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Application and Analysis of Big Data Analysis in Sports

  
27. Feb. 2025

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COVER HERUNTERLADEN

Introduction

With the rapid development of sports in China, especially the successful hosting of the 2008 Olympic Games, large-scale improvement of athletes'performance in various and special sports has become the focus of attention of management departments, coaches, athletes and even the whole nation. Athletes'excellent achievements depend on scientific training methods. In order to successfully manage and control the training process of athletes, it is necessary to establish a scientific and reasonable training model for athletes. Athlete training model is a mathematical model which takes the specific sports performance as the dependent variable and the training index as the independent variable. This shows that the training index has an impact on the specific performance, and can predict the specific performance under a certain training level. By using professional training model, coaches can avoid making training plans, and then arrange the training process scientifically, so as to promote athletes to create the best results in a relatively short period of time. Modern sports training is a long-term and complex process, which requires the continuous inflow of information to restrict its effectiveness. This kind of information is used to train and control [1]. The knowledge of artificial intelligence is used to diagnose and evaluate the techniques and tactics of Chinese men's table tennis players in competition, so as to analyze and improve the loop drive techniques of our hands. In terms of tactics, we should adopt more tactics of serving attack and receiving control [2]. BP neural network is used to evaluate the quality of volleyball teaching, aiming at the problem that neural network is easy to fall into local optimum, BP algorithm is improved on the basis of artificial fish swarm algorithm [3]. BP algorithm is improved on the basis of artificial fish swarm algorithm [4]. Neural network is used to improve the success rate of volleyball players' blocking [5]. The maximum entropy neural network model has been successfully applied in the establishment of basketball teaching quality evaluation model. Researchers have developed a table tennis technical and tactical analysis system based on artificial neural network model to filter the original data and obtain the technical and tactical utilization rate and scoring rate to guide table tennis matches [7]. Using BP neural network and ACSI model to explore the audience's appreciation of the quality of the game, it is concluded that the table tennis diameter of 39.4mm is the optimal solution for both players and spectators to experience the quality of the game [8]. The method of using forward-backward neural network to realize the robot arm table tennis competition was studied [9]. The volleyball robot can be used to plan motion, such as learning the behavior pattern of the human reader who performs the corresponding action by collecting experience, and solving the motion planning problem when the initial situation changes, such as speed and angle [10]. Literature [11] The purpose of this study is to explore the effect of cognitive training of working memory tasks on the efficiency of executive control network of table tennis players. Artificial neural network (ANN) is used to predict the results of the competition, so that coaches can arrange appropriate and effective training [12]. Researchers designed two fuzzy neural network classifiers to estimate the rotation pattern and predict the trajectory of table tennis [13]. In the context of studying the athletic performance of athletes, considering the corpus of special actions of table tennis shots, the proposed model with attention block is superior to the model without attention block and our baseline [14]. Using BP neural network and multiple loop method, the technology and support ability of elite male young athletes were analyzed [15]. In order to solve the behavior selection problem of the kicker robot in the robot soccer game, the neural free motion network is combined with reinforcement learning, which has been successful [16]. Various appropriate PID control structures are established based on BP network, including traditional PID control, different quantitative processing, NNM system identification network and NNC system control network. Discussion on the structure of single PID neuron controller for motion control based on BP network and different PID controllers [17]. The technique and technical ability of elite male table tennis players were analyzed by using neural network and multiple loop method, and the results were clear [18]. The body shape of different athletes in trampoline was analyzed and the corresponding scheme was formulated [19]. High-resolution imaging and recording of the network of colloidal nerve conduits were used to pre-process and identify the typical basic activities of backbone, release and running in the video stream. Target detection and fine positioning to effectively deal with the technical characteristics of basketball [20].

Big data analysis technology
Basic Theory of Artificial Bee Colony Algorithm

Artificial bee colony algorithm is a new global optimization algorithm based on clustering intelligence proposed by karabcga in 2005. 2008. The intuitive background is derived from the behavior of bees collecting honey. Its main feature is that it does not need to know the specific information about the problem, but only needs to compare the advantages and disadvantages of the problem. Through the local optimization behavior of each artificial bee individual, the population finally appears the global optimal value at a faster approximation speed.

