dataset preguntas y respuestas

This dissertation explores what factors influence the market value of wingers in football, using data analysis and statistical modelling. Inspired by Moneyball principles and the work of Michael Edwards and Ian Graham at Liverpool FC, it examines how different player statistics impact transfer valuations. Data Sources Transfermarkt – Provides player market values and transfer history. Fbref – Supplies performance stats such as goals, assists, dribbles, and defensive actions. Opta Power Rankings – Used to measure league strength. Methodology A multiple linear regression (MLR) model was used to find which stats best predict a winger’s market value. The key factors analysed include: Goals & Assists Expected Goals & Assists (xG, xAG) Successful Dribbles Defensive Contributions (Tackles, Pressures, Recoveries) League Strength Player Age Key Findings Goals & Assists had the strongest impact on market value. Dribbling ability also increased value, but to a lesser extent. Defensive contributions were negatively correlated, suggesting clubs prioritise attacking stats for wingers. xG and xAG were not significant in determining market value. Playing in a stronger league increased a player’s market value. Implications The study shows that attacking performance is the biggest factor in a winger’s market value. Clubs should focus on goal contributions and dribbling when evaluating players. It also highlights how data analytics is changing football, helping clubs make smarter recruitment decisions.