A Study on the Impact of Basketball on the Physical Fitness and Health of Adolescents Based on the Method of Correlation Analysis.

Basketball can improve physical fitness, keep healthy, and become the main form of modern sports. However, basketball generally has the problem of unreasonable sports schemes and even reduces physical fitness. Therefore, finding an effective basketball program is an urgent problem to be solved at present. In order to improve the level of public health, this paper proposes a correlation analysis method to study the impact of basketball on teenagers' health. The correlation analysis method is used to analyze the health data of adolescents, combined with the influence of basketball on fitness and the characteristics of basketball itself. Previous studies on basketball neglected comprehensive analysis and lack of correlation analysis of basketball indexes, resulting in unsatisfactory analysis results. The correlation analysis method can adjust the technical method, intensity, and program of basketball in combination with the physical development of teenagers. MATLAB simulation shows that the correlation analysis method can accurately analyze the impact of basketball on teenagers' health, with an accuracy of 95%. Therefore, the correlation analysis method constructed in this paper can provide guidance for basketball and improve the health level of teenagers.

1. Introduction

With the continuous improvement of public health awareness, teenagers are increasingly interested in basketball. At present, teenagers lack sports awareness and cannot master reasonable actions in basketball learning. In order to improve public health, local governments encourage teenagers to carry out basketball training and study. However, teenagers cannot accurately grasp the relationship between various movements in basketball learning and cannot carry out basketball training in combination with their own quality. Therefore, looking for an effective basketball training guidance method is an urgent problem to be solved for the healthy development of the public.

Association algorithms can deeply study the relationship between basketball and teenagers' physical health, clarify the importance of basketball, and promote the rational formulation of sports programs. The existing research methods lack intelligence and ignore the relationship between basketball and adolescent health, which leads to inaccurate analysis results of basketball. The application of the association algorithm in sports, physical health, sports indicators, and other aspects is more in-depth, but it is less used in basketball. The advantage of the correlation analysis method is that it can deeply analyze the essence of the problem, simplify the analysis steps of the problem, and improve the accuracy of the analysis results. Scholars at home and abroad mainly stay at the theoretical level and lack comprehensive analysis methods for the movement essentials, methods, and the relationship between different movements in basketball training [1]. In addition, there are many data involved in basketball training, and the original qualitative analysis method cannot carry out statistical analysis [2]. There is a lack of correlation research among sports characteristics, teaching contents, and action essentials in basketball, which cannot provide targeted programs for different athletes. The existing basketball analysis methods are qualitative [3], and the lack of comprehensive analysis methods of various indicators cannot be guided by the training steps and training contents of basketball and affect the overall effect of basketball training [4]. In short, the research on basketball training at home and abroad is mainly reflected at the theoretical level and lacks comprehensive practical analysis [5].

Based on the above background, this paper uses the metropolis acceptance standard to analyze the eigenvalues and analyzes basketball from the perspective of correlation analysis so as to clarify the corresponding scheme.

2. Literature Review

2.1. The Description of Sports Content in Basketball Practice

The data of exercise content in basketball practice have two characteristics [6]: one is a large amount, low value, and strong diversity, and the proportion of the unstructured data is more than 50% [7]; on the other hand, the data are from multisource dynamic acquisition, and the data structure is shown in Figure 1.

Figure 1

The basketball data description.

As can be seen from Figure 1, we must first obtain basketball data ⟶ calculate each data ⟶ simplify the corresponding data volume ⟶ standardize the data results ⟶ calculate the correlation between data ⟶ arrange indicators according to the correlation. It can be seen from Figure 1 that the sources of relevant data in basketball are multifaceted, which is the synthesis of different sports contents and needs to be obtained through calculation, observation, and measurement. The basketball data are mainly unstructured and structured [8], and integrated with youth health and other content to form a large amount of big data. The analysis of the impact of basketball on teenagers' health should start with health indicators [9], combined with basketball intensity, basketball effect, and teenagers' characteristics [10] so as to realize an accurate evaluation of the impact of basketball.

