# Evaluation Algorithm of Volleyball Players’ Competitive Ability Based on the Random Matrix Model

Authors: Tailin Wang; Hua Zheng; Fangshu Li; Nian Jia; Zengliang Cai
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

It is the trend of the development of modern competitive sports to put scientific and technological analysis methods and means into the study of volleyball, and it is also one of the powerful guarantee ways to promote the competitive level of all countries. The random matrix model algorithm has unique advantages to construct the team’s collective technical and tactical ability structure model. The quantitative relationship of the model describes the relationship between the technical and tactical ability structure and the result of victory and defeat and makes the advantages and disadvantages of the team clear, which is conducive to the subsequent targeted training and improvement. The technical and tactical abilities of the teams in different seasons were input to verify the prediction accuracy of the model for the teams in different seasons. In the face of the rapidly changing game situation, the coach team timely transmits the adjusted technical and tactical strategies to the players on the field and deals with the changes accurately and effectively. After the game, the opponent’s strengths and weaknesses should be clarified, and the team’s daily training details should be summarized to provide reference for the cultivation of collective technical and tactical consciousness. The random sample covariance matrix of the random monitoring matrix is constructed and the maximum and minimum eigenvalues of the sample covariance matrix are solved. The ratio of characteristic values is used to construct the detection index of characteristic values, and the detection threshold algorithm of characteristic values is determined to judge the competitive ability of volleyball players. In the case of false alarm rate and matrix size, based on Tracy-Widom distribution characteristics, the maximum eigenvalue and minimum eigenvalue approximations of sample covariance matrix are used to improve the eigenvalue index detection threshold algorithm, and the influence of false alarm rate, matrix size, and other parameters on the improved eigenvalue index detection threshold is further studied. Then, Iris data set was used to verify the effectiveness of the algorithm in terms of accuracy, recall rate, and comprehensive effective value, and the validation results proved that the accuracy of the algorithm reached more than 90%.


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## Body

## 1. Introduction

In system theory, the openness principle, one of the subordination principles, holds that the system constantly interacts and reacts with the surrounding environment, so that its internal and external factors transform each other and cause qualitative changes in the system. The principle of openness is more profound in the collective sports with the characteristics of cluster. Volleyball is one of the collective sports dominated by techniques and tactics of teams over the net on divided court. Volleyball teams make up for their shortcomings by competing with the opponent, stimulate players’ potential, and turn that into output in the match to help them win the match [1]. In our country, the technical and tactical level of the whole league team is not uniform, and the phenomenon of the alternation between the team members is not sufficient, which led to the fact that the league level has not been significantly improved. Along with the reform of the Chinese Volleyball Super League in advance and update, the local provincial should actively learn from the national team and improve the capacity analysis of competition with rivals to produce their own team characters of individual and collective tactics [2], with each specific tactical combination for the team training.Tactical ability refers to the collective skills and tactics of 6 players on the field, which is the organic sum of the technical and tactical abilities of 6 players on the field, rather than a simple sum. The structure of technical and tactical ability refers to the relationship between each component of technical and tactical ability. In volleyball, the structure of collective technical and tactical ability refers to the combination of six people’s technical and tactical ability. From the angle of system theory, the volleyball team tactics ability structure is a complete system, the technology and tactics ability is a component of the system, organization relationship between each factor, namely, the structure, determines the function of the system, the different tactics system structure of the team is not the same, and in the game it is in the form of technology and tactics ability differences.The structural models of technical and tactical ability are established by using the random matrix model algorithm. Comparing the advantages and disadvantages of each team and putting forward the corresponding suggestions to improve the ability by model quantification, the evaluation issues of competitive ability of volleyball players in the new season and the technical and tactical performance data of their opponents in a match are selected as examples and input into the established model for simulation. Finally, the simulation results show that the technical and tactical ability model constructed based on the random matrix model has the function of predicting the outcome of the team in the new season. According to the technical and tactical performance of the team and the specific opponent, the coach team clearly defines the space for improvement and the overall goal of the stage and provides visual data support for the team to standardize and implement the relevant details of the reasonable training plan and cultivate the technical and tactical awareness and improve the overall competitive ability level. The improvement of the teams’ overall competitive abilities in the CVL can promote the overall development quality of the national team. The biggest innovation of this paper is to study the structure of the collective technical and tactical ability of the Chinese Volleyball Super League team based on the random matrix model.


