# Application of Data Mining in the Analysis of Martial Arts Athlete Competition Skills and Tactics

Authors: Lingrong Tao
Journal: Journal of Healthcare Engineering
 (2021)

---

## Abstract

In martial arts, data mining technologies are used to describe and analyze the moves of athletes and changes in the process and sequences. Martial arts is a process in which athletes use all kinds of strengths and actions to make offensive and defensive changes according to the tactics of opponents. One such martial arts is Wushu arts as it has a long history in reference to Chinese martial arts. During the Wushu competition, Wushu athletes show their adaptability and technical level in complex, random, and nonlinear competitive abilities, organized and systematic skills, tactics, and position movement. Using data mining techniques, in-depth mining a particular type of martial arts competition technology and tactics behind statistical data, and using the data to find the law of change to solve some problems, for martial arts athletes in daily training to develop technology and tactics and improve competition results, is the practical significance of data mining in martial arts athletes competition. This research explored the relationship between goal-oriented and mental intensity and their effect on competitive success outcomes.


---

## Body

## 1. Introduction

Wushu has a long broad tradition and profound, but at the same time, it is also complicated. The traditional cultural content it carries and the national spirit it reflects are rich, profound, and intrinsic. Compared with domestic modern martial arts, the traditional martial arts emphasize the characteristics of attack, which is the soul of the traditional martial arts. It will lose its base if it is not attacked [1]. For a long time, the traditional martial arts are inherited by the major factions which have their own style and characteristics, whereas there are many problems in the system and practicability of modern martial arts especially in the adapted martial arts routine [2]. Due to the lack of technical support and actual combat confrontation, the content of martial arts routine is reduced or exaggerated, and also, in the competition field, the lack of application in the actual attack and defense has seriously affected the further development of martial arts. The tactics of martial arts athletes abandon the routine of martial arts that differ significantly from either the core of the martial arts and re-examine and learn about the growth and pattern of martial arts [3–6].Traditional martial arts involves many kinds and schools, and most of them hold the views of legitimate and orthodox, they have their secrets, and they seldom exchange the theory and art content of their school system in-depth. From the appearance, the traditional martial arts have been developed, showing a variety of development momentum. However, in fact, due to the influence of many kinds and schools of traditional martial arts and the different contents of techniques and tactics taught, it is difficult to form a complete set of teaching materials and training system to meet the public’s self-defense fitness and understand the traditional Chinese martial arts culture, which makes it difficult for martial arts practitioners, including overseas people, to experience [7]. The idea of western sports is to challenge the nature, the opponent, and the self, that is, to establish yourself in the pursuit of the good with the right, while the traditional Chinese martial arts in the fight are to advocate nature, pay attention to balance, and strengthen yourself and to overcome oneself in the pursuit of the right with the opposite. Therefore, with the full development of information technology today the development of traditional martial arts in China and its overseas promotion can be achieved. The use of data mining technology has been widely used in all kinds of fields.Data mining is widely used in the field of artificial intelligence (AI) [8–12], marketing, government intelligence (GI), services, and advertising. There are some other industries such as crime agencies, retail healthcare, e-commerce, telecom, biological data analysis, and information retrieval such as communication systems. In this research work, we innovative use data mining and explore the application of data mining in the analysis of martial arts athlete’s competition skills and tactics. Our research work is an important topic as it focused on the contemporary development of Chinese traditional martial arts [2, 7, 13, 14]. The primary contributions of this study were to investigate the characteristics of Chinese traditional martial arts and also design an intelligent learning model based on a data mining algorithm to monitor the process of martial arts athlete’s competition skills and tactics to analyze the process of quality.The rest of this paper is organized as follows. Section2 discusses the different methods in martial arts competitions that include traditional statistical method, video analysis method, and data mining method and establish decision tree, followed by the simulation experimental results in Section 3, and Section 4 concludes the paper with a summary and future research directions.