2.2. The Description of Basketball Sport Calculation

The diversity of influencing factors of basketball content and teenagers' physical quality increases the difficulty of correlation analysis. The previous qualitative analysis methods cannot analyze a large number of data, nor can they analyze a variety of data types. However, improving the correlation analysis method can evaluate a large number of data and meet the multifaceted needs of adolescent health. The premise of improving correlation analysis is to determine the index of basketball. The results are shown in Table 1.

Table 1

The influencing factors of basketball.

Sports content	Proportion (%)	Degree of influence (%)
Shoot	13	8
Acceleration run	11	1
Bump	6	1
Distance	8	3
Training intensity	12	12
Training time	4	17
Knowledge accumulation	9	18
Degree of collaboration	12	5
Cardiopulmonary function	7	10
Blood oxygen index	5	8
Past medical history	4	8
Age	9	9

Suppose basketball intensity is x, basketball situation is x, basketball effect is x, tactical consciousness set C, C={c1, c2,…, c}, and c=∑x+x+x, i, j, k belong to natural numbers. The data on different needs come from different competition standards [11]. The results are shown in Figure 2.

Figure 2

The basketball data description.

Suppose N is basketball, i is different competition standards, j is the type of data (structured data = 1, otherwise = 0), k is the data acquisition method (qualitative method acquisition = 1, quantitative method acquisition = 2, comprehensive acquisition method = 0), l is the intensity of basketball content (1∼5 level, the higher the value, the higher the awareness requirement), and m is the intelligent information acquisition in the process Then, the information collection item can be described as N = (1, 2, ..., n) [12], and n is a natural number.

The data are classified, and the data of similar data sources, structure types, acquisition methods [13], basketball content levels, and acquisition devices are divided into the same basketball content [14].

All processes are analyzed by the AI system of the basketball center. Under the constraints of scientific [15], time, and consciousness cultivation level, the weight and threshold of data are arranged [16].

The data collection time and basketball content level in any process are the same or similar [17].

In order to improve the stability of analysis, the Fourier series method should be used to reduce the interference of equipment, personnel, and other nonresistant factors. The calculation process is represented as

where P is the anti-interference ability in the process of data acquisition, t is the data acquisition time, y is the degree of data encryption, z is the transmission distance, and ξ is the interference coefficient [18].

2.3. Simplified Processing of Data

The data collected in different processes need to be processed by K-means, and the processing formula is expressed as follows:where S is the range of clustering, that is, the degree of data clustering, the more obvious the value is, the better the clustering effect is; I is the data information collected in the process, and i is the number of the information. In addition [19], S can be replaced by similarity J to improve the efficiency of the correlation calculation. In order to simplify the process, we can use the frequency of certain data to judge the weight, that is, to judge the data threshold collected by the equipment in the process based on the past historical data [20]. The calculation formula is expressed as follows:where G is the number of times that J device collects I information, Gis the number of times that i information appears in basketball consciousness, G is the frequency that J device obtains information, and w is the threshold of i information to ensure the data correlation <1.

3. Research Model

3.1. Data Acceptance Condition Judgment

Before the relevant analysis of the cultivation of basketball content, the process should be classified and judged according to the relevant standards. This paper adopts Metropolis's acceptance criterion of the annealing simulation algorithm. If the basketball content meets the actual needs, relevant data will be collected; otherwise, the data in the process will not be accepted. Metropolis can adjust the correlation between basketball data and content. Assuming that the state of basketball content is T, the Metropolis acceptance criterion is analyzed to determine whether the basketball content needs to be changed, that is, from I state to i + 1 state. The calculation results are expressed as follows:

Exp () is a function to judge the change of tactical consciousness; R (T) is the result of the change of tactical consciousness. If the result of R (T) is in the range of 0∼0.5, it means that the tactical consciousness of the state does not need to be changed. If the result is between 0.5 and 1, the basketball content needs to be changed [21].

