equation between the factors can be derived as follows:
(3)V02=gL2sin2θcotα+h/L.The speed of the shot is determined by four parameters: the shot height, the shooting distance, the angle of the shot, and the angle of the basket, and for a more detailed analysis, the paper makes the following analysis by setting the relevant parameters. Setting a certain shooting distance, the ball is subjected to the action of gravity only ignoring all other resistances, and the components of the ball’s exit velocity in the horizontal and vertical directions are as follows.(4)Vx=Vxsinθ,Vy=Vxcosθ.Obviously, the greater the angle of entryα, the better, but an increase in α requires the ball to gain greater speed and angle of exit at the time of the shot, undoubtedly increasing the difficulty of the shot. Good free throw shooting technique training is to get the ball in with the least effort, which is especially important in the late game of basketball when physical exertion is too high. The following theoretical analysis is used to derive the possibility and existence of minimum speed and angle of the shot. In the free throw process, the shooting distance and the height of the shot are determined. The Vom required to hit the center of the basket with an θ angle of the shot is minimal; this angle is called θ which is the minimum velocity angle. Correspondingly, the allowable error at this time is 0, so the basket angle has a range of 32.39°-90°. The muscles gain memory through a lot of repetitive training and can always be adjusted with a better power posture and shot angle to obtain an improvement in free throw shooting rate to obtain a guarantee of free throw shooting rate when it matters. The theoretical optimum provides the direction of improvement for the athlete’s daily training, as shown in Figure 2.Figure 2
Multiattribute training feature design.We analyze the players’ playing time, physical exertion, and change of status and then make reasonable substitutions to improve the team’s defensive or offensive capabilities [18]. As we all know, as the game match goes on, different players’ statuses and physical exertions are different. By establishing a video database, the physical condition of both players can be monitored in real-time to improve the overall defensive and offensive capabilities. In training, chips and sensors are used to accurately record players’ strength when shooting, running speed, and holding time, etc. These data are compiled and analyzed by analysts and coaching staff, from which the players’ technical characteristics and physical conditions are judged, and on this basis, training plans are developed to meet the players’ needs.


