Sports Intelligence Research Articles

Exploring plausible uses of artificial intelligence in sports

Artificial intelligence (AI) is fast gaining popularity in almost every field. This study explores the uses of AI in the field of sports, which has significantly evolved in technological aspects over the past two decades. Available data and expert opinions were analyzed. The analysis shows that AI has a wide variety of applications in sports, which—when implemented—can improve the field; at the same time, the data makes clear that indiscriminate use may cost the sports industry its stability and glory.

Artificial Intelligence in Sports: Data Analysis to Enhance Training

The objective of this article was to explore the role of artificial intelligence in the field of sports, focusing on data analysis to enhance training. A literature review was conducted to examine different artificial intelligence algorithms to identify patterns and trends that enable the application of effective and personalized training strategies. Additionally, practical and ethical implications of using artificial intelligence in sports were discussed, along with potential future directions for interdisciplinary research and sports development. In conclusion, the transformative potential of artificial intelligence in sports is revealed, emphasizing its ability to personalize training and enhance athlete performance.

Beyond Gates and Metal Detectors

Innovation is of vital importance in professional sport organizations, with various segments and departments catering to diverse managerial functions. While previous research has focused on ticketing, marketing, and event management, the critical aspect of security and intelligence in sports requires further exploration. With the safety of fans, players, coaches, staff, and the local community at stake, security and intelligence play a paramount role. The current research addresses the literature gap pertaining to cybersecurity through in-depth interviews involving 12 participants from major U.S. professional sports leagues and the cybersecurity industry, delving into physical security, personnel security, and cybersecurity domains.The results of the study highlighted an overall awareness and success in physical and personnel security, but also revealed a concerning deficiency in the understanding of cybersecurity threats. This shortfall emphasizes the pressing need for innovative cybersecurity solutions in the sport industry to ensure the integrity and safety of professional sport organizations in a complex environment. Through such innovative approaches, professional sport organizations can enhance their ability to effectively combat cybersecurity challenges, safeguarding their operations and protecting all stakeholders involved.

Artificial intelligence in sport: Exploring the potential of using ChatGPT in resistance training prescription.

OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) technology enables conversational interactions with applications across various fields, including sport. Here, ChatGPT's proficiency in designing a 12-week resistance training programme, following specific prompts, was investigated. GPT3.5 and GPT4.0 versions were requested to design 12-week resistance training programmes for male and female hypothetical subjects (20-years-old, no injury, and 'intermediate' resistance training experience). Subsequently, GPT4.0 was requested to design an 'advanced' training programme for the same profiles. The proposed training programmes were compared with established guidelines and literature (e.g., National Strength and Conditioning Association textbook), and discussed. ChatGPT suggested 12-week training programmes comprising three, 4-week phases, each with different objectives (e.g., hypertrophy/strength). GPT3.5 proposed a weekly frequency of ~3 sessions, load intensity of 70-85% of one repetition-maximum, repetition range of 4-8 (2-4 sets), and tempo of 2/0/2 (eccentric/pause/concentric/'pause'). GPT4.0 proposed intermediate- and advanced programme, with a frequency of 5 or 4 sessions, 60-90% or 70-95% intensity, 3-5 sets or 3-6 sets, 5-12 or 3-12 repetitions, respectively. GPT3.5 proposed rest intervals of 90-120 s, and exercise tempo of 2/0/2. GPT4.0 proposed 60-180 (intermediate) or 60-300 s (advanced), with exercise tempo of 2/1/2 for intermediates, and 3/0/1/0, 2/0/1/0, and 1/0/1/0 for advanced programmes. All derived programmes were objectively similar regardless of sex. ChatGPT generated training programmes which likely require additional fine-tuning before application. GPT4.0 synthesised more information than GPT3.5 in response to the prompt, and demonstrated recognition awareness of training experience (intermediate vs advanced). ChatGPT may serve as a complementary tool for writing 'draft' programme, but likely requires human expertise to maximise training programme effectiveness.