3.2. The Correlation Analysis of Different Basketball Content and Different Adolescent Health Data

The correlation between basketball content and different adolescent health data can be divided into local correlation P and overall correlation P, reflecting the correlation between them from different aspects. The P and P are the larger, the data correlation is the better. Due to the limitation of Metropolis acceptance criteria in 2.1, both data are required to be optimal in order to become calculation data, so the calculation formula is expressed as follows:where N1,1,0,1 is the initial value of P, N1,1,0,1 is the initial value of P, Rmin is the minimum correlation value of all data, R is the local minimum, R is the local correlation coefficient, and φ is the overall correlation coefficient [22].

3.3. The Description of Accuracy of Basketball Content Analysis

Before the analysis of basketball content, the corresponding data sequence should be constructed. In this paper, the appropriate function is used to clarify the relationship between different adolescent health data and different levels of basketball content. Under the conditions of preset precision and threshold constraints, the moderate function carries out multiple iterations of the analysis, guides the operator to calculate in the positive direction, improves the occurrence rate of local extremum and “false eigenvalue,” and improves the scientific calculation of basketball content. The specific formula is expressed as follows:where f(x⟶y) is the relationship function between data and different levels of basketball content, reflecting the degree of correlation between them; ∑max(F(x), F(x)) is the maximum value of any two different tactics correlation data, and ∑max(y), (y) is the maximum value of any two basketball content [23]; ∫(∑max(F(x), F(x)), ∑max(y)(y)) is the maximum or minimum value of two-point correlation is obtained by deriving the correlation data between process and different tactics; min[∫(∑max(F(x))), ∑max(y)] is the minimum value of all extreme values, ξ is the error value of any calculated data, and ∲sin(ξ) is the error coefficient [24].

3.4. The Calculation Steps of the Correlation of Basketball Content in Basketball

Based on the above mathematical description, the following calculations are needed:

Set the initial basketball content C={c1, c2,…, c}, the threshold of data correlation (local correlation P and global correlation P) and calculation accuracy, the data N of basketball, and the initial Metropolis acceptance criteria;

The related data were tested by gradient 200 iterations [25];

According to the Metropolis acceptance criteria and constraints, the calculation results are verified, the results that meet the requirements are included in the total scheme, and the basketball content recognition accuracy and calculation time of the overall scheme are calculated;

Judge whether all data I are traversed, if so, terminate the calculation, otherwise go to step 3;

Finally, output the minimum value in the total scheme and exit the calculation process [26].

4. Results and Discussion

Based on the public health data published by 12 provinces in China, the basketball situation of teenagers aged 15∼20 are analyzed. At the same time, according to the actual questionnaire as the verification results, carry out relevant comparative analysis to verify the rationality and accuracy of sports content and the promotion time of juvenile basketball. The results are shown in Table 2.

Table 2

Introduction to experimental conditions.

Equipment name	Test content	Quantity
Camera	Shooting action	4
Recorder	Record blood oxygen, heartbeat, blood pressure, and later physiological indexes	4
Infrared frequency meter	Monitor temperature changes	3
Relevant personnel	Tester	12
Main test	Be responsible for the whole test process	5
Recorder	Record relevant data	6
Commander	Command other personnel to test	1
Age	Tester	32
12–14 years old	None	16
15–18 years old	None	16

In the basketball game, the correlation between the basketball content (local correlation P and global correlation P) and judgment accuracy is 0.1, and the number of iterations is 100, where the data acquisition equipment includes 4 cameras, recorders, and 4 pcs, and the judgment standard meets the “youth basketball standard” issued by various regions in 2020 [27].

4.1. The Correlations between Basketball Content and Different Teenagers' Health

Through k-mean clustering and Metropolis acceptance criteria, this paper analyzes the correlation between basketball and different basketball content and carries out 150 iterations to obtain data correlation results as shown in Figures 3 and 4.

Figure 3

The location correlation between basketball and different basketball content.

Figure 4

The overall correlation between basketball and different basketball content.