## 3.2. Mining Model Construction and Simulation Experiment Design

Holdout validation is not a kind of cross-validation in the strict sense. It is based on the principle of randomly dividing the dataset into two parts, one for training the model and the remaining part for validating the model, without forming a cross-validation process in the process. Its advantage is that it can avoid the practice of training and testing on the same dataset at the same time, thus reducing the phenomenon of model overfitting caused by the reuse of training and testing data, but because it groups the original data by a random method, the final model validation effect will depend excessively on the method of grouping data, which is not conducive to widespread application in actual prediction [19]. Take each subset of data as a validation set, and the remaining K−1 subsets of data will be used as the training set. In this way, K models will be obtained, and the average of the classification accuracy of the final validation set of these K models will be used as this K. The performance index of the classifier is under CV, which is a k-fold cross-validation basic validation. The method is as follows: the original data is divided into K groups, each subset of data is used as a validation set, and the remaining K−1 subsets are used as training sets so that K models are obtained, and the average of the classification accuracy of the final validation set of these K models is used as the performance index of the classifier under K-CV. K-CV is the most commonly used cross-validation method, which can effectively avoid the occurrence of overfitting and underfitting, and the results obtained are more convincing if K is larger, and K-CV also greatly improves the computational cost from the performance perspective, as shown in Figure 3.Figure 3
Preball possession score and time statistics.Accuracy is the most basic measure of the evaluation of the binary classification model and is calculated by the formula shown in (5), which denotes the number of TP and TN divided by the number of all predicted values, and TP, TN, FP, and FN are the number of times the predicted values of the model fall into these categories.
(5)Acc=TP−TNTP+TN+FP+FN.The error rate is the proportion of incorrect classifications and is calculated as shown in (6). The error rate can also be obtained by subtracting the accuracy rate from 1.
(6)Error=1−Acc.A useful classifier often must make a good trade-off between too conservative and too aggressive decisions. This trade-off can be achieved by two metrics: sensitivity and specificity. The sensitivity of the model is used to measure the proportion of positive samples that are correctly classified, as shown in(7)Correctly=TPTP−FN.The specificity of the model is used to measure the proportion of negative samples that are correctly classified, as shown in(8)Negative=TNTN−FP.The range of values for both sensitivity and specificity is 0 to 1, with values closer to 1 being more satisfactory. It is important to find an appropriate balance between the two, but this balance is often determined on a case-by-case basis. Two evaluation metrics, prediction accuracy, and recall accuracy were initially used in the field of information retrieval to describe the relevance of the model results or whether the predictive power of the model would be diminished by meaningless noise. Predictive accuracy is defined as the proportion of true positives among all predicted positive cases, and an accurate model will predict positives only when the category is very much like a positive, in which case the prediction is very reliable. An accurate model will only predict positive when the category is very similar to positive. In this case, the prediction is very reliable. On the other hand, retrospective accuracy is a measure of the completeness of results, which is defined as the ratio of true positives to the total number of negatives. Although this is the same formula as sensitivity, its meaning is different, and a model with high retrospective accuracy can capture many positive samples, which means it has a wide range of applications. The specific formulas for prediction accuracy and retrospective accuracy are shown in(9)PAcc=TPTP+FN,(10)GAcc=TNTN+FN.A model performance measure that combines predictive accuracy and retrospective accuracy into a single value is theF-metric, which uses a harmonic mean to integrate predictive accuracy and retrospective accuracy. The summed mean is an average value used to describe the rate of change, and because both predictive and retrospective accuracies have a range of 0 to 1, the summed mean is used instead of the more common arithmetic mean. The specific formula for calculating the F-metric is shown in equation (11).