## 2. Related Work

Random matrix theory is mainly applied in quantum physics [3], biomedicine [4], social science [5], power grid distribution [6], spectrum perception [7], and other fields. Using a large number of fault and defect samples, the corresponding relationship between the confidence level of the key performance of the equipment and the state quantity is mined through association rules, and then the time series of the state quantity of the equipment is processed through the high-dimensional random matrix theory. By studying the characteristics of high-dimensional matrix and analyzing the state information of state quantity, we can complete the evaluation of the key performance of power equipment and volleyball competitive ability [8]. Based on the big data theory, a high-dimensional random matrix is constructed with grid operation acquisition parameters as elements. By characterizing the statistical characteristics of the matrix, an identification method for stolen electricity based on big data analysis is proposed [9]. In order to explore the internal relationship between various influencing factors and the operation state of distribution network, a correlation analysis method based on random matrix theory was proposed [10]. In view of the low utilization rate of smart grid data and the difficulty of screening, this paper proposes a solution based on big data drive, and designs a big data smart grid system based on the random matrix theory model construction algorithm, so that it can sample books from large databases, use data for training, learning, and establish user demand models [11]. Based on the transient information and operation data of faults, a method of correlation feature discrimination based on high-dimensional random matrix is proposed to realize the rapid location of single-phase grounding faults [12].In the application of random matrix theory, most studies focus on the eigenvalue information mining of random matrix, trying to use the eigenvalue to reflect the characteristics of engineering data as comprehensively as possible. Aiming at the low efficiency of spectrum resource utilization in spectrum sensing field, a new spectrum sensing fusion algorithm based on eigenvalue is proposed based on random matrix theory, so as to better realize dynamic spectrum sharing [13]. Because the extended information of sonar signal is contained in the covariance of the noise, after simple transformation of the covariance matrix under some conditions, the linearized measurement results can be incorporated into the existing random matrix algorithm, so that the eigenvalue theory of random matrix can be used to distinguish the sound signal. Therefore, this paper puts forward a kind of non-linear is used to measure the EOT/GTT random matrix method [14], and applied to noise of radar and sonar signal tracking. First of all, according to research random matrix tensor decomposition, the decomposition produces parallel (or coaxial) component and quadrature component; then, a metric is defined to measure the deviation of the two components relative to the nominal matrix to model the cognitive uncertainty (i.e., the modeling error) [15]. In the process of modeling, the eigenvalue decomposition of matrix is mainly used, and an abnormal state recognition method based on the spectrum distribution of eigenvalue of high-dimensional random matrix covariance matrix is proposed [16]. Random matrix theory and Wishart distribution were used to characterize the modal uncertainties caused by parametric, nonparametric, and experimental variations. To verify random matrix theory and Marchenko-Pastur (MP) density function [17, 18], according to the theory of sports training [19], sports technology refers to the method of completing sports movements and is an important determinant of athletes’ competitive ability. Tactics refer to the tactics and actions taken in order to defeat the opponent or obtain the desired results of the game. It is an indispensable means and method in competitive matches. In the competition of ball events [20], the complexity and changeability of skills and tactics and their flexible application in the competition make the performance of ball games and sports quality present a nonlinear relationship, and it is difficult to predict the performance of athletes (teams) according to sports quality, while skills and tactics play an extremely important role in the outcome of the competition. Tactics are the dominant factor in volleyball matches. However, for coaches, it is impossible to remember all tactics in order to raise the scientific level of volleyball training. In the process of training and competition, systematic statistics and analysis of techniques and tactics are needed to objectively and effectively record the application of techniques and tactics of athletes [21], so as to understand the characteristics of techniques and tactics of athletes and provide important basis for the development of training plans and competition plans in the next step. In addition, the systematic technical and tactical analysis is of great help to enhance the confidence of athletes and stabilize the mood of competition. Therefore, the technical and tactical analysis is of great significance to volleyball, and the scientific research around the technical and tactical analysis has always been one of the core contents to improve the scientific training of volleyball. The aim of volleyball competition is to win [22]. When volleyball players form a two-point attack in the first row of the competitive ability evaluation point, a static game is formed between the attacking team’s setter and the defending team’s second attack. By comparison, the attacking batsman distributed the ball 70 percent of the time, while the defending offense blocked the batsman or side 38 percent of the time. When the defensive side has a 60% chance to block the offensive side’s outside hitter, the defensive side gains 62% from the offensive side’s setting to the outside hitter, with no significant difference [23, 24]. The Nash equilibrium point is found by using the expectation effect of the other side, and the deviation is found and corresponding strategies are formulated according to the selection ratio of the offensive side and the defensive side according to the game theory to help the team win.However, the above-mentioned studies mainly use the eigenvalues of the random matrix and ignore the eigenvector information. The loss of the eigenvector damages the integrity of the data and fails to fully mine the data information, thus wasting the useful information contained in the eigenvector in the engineering application and possibly causing the deviation of the results.