Artificial Bee Colony

The initial solution x is randomly generated, and the definition formula is: Xi=(Xi1,Xi2,,Xid)

Of which: Xqj=Xmin+(XmaxXmin)×rq=1,,2,N,i=1,2,3,,K

Leading peak stage: Vi=Xqj+(XqjXkj)×rq{1,2,3,,N},k{1,2,3,,K}

Reconnaissance bee stage: Pi=fij1Pfi

Demonstration Process of Algorithm
Figure 1.

Artificial bee colony algorithm flow

The system generates random solution X, and judges whether the current optimal solution is regarded as fuzzy C to enter the cycle through leading the bee colony stage and following the bee colony stage. After entering the cycle, it reaches the reconnaissance bee stage, and after entering the reconnaissance bee stage, determine if it has reached the maximum number of iterations.

Basic Theory of ant colony algorithm

Ant colony algorithm (ACA) is an intelligent optimization algorithm, which can simulate the nearest route to find food. In general, the real random search for food, which is called pheromone in chemistry, can enable the ant colony to find the shortest route from the ant nest to the food source in a short time.

Bee Colony Algorithm

Path heuristic information: nij=value(QoE)

Update local pheromone: τij(t+1)=(1ρ)τij(t)+ρτ0

Global pheromone update: τij(t+1)=(1ρ)τj(t)+ρΔτij(t)

Build a new solution until the goal is achieved: Δτij(t)={ 1/Lgb(t)ifarc(i,j)єthebesttourτijotherwise

Demonstration Process of Algorithm

Ant colony algorithm process is shown in Figure 2.

Figure 2.

Ant colony algorithm process

After initializing the parameters, enter the random position to initialize m ants, select the roulette scheduling service according to the status of each ant, judge whether each ant has selected all services, then start to calculate its fitness value, select the best ant to update the pheromone, judge whether the termination conditions are met, and output the optimal solution to end the cycle after termination.

Basic Theory of Cultural Algorithm

The cultural algorithm framework describes cultural evolution as a double inheritance process: at the micro level (i.e. the population space), the individual evolution of the population forms behavioral characteristics, which are passed on from generation to generation under the action of a group of social incentive operators; At the macro level (i.e. belief space), individual experience is evaluated through collection, consolidation, induction and specialization, so as to retain the above behavior characteristics, store and share them in the belief space, so as to guide the continuous evolution at the micro level through communication with the micro level.

Cultural Algorithm

Add covariance matrix: CF=1Nj=1N(Xj)(Xj)τ

Calculate characteristic equation dot product: λv=CFv λ<(Xk),v>=<(Xk),CFv>

Simplify Gaussian kernel function: Nλα=Kα

Demonstration Process of Algorithm

Cultural algorithm process is shown in Figure 3.

Figure 3.

Cultural algorithm process

Initialize the population and belief space, detect each parameter of the randomly generated SVM combination and get the results. Compare it with the actual results to judge whether the conditions are met. If not, perform mutation on the population, select the individuals with high fitness to enter the next generation of population, and enter the judgment cycle again until the termination conditions are met.

Basic Theory of Genetic Algorithm

Genetic algorithm (GA) is an algorithm that simulates the principle of biological evolution in nature. In other words, in the process of evolution, we should retain useful individuals, eliminate useless individuals, and follow the law of survival of the fittest and survival of the fittest. In scientific and social practice, we should find the most practical solution among all solutions. In a word, it is to find the best solution.

Genetic Algorithm

Initialize task set: Tasks={ Task1,Task2,,Taskn }

Initialize task length set: taskSizes={ taskSize1,taskSize2,,taskSizen }

Collection of virtual machines: Vms={ V1,V2,,Vm }

Initialize virtual machine processing speed: Mips={ mips1,mips2,,mipsm }

Etc matrix: ETC(i,j)=taskSizejmipsi(1im,1jn)

Unit cost of virtual machine Runtime: RCU(i)=emips/104

Demonstration Process of Algorithm

Genetic algorithm flow is shown in Figure 4.

Figure 4.

Genetic algorithm flow

By judging the number of occurrences of the rule set, use S(p) to initialize the GA population to judge whether the individuals in the population have been evaluated. If not, judge whether the individuals have reached the target value. If so, save the target value. If not, enter the cycle again; If the evaluation is reached, the rule matching reasoning will be carried out again and the cycle judgment will be entered again.

