Design and Application of Sports Athlete Training Progress

Management System based on Big Data Analysis

Ruliang Wang

Shandong Institute of Commerce and Technology, Jinan, Shandong, China

Keywords: Big Data Analysis, Athlete Training, Management System.

Abstract: Sports athletes in order to achieve hard-won results, the daily training project content is numerous, the subject

is multifarious. Among them, there are both basic physical training, project skills training, and special training

in psychological quality. In the era of big data, big data analysis technology helps athletes to conduct training

analysis, record training results in time and convey feedback to athletes and coaches, improving the

management level of athletes' training progress, so as to realize the systematization, informatization,

intelligence and high efficiency of sports athletes' training management. Through the integration of big data

analysis technology and Internet technology, the construction of athletes' training progress management

system can provide athletes with more scientific training management decisions, so as to help them achieve

better training results.

1 INTRODUCTION

In the current era, big data has been widely used in

various industries and fields of our daily work and

life, and it has gradually become the backbone to

promote the development of the whole society and

economy. Big data analysis technology derived from

big data has also become a powerful data support tool

for people's fine management and decision making.

In the practical application process, big data analysis

technology, computer technology and Internet

application platform technology are used as tools and

means to process, store and transfer relevant

information, so as to realize prediction, control,

organization and decision making in this field (Sun

2019). In this process, the application scope of big

data analysis technology is constantly expanded and

developed, and the application mode of "Big data +"

is gradually generated, thus setting off a new wave of

development in various industries and fields in the

whole society.

At present, the application of big data analysis

technology is urgently needed to improve the training

of sports athletes in sports management. Traditional

sports athlete training management is mostly based on

manual statistical analysis, and the tools used are only

spreadsheets, with single data collection direction and

linear data analysis method. Therefore, it directly

affects the management of sports athlete training and

the evaluation of training effect. In this paper, the

author believes that the training progress

management system of sports athletes should be

reasonably constructed, so that the innovative

application of big data analysis technology can meet

the complex training management needs of current

sports athletes, and effectively improve the

information integration and information timeliness of

sports training management mode. Through the sports

athletes training progress management system can

help athletes and coaches both grasp the training

information data, so as to formulate and modify the

training plan of athletes on the basis of scientific data

information, improve the training effect of athletes.

2 OVERVIEW OF SPORTS

ATHLETE TRAINING

SCHEDULE MANAGEMENT

2.1 Meaning of Sports Training

Sports training is a planned sports activity organized

by sports athletes under the guidance of coaches in

order to improve their sports competitive ability and

sports achievements. It is the only way to improve the

sports level of sports athletes, and also an important

part of competitive sports. Sports training is mainly

232

Wang, R.

Design and Application of Sports Athlete Training Progress Management System based on Big Data Analysis.

DOI: 10.5220/0011304600003437

In Proceedings of the 1st International Conference on Public Management and Big Data Analysis (PMBDA 2021), pages 232-237

ISBN: 978-989-758-589-0

applicable to professional sports athletes, whose

purpose is to continuously explore and improve the

sports ability of professional sports athletes.

Therefore, in the course of sports training, there are

many training contents and complex subjects, and

sports training also has the characteristics of high

intensity and long time. In order to constantly change

the basic physiological state of sports athletes,

promote their higher limit forward. In addition, the

professional sports training of sports athletes also

needs professional and scientific training methods as

guidance, on the one hand, it can quickly improve the

performance of sports athletes, on the other hand, it

can fundamentally protect the health of sports

athletes, prolong their career. The improvement of

sports training effect cannot be separated from the

cooperation of coaches and athletes, and strict

progress management is also necessary to ensure the

training effect of athletes.

2.2 Sports Training Schedule

Management

The management of sports training schedule refers to

a comprehensive process of scientific and reasonable

use of various methods to improve training

effectiveness (Wang 2020). Sports training with the

athlete's career from childhood to youth to adult,

every athlete's physical, emotional, psychological

bearing capacity in different training level, therefore,

in different stages of sports training plan formulation

and implementation process must consider the

comprehensive growth state of the athletes. In

particular, the coach should be familiar with the

progress of each athlete's sports training, timely

adjust the training plan according to the emergence of

uncertain factors such as athlete's emotions and

injuries, and adopt appropriate management methods

to improve the performance of athletes' sports

training is an important subject of his research and the

core content of coaching work.

2.3 Deficiencies of Traditional Sports

Training Schedule Management

At present, in sports training management, coaches

mostly adopt the traditional management method, that

is, all athletes adopt the same "militarized"

management, and it is impossible to develop

individual training plans for each athlete in line with

their physical quality, technical and tactical

requirements and psychological development level.

In the aspect of training data recording, there are

some problems such as single data collection means,

few data information recording and mechanical data

information recording. Therefore, in the management

of sports training progress, more rely on the

experience of coaches rather than scientific analysis

of data results, which is easy to lead to deviations in

the formulation of athletes' sports training plans, and

even lead to serious consequences such as injuries of

athletes due to the wrong decisions of coaches.