## 3. Volleyball Player Competitive Evaluation Model Based on Random Matrix Eigenvalue Index

### 3.1. Volleyball Competitive Ability Evaluation Algorithm Based on the Ratio of Eigenvalues

The ratio of the maximum eigenvalue to the minimum eigenvalue is taken as the test indexb, and the appropriate threshold value is selected to judge the operating state of competitive ability evaluation. According to m-P law in spectrum analysis theory, the threshold value c is set as(1)c=1+b1−b2.Definition accuracy is the probability of volleyball competitive ability evaluation results to be correct; false alarm rate refers to the probability of error detection results, that is, when the actual running state is UI judgment forC or the actual state is U: the probability of judgment results for H, through the false alarm rate to study the change of the decision threshold. According to the definition of false alarm rate, it can be known that(2)λi=PC|H1+PB|U2.On the basis of consulting and learning related research, combining with the current situation of volleyball competitive ability evaluation research and random matrix theory research and application, this paper analyzes the advantages and disadvantages of traditional volleyball competitive ability evaluation methods and puts forward volleyball competitive ability evaluation algorithm based on random matrix theory. The technical framework is shown in Figure1.Figure 1
Frame diagram of volleyball competitive ability evaluation algorithm based on ratio of eigenvalues.


### 3.2. Construction of Three-Dimensional Evaluation Model of Volleyball Tactics

The evaluation model is composed of a series of evaluation indicators. “Indicators” is a term in social and economic statistics, which means to reveal and explain. Indicators are specific values that reflect the concept of quantity of system elements or phenomena. “It includes the name of the index and the value of the index.” The evaluation field borrows this term, takes the goal as the center, breaks the goal into some concrete and operable factors, and reflects the overall characteristics of the goal through the evaluation of these factors. The selection of indicators is the key to construct evaluation model. The principle of symmetry means that the evaluation model designed in this study should make the technical and tactical data of both sides symmetrical, which is conducive to the comparative analysis of techniques and tactics. This is a problem that has not been fully solved in the previous technical and tactical analysis methods. Therefore, this study proposes a method to divide the fifth beat. According to the volleyball rules, when the fifth beat score, that is, sixth beat lost points, the ball (fifth beat score) is classified as stalemate I section score, that is, stalemate II section lost points; when team loses point on the fifth pat, it will be regarded as team score on the fourth pat, and this ball (lost point on the fifth pat) will be classified as team lost point on the starting part, that is, team score on the catching part. After the division of the fifth beat, the structure of four sections of a match is shown in Figure2.(1)
Firstly, the input layer is determined: the input layer is the number of indicators selected for network training, and the neuron is the technical and tactical ability data indicator.(2)
Through the weight of the connection point between neurons into the next layer, the neuron output of the first layer is the neuron input layer of the next layer. As for the selection and setting of weights at the beginning of the network, the default initialization method is adopted to avoid the phenomenon of gradient descent disappearance or gradient explosion in the process of network training.(3)
The number of hidden layers is determined according to the research content. Although there is no fixed reference method to determine the number of hidden nodes, too many or too few nodes will directly affect the training effect of the network, the network convergence speed is too fast or too slow, and the change of errors. The number of hidden layer nodes is determined by some fixed empirical formulas, whereN is the number of neurons in the input layer, L is the number of neurons in the output layer, and A is the constant between N and L [1, 10]. After repeated training and adjustment, the optimal number of nodes in the hidden layer of the training structure is determined and the optimal model is constructed. The indicators are shown in Table 1.(4)
The sum of weightsw in the layers is converted into output according to the selected activation function equation.(5)
output layer is the results obtained by training the model (usually the number of output layers is 1); the above network training model can be expressed in specific quantitative mathematical formula as follows:(3)nety=tan sigωi+ωij1,2+θ.Figure 2
Flow chart of random matrix construction algorithm for monitoring data.Table 1
Indicators and description of indicators.
IndicatorsPoints indicatorIndicators descriptionServeThe straight rateNumber of direct serves/total servesFalse positivesNumber of serves in net or out/total servesBreak the attack rateIt is difficult for the receiving side to form an effective attack count/total serve countGeneral rateThe number of times served other than the above three conditions/total number of servesReceivingMust beThe receiving side will send the ball to the area near positions 2 and 3 to facilitate the passing of the setter in place to organize the number of effective attacks/servesNot must beThe setter needs to adjust the distance pass or the number of passes that cannot be made/the number of serves receivedFalse positivesThe number of times the receiving side has not hit the ball into the opponent’s area within the rules/the number of times the receiving side has received the ballTime window technology is further used to frame the monitoring data. The scale of time window is 10; that is, the time node isT. Then feature extraction is carried out on the selected monitoring data subspace. According to the characteristics of the data, 70 time-domain features are extracted to form the feature matrix Z, which is normalized to obtain the matrix. Figure 3 shows the algorithm flow of volleyball athletes’ competitive ability evaluation based on eigenvalue indicators.Figure 3
Flow chart of evaluation algorithm of athletes’ competitive ability based on eigenvalue index.In terms of utilization rate, there was almost 1 : 1:1 distribution in the snatch segment (32.44070), snatch segment (33.00070), and stalemate segment (17.72% for stalemate I and 16.84070 for stalemate II), and each segment accounted for about 1/3 of the total, in which the proportion of stalemate I and II was almost the same; it can be seen that, for either the random matrix or random matrix, the utilization rate of serving round and receiving round accounts for about 50.07%.