(11)F=2TNTN−FN,(12)θm=45°−12cot−1hL,(13)V0m=gh−h2.−L22,(14)Vin−y=LgV0sinθ+V0cosθ,(15)cotα=LgV0sin2θ+V0cos2θ.In summary, the accuracy rate is used as the first evaluation index to judge the classification model of this paper, and theF-measure will be used as the second evaluation index because the classification model of this paper satisfies the premise that the prediction accuracy and the retrospective accuracy have the same weight, and the other indexes are used as auxiliary indexes.Develop team strategy using trajectory analysis method: after the database is established, the trajectory of the target is analyzed by artificial intelligence of big data to extract multiple movement trajectories in the video database of the opposing team and select tactics with higher offensive and defensive efficiencies. Players’ running position, speed, characteristics, and game habits, footwork, blocking, and other characteristics to find out the opponent’s attacking weaknesses are captured, and corresponding defensive strategies are developed [20]. Player joint analysis method to develop team tactics: basketball is a high-level team sport. On the court, we often see the wonderful cooperation between players. The cooperation between different players will produce different offensive efficiencies. In the database, the players who often cooperate on the court are identified, and the offensive or defensive efficiency of multiple players’ partnership is analyzed from different time conditions (such as playing time and physical exertion), and the best lineup of the team is identified to enrich the team tactics and establish the best team tactical system, as shown in Figure 4.Figure 4
Schematic analysis of wrist paddle action.The main purpose is to restore the core of the whole technical movement of the free throw basketball by marking the joint points of the arm and analyzing the trajectory of the marked feature points. The research objectives in the thesis can be summarized into two parts. Let the athletes maintain the shooting state as close as possible to the ideal value, combined with the analysis of technical movements; extract the free throw rhythm that is most suitable for the athlete; and abstract it into an arm trajectory curve. Using this curve as a template, repeated training can achieve muscle memory and improve the stability of free throws in the game. The first part targets the correction of the technical movements of the free throw shooter, targeting the detection of the four steps of preparation, arm lift, squat, and extension in the first stage, and whether the technical features corresponding to each step are optimal. The second part is done by detecting the angle and speed of the ball’s shot, through statistical analysis. Comparing the difference between this data and the theoretical minimum shot speed and the corresponding optimal shot angle, the athlete maintains the shot state as close to the ideal value as possible, and combined with the analysis of technical movements, the most suitable free throw rhythm for the athlete is extracted and abstracted into an arm trajectory curve. Using the curve as a template, the repeated training achieves muscle memory and improves the stability of free throw shooting in the game.The technical elements of each phase are described, and the process of wrist flicking on the ball during the shot is analyzed [21]. By approximating the trajectory of the ball as a parabolic motion subjected to gravity only, the relationship between the angle of the shot and the angle of entry can be derived based on physical knowledge, and the existence of the optimal angle of the shot and the minimum speed of the shot is argued.The variables of the system are represented as a linear combination of a small number of common factors and special factors that affect only one variable. Factor analysis refers to the study of extracting common factors from a population of variables by grouping several variables that are more closely related in the same category. Capturing these major factors can lead to a clearer analysis and interpretation of complex problems. First, the original group of variables is examined to determine whether it is suitable for factor analysis. (i.e., the original variables should have a strong correlation with each other). A common method for determining the number of factors is to determine the number of factors with the help of two criteria. The principal components with eigenvalues greater than or equal to 1 are selected as the initial factors, while those with eigenvalues less than 1 are discarded. And the number of factors is finally determined by combining with the gravel plot for validation. After that, the factors are further subjected to an effective naming interpretation.


