## 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 spectator 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 prerequisite 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 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 circadian 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 managers 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.