## 3.1. Volleyball Competitive Ability Evaluation Algorithm Based on the Ratio of Eigenvalues

The ratio of the maximum eigenvalue to the minimum eigenvalue is taken as the test indexb, and the appropriate threshold value is selected to judge the operating state of competitive ability evaluation. According to m-P law in spectrum analysis theory, the threshold value c is set as(1)c=1+b1−b2.Definition accuracy is the probability of volleyball competitive ability evaluation results to be correct; false alarm rate refers to the probability of error detection results, that is, when the actual running state is UI judgment forC or the actual state is U: the probability of judgment results for H, through the false alarm rate to study the change of the decision threshold. According to the definition of false alarm rate, it can be known that(2)λi=PC|H1+PB|U2.On the basis of consulting and learning related research, combining with the current situation of volleyball competitive ability evaluation research and random matrix theory research and application, this paper analyzes the advantages and disadvantages of traditional volleyball competitive ability evaluation methods and puts forward volleyball competitive ability evaluation algorithm based on random matrix theory. The technical framework is shown in Figure1.Figure 1
Frame diagram of volleyball competitive ability evaluation algorithm based on ratio of eigenvalues.


## 3.2. Construction of Three-Dimensional Evaluation Model of Volleyball Tactics

The evaluation model is composed of a series of evaluation indicators. “Indicators” is a term in social and economic statistics, which means to reveal and explain. Indicators are specific values that reflect the concept of quantity of system elements or phenomena. “It includes the name of the index and the value of the index.” The evaluation field borrows this term, takes the goal as the center, breaks the goal into some concrete and operable factors, and reflects the overall characteristics of the goal through the evaluation of these factors. The selection of indicators is the key to construct evaluation model. The principle of symmetry means that the evaluation model designed in this study should make the technical and tactical data of both sides symmetrical, which is conducive to the comparative analysis of techniques and tactics. This is a problem that has not been fully solved in the previous technical and tactical analysis methods. Therefore, this study proposes a method to divide the fifth beat. According to the volleyball rules, when the fifth beat score, that is, sixth beat lost points, the ball (fifth beat score) is classified as stalemate I section score, that is, stalemate II section lost points; when team loses point on the fifth pat, it will be regarded as team score on the fourth pat, and this ball (lost point on the fifth pat) will be classified as team lost point on the starting part, that is, team score on the catching part. After the division of the fifth beat, the structure of four sections of a match is shown in Figure2.(1)
Firstly, the input layer is determined: the input layer is the number of indicators selected for network training, and the neuron is the technical and tactical ability data indicator.(2)
Through the weight of the connection point between neurons into the next layer, the neuron output of the first layer is the neuron input layer of the next layer. As for the selection and setting of weights at the beginning of the network, the default initialization method is adopted to avoid the phenomenon of gradient descent disappearance or gradient explosion in the process of network training.(3)
The number of hidden layers is determined according to the research content. Although there is no fixed reference method to determine the number of hidden nodes, too many or too few nodes will directly affect the training effect of the network, the network convergence speed is too fast or too slow, and the change of errors. The number of hidden layer nodes is determined by some fixed empirical formulas, whereN is the number of neurons in the input layer, L is the number of neurons in the output layer, and A is the constant between N and L [1, 10]. After repeated training and adjustment, the optimal number of nodes in the hidden layer of the training structure is determined and the optimal model is constructed. The indicators are shown in Table 1.(4)
The sum of weightsw in the layers is converted into output according to the selected activation function equation.(5)
output layer is the results obtained by training the model (usually the number of output layers is 1); the above network training model can be expressed in specific quantitative mathematical formula as follows:(3)nety=tan sigωi+ωij1,2+θ.Figure 2
Flow chart of random matrix construction algorithm for monitoring data.Table 1
Indicators and description of indicators.
IndicatorsPoints indicatorIndicators descriptionServeThe straight rateNumber of direct serves/total servesFalse positivesNumber of serves in net or out/total servesBreak the attack rateIt is difficult for the receiving side to form an effective attack count/total serve countGeneral rateThe number of times served other than the above three conditions/total number of servesReceivingMust beThe receiving side will send the ball to the area near positions 2 and 3 to facilitate the passing of the setter in place to organize the number of effective attacks/servesNot must beThe setter needs to adjust the distance pass or the number of passes that cannot be made/the number of serves receivedFalse positivesThe number of times the receiving side has not hit the ball into the opponent’s area within the rules/the number of times the receiving side has received the ballTime window technology is further used to frame the monitoring data. The scale of time window is 10; that is, the time node isT. Then feature extraction is carried out on the selected monitoring data subspace. According to the characteristics of the data, 70 time-domain features are extracted to form the feature matrix Z, which is normalized to obtain the matrix. Figure 3 shows the algorithm flow of volleyball athletes’ competitive ability evaluation based on eigenvalue indicators.Figure 3
Flow chart of evaluation algorithm of athletes’ competitive ability based on eigenvalue index.In terms of utilization rate, there was almost 1 : 1:1 distribution in the snatch segment (32.44070), snatch segment (33.00070), and stalemate segment (17.72% for stalemate I and 16.84070 for stalemate II), and each segment accounted for about 1/3 of the total, in which the proportion of stalemate I and II was almost the same; it can be seen that, for either the random matrix or random matrix, the utilization rate of serving round and receiving round accounts for about 50.07%.