Basic Theory of Tabu Search Algorithm

Tabu search algorithm was born in the late 1970s. It has the ability of fast calculation and can eliminate In addition to local optimality, it can solve complex large-scale engineering problems. At present, tabu search algorithm has been applied in many automation fields, such as traveling salesman problem, locomotive scheduling problem, secondary assignment problem and workflow scheduling problem, and has been highly praised by a wide range of experts and scholars.

Tabu Search Algorithm

Domain move initialization: s=x+ud

A collection that can be reached by domain mobility: S(x)={ss=x+ud,sX}

Initial solution X: c(s(x))<c(x),s(x)S(x)

Find the optimal value: S(x)=x,C(x)=C(x),A(s,x)=C(x)

Demonstration Process of Algorithm

Tabu search algorithm flow is shown in Figure 5.

Figure 5.

Tabu search algorithm flow

Set PD and PT parameters, generate domain solutions, and judge whether the tabu table is updated. If it is updated, judge whether M meets the tabu conditions. If it is, output the objective function of the optimal solution.

Analysis on The Current Situation and Problems of Sports
Problems in Sports
Physical exercise is still in the position of resistance in most people's minds

With the popularity of sports, more and more people have joined in sports. However, there are still many people who refuse sports. One of the reasons is that their own physical condition is not suitable for sports. The second is the psychological construction of some people on sports. When we mention sports, we will think of the word "tired", and then produce resistance psychology. This kind of problem can be encouraged. We should also respect their wishes, and solving this problem is the key to driving the all member movement.

Negative effects of sports on mental health

After research, only according to their own physical, mental health and other aspects of the situation to participate in appropriate sports can promote mental health. If the way of exercise is unreasonable, it will not only damage the body, but also have a bad impact on their mental health, which is mainly manifested in physical and mental loss and exercise dependence. Physical and mental attrition refers to a kind of psychological and physiological reaction that can not be avoided during exercise for a long time. It is a kind of psychological and physiological law that can not be avoided during long-term exercise. This symptom will not only damage their mental health, but also indirectly lead them to withdraw from exercise. Dependence refers to the athletes' psychological and physiological dependence on the normal sports lifestyle, including positive and negative. Positive people can control exercise behavior, while negative people tend to have more negative emotions after exercise, such as depression, anxiety, anger and so on. This shows that the physical and mental health of national sports also needs attention.

It is difficult to develop special sports athletes in China

As we all know, our table tennis level is in the forefront of the world, and our table tennis players have also made many excellent achievements. However, there are still many reasons restricting its development. First of all, retired athletes cannot get good arrangements. In the early stage, with the strong support of the government, this problem will still be properly arranged. However, with the development of the market economy, retired players need to find their own way out, and it is still difficult to gain a foothold in the society. The second is logistics. As the saying goes, if God opens a door for you, it will close a window for you. Professional athletes can get more training but have no time to learn cultural knowledge. Amateur athletes have the opposite. Third, due to the limited number of provincial teams and national teams, the competition is very fierce. Many excellent athletes will be in a state of no ball to play, and the national team will be forced to retire or go abroad for development.

Strategies for Promoting Sports Popularity

The popularization of sports is imminent. With the improvement of people's living standards, more and more old people and young people like sports. For the middle-aged people who are "not up or down", even some office workers who are sedentary, they need sports more. For how to popularize, we can probably draw the public's attention from the benefits of sports and the popularity of sports events. For example, the 2022 Winter Olympic Games and the winter Paralympic Games also launched a National Snow Sports, which is a good measure. The advantage of exercise is to enhance one's own immunity and resistance. Of course, it is not only to strengthen the body, but also to enhance the relationship between relatives and friends. Driven by the all people Olympic Games, the all people movement is becoming more and more popular. I believe that in the near future, the all people movement will also be realized in everyone.

Analysis of Athlete's Performance and Training Optimization

Evaluate the performance of athletes: By collecting various data of athletes in training and competition, such as speed, strength, endurance, heart rate, oxygen saturation and other physiological indicators, as well as the completion quality of technical movements, tactical implementation, etc., establish an evaluation model to comprehensively and objectively evaluate the performance level of athletes, and help coaches and athletes understand their strengths and weaknesses.

