(untitled) (LACCEI, 2024)
Data analytics systems are increasingly popular as an effective framework for comprehending and enhancing sports performance. Consequently, they are commonly utilized by technical staff in several sports organizations, including national teams and clubs, with positive outcomes reflected in their victories. Nevertheless, accurately measuring the positioning of players remains a challenge. For instance, some technical staff of soccer institutions rely on their empirical knowledge to determine player positions, which can be time-consuming and imprecise. This research introduces FuzzyFootball, a web application intended primarily for soccer technical staff and, to a lesser extent, soccer fans. The purpose of the application is to determine the positions of soccer players on the field using logic that considers each player’s most critical attributes, rather than relying on empirical decision-making. The application’s solution is based on fuzzy logic, wherein quantitative values are assigned to player attributes, providing a performance score for each player in each position. As part of the validation process, indicators were established, interviews and surveys were conducted with defined users. The results indicate that the average time required to conduct a player analysis was reduced by more than 87%. Moreover, since the application’s results are generated using logic rather than empiricism, they are more precise and accurate, which is supported by the satisfaction level of respondents.