Abstract
n elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes’ performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete’s Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes
the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes’ feedback interven- tions. This framework has the potential to revolutionize how athletes’ performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine. Keywords:data analytics; artificial intelligence; ubiquitous computing; wearables; sport science; athlete support 1. Introduction Sport analytics is a relatively new area that incorporates frequently disconnected analyses related to sports in general. It is important to acknowledge early in this discussion paper that the term “sport analytics” holds different meanings across the diverse spectrum of sports stakeholders. Initially, the adoption of sports analytics can be traced back to broad- casters, who recognized its potential in enhanced the viewing experience by providing deeper insights into performance metrics and player statistics. This not only enriched spec- tator engagement with specific sports or disciplines but also facilitated the understanding of performance dynamics. Such potential of sports analytics for performance evaluation and forecasting was rapidly recognized by sports managers and scouts. Its application has since expanded, becoming a staple in strategic sports management and talent scouting. In the sport science area, statistics has been omnipresent since its emergence nearly a century ago. In contemporary research, robust statistical validation of findings is a prerequi- site for publication in respected journals. The statistical principles have been adapted from fields such as medicine, social sciences, and humanities to meet the requirements of sports Appl. Sci.2024,14, 3361.
Appl. Sci.2024,14, 3361 2 of 18 science. A common challenge in this regard is the reliance on small cohorts, a byproduct of specific selection criteria inherent to sports-driven studies. Unlike medical research, where extensive participant pools are often the norm, sports science frequently deals with limited cohort sizes and the restricted accessibility of individual data, complicating traditional statistical analysis. The introduction of data analytics into sports science represents a shift towards accommodating the inherent constraints and leveraging the potential of smaller, more specialized datasets. Despite its advantages, the integration of data analytics has been sporadic compared to conventional statistical methods. This paper aims to explore the reasons behind the underutilization of data analytics in sports science and to highlight the practical benefits of this approach, especially in scenarios where large group studies are impractical due to the elite nature of participants or confidentiality concerns. As suggested above, there is a clear need for science-backed analytic tools that connect the knowledge pool of different sport science fields with the novel statistical techniques that produce reliable quantitative models. Additionally, such reliable tools need to focus on meticulous data collection and analysis, validate the outcomes to rigorously assess the strength and weaknesses of the proposed tools. Thus, the present work focuses on areas of importance in elite sports that are benefiting from the implementation of sport analytics, discussing the topic in three parts. First, for Elite Sports Performance Optimization, we showcase interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will briefly discuss also how the betting industry has been relying and developing sports analytics. In the second part, Elite Athletes’ Wellness and Wellbeing, we discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circa- dian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the third and final part of this paper, we argue that a critical issue for man- agers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach.
to assess physical activity and sedentary behavior profiles, sleep and circa- dian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the third and final part of this paper, we argue that a critical issue for man- agers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. We also present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. 2. Elite Sports Performance Optimization The astounding improvement of athletes’ performance in the last few decades has brought the required levels of performance to the very limit of human abilities such as marathon running approaching the 2 h mark. The women’s world records in the high jump from 1987 and the 400 m from 1985 serve as evidence of these boundaries. Consequently, elite sport performance has, in effect, become the science of marginal gains, meaning that athletes need to be at their optimum to deliver on a particular occasion over a given period. The definition of optimum is not easy to provide as it is a dynamic state affected by numerous factors including training, health, and lifestyle. The nature of each sport competition provides the framework for the definition of optimum performance. In sports where points are accumulated during an entire season, it is crucial to focus on building endurance at the pre-season to prepare athletes to overcome challenges posed by numerous competitions, time zone changes, and varying climatic conditions. As competitions approach, the focus shifts to refining sport specific perfor- mance parameters despite the ongoing challenge of managing inevitable performance fluctuations [1]. This is particularly evident in Olympic competitions for such sports, with tennis being a prime example where top-seeded players rarely secure the gold medal. Moreover, it is essential to implement effective measures for monitoring and timely intervention, especially considering that athletes at their peak are often more vulnerable to viral infections, for example. The following sections below will highlight the most promising areas where data analytics facilitate the achievement of this optimum perfor-
a prime example where top-seeded players rarely secure the gold medal. Moreover, it is essential to implement effective measures for monitoring and timely intervention, especially considering that athletes at their peak are often more vulnerable to viral infections, for example. The following sections below will highlight the most promising areas where data analytics facilitate the achievement of this optimum perfor- mance level.