## 2. Methods

In martial arts competitions, there are specific actions of kicking, beating, holding, leaning, falling, hitting, and stabbing. In the historical competition records, there are effective attacks, fouls, mistakes, and other actions of the martial arts athletes. Technical and tactical actions are the objects of data mining. The relevant technical and tactical statistical data are obtained, through computer software recording or video analysis, the average value and limit value are calculated, and the frequent item sets and some association rules are generated [15].In the last five years, researchers have carried out more interdisciplinary research on the combination of physical education, sports training, and data mining technology. For example, the application of data mining algorithms in volleyball, table tennis, and cricket. The correlation degree between variables is determined by calculating the correlation coefficient between variables, and frequent item set pattern knowledge is found from a given dataset. Data mining is a technology to find the rules from a large number of data by analyzing each data. It mainly includes three steps: data preparation, rule finding, and rule representation. After obtaining the competition data, how to do data mining well depends on whether we can find the right rules [16, 17].


### 2.1. Traditional Statistical Method

In recent years, statistical technology has also made great progress. Statistical methods, statistical classification, statistical models, and statistical data processing have developed very maturely. However, because the models are also very fixed, there is no room for statistical methods to make progress. Also, with the improvement of information technology, there will be more and more martial arts competition data in the database. Statistical methods cannot make good use of the massive data in the database [18]. The application of data mining technology is an inevitable trend. Data mining can comprehensively use association rule discovery, cluster analysis, anomaly analysis, special group analysis, and evolution analysis to mine a large number of data in a certain type of martial arts technology and tactics and form a mining tool for martial arts competition technology and tactics.


### 2.2. Video Analysis Method

The quantity and quality of samples collected in Wushu competitions are also important factors affecting the results of data mining. At present, the analysis of martial arts athletes’ competition techniques and tactics are mainly based on the collection points and strength of martial arts competition information. The standardization of body script language affects the effectiveness of martial arts techniques and tactics analysis [19]. The degree of data integrity and professional support will have an impact on the establishment of the data mining process. In the past, the analysis of techniques and tactics in martial arts competitions was relatively subjective, and the data sources were only intuitive data such as historical victory and defeat rate, age, height, and weight. With the continuous improvement of computer technology, it can be determined to use video to analyze a martial arts athlete’s ability to use technology, a certain position technology, and a certain tactical effect, with the help of video analysis software. In a certain scene of the game, the data of the game video are analyzed and collected through the frame analysis technology.


### 2.3. Data Mining Method

With the development of computer technology, the application of fuzzy mathematics, and the development of matrix analysis theory, data mining came into being. As new technology and deepening of new research, data mining was widely applied in many fields, i.e., military, financial, and insurance industries to solve practical problems. Data mining is used to examine or explore the data using queries. These queries can be fired on the data warehouse. The rise of data mining technology undoubtedly provides powerful help for the analysis of technology and tactics. There are several data mining tools that companies can use to translate raw data into actionable insights. This includes Statistics, Artificial Intelligence, Hidden Markov models, Metalearning, Genetic Algorithm, Machine learning [20–25], and Decision Tree. It obtains the solution from the whole situation and applies it to formulate the combination of technology and tactics to solve the problem of the same hidden essence. Data mining technology is different from traditional statistical methods. The traditional statistics method is a science that studies how to collect, organize, analyze, and interpret digital information in data. Traditional methods use basic algorithms to predict the future. However, it does not provide reliable findings as compared to data mining. The traditional statistics method can be divided into two categories: descriptive statistics and inferential statistics. Descriptive statistics involves organizing, accumulating, and depicting information in data, where inferential statistics involves using sampling data to infer populations. Data mining is a kind of practical application algorithm (mostly machine learning algorithm), which uses data from various fields to solve problems related to various fields. Combining this information technology with the analysis of martial arts athletes’ skills and tactics in the process of competition can provide an effective scientific base for coaches and martial arts athletes’ on-site and off-site training methods [26].Since the 1990s, data mining technology has been developed by leaps and bounds. SQL Server Data Mining offers office 2007 data mining additives for the detection of data patterns and relationships. This is also helpful in improving the analysis. The add-in called Data Mining Client for Excel is used first to plan, compile, analyze, handle, and forecast data. Because of its broad application prospects, data mining technology has been widely used in eight fields, such as finance, health care, marketing, retail, manufacturing, justice, engineering and science, and insurance, and has just emerged in the field of sports information analysis. The technology of data mining is not the main problem that affects the result of data processing. It depends more on data. Data acquisition, rule analysis, and rule representation are the three steps of data mining technology. Using modern video analysis technology, we can obtain a large number of real and noisy data. With these preconditions, for martial arts competitions, we need to collect a large number of data first real and effective data; the more and more specific these data are, the more realistic the results of data mining can show the simulation results, followed by the reuse of data. How to improve the model plays the guiding role of existing data for martial arts athletes in the analysis of competition tactics in daily training, and improving the competitive performance is the important significance of data mining. With the improvement of CPU core number and architecture of a modern home computer, an ordinary home computer can complete simple data modeling and analysis, which lays the hardware foundation for the broad application prospect of data analysis.


### 2.4. Establish Decision Tree

For a competition instance set, many decision trees may be generated. The basic idea of using decision tree is to divide the search space of the target problem into several subsets by means of the classification, clustering and prediction algorithms, and idea of divide and conquer. The training set is used to generate all possible decision trees, and the simplest one is selected from them. Decision tree is widely used in data analysis in competitions. Due to the inherent advantages of tree structure, it is clear at a glance and easy to find features quickly.The random forest model is used to model the data mining problem. The main reasons for using this model include, firstly, compared with other classification and regression models, the random forest has many good characteristics, such as supporting multicategory classification, processing missing values, and supporting mixed types of data. Secondly, random forest is usually known as one of the best classification models and has been widely used in practical problems, such as Kinect object recognition and camera face recognition. In some data mining competitions, such as web search ranking, it has been called one of the methods that must be tried.It should be pointed out that although the random forest model is used in this paper, the method proposed in this paper is also suitable for similar decision tree combination models, such as the AdaBoost model based on decision tree and gradient boosting trees model (as shown in Figure1).Figure 1
Random forest model [16].In the process of building the random forest model, a dataset {x, y} is given, wherex = {x1, xn} is the set of attribute vectors and y = {Y1, Yn} is the set of classification tags. Each attribute vector x⇀j=x1i,…,xMj has m attribute values, in which each attribute value xj∈Dj has a finite range of values Dj. xj can be classified or numeric. The classification label y has a finite range of values Dy. In the case of no ambiguity, x = (x1, XM) denotes x⇀i=x1i,...,xMi. In order to simplify the description, this paper only considers the binary case, that is, y ∈ {0, 1}. The binary case can be extended to the multiclassification model with only a small change. A random forest model is generally composed of K decision trees. Each decision tree K has an output function OK, which means that when the input attribute vector x is given, a classification label is output Therefore, the output of the whole random forest is y∈DY:(1)Hx=∑k=1kϖkokx⇀,where ωk∈R is the weight of each decision tree. In addition, when a classification label c∈DY is given, the probability Hx⇀ expression of the classification label is as follows:(2)py=cx⇀=∑k=1kωkIokx⇀=c∑k=1Kωk,where Iokx⇀=c is the indicator function; when okx⇀=c, its value is 1, otherwise it is 0. Given an input attribute vector x⇀, the output prediction classification label yH of the random forest model Hx⇀ is denoted as(3)y∏=argmaxpy=cx⇀c∈DY.Evaluation function is used to measure the expected cost from the current state to a target state:(4)gx⇀=∑i=1mωx⇀i−1,x⇀i=∑i=1mπai.Given a random forest modelHx⇀, for any vector x⇀, the heuristic function hrf is defined as(5)hrfx=0Rcz−px⇀.


## 2.1. Traditional Statistical Method

In recent years, statistical technology has also made great progress. Statistical methods, statistical classification, statistical models, and statistical data processing have developed very maturely. However, because the models are also very fixed, there is no room for statistical methods to make progress. Also, with the improvement of information technology, there will be more and more martial arts competition data in the database. Statistical methods cannot make good use of the massive data in the database [18]. The application of data mining technology is an inevitable trend. Data mining can comprehensively use association rule discovery, cluster analysis, anomaly analysis, special group analysis, and evolution analysis to mine a large number of data in a certain type of martial arts technology and tactics and form a mining tool for martial arts competition technology and tactics.


## 2.2. Video Analysis Method

The quantity and quality of samples collected in Wushu competitions are also important factors affecting the results of data mining. At present, the analysis of martial arts athletes’ competition techniques and tactics are mainly based on the collection points and strength of martial arts competition information. The standardization of body script language affects the effectiveness of martial arts techniques and tactics analysis [19]. The degree of data integrity and professional support will have an impact on the establishment of the data mining process. In the past, the analysis of techniques and tactics in martial arts competitions was relatively subjective, and the data sources were only intuitive data such as historical victory and defeat rate, age, height, and weight. With the continuous improvement of computer technology, it can be determined to use video to analyze a martial arts athlete’s ability to use technology, a certain position technology, and a certain tactical effect, with the help of video analysis software. In a certain scene of the game, the data of the game video are analyzed and collected through the frame analysis technology.


## 2.3. Data Mining Method

With the development of computer technology, the application of fuzzy mathematics, and the development of matrix analysis theory, data mining came into being. As new technology and deepening of new research, data mining was widely applied in many fields, i.e., military, financial, and insurance industries to solve practical problems. Data mining is used to examine or explore the data using queries. These queries can be fired on the data warehouse. The rise of data mining technology undoubtedly provides powerful help for the analysis of technology and tactics. There are several data mining tools that companies can use to translate raw data into actionable insights. This includes Statistics, Artificial Intelligence, Hidden Markov models, Metalearning, Genetic Algorithm, Machine learning [20–25], and Decision Tree. It obtains the solution from the whole situation and applies it to formulate the combination of technology and tactics to solve the problem of the same hidden essence. Data mining technology is different from traditional statistical methods. The traditional statistics method is a science that studies how to collect, organize, analyze, and interpret digital information in data. Traditional methods use basic algorithms to predict the future. However, it does not provide reliable findings as compared to data mining. The traditional statistics method can be divided into two categories: descriptive statistics and inferential statistics. Descriptive statistics involves organizing, accumulating, and depicting information in data, where inferential statistics involves using sampling data to infer populations. Data mining is a kind of practical application algorithm (mostly machine learning algorithm), which uses data from various fields to solve problems related to various fields. Combining this information technology with the analysis of martial arts athletes’ skills and tactics in the process of competition can provide an effective scientific base for coaches and martial arts athletes’ on-site and off-site training methods [26].Since the 1990s, data mining technology has been developed by leaps and bounds. SQL Server Data Mining offers office 2007 data mining additives for the detection of data patterns and relationships. This is also helpful in improving the analysis. The add-in called Data Mining Client for Excel is used first to plan, compile, analyze, handle, and forecast data. Because of its broad application prospects, data mining technology has been widely used in eight fields, such as finance, health care, marketing, retail, manufacturing, justice, engineering and science, and insurance, and has just emerged in the field of sports information analysis. The technology of data mining is not the main problem that affects the result of data processing. It depends more on data. Data acquisition, rule analysis, and rule representation are the three steps of data mining technology. Using modern video analysis technology, we can obtain a large number of real and noisy data. With these preconditions, for martial arts competitions, we need to collect a large number of data first real and effective data; the more and more specific these data are, the more realistic the results of data mining can show the simulation results, followed by the reuse of data. How to improve the model plays the guiding role of existing data for martial arts athletes in the analysis of competition tactics in daily training, and improving the competitive performance is the important significance of data mining. With the improvement of CPU core number and architecture of a modern home computer, an ordinary home computer can complete simple data modeling and analysis, which lays the hardware foundation for the broad application prospect of data analysis.


## 2.4. Establish Decision Tree

For a competition instance set, many decision trees may be generated. The basic idea of using decision tree is to divide the search space of the target problem into several subsets by means of the classification, clustering and prediction algorithms, and idea of divide and conquer. The training set is used to generate all possible decision trees, and the simplest one is selected from them. Decision tree is widely used in data analysis in competitions. Due to the inherent advantages of tree structure, it is clear at a glance and easy to find features quickly.The random forest model is used to model the data mining problem. The main reasons for using this model include, firstly, compared with other classification and regression models, the random forest has many good characteristics, such as supporting multicategory classification, processing missing values, and supporting mixed types of data. Secondly, random forest is usually known as one of the best classification models and has been widely used in practical problems, such as Kinect object recognition and camera face recognition. In some data mining competitions, such as web search ranking, it has been called one of the methods that must be tried.It should be pointed out that although the random forest model is used in this paper, the method proposed in this paper is also suitable for similar decision tree combination models, such as the AdaBoost model based on decision tree and gradient boosting trees model (as shown in Figure1).Figure 1
Random forest model [16].In the process of building the random forest model, a dataset {x, y} is given, wherex = {x1, xn} is the set of attribute vectors and y = {Y1, Yn} is the set of classification tags. Each attribute vector x⇀j=x1i,…,xMj has m attribute values, in which each attribute value xj∈Dj has a finite range of values Dj. xj can be classified or numeric. The classification label y has a finite range of values Dy. In the case of no ambiguity, x = (x1, XM) denotes x⇀i=x1i,...,xMi. In order to simplify the description, this paper only considers the binary case, that is, y ∈ {0, 1}. The binary case can be extended to the multiclassification model with only a small change. A random forest model is generally composed of K decision trees. Each decision tree K has an output function OK, which means that when the input attribute vector x is given, a classification label is output Therefore, the output of the whole random forest is y∈DY:(1)Hx=∑k=1kϖkokx⇀,where ωk∈R is the weight of each decision tree. In addition, when a classification label c∈DY is given, the probability Hx⇀ expression of the classification label is as follows:(2)py=cx⇀=∑k=1kωkIokx⇀=c∑k=1Kωk,where Iokx⇀=c is the indicator function; when okx⇀=c, its value is 1, otherwise it is 0. Given an input attribute vector x⇀, the output prediction classification label yH of the random forest model Hx⇀ is denoted as(3)y∏=argmaxpy=cx⇀c∈DY.Evaluation function is used to measure the expected cost from the current state to a target state:(4)gx⇀=∑i=1mωx⇀i−1,x⇀i=∑i=1mπai.Given a random forest modelHx⇀, for any vector x⇀, the heuristic function hrf is defined as(5)hrfx=0Rcz−px⇀.


## 3. Experimental Results and Discussion

In this section, we will discuss the experimental result and discussion from model optimization algorithm and statistical descriptions.


### 3.1. Model Optimization Algorithm

Based on obtaining the data of martial arts athletes’ competition skills and tactics in different years of their career, this paper calculates the correlation between the display of skills and tactics and the data of martial arts athletes in different years and deeply excavates the growth track of martial arts athletes, according to the specific characteristics and functions of the interrelationship and interaction mode among the elements of the competitive ability of excellent martial arts athletes in this position. In the training, we strengthen the training of strong points and weak points and combine certain technical and tactical arrangements to make up for and compensate for the lack of competitive ability. The schematic diagram of the decision tree model is shown in Figure2.Figure 2
The schematic diagram of decision tree model.From Figure2, the decision tree algorithm is one of the main mining technologies to deal with dataset classification and prediction. A decision tree algorithm is to learn from the set of examples and induce logical classification rules from a large number of unordered information. The algorithm uses top-down recursion to compare the attribute values among the internal nodes of the tree, judge the direction of the branch, and get the conclusion at the leaf nodes of the spanning tree.Taking a martial arts athlete as an example, this paper makes statistics on the technical statistics of participation in previous world competitions, calculates the correlation between the data and the improvement of martial arts athletes technical level, and deeply excavates the potential data, so as to explore the knowledge that coaches and martial arts athletes are interested in and provide guidance for the subsequent growth and development of martial arts athletes.


### 3.2. Descriptive Statistics

The mean standard deviations are shown in Table1. Important correlations were established between goal orientation (task and ego) and mental capacity (Table 2). There was a mild, but substantial correlation of task and ego (r = 0, 36, p<0.05), task and confidence (r = 0, 45, p<0.05), task and positive energy management, task control and attitude control (r = 0, 36, p<0.05), and ego and negative energy control (r = 0, 36, p<0.05).Table 1
Mean and SD of descriptive statistics.
VariableRangeMeanSDαCompetitive experiences1–82.661.6Age19–2721.431.66Goal orientationsEgo1.67–5.003.150.70.88Task2.57–5.003.860.510.84Mental toughnessMotivational level13–2920.533.690.79Visual and imagery control13–2919.633.390.79Negative energy control14–2318.882.240.71Self confidence15–2820.383.180.79Attention control14–2218.771.850.58Positive energy control16–2620.852.640.65Attitude control15–2920.272.770.73Overall116–181139.314.22—Table 2
Intercorrelations between goal orientations and mental toughness.
123456789Task1Ego0.361Self-confidence0.10.051Negative energy control0.250.40∗0.36∗1Attention control0.120.280.03∗∗0.311Visualization and imagery0.290.280.58∗∗0.270.191Motivational level0.45∗0.270.48∗∗0.41∗∗0.160.63∗∗1Positive energy control0.54∗0.050.49∗∗0.20.090.56∗∗0.62∗∗1Attitude control0.360.070.67∗∗0.46∗∗−0.090.47∗∗0.60∗∗0.56∗∗1