To sum up, based on analysis of large data sports

athletes training schedule management system can

according to the need of sports training, through the

technology of data in the process of athletes training

a variety of data collection, cleaning, storage, analysis

and mining, the formation of intelligent sports

training model, help coaches and athletes to deeper

and more intuitive to see the effect of sports training,

At the same time, it can also supervise athletes'

physical functions accordingly, which is convenient

for coaches to adjust training plans according to

training effects in time, control training intensity, and

realize professional and scientific management of

sports training schedule.

3 BIG DATA ANALYSIS

TECHNOLOGY AND

TRAINING PROGRESS

MANAGEMENT SYSTEM

3.1 Big Data Technology

In the current era of big data, data development

promotes the progress of the society as a whole, and

massive data information brings new opportunities

and challenges to various industries and fields in the

whole society. Big data technology is an emerging

technology in data capture, data storage, data

analysis, data application and other aspects far

beyond the traditional data management tools and

manual data analysis capabilities. Big data has the

obvious characteristics of large data scale, fast flow

speed, multiple data types and low value density. So

we need new data processing models to realize the

true value of big data. In the face of a large number of

inaccurate, unstructured and incorrectly utilized data

information, big data technology can obtain results

through a series of analysis and processing, clarify the

correlation between data, and be applied to practical

work production and decision-making analysis and

making, providing accurate and scientific data

information support. And gradually realize the digital,

information and intelligent development of the

industry or field.

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233

There are five steps in the whole life cycle of big

data from generation to practical application, namely

big data collection, big data pre-processing, big data

storage and management, big data analysis and

mining, big data presentation and application (Yang

2017). This process also corresponds to the five core

technologies covered by big data technology. In

addition, the application process of big data

processing is also an important basis for the

construction of big data technology system, as shown

in Figure 1, which is the big data technology

architecture diagram.

Figure 1: Big data technology architecture diagram.

3.2 Big Data Analysis Technology

Big data analysis technology is one of the cores of big

data technology. It is the whole process of extracting,

refining and analyzing massive low-value density

data from the aspects of big data visualization

analysis, data mining and predictive analysis. In

short, big data analysis technology is data mining

technology. The common technical methods involved

in data mining include prediction model discovery,

sequence model discovery, dependency model

discovery and so on. The data object can also be

divided into relational database, object-oriented

database, text data source, multimedia database and

so on. Big data analysis and data mining is the main

process: set the goal of data analysis and mining, the

corresponding data extracted from the target database

objects, and then use data mining methods for data

analysis, the final will be the result of the analysis of

data mining in the form of visual intuitive display,

also can support to make decisions on the predictive

data results.

3.3 Sports Training Progress

Management System

Sports training progress management system is an

innovative practice of integrating big data technology

and Internet application platform technology into

sports training progress management. Firstly, all the

data of athletes in the process of sports training are

collected and classified and stored by wearable

devices and high-speed cameras, such as heart rate,

blood pressure, body temperature, energy

consumption and other athletes' physical data. There

are such as speed, strength, distance, habitual

movements, jumping athlete technical data. In

addition, the data of athletes' mental states such as

tension, depression, excitement and so on will be

classified and retained. Secondly, through the large

data analysis technology will be more integrated

processing data information, the data results build

intelligent model for athletes personality and

preferences, and make the athlete daily training plan

and schedule according to the can depend on, to help

athletes and coaches comprehensive, intuitive

understanding of real effect of exercise training,

improve the effect of exercise training. Thirdly, under

the technology of Internet application platform, the

sports training progress management system is built

to facilitate athletes and coaches to implement the

overall, scientific and intelligent management and

monitoring of the sports training system engineering.

4 THE DESIGN AND

IMPLEMENTATION OF

SPORTS TRAINING PROGRESS

MANAGEMENT SYSTEM

4.1 Overall Framework

The sports training progress management system

adopts B/S architecture in the system hardware

deployment, in which the system background server

includes Web server, database service and business

logic server. Under B/S structure, sports training

schedule management system control can be divided

into the front end the application layer, business layer

and data access layer, business logic as the user to the

server's instruction in between the user and the

database server deployment and response, so as to

realize the human-computer interaction between

users and system, and user calls to the data. Figure 2

shows the architecture diagram of sports training

progress management system.

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234

Figure 2: Architecture diagram of sports training progress management system.

In the B/S architecture, users can log in to the

system through a browser that accesses the Internet

on a desktop client and use various system functions.

And the overall business expansion of the system is

simple and convenient, only by adding the function

page can be completed. In addition, it has the

advantages of low development and maintenance cost

and strong data sharing. The normal operation of

sports training progress management system is

inseparable from the support of software system and

hardware equipment. Table 1 shows the specific

system hardware and software configurations.

Table 1: Hardware and software environment configuration of sports training progress management system.