## 4. Analysis of Results

### 4.1. Big Data Mining Performance Results

Firstly, the k-s test was performed on the game data, the two-sided asymptotic significance of the test results for each group of values was greater than 0.1, and these data were considered to conform to a normal distribution. Secondly, the resultant data derived from the original algorithm were standardized byZ-scores, and the processing principles were as follows: in the statistics of the data, there were differences in the indicators, such as the number of hits and rebounds. Therefore, the differences in the units of indicators were eliminated; indicators with obvious functional relationships were excluded, for example, the total number of shots is the sum of the number of two-point shots and the number of three-point shots. Finally, 14 indicators such as i1, i2, i3, i4, i7, i10, i13, i17, i18, i20, and i35 were excluded; to facilitate in-depth analysis of team differentiation, the negative indicators were converted into positive indicators.KMO and Bartlett’s sphericity tests were performed on the data, and the KMO value of the data was 0.786 with atwo−sidedsig<0.001. This indicates that common factors exist between the correlation matrices of the data clusters, and the data are suitable for factor analysis. The remaining 23 indicators were subjected to correlation matrix analysis to extract the common factors, and the results were rotated by the method of maximum variance to exclude the lower factor loadings (<0.60), resulting in the rotated factor loading matrix. And the principal components with eigenvalues less than 1 were excluded. The results are shown in the table; there are 7 factors with eigenvalues greater than 1, which are compatible with the theoretical concept. Their eigenvalues were 5.394, 3.842, 2.638, 2.353, 2.100, 1.730, and 1.506, with a cumulative contribution rate of 72.455%, as shown in Figure 5.Figure 5
The characteristic roots of the factors and the contribution of the total variance explained.
(a)(b)(c)(d)Although factor analysis can reduce the influence of artificial subjective factors, the shortcoming is that it relies too much on sample indicator data and cannot reflect the preferences of decision-makers. In principle, the selection of question items not only is simply removed from the statistical strict requirements but also needs to be integrated according to the different research questions, the completeness of the meaning of the conception of the theoretical dimensions, and the significance level of each factor loading, high and low grouping, and other conditions under consideration. To ensure the applicability of the system factors, basketball experts and coaches were consulted to correct the categorization of indicators and determine the naming of factors according to the characteristics of basketball.Its eigenvalues are 5.394, 3.842, 2.638, 2.353, 2.100, 1.730, and 1.506, and the cumulative contribution rate is 72.455%.Only indicator i23 on the F6 principal component, for the number of steals, was loaded as 0.733. The ball-breaking skill is a very important basketball defensive skill. It is a comprehensive reflection of the team’s defensive ability and the team’s collaborative defensive formation. The i23 is more clearly defined in basketball theory and is close and highly correlated with the theoretical conception of F4 latitude (0.602). Therefore, i23 is classified as one of the F4 dimensions. The game of basketball requires not only technical and tactical cooperation but also perseverance, and high morale can be a factor in winning or losing the game. Among the many ways to boost morale—steals, caps, etc.—dunking is one of the most effective ways. The dunking technique has its unique characteristics: agitation, aggression, artistry, and skill. Expert opinions on this are divided. i21 appears less frequently in Cuban games but plays a good role in boosting team morale and can be listed separately as an indicator of psychological aspects. However, there are more suggestions that dunks should be included as an offensive strength indicator. The subject is intended to be universal in the system and has a loading of 0.627 in the F1 latitude; therefore, i21 is incorporated into F1.This research is aimed at the universality of the system, and the load in the F1 latitude is 0.627; therefore, i21 is incorporated into F1. These indicators are a better representation of a team’s defensive skills and individual defensive skills. Therefore, it is defined as a defensive quality; the factors with high loadings in F5 are the number of three-point shots (0.647), two-point shots (0.838), and two-point tendencies (0.941). The two indicators of three-point tendency and two-point tendency can better evaluate a team’s choice of offensive style for the outside and inside. Therefore, this dimension was named an offensive choice. The factor loadings and common degree statistics of each dimension are shown in Figure6.Figure 6
Factor loading statistics for each dimension.In the study, the input video unit is firstly extracted in the foreground, based on which only the foreground pixels are traversed, which can improve the efficiency of the algorithm, and then, the corresponding RGB values are compared, and the pixel points that satisfy the marker color range are set to 255 and the other pixel points are set to 0 to obtain the feature binary map containing the marker points. To remove the possible irrelevant feature points, the corresponding erosion and expansion are performed. The binary map containing only the marker points is obtained by the above method. The contour of the binary map is then extracted to obtain the contour that contains each marker point, and the statistical analysis of the pixel coordinates of the contour block is performed to obtain the statistical center of mass of the pixel block as the node coordinates.


