Sports Policy and Training Decision Support Method Based on Wireless Sensor Network

In recent years, the awareness of sports departments at all levels of society to promote sports through science and has been increasing, and the scientific decision-making and management of sports have been improved to a great extent. With the application of scientific decision-making combined with a real-time sports data monitoring network, the opponent’s advance information can be effectively observed during the game and reasonable decisions can be made to deal with the opponent’s offense. Therefore, high-level athletes appear to be more relaxed and calm in the game. It first requires the application of advanced information collection methods to obtain sports data quickly, in real time and at low cost, and extract information about athletes’ scientific management decision-making from massive data and then make scientific management decisions for sports training. The modern sports method is highly open, and big data mining also profoundly affects the relevant decision-making of sports training. How to design appropriate decision support tools to grasp the key points of the problem in sports information data and make reasonable and correct decisions is a problem that is closely watched by macro decision-makers and coaches at all levels. This article mainly introduces the training decision support method derived from data mining and intends to provide some technical directions for making scientific decisions in sports training. This paper proposes related algorithms of a training decision support method derived from data mining, including training effectiveness prediction model and decision tree algorithm, for the design of the training decision support method derived from data mining. Experimental data shows that the average error between the prediction of the effectiveness of the training method and the actual situation of the training decision support method in this paper is 0.913%, which is helpful for the management or coach to make decisions.

A Multidisciplinary Perspective on Publicly Available Sports Data in the Era of Big Data: A Scoping Review of the Literature on Major League Baseball

Sports big data has been an emerging research area in recent years. The purpose of this study was to ascertain the most frequent research topics, application areas, data sources, and data usage characteristics in the existing literature, in order to understand the development of data-driven baseball research and the multidisciplinary participation in the big data era. A scoping review was conducted, focusing on the diversity of using publicly available major league baseball data. Next, the co-occurrence analysis in bibliometrics was used to present a knowledge map of the reviewed literature. Finally, we propose a comprehensive baseball data research domain framework to visualize the ecosystem of publicly available sports data applications mapped to the four application domains in the big data maturity model. After searching and screening process from the Web of Science, Science Direct, and SPORTDiscus database, 48 relevant papers with clearly indicated data sources and data fields used were finally selected and full reviewed for advanced analysis. The most relevant research hotspots for sports data are sequentially economics and finance, sports injury, and sports performance evaluation. Subjects studied ranged from pitchers, position players, catchers, umpires, batters, free agents, and attendees. The most popular data sources are PITCHf/x, the Lahman Baseball Database, and baseball-reference.com. This review can serve as a valuable starting point for researchers to plan research strategies, to discover opportunities for cross-disciplinary research innovations, and to categorize their work in the context of the state of research.

Self-Reported Restrictive Eating, Eating Disorders, Menstrual Dysfunction, and Injuries in Athletes Competing at Different Levels and Sports

The purpose of this study was to investigate the prevalence of self-reported restrictive eating, current or past eating disorder, and menstrual dysfunction and their relationships with injuries. Furthermore, we aimed to compare these prevalences and associations between younger (aged 15–24) and older (aged 25–45) athletes, between elite and non-elite athletes, and between athletes competing in lean and non-lean sports. Data were collected using a web-based questionnaire. Participants were 846 female athletes representing 67 different sports. Results showed that 25%, 18%, and 32% of the athletes reported restrictive eating, eating disorders, and menstrual dysfunction, respectively. Higher rates of lean sport athletes compared with non-lean sport athletes reported these symptoms, while no differences were found between elite and non-elite athletes. Younger athletes reported higher rates of menstrual dysfunction and lower lifetime prevalence of eating disorders. Both restrictive eating (OR 1.41, 95% CI 1.02–1.94) and eating disorders (OR 1.89, 95% CI 1.31–2.73) were associated with injuries, while menstrual dysfunction was associated with more missed participation days compared with a regular menstrual cycle (OR 1.79, 95% CI 1.05–3.07). Our findings indicate that eating disorder symptoms and menstrual dysfunction are common problems in athletes that should be managed properly as they are linked to injuries and missed training/competition days.

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

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.

Sports Settings in Management Research

In recent years, scholars have increasingly engaged with the use of sports settings to advance management theory. This stream of literature departs from the ‘Sport Management’ conversation, as it aims to move beyond the appreciation of the mere empirical sport phenomenon to advance a theoretical contribution with broader generalizability to other settings. The purpose of this bibliography is to present an organized overview of some of the relevant empirical works which can act as guides to scholars interested in conducting management research using sports data. Sports settings are becoming increasingly popular among management scholars due to the large availability of fine-grained data, well-defined performance metrics, and transparency of changes in strategies and processes. Also, sports settings are considered to be relatively controlled environments, which resemble laboratory conditions. These factors make sports data particularly suitable for quantitative studies, which have been so far more common than qualitative ones. Yet sports data can also ideally suit qualitative research. For example, sporting events are incredibly well documented and often collect multiple informants’ interviews impromptu, thus making them excellent settings for archival, historical, and in-depth case studies. This bibliography aims to provide readers with selected examples of both excellent quantitative and qualitative studies in different sports settings. The first section of the bibliography presents some past literature reviews on how sports data has been used in management research and some suggestions on future research topics in sports settings. Following this, the bibliography summarizes the sports settings which are most popular in management research. We show that especially the various US major league sports (baseball, basketball, football, ice hockey) have been prominent settings for management research. In addition to US major sports, other popular sports settings included in this bibliography are soccer, motorsports, national sports organizations (NSOs) and the Olympics. The bibliography also includes a section on the less common sports that have been used in previous research and a section on studies examining other actors in sports than players and managers.

Volleyball Data Analysis System and Method Based on Machine Learning

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.