The Role of Artificial Intelligence in Physical Education and Sports: A Review of Current Applications and Future Potential

Artificial Intelligence (AI) has emerged as a groundbreaking technology with great potential to transform various industries, and the realm of physical education and sports is no exception. This research paper aims to explore the role of AI in enhancing performance and personalization in physical education and sports activities. The paper will provide an overview of AI technologies and their applications in this field, highlighting key advancements and their impact on athletes, coaches, and trainers. The use of AI in physical education and sports offers numerous benefits. AI-powered sensors and wearables can provide real-time feedback on biomechanics, technique, and performance, allowing athletes to identify and correct weaknesses. Machine learning algorithms can analyze vast amounts of data, enabling coaches to design personalized training programs tailored to each athlete’s unique characteristics and needs. AI also facilitates the development of virtual coaching platforms and simulators, providing athletes with immersive and dynamic training experiences. Additionally, AI can assist in injury prevention and management. Intelligent systems can assess an athlete’s physical condition, track fatigue levels, and provide recommendations for recovery and injury rehabilitation. Furthermore, AI can analyze historical data to predict potential injuries or evaluate an athlete’s readiness to return to competition. This proactive approach to injury prevention can significantly reduce the risk and impact of sport-related injuries. However, the implementation of AI in physical education and sports comes with challenges and ethical considerations. Privacy and security issues arise with the collection and analysis of athletes’ personal data. Moreover, the reliance on AI can lead to a lack of human connection and intuition in coaching relationships. These concerns need to be addressed to ensure the responsible and effective use of AI in this context. Overall, this research paper presents a comprehensive analysis of the role of AI in physical education and sports. By leveraging AI technologies, athletes can optimize their performance, trainers can provide personalized coaching, and the overall sports ecosystem can be enhanced. Future research will delve into the practical implications, implementation strategies, and potential ethical implications involved in incorporating AI into physical education and sports.

Analysis of the Development of Sports Intelligence From the Application of "Intelligent Robot Dogs" in Competitive Sports

The 19th Asian Games, hosted in Hangzhou, China, have been described as a large-scale event of sports intelligence, with high-tech technology such as "digital torchbearers", autonomous buses, 5G information networks, meta-universes, and intelligent robots on show. This paper will use the "intelligent robot dog" as the research object and will use the literature method, case study method, logical reasoning method, and other research methods to describe the concepts of digitalization and intelligent robots and analyse the positive effects that the combination of sports and intelligence brings to sports competitions. Using the application of an intelligent robot dog in sports competition at the Hangzhou Asian Games as a starting point, this paper will analyse the current state of sports intelligence, present the problems encountered as well as the solutions, and finally forecast the future market development of the combination of sports and artificial intelligence applied in competitive sports.

TAEKWONDO POSE ESTIMATION WITH DEEP LEARNING ARCHITECTURES ON ONE-DIMENSIONAL AND TWO-DIMENSIONAL DATA

Practicing sports is an activity that helps people maintain and improve their health, enhance memory and concentration, reduce anxiety and stress, and train teamwork and leadership ability. With the development of science and technology, artificial intelligence in sports has become increasingly popular with the public and brings many benefits. In particular, many applications help people track and evaluate athletes' achievements in competitions. This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Long-term Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. Second, we use sequence images to train using video classification architecture. Finally, we recognize poses in sports lessons using skeleton data to remove noise in the image, such as background and extraneous objects behind the exerciser. As a result, our proposed method has achieved promising performance in pose classification tasks in an introductory Taekwondo lesson.

Volleyball Action Recognition based on Skeleton Data

This research explores the intricacies of volleyball action recognition using skeleton data through the lens of the Long Short-Term Memory (LSTM) model. With the objective of accurately identifying distinct volleyball actions—Serve, Spike, Block, Dig, and Set—the study implemented a structured LSTM network, achieving a commendable 95% accuracy rate consistently across all actions. The findings underscore the transformative potential of deep learning, particularly the LSTM network, in sports analytics, suggesting a paradigm shift in understanding and analyzing sports actions. The research serves as a foundation for future studies, offering insights into the blend of artificial intelligence in sports, with applications extending to coaching support and enhanced sports broadcasts.