### 4.2. Simulation Results of Multiattribute Training Data

The data used in the paper for the arm trajectory analysis were a basketball coach with standard skills and two average students, and the three could perform 80 sets of free throws each, with every 20 sets as a video unit. By performing image preprocessing algorithms on more than 15,000 frames of the three video units, to obtain the corresponding trajectory change characteristics, it can be found that there are respective periodic repetitions of the longitudinal coordinate trajectories of the three subjects’ joint points, and Figure7 is analyzed by plotting the individual free throw cycle trajectories of everyone.Figure 7
Longitudinal trajectory of arm joint points.Figure7 shows the trajectory changes of the arm joint points of the two regular students, and it can be found that although there is no similarity in the trajectories as in Figure 7, the degree of similarity is still 75%, especially in the later trajectory transformation showing the same characteristics. By analyzing the frames corresponding to the maximum point in the trajectory change, it is found that in all the video samples, although the trajectories of each person are different, it can be found that the subject, as a basketball coach, can throw the ball at the highest point of his arm as much as possible due to his professionalism and the repeatability of his movements, i.e., the shot frames correspond to T frames; however, students A and B cannot do so, i.e., the distribution of the strike frames is from T-1 to T-3 frames. Therefore, to obtain the shot frames more accurately, a basketball localization detection algorithm is proposed to locate the coordinates of the center of the ball by improving the Hough transform. Then, we calculate the Euclidean distance D from the center of the ball to the wrist marker C and analyze its change curve to further analyze the characteristics of the shot frames.We obtain the trajectory of the longitudinal coordinates of the arm joint A from T1 to T2 of the tested player and compare it with the technical model of that phase, and if the similarity reaches 60%, the player is considered to have a squatting step, and we further calculate the squatting depth and the minimum knee angle to judge whether the player’s squat is in place or not and give the corresponding modifications. By obtaining the trajectory curve of the wrist joint C at the vertical coordinate from T2 to T3 during the extension process, the fluency of the extension is judged by comparing it with the standard extension curve. For example, Figure8 shows the average trajectory graph normalized by the data of more than 500 hit free throws of athletes from multiple varsity men’s basketball teams. As shown in Figure 8, the trajectory change from T2 to T3 is processed by normalization. The trajectory of the athlete’s input data is matched with the trajectory of the T2 to T3 segment, and a match of 80% is considered as the rhythm of the stretching process meets the criteria; otherwise, it is considered that there is a lack of fluency in the stretching. The angle and speed of the stroke are compared with the optimal speed, angle, and angle between the arm and the horizontal plane corresponding to the height of the individual stroke to determine whether the stroke is reasonable.Figure 8
Template diagram of free throw extension trajectory.We obtain the best shooting speed, the angle, and the angle between the arm and the horizontal plane corresponding to the shooting angle and speed with the individual shooting height and compare it to judge whether this shot is reasonable. Suggestions are given for “spinning the wrist too fast” or “spinning the wrist too slow” and “raise the arm appropriately to increase the angle to obtain a higher shot height.” Suggestions are given for “too fast wrist flick” or “too slow wrist flick” and “raise the angle of the arm to obtain a higher strike height.” Firstly, the algorithm is based on the automatic positioning of the shot frame. Secondly, a basketball detection and positioning algorithm is proposed using an improved Hough transformation algorithm to calculate hand speed status information. Finally, the technical actions are guided by the four steps of the free throw technique, and the technical trajectory and technical parameters of each step are analyzed to guide the athletes in their actions.