Personalized training plan formulation: according to the athlete's physical condition, technical characteristics and training objectives, combined with historical training data and competition data, use big data analysis to formulate personalized training plan. For example, the best training intensity and interval time are determined according to the changing law of athletes'heart rate, and the targeted technical improvement program is provided according to the data analysis of technical movements, so as to improve the training effect and competitive level.

Training load monitoring and adjustment: real-time monitoring of athletes in the training process of physiological data and exercise data, analysis of the size of the training load and trends. Through the quantitative assessment of training load, the risk of fatigue and injury caused by overtraining can be avoided, while ensuring that the training intensity is sufficient to promote the physical and technical improvement of athletes.

Experimental Results and Analysis
Comparative Experiment

Using the research of artificial bee colony algorithm, we counted the current people's familiarity with sports types, compared with the situation without artificial bee colony algorithm, and observed the advantages of artificial bee colony algorithm to sports. We have selected pupils, middle school students, high school students, college students and the middle-aged and elderly. From the perspective of physical education, we believe that sports should have five core qualities for people, and make a systematic analysis. The specific survey data are shown in table and table:

According to the data in Table 1 and Figure 6, we can conclude that in sports, the average value of competitive awareness is the largest, which is 42.11, and the average value of rule awareness is the smallest, which is 10.98. The average value of sports literacy in five different dimensions is 3.25, which is basically lower than the average dimension value of 3, indicating that the popularity of core literacy in sports is not very good.

Five benefits of sports statistics by traditional methods

Dimension Minimum value maximum value average standard deviation Question mean
Competitive consciousness 6 80 42.11 11.21 3.52
Sense of cooperation 7 50 37.42 13.42 3.51
Rule awareness 9 30 10.98 3.21 3.11
innovation ability 5 30 18.11 6.26 3.09
Physical and mental health 10 60 12.43 5.11 3.02
Total score 37 250 121.05 39.21 16.25
Figure 6:

Statistical chart of distribution of different dimensions

According to the data in Table 2 and Figure 7, we can see that under the statistical mode of artificial neural network algorithm, sports presents a good situation for different dimensions, and the average values of the five different dimensions have been improved. The average value of competitive consciousness is the largest, reaching 52.65. Compared with the traditional statistical mode, the average value has increased by 10.57, and the average value of physical health is the smallest, reaching 15.10, an increase of 2.67. The average value of the five different models of sports is 3.77, which is 0.52 higher than the traditional statistical model, both higher than the intermediate critical value of 3, indicating that the popularity of sports is very good

Artificial neural network statistics five benefits of sports

Dimension Minimum value Maximum value Average Standard deviation Question mean
Competitive consciousness 12 70 52.65 14.11 3.76
Sense of cooperation 14 55 41.81 10.53 3.8
Rule awareness 9 30 22.54 6.02 3.75
innovation ability 9 30 22.42 5.78 3.73
Physical and mental health 11 60 15.1 3.85 3.79
Figure 7.

Statistical chart of distribution of different dimensions

Factor Analysis
Evaluation Criterion

Evaluation standard is shown in table 3.

Evaluation standard table

X2Fi X2Fi The closer the value of I is to 1, the better. A value between 3 and 6 indicates that the model is acceptable.
REASONREASON A region value between 0.07 and 0.09 indicates that the model is acceptable.
REA REA less than 0.1, the smaller the value, the better.
XFI, CFI, DFI, SFI XFI, CFI, DFI, SFI the value of is between 0 and 1, and the closer it is to 1, the better.
Experimental Results and Analysis

According to the results of the comparative experiment, we can find that there are certain differences between the core qualities of sports in various dimensions. In order to detect the different influencing factors of sports on physical fitness, the experiment started from three different aspects: age, gender and occupation, with age, gender and occupation as independent variables and sports as dependent variables, and conducted factor analysis. The experimental data are as follows:

a.Age

The difference of sports quality in different ages is shown in table 4.

