Sports analysis is a broad topic that can be approached from various research directions. In this regard, I could list economic informatics through which the participation of supporters in matches played between football academies can be analyzed; in the field of sports, a research may focus on the analysis of individual performance (player performance) or group performance (team performance); in the field of education, the emphasis can be placed on learning strategies; in the field of health, the physical condition of the players can be monitored, and the examples can certainly continue.
For this paper, the main objective is to synthesize the most relevant sports analysis works for football academies, starting from exploratory data analysis machine learning-based techniques and presenting the most current and accessible sports prediction techniques that can be used with the ease of non-programmers, as well as to present future research directions for the field of sports, especially football; hence, the desire to promote exploratory techniques for analyzing data that can be easily collected in electronic form and directly secured from training or matches to keep the data history for each individual academy.
Although the main focus is most often on professional football teams, it is necessary to introduce and take into account the performances of academy players who will be selected for professional teams in the near future. In any case, it depends on the management of the academies to identify potential talents and create the most appropriate strategies to achieve their goals.
Keywords: football academy, exploratory data analysis, types of sports performance, literature overview
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