Feedback delay of sports intelligent learning system based on model predictive control and artificial intelligence

At present, in terms of model predictive control theory and model applications, two-layer segmented linear grid models are dominant. The two-layer piecewise linear lattice model is more effective, does not require too many parameters and is easy to calculate. Since the multi-layer model predictive control is based on a two-layer model, it is very important to study the mechanism and optimization methods of the two-layer model. Artificial intelligence comes from computer science. It tries to understand the nature of intelligence and create new intelligent machines that can react like human intelligence. Research in this field involves robotics, speech recognition, image recognition, natural language processing, etc. Since the emergence of artificial intelligence, theory and technology have gradually matured, and its application fields have also increased. The technological products created by artificial intelligence will embody the development of human wisdom in the future. Sports intelligence learning was first proposed by Lins. Linsley found that under laboratory conditions, by observing and recording the learner's behavior frequency and reaction speed, and adjusting the learning activities based on this, a better learning effect can be obtained. The combination of sports intelligence learning and behaviorist learning theory forms a systematic and accurate teaching method. Video system feedback delayed learning is a part of multimedia education technology. In recent years, multimedia technology has been widely used in various educational fields. By reviewing the application research of multimedia teaching technology in education, we have determined the areas related to the delay of the video system feedback, and guided the application research of the video feedback system in education.

Integrating Emotional Intelligence and Mental Education in Sports to Improve Personal Resilience of Adolescents

Adolescents are the nation's future leaders, so their immediate environment, including their families, schools, and the government, must promote a healthy environment for them both physically and mentally. This is necessary because improving an adolescent's psychological condition is one of the most crucial investments for raising a good generation, but more and more young people are developing psychological disorders. This study employed a pseudo-experiment method of research. The findings of the study in the treatment group, as shown in the "Paired Samples Test" output table above, indicate that the t count negative value is -45,020. This difference between the average Pre-Test and the average Post-Test is what causes the t count negative value to be negative. A negative t-count may turn positive in this situation. As a result, the computed t-value is 45,020. While in the control group, it is evident from the "Paired Samples Test" output table above that the negative value t count of -7,760 t counts is due to the average Pre-Test value being lower than the average Post-Test value. A negative t-count may turn positive in this situation. Consequently, the computed t value is now -7.760. Despite the fact that both groups have an impact on pre- and post-test results, the outcomes can be seen if there is a sizable difference between the treatment and control groups. This demonstrates that the treatment group receiving mental education achieves greater results than the group not receiving such treatment. Teenagers' pre-test and post-test personal endurance results for each group show this. Keywords: emotional intelligence, mental health education, youth resilience, physical education

Artificial Intelligence in Sports

Artificial Intelligence (AI) is the capacity of machines to perform cognitive tasks such as thinking, perceiving, learning, problem solving, and decision making; it is inspired by the way humans perceive, learn, reason out, and make decisions. Artificial intelligence is a developing advanced field of study and a key research direction in the domains of sports and technology. Coach's Eye is a video analysis programme for multiple sports. Appropriate for the majority of sports requiring close-up analysis of human athletic movement, such as bat swing or jump analysis. Dartfish : Apps for video analysis are ideal for capturing skill-based video and providing immediate feedback. In Officiating AI and Machine Learning are also assisting game Judge, Referee, and umpires to make informed decisions. Cricket uses the third umpire that relies on AI-driven visualization to assist umpire in making a decision where the human eye cannot catch the minute split second or millimeter level differences. Artificial intelligence is becoming increasingly important in modern sports and games. Artificial intelligence has brought about several changes in sports. AI in sports is here to stay, and it will become even more essential as the technology develops with better sensors, processors, and algorithms. Sports organizations increasingly require AI to compete at the highest levels, whether through an internal IT organization or external AI platforms.

