• soccer
• players
• machine
• learning
• data
• football
• analysis
• market
• evaluation

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no accepted metric for measuring performance quality in all of its aspects. This thesis aims to develop a model for the market evaluation of football players. The model will utilize various player attributes such as age, position, past performance, and physical characteristics to forecast a player's market value. This thesis will use machine learning techniques, like regression analysis, to build and train the model. The dataset used for the study will consist of statistical and personal data of football players, including transfer market values, age and various attributes registered from an entire football season. The model's accuracy will be evaluated using metrics such as mean absolute error and root mean squared error. The research aims to contribute to the field of sports analytics by providing a reliable and accurate tool for evaluate football player market values, which can be useful for football clubs, agents, and other stakeholders in the football industry.