## 4. Study on the Spatial and Temporal Characteristics of Volleyball Players’ Competitive Ability Evaluation

Most sports competitions are carried out in a relatively fixed three-dimensional space, and the winners and losers are determined in a limited time. Competition is essentially a competition for time and space. Due to the limitation of measurement methods, most of the research on the space-time characteristics of the early movement remained at the level of qualitative description of space-time three-dimensional features and two-dimensional parameter analysis. The human body is the most complex system of space-time characteristics. The transformation of external space-time (opponents, rules, environment, judges, etc.) into internal space-time (basic technology, etc.) is the automatic process of technical training and tactical cooperation. The clever and reasonable use of space-time characteristics can lay a solid foundation for the victory of the game. With the increasing movement speed of athletes in the game, the space range is relatively compressed, and the essence of the competition behavior of the athletes on both sides in the transition between offense and defense is to continuously pursue the maximization of the interests of space-time and displacement, but they are restricted by the law of space-time.The attacking position refers to the position where the attacking player’s feet stand in the field when player spikes the ball. According to the conventional understanding of volleyball, the main factors affecting the position of the attacking ball are the front and back direction; that is, the closer the position is to the net when the attacking ball, the greater the angle between the line of attacking ball and the ground plane is, the shorter the flight distance is, and the larger the attacking ball is. This paper studies the selection method of attack position. In order to facilitate quantitative analysis, taking the volleyball field finish line as a reference, measure the distance from the ball’s landing point to the finish line when the attacking player contacts the racket and volleyball, and take this as the value standard of attack position: the farther away from the finish line, the closer to the net. It can be seen from the statistical data in Table2 that the highest incidence of blocking occurs 1.2 meters away from the end line, and the inflection point of increasing the incidence of blocking is 0.8 meters away from the middle line, while the distance between the attack line and the end line in the volleyball court is 6 meters (0.52 + 0.04 + 0.04). The offensive line is the generally recognized reference line for judging whether the baseline ball is in the marking line in the volleyball training field. Its basic meaning is that the lob or the touchdown ball is “not in place” before the offensive line. The drop point of the lob or lob behind the block is “in place,” indicating whether it can be attacked or not. “The distance is 0 meters,” indicating that the high ball of the defense side is 100% in place, and the offensive side is generally not easy to attack. Because, at this time of attack, the ball has a long flight distance, the attack landing point is not deep, and the ball speed is more attenuated, so it is easy to be attacked by the defense. Therefore, the incidence of net blocking is obviously very low, only 0.68%. With the advance of the attack point to the front of the net, the incidence of blocking and the proportion of score gradually increased, significantly increased when advancing to 0.8 meters, and reached the peak when advancing to 1.2 meters. Since then, as the position of the attack point continued to advance to the front of the net, the incidence of blocking and the proportion of the score began to decline, and in the case of more than 2 meters, there was an inflection point of decline. Because the attack position at this time has always been “half,” the probability of direct attack score is very high, but the proportion and effect of blocking have decreased significantly.Table 2
