This article describes the development of a recommender system for selecting players based on machine learning. The system introduced the example of hockey with the possibility of expanding its use in various team sports. For each sport different roles and characteristics of the players were considered. The article analyzes information about hockey, football, basketball and volleyball. The characteristics of the players are structured and divided into general groups. For each parameter coefficients are displayed that show the impact on the result of the match. Various machine learning algorithms were used to build the model. The web interface of the application has been created.

In this paper one of the possible implementations of cooperative education models based on STAD (Student Teams-achievement Divisions) strategy of cooperative learning in LMS (Learning Management System) is described. The approaches of this learning methodology increase the level of teamwork skills, need in further professional activity, and their injection into LMS can help to automate and optimize some processes and open new opportunities for implementation of new instruments.