Server

Configuration

The Web server Apache

Database server

Oracle database management

software

Hardware Server CPU 3.00GHz

Hardware Server Memory 16GB

Hardware Server Hard disk 2TB

System development operating

platform

Windows Sever 2019

Client

Configuration

Software operating system Windows 10.0

Recommended Browser IE/Chrome

In addition, the main application of big data

technology in sports training progress management

system is to collect, store, analyze and mine various

data of athletes in the process of sports training as

well as the application of data results. The big data

analysis system adopts Hadoop distributed system

architecture of cloud server to deal with the parallel

operation and resource allocation of multiple data in

the training process of athletes. Meanwhile, it

provides outgoing API and Web interface to facilitate

the collection of athletes' training data and the

invocation of sports training progress management

system. The collection of athletes' training data

mainly relies on intelligent wearable devices and

high-speed cameras. Intelligent wearable devices can

directly obtain the physical function data of athletes,

while high-speed cameras can extract and analyze the

athletes' habitual movements, preferred routes,

technical skills and other small data information that

cannot be distinguished by naked eyes through the

recording of athletes' training process frame by frame.

The two kinds of data cooperate with each other to

improve the data record of sports training, realize the

comprehensive perception of athletes and training

process, and provide necessary data support for the

management of sports training progress.

4.2 Specific Functions

4.2.1 System Management

Sports training progress management system

supports different users and different roles for login,

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235

role is divided into athletes, coaches and

administrators. Users can log in to the system to use

system functions after registering the account and

password with unique identification. Different roles

have different functions displayed after login, and

different roles have different permissions. The

administrator can assign different roles and adjust

permissions for different users.

4.2.2 Coach End

In the coach end of sports training progress

management system, the functional modules

supported by the system include training progress

management and athlete management. Among them,

the training progress management function module

also includes basic training management, special

training management, training objectives, training

progress adjustment, training data recording, training

results evaluation and other contents. In the athlete

management module, the coach will check the

specific training progress of different athletes and

various analysis data in the training process, so as to

adjust the training progress and change the training

intensity, training subjects and training methods

accordingly. To achieve personalized training

program development, diagnosis, decision making

and evaluation for each athlete.

4.2.3 Athlete End

At the athlete end of sports training progress

management system, the functional modules

supported by the system include training plan

management, training progress management and

personal information management. Among them,

training plan management includes short-term plan,

long-term plan, special training plan and training plan

modification. In the training progress management,

athletes can view the training arrangements, training

plans and corresponding coach information of

different training subjects, as well as see all the data

visualization results and intelligent data model of

personality and preference in their training process. It

is convenient for athletes to find their own

shortcomings and strengths in the training process,

and also provides decision-making data support for

timely adjustment of training schedule. In addition,

under the functional module of training progress

management, it also supports the evaluation feedback

of coaches and the statistics of training items reaching

the standard, so as to improve the refinement of the

control of personalized training progress of different

athletes and the effectiveness of training effects. In

personal information management, athletes can view

and update their personal information at any time, see

the change of their own body function data, but also

can obtain the corresponding healthy diet information

and life tips information, so as to form a systematic

management of sports training, comprehensively

improve the effect of athletes sports.

4.3 Technical Support

According to the design requirements and functional

modules of the sports training progress management

system, the system needs to take into account the

function switch between different roles of athletes

and coaches, as well as the transfer and call of Internet

application technology platform and big data analysis

and mining data information. Therefore, in the

development platform and system function module

selection, it is necessary to pay attention to the

compatibility between each module and function

practicality. Java language and MCV mode are used

to build the application layer in the front part of the

system, and Oracle 11.2 is used to select the database

server. In the big data analysis technology

infrastructure module, Java, Python, Shell three

languages are selected to complete data analysis and

mining function interpretation and script compilation.

HDFS technology is used for task management of big

data analysis and mining. Apache, Hadoop and

YARN technology are used for parallel computing to

coordinate external Web server and big data analysis

module. Hbase distributed database and Hive data

warehouse technology are used for data storage of

cloud server for big data analysis.

In addition, data results generated by big data

analysis and mining are realized by data visualization

analysis, and displayed in the front-end application

layer page with D3.js. D3.js can display data in

HTML, SVG, and CSS. Like jQuery, D3 directly

manipulates the DOM (Liu 2020).

5 CONCLUSIONS

The construction of sports athlete training progress

management system based on big data analysis is a

pioneering practice in the integration of big data

analysis technology and Internet application platform

technology innovation in athlete training

management. Relying on big data analysis and

mining technology, the data information of athletes'

training progress will be fully perceived, which is

convenient for coaches and athletes to find their

strengths and weaknesses in the training process, and

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timely improve the training plan to achieve fine

control and personalized needs of sports training

progress. It improves the effectiveness of sports

training and provides a new idea for the digitalization,

information, intelligence and scientific development

of sports training for athletes.

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