A Data-Driven Approach to Business Intelligence for Evaluating Football Player Performance

Abstract

Analyzing football players' performance is a crucial focus in modern sports science, providing insights into player efficiency and team strategies. This paper proposes a comprehensive framework for evaluating player performance by integrating statistical metrics, match data, and advanced analytics techniques. Key performance indicators (KPIs), including passing accuracy, goal contributions, and defensive actions, are analyzed alongside contextual factors such as match conditions and opposition strength. Using a dataset of per-90-minute statistics for the 2022-2023 season, this study covers players from top European leagues: the Premier League, Ligue 1, Bundesliga, Serie A, and La Liga. The proposed model offers coaches, analysts, and researchers actionable insights to enhance player development and optimize team strategies.