Appl. Sci.2024,14, 3361 3 of 18 2.1. Data Analytics for Performance Improvement Data analytics have been predominantly used to interrelate the execution of different performance elements to a desired performance outcome. For example, there are numerous studies that correlate tennis player’s body configuration to the speed and accuracy of the serve, or sprinters push from the starting blocks, and basketball free throws [2–6]. Although valuable, such results do not always provide clear guidance for performance improvement as they are result of a cohort analysis and may not benefit a particular athlete, even if a part of a cohort (for example if the athlete is a positive outlier). In short, researchers have been quite successful in establishing fundamental principles for effective coaching for the athletes’ broad population but less successful in supporting the individual athlete (i.e., improving and fine-tuning their skills to reach their natural limit). Current progress in technology, wearables and especially artificial intelligence tools enable the facilitation of intensive and rapid feedback that in turn helps to deliver superior athletic performance. This is perfectly illustrated in this issue by the work of Cizmic et al. [7]. The work illustrates how smart equipment could evolve into an on-field assistant trainer with the help of sport analytics that become a part of a broader coaching system. The analytics are the backbone of the classification algorithm that is the heart of the system. This, we believe, is the future trend of the sport analytics implementation that would widen the access of less supported athletes to indirect expert support and would help them self-support and improve. It is a fascinating prospect that relatively low-cost equipment and software will help athletes with less access to funding for specialized staff to access proper support and stay competitive. When the application of analytics is backed by rigorous science, it has the potential to deepen our understanding of execution techniques. Again, this is aptly demonstrated by Vives et al. [8] who show how performance analysis of tennis doubles serves is better understood by applying machine learning approach to identify key performance variables such as serve speed, serve
stay competitive. When the application of analytics is backed by rigorous science, it has the potential to deepen our understanding of execution techniques. Again, this is aptly demonstrated by Vives et al. [8] who show how performance analysis of tennis doubles serves is better understood by applying machine learning approach to identify key performance variables such as serve speed, serve angle, distance to sideline, and net clearance. In both cases above, we see that in-depth knowledge of the respective sport is used to fine-tune and validate the outcomes from the software and achieve a high level of variability Some of those requirements are inherently contradictory as in some instances, performance improvement in terms of higher-level achievement could substantially increase the risk of injury due, for example, to increased mechanical loading on certain parts of the human body or alleviated physiological demands. Equipment design in sports like tennis, cycling and skiing underwent technological revolution that altered the performance and physical demands of athletes. On occasions, there were periods of high injury incidence as safety was sacrificed for performance. It is a well-known fact that the large new head of powerful tennis rackets curtailed the career span of several top-level players, notably highest-level female ones. Equipment design has rapidly evolved with the continuous discovery and utiliza- tion of advanced materials, and new technology solutions and computer-aided design, while equipment selection is still shrouded in secrecy, and very little research has been published on its influence on specific performance and competition outcomes as well as associated injuries potentially attributable to the equipment in use. That is partially due to industry confidentiality rules, but one should not discount the sometimes-damaging effect of sponsorship that compels athletes to use specific brands that may not be the best fit for them. The authors carried out personal observations on sports like cycling, tennis, rowing, and ice track sports where selection is rigorously conducted utilizing performance analysis and proactive user feedback. Still, to the best of our knowledge, there is limited use of analytics tools and this area. The lack of published results and the confidentiality agreements prohibiting
the best fit for them. The authors carried out personal observations on sports like cycling, tennis, rowing, and ice track sports where selection is rigorously conducted utilizing performance analysis and proactive user feedback. Still, to the best of our knowledge, there is limited use of analytics tools and this area. The lack of published results and the confidentiality agreements prohibiting us from disclosing equipment data from our own research prevents us from providing more detailed analysis in the second part of this work that would then be used for informed decision making and assessment. What we have in the end is a system that allows us to progress further current knowledge and associated practices. Importantly, this includes such systems that can be used outside labs and bring athletes to actively participate in the process. Furthermore, with the help of these tools, athletes could work on
Appl. Sci.2024,14, 3361 4 of 18 their individual improvement with the support of a system that has the capability to detect both negative and positive developments without being overly prescriptive. However, success of such developments could only be achieved through proliferation across the respective sports to enable the creation of a large data base and most importantly, the publication of the results to ensure transparency and knowledge enhancement. 2.2. Equipment Design and Selection Sport equipment design needs to satisfy three main objectives [9]: •performance enhancement, •safety improvement, and •injury prevention. Some of those requirements are inherently contradictory. For example, improvements aimed at achieving higher levels of performance can substantially increase the risk of injury due to increased mechanical loading on specific body parts or elevated physiological demands. In sports like tennis, cycling, and skiing, equipment design has undergone a technological revolution, transforming both performance levels and physical demands on athletes. This has occasionally led to periods of high injury incidence, as safety was compromised for enhanced performance [5,10–12]. It is well known that the larger heads of powerful tennis rackets have shortened the career spans of several top-level players, particularly among the highest-ranking female athletes. Equipment design has rapidly evolved with the continuous discovery and utilization of cutting-edge materials, alongside innovations in technology and computer-aided design. However, the process of selecting equipment remains largely confidential, with minimal research available regarding its specific impact on performance, competition results, and injuries that may be linked to the equipment in use. This lack of transparency in equipment selection can be attributed in part to the confidentiality policies within the industry. Still, the potentially negative influence of sponsorships, which may obligate athletes to use specific brands not ideally suited for them, should not be overlooked. The authors of this perspective have personally observed that in sports such as cy- cling, tennis, rowing, and ice-track sports, equipment selection is meticulously carried out through performance analysis and proactive user feedback. This rigorous approach ensures that the equipment chosen is optimally suited to enhance athletic performance and meet the specific needs of the athletes. For instance, in
overlooked. The authors of this perspective have personally observed that in sports such as cy- cling, tennis, rowing, and ice-track sports, equipment selection is meticulously carried out through performance analysis and proactive user feedback. This rigorous approach ensures that the equipment chosen is optimally suited to enhance athletic performance and meet the specific needs of the athletes. For instance, in individual equipment selection, it’s essential to pinpoint critical performance indicators and monitor them across various conditions and fatigue levels, as demonstrated in [13], though not at the highest level of performance. After establishing these key parameters, the corresponding design elements believed to influence performance are identified. A method akin to the design of experiments is then crafted, narrowing down to a select few combinations for trial. Athletes are asked to test these combinations without prior knowledge of the specifics, ensuring that the assessment of effects is both objective and supplemented by the athlete’s feedback. Interestingly, this process sometimes necessitates adjusting parameters for varying conditions within a match, such as altering the string tension in tennis rackets or the shear stiffness of skis depending on snow conditions. One challenge encountered is that such adaptations may lead to changes in technique, potentially causing long-term issues. Despite these insights, to the best of our knowledge, there is limited use of analytics tools in this area. The lack of published findings and confidentiality agreements that restrict the sharing of equipment data from our own research hinder a more detailed discussion in the latter part of this paper. However, certain high-tech sports, like ice track sports, demon- strate how equipment optimization can be fitted to the team’s characteristics[14–16] . These examples show that equipment performance is not isolated from the athletes; adjustments in positioning, team lineup, and technical specifications should be considered collectively. This approach also facilitates adaptations to female anthropometry, enhancing performance while mitigating injury risks.
