Intelligent Sports Team Management Powered By Machine Learning

Abstract

Sports like basketball and baseball have seen significant advancements through the effective use of sports analytics. In contrast, machine learning applications in football have largely concentrated on outcome prediction rather than player evaluation. This study aims to bridge that gap by presenting a descriptive analysis of football player performance using a football-specific dataset. Traditionally, player performance assessments rely on expert panels, though the criteria they use remain undisclosed. In this research, the Support Vector Classifier (SVC) algorithm is employed to analyze and classify player performance data, identifying key functional attributes relevant to different playing positions. By tuning kernel functions and hyperparameters, the model effectively highlights the most impactful performance metrics, offering objective insights that align with expert evaluations. The dataset used comprises detailed performance data from football matches, making the analysis specific and relevant to the sport. The application of SVC allowed the development of highly accurate classifications with minimal error, thus validating the algorithm’s effectiveness in rating prediction tasks. The results indicate that SVC can serve as a powerful tool in football analytics, enabling data-driven decision-making for coaches, analysts, and scouts. This approach not only enhances transparency in player assessment but also supports more strategic planning based on performance-driven evidence.