USING DATA SCIENCE TO ANALYZE SPORTS DATA FOR IMPROVING TEAM AND PLAYERS PERFORMANCE

In this paper, we consider the use of data science for the improvement of player and collaboration performance with reference to football. The research fascines goal-scoring trends in the period 1992-2023, analyzing player performance trends and club efficiencies from Kaggle and other reliable sources. Modern tools, such as Python and data visualization libraries, allowed for structured analysis with the aforementioned types of methods. Results obtained show a positive influence of appearances on goals scored; this indicates the importance of regular playing time in scoring optimal results. Furthermore, the study, demonstrates how analytical findings can inform player acquisition decisions and determine the appropriate team structures making the work valuable for coaches and analysts. Some limitations include the fact that data is collected from the Premier League only, and some important contributions to goals such as assists and tackles did not make the list. The study encourages further comprehensive comparisons across leagues and stresses that data science can drastically transform the field and promote sports analytics in general for all parties involved. Authors of future works could move to other leagues or include other performance indicators to increase external validity and practicality. Therefore, the present research is part of a vast list of investigations that explain how the utilization of data science transforms decision-making in sports.