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Development of a Market Value Metric – Made in Benfica
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This thesis focuses on the analysis of player transfers in football, with a specific emphasis on Benfica,
the Portuguese club known for its significant investments in player acquisitions. The study aims to
determine a market value that aligns closely with actual transfer values while identifying the key
features and factors that play a crucial role in this context. By understanding these factors, the research
aims to predict the market value of the players, in order to help the club's decision-making process. To
achieve these objectives, the data was collected from various leagues, including the Portuguese,
English, and French leagues, which are of interest to Benfica's scouting department. The
TransferMarket website served as the data source, with data extraction performed using the
BeautifulSoup library in Python. The analysis phase involved exploring and identifying the most
relevant variables and their impact on calculating the market value using three modeling techniques:
Decision Trees, Random Forest, and XGBoost. This process was conducted twice, once for the global
dataset and once specifically for Benfica's purchases, allowing for a comparison between the two
scenarios. The conclusions revealed that players who have previously played in the Premier League or
Eredivisie and are international players for their respective national teams are highly prioritized by
Benfica. Furthermore, factors such as the time remaining on a player's contract and their age were
identified as important variables in determining market value. The Random Forest model exhibited the
best performance, with a higher r-squared value (0,919 for Benfica dataset and 0,953 for the Global
dataset) and a lower Mean Absolute Error (3818086,11 for Benfica dataset and 4038199,56 for the
Global dataset) for both the TransferMarket and Actual Transfer Value problems, respectively. This
study points to the significance of player transfers in professional football and highlights the potential
benefits of employing data-driven approaches for decision-making by clubs. By incorporating these
methodologies and leveraging comprehensive information, clubs can make more informed decisions
and improve their overall strategic planning.
Descrição
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Sports Analytics Market Value Data Analysis Decision Trees Random Forest XGBoost Decision Making