It can be seen from Figure 3 that there is a strong correlation between basketball and basketball training content, and the degree of correlation changes regularly. The reason for the abovementioned problems is to cluster basketball data early, to reduce the amount of data, and to analyze the correlation problems more deeply.

It can be seen from the above Figures 3 and 4 that the local correlation and overall correlation of teenagers' health and different basketball content are >95%, while the overall correlation is low [28], but also >90%, indicating that the overall correlation is high, which also meets the requirement of 10% threshold set by K-mean clustering and Metropolis acceptance criteria. In addition, the correlation analysis method has a strong correlation and the correlation between different indexes is between 1 and 1.5. Moreover, the intuitive correlation degree is relatively average, which shows that the results of correlation analysis are relatively stable. The reason is that the association analysis method can cluster the initial data analysis. Ding et al. [29] improved the preprocessing of data and further simplify the data analysis steps.

4.2. The Accuracy of Basketball Scheme

The four evaluation indexes of the correlation between basketball and basketball content are basketball intensity judgment x, basketball situation judgment x, and basketball effect judgment x. The evaluation index directly determines the effectiveness of the whole model. Through MATLAB simulation analysis, it can be seen that the recognition accuracy of basketball content in the above three indicators is more than 95%, and the initial threshold is 0.1, so the simulation result is better, as shown in Figure 5.

Figure 5

The accuracy of the basketball scheme.

As can be seen from Figure 5, the calculation accuracy of training intensity is the highest, ranging from 96% to 100%, followed by training environment and the accuracy of training effect is the lowest. However, the overall analysis results are above 78%, indicating that the physical health analysis effect of basketball is ideal, in line with the actual requirements.

4.3. The Identification Time of Basketball Scheme

Due to the large amount of basketball data processing and high processing complexity, the identification time of basketball content is another validation metric. The results are shown in Figure 6.

Figure 6

The identification time of basketball scheme.

The results show that the identification time periods of basketball content in basketball intensity judgment x, basketball situation judgment x, and basketball effect judgment x are less than 7 seconds, and the identification time of basketball content in basketball effect and intensity judgment is less than 4 seconds, which is mainly due to the relatively complex calculation process of the situation. However, the identification time of the overall basketball content meets the requirements of men's basketball Asian Championship 2020. The results are shown in Figure 6. In addition, in terms of experimental environment and experimental effect, the results of basketball program are ideal, which is basically in the position of median line, indicating that the comprehensiveness of the two indexes is strong [30].

5. Conclusion

With the continuous improvement of mass fitness, scholars at home and abroad have strengthened the research on the content of basketball, especially the health of basketball teenagers. However, the research of relevant scholars is mainly qualitative theoretical analysis, lacking quantitative practical analysis. Finding out the relationship between basketball and physical health is beneficial to improve the implementation scheme of basketball and give full play to the exercise function of basketball. At the same time, correlation analysis can find out the essence of exercise more deeply and make corresponding exercise strategies more reasonably. Therefore, correlation analysis has obvious advantages for basketball physical health. The correlation analysis method can more accurately analyze the physical health problems of basketball, dig deep into the correlation between related indicators, and improve the accuracy of the results. In this study, the advantages of correlation analysis method have been effectively reflected to promote the rationalization of basketball program. In addition, the previous correlation analysis method cannot carry out basketball analysis with strong complexity and large amount of data, and there is a problem of poor accuracy in the formulation of basketball scheme. This paper puts forward a method based on correlation analysis, through different basketball requirements, the basketball content and judgment, and the accuracy of judgment is 0.1. Then, by using iterative calculation, the recognition rate of basketball content of basketball intensity judgment x, basketball situation judgment x, and basketball effect judgment x, and other indicators is obtained. MATLAB simulation results show that the recognition accuracy of basketball content in the three indicators is more than 95%, while the requirement in the initial threshold is 0.1. At the same time, the identification time of basketball content in the three indicators is less than 7 seconds, and the identification time of basketball content in basketball effect and intensity judgment is less than 4 seconds, so the overall judgment result is better.