A Data Science Approach to Football Team Player Selection

FIFA (Federation Internationale de Football Association) is world football (soccer) league that is separate from Olympics. FIFA been largely instrumental for making soccer as the most popular game in the world. It has led to development of many private soccer clubs all over the world. Creating new clubs with young players, loaning players from other clubs, picking choice positions, determining wages and remuneration to players based on performance and international rankings is complicated decision process in terms of global business perspective. This paper presents a data science approach to minimize the time taken in selecting a player for a team by considering the cost and player's skills as constraints. Such an analysis will help an owner to maximize the profit and popularity of an existing club or to create a new club. We present statistical analysis of player performance based on abilities and skills for a new team using powerBl and Python Pandas by minimizing the cost. The results show that it leads to improved business profits through a systematic enhancement to football data sets. These kind of approaches and analytical results can be useful to franchisor of proprietary knowledge to form group of selected players as team.