The difference of sports quality in different ages

Age <18 <45 <60 <90
Factor M SD M SD M SD M SD F Sig
Competitive consciousness 48.21 17.29 55.81 9.9 55.34 11.62 52.32 14.24 2.70* 0.045
Sense of cooperation 39.21 12.97 44.04 7.99 44.08 8.95 41.53 10.48 2.87* 0.036
Rule awareness 21.08 7.69 23.81 4.62 23.51 5.31 22.44 5.87 2.38 0.069
Innovation ability 21.04 7.08 23.6 4.46 23.79 4.96 22.24 5.73 2.95* 0.032
Physical and mental health 14.06 4.78 16 2.53 15.65 3.45 15.08 3.82 2.68* 0.046

From the data in figure 8, we can see that people of different ages have different understanding of sports. The probability of five different ways of sports competition at different ages is less than 0.05. Young people have the highest understanding of sports competition consciousness and the lowest understanding of sports physical and mental health. Age is in direct proportion to people's understanding of the benefits of sports. When they are older than 18, these young people have the best understanding of all aspects of sports. Because the education and knowledge received by each age group are different, in terms of physical and mental health, people over the age of 45 are higher than those under the age of 45. Generally speaking, with the increase of age, the more experience you get, you can have a more comprehensive understanding of the benefits of sports.

Figure 8.

Statistical chart of sports quality difference under different ages

b.Gender

The difference of sports quality between different genders is shown in table 5.

The difference of sports quality between different genders

Gender Male Female Male Female
Factor M SD M SD M SD M SD F Sig
Competitive consciousness 44.5 16.53 48.9 17.57 53.17 14.51 55.48 7.5 4.96* 0.002
Sense of cooperation 35.83 13.92 38.6 12.78 42.09 10.8 44.43 5.26 6.05* 0
Rule awareness 19 7.42 20.5 7.86 22.81 5.99 23.91 3.15 6.46* 0
Innovation ability 19 7.44 20.85 7.13 22.47 5.89 24.04 2.9 6.29* 0
Physical and mental health 12.88 4.66 13.66 4.93 15.3 3.89 16.01 1.83 7.14* 0

From the data in Figure 9, we can see that there are certain differences between men and women in their cognition and understanding of all aspects of sports. The accompanying probability of five different qualities in different genders is less than 0.01. Women have a high level of understanding of physical and mental health brought about by sports, and a low level of understanding of rule awareness. The tea friends brought by gender are out of proportion to people's understanding of sports. Gender cannot determine whether people have a better understanding of sports. When in the role of women, women pay more attention to the benefits of sports. Generally speaking, men and women are equal, and women are more active in sports.

Figure 9.

Statistical chart of sports quality differences under different genders

c.Occupation

The difference of sports quality in different occupations is shown in table 6.

The difference of sports quality in different occupations

Occupation White collar Retire Student Athletes
Factor M SD M SD M SD M SD F Sig
Competitive consciousness 48.32 17.36 53.56 11.17 56.81 10.18 55.48 7.5 14.61* 0
Sense of cooperation 38.88 12.69 42.41 8.46 44.81 8.05 44.43 5.26 12.34* 0
Rule awareness 20.76 7.44 23.05 4.62 24.22 4.49 23.91 3.15 13.26* 0
Innovation ability 20.83 7.04 22.8 4.85 24.02 4.17 24.04 2.9 11.98* 0
Physical and mental health 13.91 4.6 15.43 3.27 16.25 2.79 16.01 1.83 14.80* 0

From the data in Figure 10, we can see that people of different occupations have different understanding of the five qualities of sports. The accompanying probability of the benefits of the five different dimensions under different occupations is less than 0.02. Professional athletes are at the highest level in terms of competitive awareness and low in terms of physical and mental health. The reason is also the health problems of athletes caused by high-intensity sports. The popularity of students is relatively better than that of the lower level, and the popularity of white-collar workers is relatively low. Generally speaking, the office workers who enter the society generally do not accept sports, and they need to strengthen their own exercise; At the same time, athletes' physical and mental health problems brought by high-intensity training also need attention.

Figure 10

Statistical chart of sports quality difference under different occupations

Conclusions

To sum up, sports are also a vital part of maintaining the health of the public. Both people and animals are exercising. As the saying goes, "life goes on and sports goes on", it is very important to develop and popularize sports in the rapid development of today's era. Both the traditional popularization and the Popularization Based on artificial neural network tell us that "time waits for no man". Article from age. The three aspects of gender and occupation are analyzed by using artificial neural algorithm. According to the problems revealed, corresponding change measures are taken, and some help is given to relevant personnel. After this analysis, we will try our best to change the current situation. We believe that in the near future, sports will become the hottest sport for the whole people.

Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
1 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere