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

Hindawi
Journal of Healthcare Engineering
Volume 2021, Article ID 5574152, 6 pages
https://doi.org/10.1155/2021/5574152

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

 Lingrong Tao
 Physical Education Institute, Jimei University, Xiamen 361021, China

 Correspondence should be addressed to Lingrong Tao; ydsu2950@jmu.edu.cn

 Received 24 February 2021; Revised 5 March 2021; Accepted 19 March 2021; Published 5 April 2021

 Academic Editor: Fazlullah Khan

 Copyright © 2021 Lingrong Tao. This is an open access article distributed under the Creative Commons Attribution License,
 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 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.

1. Introduction Traditional martial arts involves many kinds and schools,
 and most of them hold the views of legitimate and orthodox,
Wushu has a long broad tradition and profound, but at the they have their secrets, and they seldom exchange the theory
same time, it is also complicated. The traditional cultural and art content of their school system in-depth. From the
content it carries and the national spirit it reflects are rich, appearance, the traditional martial arts have been developed,
profound, and intrinsic. Compared with domestic modern showing a variety of development momentum. However, in
martial arts, the traditional martial arts emphasize the fact, due to the influence of many kinds and schools of
characteristics of attack, which is the soul of the traditional traditional martial arts and the different contents of tech-
martial arts. It will lose its base if it is not attacked [1]. For a niques and tactics taught, it is difficult to form a complete set
long time, the traditional martial arts are inherited by the of teaching materials and training system to meet the
major factions which have their own style and character- public’s self-defense fitness and understand the traditional
istics, whereas there are many problems in the system and Chinese martial arts culture, which makes it difficult for
practicability of modern martial arts especially in the martial arts practitioners, including overseas people, to
adapted martial arts routine [2]. Due to the lack of technical experience [7]. The idea of western sports is to challenge the
support and actual combat confrontation, the content of nature, the opponent, and the self, that is, to establish
martial arts routine is reduced or exaggerated, and also, in yourself in the pursuit of the good with the right, while the
the competition field, the lack of application in the actual traditional Chinese martial arts in the fight are to advocate
attack and defense has seriously affected the further de- nature, pay attention to balance, and strengthen yourself and
velopment of martial arts. The tactics of martial arts athletes to overcome oneself in the pursuit of the right with the
abandon the routine of martial arts that differ significantly opposite. Therefore, with the full development of infor-
from either the core of the martial arts and re-examine and mation technology today the development of traditional
learn about the growth and pattern of martial arts [3–6]. martial arts in China and its overseas promotion can be

2 Journal of Healthcare Engineering

achieved. The use of data mining technology has been widely methods to make progress. Also, with the improvement of
used in all kinds of fields. information technology, there will be more and more
 Data mining is widely used in the field of artificial in- martial arts competition data in the database. Statistical
telligence (AI) [8–12], marketing, government intelligence methods cannot make good use of the massive data in the
(GI), services, and advertising. There are some other in- database [18]. The application of data mining technology is
dustries such as crime agencies, retail healthcare, e-com- an inevitable trend. Data mining can comprehensively use
merce, telecom, biological data analysis, and information association rule discovery, cluster analysis, anomaly analysis,
retrieval such as communication systems. In this research special group analysis, and evolution analysis to mine a large
work, we innovative use data mining and explore the ap- number of data in a certain type of martial arts technology
plication of data mining in the analysis of martial arts and tactics and form a mining tool for martial arts com-
athlete’s competition skills and tactics. Our research work is petition technology and tactics.
an important topic as it focused on the contemporary de-
velopment of Chinese traditional martial arts [2, 7, 13, 14].
 2.2. Video Analysis Method. The quantity and quality of
The primary contributions of this study were to investigate
 samples collected in Wushu competitions are also important
the characteristics of Chinese traditional martial arts and
 factors affecting the results of data mining. At present, the
also design an intelligent learning model based on a data
 analysis of martial arts athletes’ competition techniques and
mining algorithm to monitor the process of martial arts
 tactics are mainly based on the collection points and strength
athlete’s competition skills and tactics to analyze the process
 of martial arts competition information. The standardization
of quality.
 of body script language affects the effectiveness of martial
 The rest of this paper is organized as follows. Section 2
 arts techniques and tactics analysis [19]. The degree of data
discusses the different methods in martial arts competitions
 integrity and professional support will have an impact on the
that include traditional statistical method, video analysis
 establishment of the data mining process. In the past, the
method, and data mining method and establish decision
 analysis of techniques and tactics in martial arts competi-
tree, followed by the simulation experimental results in
 tions was relatively subjective, and the data sources were
Section 3, and Section 4 concludes the paper with a summary
 only intuitive data such as historical victory and defeat rate,
and future research directions.
 age, height, and weight. With the continuous improvement
 of computer technology, it can be determined to use video to
2. Methods analyze a martial arts athlete’s ability to use technology, a
 certain position technology, and a certain tactical effect, with
In martial arts competitions, there are specific actions of the help of video analysis software. In a certain scene of the
kicking, beating, holding, leaning, falling, hitting, and game, the data of the game video are analyzed and collected
stabbing. In the historical competition records, there are through the frame analysis technology.
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 2.3. Data Mining Method. With the development of com-
statistical data are obtained, through computer software puter technology, the application of fuzzy mathematics, and
recording or video analysis, the average value and limit value the development of matrix analysis theory, data mining
are calculated, and the frequent item sets and some asso- came into being. As new technology and deepening of new
ciation rules are generated [15]. research, data mining was widely applied in many fields, i.e.,
 In the last five years, researchers have carried out more military, financial, and insurance industries to solve practical
interdisciplinary research on the combination of physical problems. Data mining is used to examine or explore the
education, sports training, and data mining technology. For data using queries. These queries can be fired on the data
example, the application of data mining algorithms in warehouse. The rise of data mining technology undoubtedly
volleyball, table tennis, and cricket. The correlation degree provides powerful help for the analysis of technology and
between variables is determined by calculating the corre- tactics. There are several data mining tools that companies
lation coefficient between variables, and frequent item set can use to translate raw data into actionable insights. This
pattern knowledge is found from a given dataset. Data includes Statistics, Artificial Intelligence, Hidden Markov
mining is a technology to find the rules from a large number models, Metalearning, Genetic Algorithm, Machine learning
of data by analyzing each data. It mainly includes three steps: [20–25], and Decision Tree. It obtains the solution from the
data preparation, rule finding, and rule representation. After whole situation and applies it to formulate the combination
obtaining the competition data, how to do data mining well of technology and tactics to solve the problem of the same
depends on whether we can find the right rules [16, 17]. hidden essence. Data mining technology is different from
 traditional statistical methods. The traditional statistics
 method is a science that studies how to collect, organize,
2.1. Traditional Statistical Method. In recent years, statistical analyze, and interpret digital information in data. Tradi-
technology has also made great progress. Statistical methods, tional methods use basic algorithms to predict the future.
statistical classification, statistical models, and statistical data However, it does not provide reliable findings as compared
processing have developed very maturely. However, because to data mining. The traditional statistics method can be
the models are also very fixed, there is no room for statistical divided into two categories: descriptive statistics and

Journal of Healthcare Engineering 3

inferential statistics. Descriptive statistics involves orga- processing missing values, and supporting mixed types of
nizing, accumulating, and depicting information in data, data. Secondly, random forest is usually known as one of the
where inferential statistics involves using sampling data to best classification models and has been widely used in
infer populations. Data mining is a kind of practical ap- practical problems, such as Kinect object recognition and
plication algorithm (mostly machine learning algorithm), camera face recognition. In some data mining competitions,
which uses data from various fields to solve problems related such as web search ranking, it has been called one of the
to various fields. Combining this information technology methods that must be tried.
with the analysis of martial arts athletes’ skills and tactics in It should be pointed out that although the random
the process of competition can provide an effective scientific forest model is used in this paper, the method proposed in
base for coaches and martial arts athletes’ on-site and off-site this paper is also suitable for similar decision tree com-
training methods [26]. bination models, such as the AdaBoost model based on
 Since the 1990s, data mining technology has been decision tree and gradient boosting trees model (as shown
developed by leaps and bounds. SQL Server Data Mining in Figure 1).
offers office 2007 data mining additives for the detection of In the process of building the random forest model, a
data patterns and relationships. This is also helpful in dataset {x, y} is given, where x � {x1, xn} is the set of attribute
improving the analysis. The add-in called Data Mining vectors and y � {Y1, Yn} is the set of classification tags. Each
 ⇀j
Client for Excel is used first to plan, compile, analyze, attribute vector x � (xi1 , . . . , xjM ) has m attribute values, in
handle, and forecast data. Because of its broad application which each attribute value xj ∈ Dj has a finite range of
prospects, data mining technology has been widely used in values Dj . xj can be classified or numeric. The classification
eight fields, such as finance, health care, marketing, retail, label y has a finite range of values Dy. In the case of no
manufacturing, justice, engineering and science, and in- ⇀i
 ambiguity, x � (x1, XM) denotes x � (xi1 , ..., xiM ). In order
surance, and has just emerged in the field of sports in- to simplify the description, this paper only considers the
formation analysis. The technology of data mining is not binary case, that is, y ∈ {0, 1}. The binary case can be extended
the main problem that affects the result of data processing. to the multiclassification model with only a small change. A
It depends more on data. Data acquisition, rule analysis, random forest model is generally composed of K decision
and rule representation are the three steps of data mining trees. Each decision tree K has an output function OK, which
technology. Using modern video analysis technology, we means that when the input attribute vector x is given, a
can obtain a large number of real and noisy data. With classification label is output Therefore, the output of the
these preconditions, for martial arts competitions, we need whole random forest is y ∈ DY :
to collect a large number of data first real and effective k
 ⇀
data; the more and more specific these data are, the more H(x) � ϖk ok (x), (1)
realistic the results of data mining can show the simulation k�1
results, followed by the reuse of data. How to improve the
model plays the guiding role of existing data for martial where ωk ∈ R is the weight of each decision tree. In addition,
arts athletes in the analysis of competition tactics in daily when a classification label c ∈ DY is given, the probability
 ⇀
training, and improving the competitive performance is H(x) expression of the classification label is as follows:
the important significance of data mining. With the im- ⇀
 c kk�1 ωk I ok (x) � c 
provement of CPU core number and architecture of a p y � ⇀ � , (2)
modern home computer, an ordinary home computer can x K k�1 ωk
complete simple data modeling and analysis, which lays ⇀
 where I(ok (x) � c) is the indicator function; when
the hardware foundation for the broad application pros- ⇀
 ok (x) � c, its value is 1, otherwise it is 0. Given an input
pect of data analysis. ⇀
 attribute vector x, the output prediction classification label
 ⇀
 yH of the random forest model H(x) is denoted as
2.4. Establish Decision Tree. For a competition instance set, c
 y � arg max p y � ⇀ . (3)
many decision trees may be generated. The basic idea of x
using decision tree is to divide the search space of the target c∈DY
problem into several subsets by means of the classification,
clustering and prediction algorithms, and idea of divide and Evaluation function is used to measure the expected cost
conquer. The training set is used to generate all possible from the current state to a target state:
decision trees, and the simplest one is selected from them. m m
 ⇀ ⇀ ⇀
Decision tree is widely used in data analysis in competitions. g(x) � ω xi−1 , xi � π ai . (4)
 i�1 i�1
Due to the inherent advantages of tree structure, it is clear at
a glance and easy to find features quickly. ⇀ ⇀
 Given a random forest model H(x), for any vector x, the
 The random forest model is used to model the data heuristic function hrf is defined as
mining problem. The main reasons for using this model
include, firstly, compared with other classification and re- ⎨0
 ⎧
gression models, the random forest has many good char- hrf (x) � ⎩ ⇀
 . (5)
acteristics, such as supporting multicategory classification, Rc (z − p(x))

4 Journal of Healthcare Engineering

 Regressor tree 1

 Prediction
 Regressor tree 2 Average

 Regressor tree 3

 Figure 1: Random forest model [16].

 Feature (f) Feature (f)

 ∑

 Figure 2: The schematic diagram of decision tree model.

3. Experimental Results and Discussion and prediction. A decision tree algorithm is to learn from the
 set of examples and induce logical classification rules from a
In this section, we will discuss the experimental result and large number of unordered information. The algorithm uses
discussion from model optimization algorithm and statis- top-down recursion to compare the attribute values among
tical descriptions. the internal nodes of the tree, judge the direction of the
 branch, and get the conclusion at the leaf nodes of the
 spanning tree.
3.1. Model Optimization Algorithm. Based on obtaining the Taking a martial arts athlete as an example, this paper
data of martial arts athletes’ competition skills and tactics in makes statistics on the technical statistics of participation in
different years of their career, this paper calculates the previous world competitions, calculates the correlation
correlation between the display of skills and tactics and the between the data and the improvement of martial arts
data of martial arts athletes in different years and deeply athletes technical level, and deeply excavates the potential
excavates the growth track of martial arts athletes, according data, so as to explore the knowledge that coaches and martial
to the specific characteristics and functions of the interre- arts athletes are interested in and provide guidance for the
lationship and interaction mode among the elements of the subsequent growth and development of martial arts athletes.
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 3.2. Descriptive Statistics. The mean standard deviations are
tactical arrangements to make up for and compensate for the shown in Table 1. Important correlations were established
lack of competitive ability. The schematic diagram of the between goal orientation (task and ego) and mental capacity
decision tree model is shown in Figure 2. (Table 2). There was a mild, but substantial correlation of
 From Figure 2, the decision tree algorithm is one of the task and ego (r � 0, 36, p < 0.05), task and confidence (r � 0,
main mining technologies to deal with dataset classification 45, p < 0.05), task and positive energy management, task

Journal of Healthcare Engineering 5

 Table 1: Mean and SD of descriptive statistics.
Variable Range Mean SD α
Competitive experiences 1–8 2.66 1.6
Age 19–27 21.43 1.66
Goal orientations
Ego 1.67–5.00 3.15 0.7 0.88
Task 2.57–5.00 3.86 0.51 0.84
Mental toughness
Motivational level 13–29 20.53 3.69 0.79
Visual and imagery control 13–29 19.63 3.39 0.79
Negative energy control 14–23 18.88 2.24 0.71
Self confidence 15–28 20.38 3.18 0.79
Attention control 14–22 18.77 1.85 0.58
Positive energy control 16–26 20.85 2.64 0.65
Attitude control 15–29 20.27 2.77 0.73
Overall 116–181 139.3 14.22 —

 Table 2: Intercorrelations between goal orientations and mental toughness.
 1 2 3 4 5 6 7 8 9
Task 1
Ego 0.36 1
Self-confidence 0.1 0.05 1
Negative energy control 0.25 0.40∗ 0.36∗ 1
Attention control 0.12 0.28 0.03∗∗ 0.31 1
Visualization and imagery 0.29 0.28 0.58∗∗ 0.27 0.19 1
Motivational level 0.45∗ 0.27 0.48∗∗ 0.41∗∗ 0.16 0.63∗∗ 1
Positive energy control 0.54∗ 0.05 0.49∗∗ 0.2 0.09 0.56∗∗ 0.62∗∗ 1
Attitude control 0.36 0.07 0.67∗∗ 0.46∗∗ −0.09 0.47∗∗ 0.60∗∗ 0.56∗∗ 1

control and attitude control (r � 0, 36, p < 0.05), and ego and Conflicts of Interest
negative energy control (r � 0, 36, p < 0.05).
 The authors declare that they have no conflicts of interest.
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