Analysis of auxiliary modes for sports intelligence training system based on nonlinear model optimization and improved algorithms

Analysis of auxiliary modes for sports intelligence training system based on nonlinear model optimization and improved algorithms

The Influence of Emotional Intelligence on Coping Ability in Senior Female Field-Hockey Players in South Africa.

Research has suggested that coping under pressure could be rooted in the ability to identify and manage one's emotions. In this study, we investigated this hypothesis using cross-sectional data obtained from a sample of South African national and university level female field-hockey players (N = 60, Mage = 21.57, SD = 3.65). A correlational research design was adopted of which a pen-and-paper survey containing the Emotional Intelligence Scale and Athletic Coping Skills Inventory-28 was used to collect the data. Descriptive results exposed players to yield higher than average levels of total emotional intelligence and coping ability in sport with significant differences noted between the national and university level players in terms of their ability to manage their own emotions (p = 0.018), utilise emotions (p = 0.007, d = 0.74), coping with adversity (p = 0.002, d = 0.84), coachability (p < 0.01, d = 3.17), and overall coping ability (p < 0.01, d = 1.00). After controlling for the level of participation, hierarchical linear regression analyses confirmed the relationship between the study variables exposing total emotional intelligence to be a significant predictor of players' ability to cope with adversity (β = 0.55, p = 0.006), concentrate (β = 0.43, p = 0.044), maintain confidence and achievement motivation (β = 0.42, p = 0.027), as well as overall coping ability (β = 0.28, p = 0.023). It was concluded that emotional intelligence may be a worthy contributor in the psychological profiling of players and a plausible intervention mapping tool in sport psychology practice to potentially enhance the coping ability of female field-hockey players.

Sports Intelligence Training Framework Based on Image Processing Chip Fusion Multimedia

On the basis of analyzing and studying the relevant theories of image fusion, this paper introduces a variety of fusion methods that can be run on the platform, as well as the method of constructing the fusion algorithm function chip. The decline in physical fitness is getting more and more serious, which has gradually attracted the attention of schoolteachers and parents. All sectors of society are paying close attention to the school's actions. A healthy body is the foundation of students' learning and the driving force for students' development. The way to generate the teaching wisdom of physical education teachers is to first get rid of the constraints of traditional physical education teaching concepts on teaching, improve the professional quality of physical education teachers, secondly strengthen the practical reflection of physical education, strive to improve the management system of the campus, and finally strive to improve the professional skills of physical education teachers. Ability to integrate with Internet technology.

The Application of Big Data and Artificial Intelligence in Sports Industry

In the age of technology, intelligent and innovative sports events present a brand new look, which has affected how many enterprises operate worldwide. Traditional sports events are upgraded to artificial intelligence. While broadcasting baseball, basketball, and other games, the automatic tracking system is used to perform real-time technical analysis, and the player's course can be analyzed on the spot. In the event broadcast part, the simulation technology, virtual and real integration are widely used, and the events could also be broadcasted live on the streaming audio-visual platform. Therefore, how to use big data analysis has become a key factor for the success of professional teams or sports marketing companies. This research explores the application classification and significance of big data analysis and artificial intelligence in the field of sports by analyzing relevant literature. Through this study, the significance and application of big data, the importance of big data analysis and artificial intelligence in sports industry, and the development trend of sports big data are preliminarily understood. It is also expected that the application and development trend of sports intelligence technology could help sports industry-related companies improve their competitiveness in terms of development strategies, thereby enhancing the visibility of Taiwan's sports industry in the world and making the research and development strength of Taiwan's sports enterprises visible to the world.

Application of Artificial Intelligence in Sports: A case study of cricket.