## 4.1. Big Data Mining Performance Results

Firstly, the k-s test was performed on the game data, the two-sided asymptotic significance of the test results for each group of values was greater than 0.1, and these data were considered to conform to a normal distribution. Secondly, the resultant data derived from the original algorithm were standardized byZ-scores, and the processing principles were as follows: in the statistics of the data, there were differences in the indicators, such as the number of hits and rebounds. Therefore, the differences in the units of indicators were eliminated; indicators with obvious functional relationships were excluded, for example, the total number of shots is the sum of the number of two-point shots and the number of three-point shots. Finally, 14 indicators such as i1, i2, i3, i4, i7, i10, i13, i17, i18, i20, and i35 were excluded; to facilitate in-depth analysis of team differentiation, the negative indicators were converted into positive indicators.KMO and Bartlett’s sphericity tests were performed on the data, and the KMO value of the data was 0.786 with atwo−sidedsig<0.001. This indicates that common factors exist between the correlation matrices of the data clusters, and the data are suitable for factor analysis. The remaining 23 indicators were subjected to correlation matrix analysis to extract the common factors, and the results were rotated by the method of maximum variance to exclude the lower factor loadings (<0.60), resulting in the rotated factor loading matrix. And the principal components with eigenvalues less than 1 were excluded. The results are shown in the table; there are 7 factors with eigenvalues greater than 1, which are compatible with the theoretical concept. Their eigenvalues were 5.394, 3.842, 2.638, 2.353, 2.100, 1.730, and 1.506, with a cumulative contribution rate of 72.455%, as shown in Figure 5.Figure 5
The characteristic roots of the factors and the contribution of the total variance explained.
(a)(b)(c)(d)Although factor analysis can reduce the influence of artificial subjective factors, the shortcoming is that it relies too much on sample indicator data and cannot reflect the preferences of decision-makers. In principle, the selection of question items not only is simply removed from the statistical strict requirements but also needs to be integrated according to the different research questions, the completeness of the meaning of the conception of the theoretical dimensions, and the significance level of each factor loading, high and low grouping, and other conditions under consideration. To ensure the applicability of the system factors, basketball experts and coaches were consulted to correct the categorization of indicators and determine the naming of factors according to the characteristics of basketball.Its eigenvalues are 5.394, 3.842, 2.638, 2.353, 2.100, 1.730, and 1.506, and the cumulative contribution rate is 72.455%.Only indicator i23 on the F6 principal component, for the number of steals, was loaded as 0.733. The ball-breaking skill is a very important basketball defensive skill. It is a comprehensive reflection of the team’s defensive ability and the team’s collaborative defensive formation. The i23 is more clearly defined in basketball theory and is close and highly correlated with the theoretical conception of F4 latitude (0.602). Therefore, i23 is classified as one of the F4 dimensions. The game of basketball requires not only technical and tactical cooperation but also perseverance, and high morale can be a factor in winning or losing the game. Among the many ways to boost morale—steals, caps, etc.—dunking is one of the most effective ways. The dunking technique has its unique characteristics: agitation, aggression, artistry, and skill. Expert opinions on this are divided. i21 appears less frequently in Cuban games but plays a good role in boosting team morale and can be listed separately as an indicator of psychological aspects. However, there are more suggestions that dunks should be included as an offensive strength indicator. The subject is intended to be universal in the system and has a loading of 0.627 in the F1 latitude; therefore, i21 is incorporated into F1.This research is aimed at the universality of the system, and the load in the F1 latitude is 0.627; therefore, i21 is incorporated into F1. These indicators are a better representation of a team’s defensive skills and individual defensive skills. Therefore, it is defined as a defensive quality; the factors with high loadings in F5 are the number of three-point shots (0.647), two-point shots (0.838), and two-point tendencies (0.941). The two indicators of three-point tendency and two-point tendency can better evaluate a team’s choice of offensive style for the outside and inside. Therefore, this dimension was named an offensive choice. The factor loadings and common degree statistics of each dimension are shown in Figure6.Figure 6
Factor loading statistics for each dimension.In the study, the input video unit is firstly extracted in the foreground, based on which only the foreground pixels are traversed, which can improve the efficiency of the algorithm, and then, the corresponding RGB values are compared, and the pixel points that satisfy the marker color range are set to 255 and the other pixel points are set to 0 to obtain the feature binary map containing the marker points. To remove the possible irrelevant feature points, the corresponding erosion and expansion are performed. The binary map containing only the marker points is obtained by the above method. The contour of the binary map is then extracted to obtain the contour that contains each marker point, and the statistical analysis of the pixel coordinates of the contour block is performed to obtain the statistical center of mass of the pixel block as the node coordinates.