Incidence and efficiency of blocking at different offensive positions.
Attack lineClosing scoreTapping seal numberIncidence of network blocking %Percentage of score %Attack and penetration efficiency %0.6 m from attack line271217.636.6423.760.8 m from attack line4117210.499.6524.721 m from attack line4319812.509.6221 .141.2 m from attack line5420512.6611.8525.021.4 m from attack line4616710.1310.2427.921.6 m from attack line531629.9811 .8532.511.8 m from attack line421207.639 .4533.63According to the aerodynamic law of volleyball flight, the longer the ball flies in the air, the greater the attenuation of ball speed is; therefore, the smaller the threat to defense, the greater the flexibility of defense, and the higher the difficulty of attack. Correlation analysis was conducted between the attacking and penetration efficiency and the flight time in the area with dense incidence of blocking within 0.28–0.52 seconds (accounting for 97.31% in total). Statistical results (Table3) show that there is a negative correlation between the attacking flight time and the attacking and penetration efficiency. The longer the flight time, the lower the attacking and penetration efficiency, and Pearson correlation coefficient R = −0.757. The significance level was 0.05.480 ms which is an important critical value. Flight time is closely related to offensive force, offensive position, and offensive line; that is, smaller offensive force and longer offensive line are related to longer flight time, which is not conducive to the realization of attack-and-penetrate cooperation.Table 3
Incidence rate and efficiency of sealing under different flight time of attacking spheres.
Time of flightClosing scoreTapping seal numberIncidence of blocking %Percentage of score %Attack and penetration efficiency %0.26 s15422.543.2634.130.3 s9434521.5622.0528.140.34 s10333221.2323.2329.760.42 s9637623.6522.3225.530.46 s8833220.8720.4426.450.44 s22674.254.6229.430.54 s8563.672.0515.51After the model is established, the random matrix model of network training error is shown in Figure4, in which the horizontal axis is trained and the vertical axis is taken as the mean square error of the dataset. After 17 times of learning, the random matrix model network converges successfully. The best mean square error of the validation set is 0.00075811, and the mean square error of the training set is 0.00014371. The random matrix model after five studies the network converges successfully, the best mean square error of the verification set is 0.00046615, and the mean square error of the training set is 0.00014232, indicating that the network training speed of the random matrix model is fast, and the training effect is good.Figure 4
Training error curve of the random matrix model.Situation diagnosis includes overall situation diagnosis and individual situation diagnosis. Gap diagnosis completes the calculation and ranking of individual level index gap coefficient. According to the radar analysis of individual situation diagnosis according to certain analysis standards, the advantages and disadvantages of athletes are automatically determined. According to the special physical fitness target challenge model, the athletes’ physical fitness index gap diagnosis, and automatic conversion of physical fitness index gap coefficient and ranking, the data query module can query and modify the basic personal information and raw physical data of athletes and realize the establishment of initial data analysis and evaluation criteria, as well as system data backup and other functions.


