Linear Regression Analysis of Football Player Market Value Fluctuation Factors

Abstract

 This study employs linear regression analysis to examine the factors influencing football player value fluctuations based on market value data from 44 football players. Through constructing a multiple linear regression model, the research findings reveal: Each unit increase in event intensity leads to an average increase of 324.5 percentage points in value change rate; The Big Five leagues bring an additional 298.7 percentage points of value growth compared to other leagues; Each unit increase in player level results in an average decrease of 156.3 percentage points in value change rate; The linear regression model effectively explains the value fluctuation phenomenon (R²=0.612). This research provides crucial quantitative insights into football's economic dynamics by identifying key factors affecting player valuation. The findings offer evidence-based guidance for stakeholders in player development, investment strategies, and market regulation, contributing to more efficient resource allocation and strategic decision-making across the football industry. Additionally, the study establishes a methodological framework for analyzing value volatility that can be applied to talent evaluation across sports markets, while enhancing transparency in the increasingly commercialized football transfer ecosystem.