Tools and Systems for Sports Data Analysis

2010 ◽  
pp. 45-53 ◽  
Author(s):  
Robert P. Schumaker ◽  
Osama K. Solieman ◽  
Hsinchun Chen
Keyword(s):  
Data Analysis ◽  
Sports Data

Dark data analysis using Intuitionistic Plithogenic graphs

2021 ◽  
pp. 80-100
Author(s):  
Prem Kumar Singh ◽  
Keyword(s):  
Data Analysis ◽  
Decision Process ◽  
Mathematical Representation ◽  
Data Sets ◽  
Current Time ◽  
Graphical Visualization ◽  
Precise Analysis ◽  
Sports Data

The precise analysis of uncertainty in given data sets and its mathematical representation is considered as one of the major issues at current time. The problem become more complex when the data sets contains several mutliattributes and its non-opposite sides. One of the suitable examples is cricket or sports data sets which create conflict among the experts in case of multi-decision process. The problem arises when the expert want to chategorize the performance of players based on its acceptation and rejection regions considering the contradiction. To deal with these types of data which contains human intuition in true and false regions intuitionistic Plithogenic set and its graphical visualization is introduced in this paper with an illustrative examples.

scholarly journals PMData: A sports logging dataset

2020 ◽  
Author(s):  
Vajira Thambawita ◽  
Steven Hicks ◽  
Hanna Borgli ◽  
Svein Arne Pettersen ◽  
Håkon Kvale Stensland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Smartphone Application ◽  
Sleep Patterns ◽  
Sports Activity ◽  
Activity Data ◽  
Machine Learning Applications ◽  
Sports Data

In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. \datasetname combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.

scholarly journals Data Journalism as an innovation in social communication: The case in sports industry

2021 ◽  
Vol 6 (1) ◽  
pp. 42-55
Author(s):  
Zeliha Işıl Vural ◽  
◽  
Pere Masip
Keyword(s):  
Content Analysis ◽  
Data Analysis ◽  
Quantitative Analysis ◽  
Social Communication ◽  
Large Datasets ◽  
Sports Industry ◽  
Data Journalism ◽  
Similarities And Differences ◽  
Technical Features ◽  
Sports Data

Data analysis has always been an integral part of journalism but combining it with technology was a novelty for newspapers. Journalism’s combination with technology was an innovation because of processing, interpretation, and visualization of large datasets in a journalistic content. In recent years, newspapers have started to adapt data journalism and integrated it to sports for better storytelling and making sports more understandable for readers. This research aims to analyse sports data journalism practices in Spain with a quantitative approach with content analysis of 1068 data journalism articles published by 6 newspapers (Marca, Mundo Deportivo, AS, El Mundo, El Periódico, El Pais) between 2017-2019. Quantitative analysis focuses on how sports data journalism is being adapted in Spain, technical features of articles, and the similarities and differences between sports and national newspapers to identify integration of sports data journalism.

scholarly journals Sports Big Data: Management, Analysis, Applications, and Challenges

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongbo Bai ◽  
Xiaomei Bai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Management ◽  
Rapid Growth ◽  
Growth Trend ◽  
Research Directions ◽  
Research Issues ◽  
Big Data Applications ◽  
Rich Information ◽  
Sports Data

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.

scholarly journals Research on E-sports session identification

2018 ◽  
Vol 189 ◽  
pp. 10011 ◽  
Author(s):  
Cheng Yu ◽  
Zhu Wan Ning ◽  
Li LiuLei ◽  
Sun Yu Meng ◽  
Liqing Nong
Keyword(s):  
Data Analysis ◽  
Heat Maps ◽  
Time Threshold ◽  
Spatial Range ◽  
Individual Character ◽  
Partitioning Algorithm ◽  
Sports Data

With the development of the e-sports, data analysis has gained more and more attention in the use of it. For the important problem that the analysis of behaviour patterns of the e-sports’ individual character is not solved directly and solved by anyone, this paper has put forward the session partitioning algorithm based on heat maps of jungle which adopts the clustering prototype thinking that partitions spatial range in advance and then determines by the range of time threshold. Experiments show that it solves the problem of roles of the session which is difficult to be partitioned.

scholarly journals Plithogenic set for multi-variable data analysis

2020 ◽  
pp. 81-89
Author(s):  
Prem Kumar Singh ◽  
Keyword(s):  
Data Analysis ◽  
Mathematical Analysis ◽  
Current Paper ◽  
Data Sets ◽  
Research Communities ◽  
Variable Data ◽  
Precise Representation ◽  
The Given ◽  
Sports Data

The m-polar and multi-dimensional data sets given a platform to deal with multi--valued attributes. In this case, a problem addressed that sometimes the attributes may contain many types of opposites, non--opposites and neutrals values as for example Rainbow. One of the best examples is sports data sets where each time the value of an attribute changes several time towards the given team, the opposition of the given team as well as draw conditions. The precise representation of these types of data sets and their mathematical analysis are crucial tasks for the research communities. The current paper tried to develop new mathematical set theories for precise representation and analysis of sports data via plithogenic set and its mathematical algebra.

scholarly journals Volleyball Data Analysis System and Method Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianyan Dai ◽  
Shangbin Li
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Rapid Development ◽  
Gluteus Maximus ◽  
Economic Benefits ◽  
Practical Significance ◽  
Beijing Olympics ◽  
Type Group ◽  
Volleyball Players ◽  
Sports Data

After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two-arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front-row 4th position strong attack and the back-row 6th position for comparison and analysis, and analyze the process from the take-off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows ( P < 0.05 ). The discharge volume of the left gluteus maximus during the front-row spiking process is greater than that of the back-row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.

Microchemical imaging in biomedical research

1992 ◽  
Vol 50 (2) ◽  
pp. 1118-1119
Author(s):  
P. Ingram
Keyword(s):  
Data Analysis ◽  
Electron Probe ◽  
The Other ◽  
X Ray ◽  
Cell Element ◽  
Physiological Information ◽  
Functional States ◽  
Elemental Maps ◽  
Electron Energy Loss ◽  
Secondary Ion

It is well established that unique physiological information can be obtained by rapidly freezing cells in various functional states and analyzing the cell element content and distribution by electron probe x-ray microanalysis. (The other techniques of microanalysis that are amenable to imaging, such as electron energy loss spectroscopy, secondary ion mass spectroscopy, particle induced x-ray emission etc., are not addressed in this tutorial.) However, the usual processes of data acquisition are labor intensive and lengthy, requiring that x-ray counts be collected from individually selected regions of each cell in question and that data analysis be performed subsequent to data collection. A judicious combination of quantitative elemental maps and static raster probes adds not only an additional overall perception of what is occurring during a particular biological manipulation or event, but substantially increases data productivity. Recent advances in microcomputer instrumentation and software have made readily feasible the acquisition and processing of digital quantitative x-ray maps of one to several cells.

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