## 5. Example Verification

The model training parameters of the four teams were selected with 1000 iterations and convergence error within 0.0001. The specific operation results are shown in the following figure: From the results of competitive ability evaluation points of Beijing volleyball players in Figure5, it can be seen that when the training times of the model reach the eighth step, the error converges to 2.9268E − 08. Within the error range, it shows that the random matrix model R = 0.96602, and when the result of R is closer to 1, it proves that the training fitting accuracy of the model is high. In this case, the network convergence is the best, the training speed is fast, and the whole training process takes a short time.Figure 5
Convergence of training error of competitive ability evaluation points of volleyball players.Taking the scoring rate of the four sections (X1, X2, X3, and X4) as the tactical factor, the path analysis model diagram between the winning probability and Y is established. The volleyball athletes’ competitive and competitive winning probability conforms to the normal distribution after the test. According to the correlation between the four independent variables and dependent variables, the direct and indirect path coefficients of volleyball athletes and volleyball athletes were calculated, respectively, and the results are shown in Table 4.Table 4
Index evaluation list of overall strength dimensions of Chinese and foreign elite athletes.
SourceDependent variableDegrees of freedomMean squareFInterceptSend grab section technical benefit112.8741658.72Technical benefit of snatch section214.1632763.621Phase I technical benefits216.5432842.960Phase II technical benefits211.2322842.139Send grab section technical benefit20.0243.383OtherTechnical benefit of snatch section20.0356.645Phase I technical benefits10.0335.655Phase II technical benefits10.0225.145Compared with the traditional kurtosis index method shown in Figure6, it can be seen that the eigenvalue detection index can find the evaluation points of the early competitive ability of volleyball players in advance compared to the kurtosis index method under the conditions of different matrix row ratios. The advance time is the largest when C = 0.4, 536 time series. It can be found that, under different matrix sizes, the judgment results of evaluation points of early volleyball players’ competitive ability are roughly the same, but, compared with the traditional algorithm, the eigenvalue index detection algorithm can significantly advance the discovery time of evaluation points of early volleyball players’ competitive ability.Figure 6
Results of kurtosis index method based on random matrix model data for volleyball athletic ability evaluation.As shown in Figure7, when the training times of the random matrix model of competitive ability evaluation points of volleyball players reached the eighth step, the error convergence was 1.8547E − 06, which was within the error range set in advance.Figure 7
Regression diagram of competitive ability evaluation point network of volleyball players.


## 6. Conclusion

With the help of the nonlinear quantitative mathematical expression of the model, the random matrix model is used to analyze and explore the relationship between the technical and tactical abilities of the four teams and the outcome of the match. The results obtained are consistent with the realistic technical and tactical performances to a high degree, whether compared to the top four teams as a whole or each team in 2018-2019 season. The random matrix model is effective in analyzing the structure of collective technical and tactical ability of CVL women’s teams. By random matrix model and multiple regression model, the relationship between four indexes of excellent athletes to explore in detail found that excellent volleyball players tactical factors characteristic main performance is as follows: (1) Different tactical factors on the importance of the elite and other players, as well as volleyball athletic competition, according to the important degree sorting to spike, block, and penetration. (2) There is a compensation effect among the tactical factors in volleyball competition. The compensation effect is different due to the types and grades of tactical factors. Top teams often use some special tactics, such as the “two hitters for three” attack tactics in the front and back rows, arrange setters and attacking players in specific rotation, and maximize the richness of attack means in each rotation of the teams. (3) There is an interaction effect among tactical factors in volleyball matches. The interaction effect is different between tactical standard and utilization and different sections. The interaction effect from elite players of each team in competitive matches is higher than that of the other players in competitive matches, and the interaction effect of tactical system of top teams from where close to middle line and place near attack line is higher and more complex than that of other attacking sections. The next step is to make a proper assessment about the potential athletes physical changes, for athletes could reach the target state to make accurate predictions and provided the basis and starting point for research and training program, through periodic inspection, can work to the training of the previous phase and its effect is to make objective evaluation and then make the necessary adjustment to ensure the physical training process in accordance with the prescribed way to run, for the coach scientific planning and formulation of athletes physical training strategy to provide the basis and operation method.



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