Appl. Sci.2024,14, 3361 5 of 18 However, we still would like to point out that the integration of ubiquitous computing and wearable technologies with sports analytics can significantly boost our understanding of the interaction between athletes and their equipment, and its impact on performance and injury prevention. This topic is discussed in depth by Baca et al. [17], and the enhanced use of sport analytics will accelerate this development, highlighting how the advanced application of sports analytics can propel these improvements forward. We are convinced that the personalization and optimal selection of sports equipment and apparel hold the key to minimizing career-threatening injuries. Nevertheless, the field suffers from a lack of longitudinal research focused on the long-term effects on athletes, as the primary focus often remains on immediate outcomes rather than the overall impact. It is certainly very difficult to convince both athletes and managers to compromise performance for the sake of safety or injury. Still, contemporary design methodologies that incorporate statistical principles at their core, such as the Taguchi method and principles of quality design, offer a pathway to a more balanced approach, provided that there are sufficient data to refine analytics tools. Once again, a major barrier is the lack of transparency, as the major goal of maintaining secrecy is a competitive advantage. 2.3. Betting Industry and Sports Analytics The potential for performance analysis using sport analytics has been quickly noticed by sports managers and scouts, who are now widely using analytics for both assessment and prediction. It is intriguing though that the betting industry, despite being at the cutting edge of this field and experiencing significant growth and innovation, has been largely overlooked in academic research. The very nature of their business requires them to be ahead of the pack, necessitating the production of highly accurate performance predictions and comprehensive analyses of trends through advanced sports analytics and statistical methodologies [18]. The domains of broadcasting, management, and betting command the majority of investment in this sector, attracting a wide array of consultancies, startups, and well- organized consortia. These entities are dedicated to developing and advancing analytical tools
of the pack, necessitating the production of highly accurate performance predictions and comprehensive analyses of trends through advanced sports analytics and statistical methodologies [18]. The domains of broadcasting, management, and betting command the majority of investment in this sector, attracting a wide array of consultancies, startups, and well- organized consortia. These entities are dedicated to developing and advancing analytical tools tailored to their specific needs, which, in turn, has contributed to the controversial reputation of this sector. As in many other analytics applications, business have taken over unfinished research and implemented it commercially to a myriad of applications. It is notable that there are very few publications detailing the exact statistical methods implemented by the developers. The issue was perfectly captured by Szymanski [19] in his seminal work “Sport analytics: Science or alchemy?”. Szymanski critiques the ten- dency among many developers to keep their methodologies secret, lacking transparent scientific evidence and support. His arguments are compelling and well-founded. Despite recent counterarguments from figures like Charles Mountifield [20], we agree with Szyman- ski’s perspective that sports analytics tools should undergo rigorous and critical scientific evaluation to ensure their credibility and effectiveness. 2.4. Team Members Recruitment and Selection Major sporting events like the Olympics, held every 4 years, have a significant impact on funding decisions by both public authorities and private sponsors. This creates a sub- stantial responsibility for sports governing bodies to select athletes who have the highest likelihood of achieving top performances at these elite levels, sometimes even prioritizing this over an athlete’s historical performance. The days of using simple elimination competi- tions to select representatives, where the best performers on the day were automatically chosen, are now behind us. Such methods, apart from being simplistic, do not support athletes’ preparation adequately and are prone to legal challenges, such as from athletes unable to compete on selection day due to personal or health issues. Consequently, most governing bodies have developed comprehensive selection poli- cies tailored to specific sports, laying the groundwork for the application of sports analytics. These policies aim to establish a framework within which sports can identify athletes
Description
The article discusses data analytics' role in enhancing athlete performance and well-being.