In football, the process of players' identification still strongly relies on the subjective opinions of football experts, such as talent scouts or sporting directors. Especially for medium and small clubs, embracing data analysis represents an unprecedented opportunity to gain a more insightful and objective overview of virtually every player worldwide. This thesis proposes two distinct approaches that aim to enhance the transfer market. In the first section, called "players' embeddings", deep learning techniques are employed to represent football players as vectors in a lower-dimensional space. By leveraging this approach, it is possible to analytically characterise footballers, analyse their similarities and differences, and identify suitable replacements in the event of a player's departure.