Abstract:
Player recommendation systems are essential for effective player transfers in football club management. The most popular sport in the world, soccer, is highly competitive as teams try to assemble strong squads. Financial limitations frequently make it difficult to pursue the best talent, which emphasizes how crucial it is to find individuals who fit the needs of the team rather than just picking the best player available. The objective of this project is to create an advanced recommendation system for football players to maximize player scouting and recruitment. The suggested approach helps clubs to make more strategic hiring selections by carefully examining player performances and taking into account different information and attributes including player profiles and statistics. Rather than focusing only on the best talent, the goal is to identify players whose characteristics and style of play mesh well with the group dynamic. The purpose of this study is to assess advanced analytical methods for improving player recruitment and scouting. Effective squad planning is intended to be aided by creating player profiles that consider a variety of attributes. By means of practical evaluation, the study seeks to illustrate the value of the enhanced recommendation mechanism in guiding teams toward specific player acquisitions, hence enhancing football's continuous advancement and success. PlayerPulse's scatter plot, with its well-organized clusters, shows how well players are classified into different profiles. PlayerPulse demonstrates accurate player classification with a high Calinski-Harabasz score of 773.69 showing considerable variances between player profiles and a Davies-Bouldin score of 0.75 indicating low within-cluster scatter. The specific organization of players in their individual profiles provides another evidence of PlayerPulse's efficiency in improving football scouting and hiring decisions by means of tactical player suggestions