## 4.2. Simulation Results of Multiattribute Training Data

The data used in the paper for the arm trajectory analysis were a basketball coach with standard skills and two average students, and the three could perform 80 sets of free throws each, with every 20 sets as a video unit. By performing image preprocessing algorithms on more than 15,000 frames of the three video units, to obtain the corresponding trajectory change characteristics, it can be found that there are respective periodic repetitions of the longitudinal coordinate trajectories of the three subjects’ joint points, and Figure7 is analyzed by plotting the individual free throw cycle trajectories of everyone.Figure 7
Longitudinal trajectory of arm joint points.Figure7 shows the trajectory changes of the arm joint points of the two regular students, and it can be found that although there is no similarity in the trajectories as in Figure 7, the degree of similarity is still 75%, especially in the later trajectory transformation showing the same characteristics. By analyzing the frames corresponding to the maximum point in the trajectory change, it is found that in all the video samples, although the trajectories of each person are different, it can be found that the subject, as a basketball coach, can throw the ball at the highest point of his arm as much as possible due to his professionalism and the repeatability of his movements, i.e., the shot frames correspond to T frames; however, students A and B cannot do so, i.e., the distribution of the strike frames is from T-1 to T-3 frames. Therefore, to obtain the shot frames more accurately, a basketball localization detection algorithm is proposed to locate the coordinates of the center of the ball by improving the Hough transform. Then, we calculate the Euclidean distance D from the center of the ball to the wrist marker C and analyze its change curve to further analyze the characteristics of the shot frames.We obtain the trajectory of the longitudinal coordinates of the arm joint A from T1 to T2 of the tested player and compare it with the technical model of that phase, and if the similarity reaches 60%, the player is considered to have a squatting step, and we further calculate the squatting depth and the minimum knee angle to judge whether the player’s squat is in place or not and give the corresponding modifications. By obtaining the trajectory curve of the wrist joint C at the vertical coordinate from T2 to T3 during the extension process, the fluency of the extension is judged by comparing it with the standard extension curve. For example, Figure8 shows the average trajectory graph normalized by the data of more than 500 hit free throws of athletes from multiple varsity men’s basketball teams. As shown in Figure 8, the trajectory change from T2 to T3 is processed by normalization. The trajectory of the athlete’s input data is matched with the trajectory of the T2 to T3 segment, and a match of 80% is considered as the rhythm of the stretching process meets the criteria; otherwise, it is considered that there is a lack of fluency in the stretching. The angle and speed of the stroke are compared with the optimal speed, angle, and angle between the arm and the horizontal plane corresponding to the height of the individual stroke to determine whether the stroke is reasonable.Figure 8
Template diagram of free throw extension trajectory.We obtain the best shooting speed, the angle, and the angle between the arm and the horizontal plane corresponding to the shooting angle and speed with the individual shooting height and compare it to judge whether this shot is reasonable. Suggestions are given for “spinning the wrist too fast” or “spinning the wrist too slow” and “raise the arm appropriately to increase the angle to obtain a higher shot height.” Suggestions are given for “too fast wrist flick” or “too slow wrist flick” and “raise the angle of the arm to obtain a higher strike height.” Firstly, the algorithm is based on the automatic positioning of the shot frame. Secondly, a basketball detection and positioning algorithm is proposed using an improved Hough transformation algorithm to calculate hand speed status information. Finally, the technical actions are guided by the four steps of the free throw technique, and the technical trajectory and technical parameters of each step are analyzed to guide the athletes in their actions.


## 5. Conclusion

The basketball free throw-assisted training system applies computer, image processing, database, and other technologies to assist in physical training, which is becoming increasingly responsive to the times in today’s intelligent and data-oriented world. This paper takes the recorded video of free throw shooting by the player as the original material and studies the technical steps and essentials steps for free throw shooting by analyzing the trajectory of the arm in the process of free throw shooting. By reviewing the relevant literature, the free throw technique is theoretically analyzed and summarized to deduce the existence of optimal shot speed and angle under the corresponding height. Then, the correctness of the theory was verified by analyzing a large amount of video data of free throw shooting by professional athletes and coaches, and the trajectory template of the arm in the free throw shooting process was extracted by digitizing the relevant technical characteristics through trajectory analysis. Through the actual movements of the athletes, combined with the theoretical analysis, the technical steps of free throw shooting are proposed: four steps of preparation, arm raising, squatting, and extension, and the time nodes of each step are obtained using the free throw shooting step time point acquisition algorithm. For the trajectory characteristics and technical parameters of arm raising, squatting, and extension, a large amount of statistical analysis was conducted to obtain the corresponding template trajectory. The corresponding technical guidance is given to the free throw shooter by comparing with the template trajectory in the assisted training system. At the same time, the technical parameters corresponding to the subject are calculated and compared with the standard parameters to achieve the purpose of assisting athletes’ training.



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*Source: 6399266-2021-09-30.xml*