Application of Multiple Linear Regression Models in Predicting the Value of Football Players

Abstract

In this study, the key factors affecting football player value were analyzed using a multiple linear regression model. The analysis considered variables such as nationality, location, historical achievements, experience, skill level, and market value. The results indicate that a player’s competitive level significantly enhances their value, while there is a negative correlation between market value and player value. The relationship between experience and value is complex and requires further exploration. Additionally, the study examined the distribution of players from various countries in the English Premier League, with French, Spanish, and Brazilian players ranking highest in appearances. French and Spanish players are versatile across positions, while Brazilian players excel primarily as forwards. Visual analyses demonstrated the proportions and performances of players in different positions, confirming the positive correlation between competitive level and net worth and the negative correlation with market value. The intricate relationship between experience and net worth was also highlighted. These findings offer valuable insights into the determinants of football player value and provide data to support team management and future research.