## 3.1. Model Optimization Algorithm

Based on obtaining the data of martial arts athletes’ competition skills and tactics in different years of their career, this paper calculates the correlation between the display of skills and tactics and the data of martial arts athletes in different years and deeply excavates the growth track of martial arts athletes, according to the specific characteristics and functions of the interrelationship and interaction mode among the elements of the competitive ability of excellent martial arts athletes in this position. In the training, we strengthen the training of strong points and weak points and combine certain technical and tactical arrangements to make up for and compensate for the lack of competitive ability. The schematic diagram of the decision tree model is shown in Figure2.Figure 2
The schematic diagram of decision tree model.From Figure2, the decision tree algorithm is one of the main mining technologies to deal with dataset classification and prediction. A decision tree algorithm is to learn from the set of examples and induce logical classification rules from a large number of unordered information. The algorithm uses top-down recursion to compare the attribute values among the internal nodes of the tree, judge the direction of the branch, and get the conclusion at the leaf nodes of the spanning tree.Taking a martial arts athlete as an example, this paper makes statistics on the technical statistics of participation in previous world competitions, calculates the correlation between the data and the improvement of martial arts athletes technical level, and deeply excavates the potential data, so as to explore the knowledge that coaches and martial arts athletes are interested in and provide guidance for the subsequent growth and development of martial arts athletes.


## 3.2. Descriptive Statistics

The mean standard deviations are shown in Table1. Important correlations were established between goal orientation (task and ego) and mental capacity (Table 2). There was a mild, but substantial correlation of task and ego (r = 0, 36, p<0.05), task and confidence (r = 0, 45, p<0.05), task and positive energy management, task control and attitude control (r = 0, 36, p<0.05), and ego and negative energy control (r = 0, 36, p<0.05).Table 1
Mean and SD of descriptive statistics.
VariableRangeMeanSDαCompetitive experiences1–82.661.6Age19–2721.431.66Goal orientationsEgo1.67–5.003.150.70.88Task2.57–5.003.860.510.84Mental toughnessMotivational level13–2920.533.690.79Visual and imagery control13–2919.633.390.79Negative energy control14–2318.882.240.71Self confidence15–2820.383.180.79Attention control14–2218.771.850.58Positive energy control16–2620.852.640.65Attitude control15–2920.272.770.73Overall116–181139.314.22—Table 2
Intercorrelations between goal orientations and mental toughness.
123456789Task1Ego0.361Self-confidence0.10.051Negative energy control0.250.40∗0.36∗1Attention control0.120.280.03∗∗0.311Visualization and imagery0.290.280.58∗∗0.270.191Motivational level0.45∗0.270.48∗∗0.41∗∗0.160.63∗∗1Positive energy control0.54∗0.050.49∗∗0.20.090.56∗∗0.62∗∗1Attitude control0.360.070.67∗∗0.46∗∗−0.090.47∗∗0.60∗∗0.56∗∗1


## 4. Conclusions

Wushu is not only an important member of Chinese traditional sports but also an important symbol of Chinese traditional culture. With the acceleration of the internationalization process of competitive Wushu, Wushu is gradually returning to the essence of fighting, which makes the influence of Chinese Traditional Wushu in the world gradually reduce. In this research, the higher task ego goal profile athletes scored significantly higher for negative energy control than for positive energy control. Successful effects of competition success have not been a function of the target profile. The traditional Wushu improves the performance of Wushu Athletes in the competition and innovates tactics which is of great significance to improve the social influence of Wushu, which is beneficial to our national development. The healthy development of traditional sports is also an effective inheritance of national personality culture. In the future, we are planning to incorporate and compare as well as to add tools and the effects of more in-depth learning frameworks such as the RNN, CNN, and BNN.



---

*Source: 5574152-2021-04-05.xml*