The game of cricket has developed over the years and has completely transformed. Very obviously technology has affected the game a lot. Many developments have come in the game as a concept, rules and even how teams and players look and prepare for a match. This paper looks at the various uses of Artificial Intelligence as a whole.

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review.

Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the research field, there is a lack of bibliometric analysis, which would prove useful for obtaining insights into the large amounts of available literature. Therefore, the present work aims to identify important research issues, elucidate the conceptual structure of the research field, and unpack the evolutionary trends and the direction of hot topics regarding key themes in the research field of artificial intelligence in sports. Using the Scopus database, 1215 documents (reviews and articles) were selected. Bibliometric analysis was performed using VOSviewer and bibliometrix R package. The main findings are as follows: (a) the literature and research interest concerning AI and its subcategories is growing exponentially; (b) the top 20 most cited works comprise 32.52% of the total citations; (c) the top 10 journals are responsible for 28.64% of all published documents; (d) strong collaborative relationships are present, along with small, isolated collaboration networks of individual institutions; (e) the three most productive countries are China, the USA, and Germany; (f) different research themes can be characterized using author keywords with current trend topics, e.g., in the fields of biomechanics, injury prevention or prediction, new algorithms, and learning approaches. AI research activities in the fields of sports pedagogy, sports sociology, and sports economics seem to have played a subordinate role thus far. Overall, the findings of this study expand knowledge on the research situation as well as the development of research topics regarding the use of artificial intelligence in sports, and may guide researchers to identify currently relevant topics and gaps in the research.

A Stable and Durable Triboelectric Nanogenerator for Speed Skating Land Training Monitoring

In the current IoT era, the key to sports intelligence is the effective collection and analysis of sports data. Sports data can accurately reflect an athlete’s athletic status and help coaches to develop competitive tactics and training programs. Wearable electronic devices used to collect sports data currently have several drawbacks, including their large size, heavy weight, complex wiring, high cost, and need for frequent power replacement. In this work, transparent polyamide-66 (PA-66) and transparent polytetrafluoroethylene (PTFE) films were used as friction layers, polydimethylsiloxane (PDMS) was used as a support layer, and conductive hydrogels were used as electrodes, which were simply combined to create stable and durable triboelectric nanogenerators (SD-TENG) with good mechanical and triboelectric properties. In the test, the output power was 1mW under a load resistance of 10MΩ. In addition, the integrated intelligent speed skating land training assistance system monitors the changes in the joints and joint chains of skaters during land training in real time. The successful demonstration of the use of SD-TENG in speed skating land training will help to promote the development and application of TENG in the fields of intelligent sport monitoring, smart wearable devices, and big data analysis.

College Sports Intelligence Using Human–Computer Interaction System for Education

The emergence of the “internet plus” era marks a significant gap in sports education for higher level training, which has laid the groundwork for mixed teaching. This article briefly discusses “the internet plus” and artificial intelligence mixed learning principles. This article introduces a problem-based Cognitive Reasoning Sports Teaching Model (CRSTM) designed to provide a transitory response to dynamic trails according to research theory and observations in teaching computer based sports learning techniques. Computational mathematics training typically starts with identifying behavior expectations or anticipated performance. The field of educational psychology has long been receptive to the desirability of setting educational goals. Motor skill training is focused primarily on a set of options to incorporate critical task components. Human–Computer Interaction System for Education makes students and scholars learn about human features and acquire the skills essential to investigate people’s interactions with and around technologies.

Different Stages of Rally Score Distinguish Performance Level in Badminton: A preliminary study

The study aimed to investigate the use of visual display badminton game rallies to determine the level of sports intelligence. Methods of this study reveal the study participants with video display real game of badminton where some strokes rally divided into several shots. The result showed expert level players led the perceptual-cognitive task among another tested group. This preliminary study could suggest that the 'software' approach in skilled performance has been heading the search for systematic differences in information processing strategies between expert and novice athletes. Keywords: perceptual-visual; prediction; decision making; video task eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3822