# Analysis and Parameterization of Sports Performance: A Case Study of Soccer
## 1 Introduction  
Today, the impact of professional sports, particularly soccer, on every facet of society is substantial. Economically speaking, the capital tied to sports is significant, resulting in clubs and top-tier athletes deploying vast resources to remain competitively relevant and achieve the best performance, often surpassing established financial capacities [1]. It is unequivocal that data science is proliferating across all life domains. As one would expect, its application has also penetrated the sports industry, especially soccer, with elite teams leveraging it to gain a competitive advantage [2]. Given that data analysis inherently involves handling numerous variables, adopting these tools in professional clubs yields scarce information about their modus operandi or the parameters utilized because any disclosure could jeopardize the team. Analogously, numerous amateur-level analyses and academic studies focus merely on raw data compilation and present them as is or employ them for comparisons without any initial statistical treatments [3; 4; 5]. In view of the foregoing, there is a growing necessity to harness the potential of Big Data in conducting analysis, designing, and implementing an expert system (ES) that will enable assessment of a player's or team's prowess based on specific sensitive, analyzable, and paramelizable factors. In this article, a fuzzy system is proposed as an effective solution for rating a player's aptitude in various game aspects or determining the probabilities of winning, drawing or losing for their team in a clash. In summary, this study proposes the following objectives:* Analyzing, designing, and implementing an ES that enables the performance of a soccer player to be parameterized based on raw data collected and statistics from every match.
* Comparing teams based on the parameterization of player characteristics to estimate probabilities of a match's victory, draw, or defeat.
* Evaluating a player's talent per parameter established using fuzzy logic functions to categorize players.
* Designing study findings' visualization that portrays results in a simple, precise, and uncluttered fashion.  
Given these objectives, this research represents the work carried out on the design and implementation of a fuzzy logic-based ES that allows the performance of soccer players and teams to be parameterized, facilitating the estimation of an optimal team composition and prediction of a clash's outcome based on these parameters.  
The remainder of the article is organized as follows. Section 2 presents a review of the work related to the proposal put forward in the article. Section 3 introduces the theoretical framework in reference to ESs and fuzzy logic. Then, Section 4 describes the data used for experimentation and the process followed for their treatment. Section 5 describes the application of fuzzy logic functions to our problem. Section 6 shows the system of visualization and presentation of results. Section 7 shows the discussion and a comparison with other forecast tools, and finally, Section 8 shows the conclusions obtained in this work.