Social Networks and the Economics of Sports. This item is unavailable.Social Networks and the Economics of Sports

This book presents recent research developments in social networks, economics, management, marketing and optimization applied to sports. The volume will be of interest to students, researchers, managers from sports, policy makers and as well athletes. In particular the book contains research papers and reviews addressing the following issues: social network tools for player selection, movement and pricing in team sports, methods for ranking teams and evaluating players' performance, economics and marketing issues related to sports clubs, techniques for predicting outcomes of sports competitions, optimal strategies in sports, scheduling and managing sports tournaments, optimal referee assignment techniques and the economics and marketing of sports entertainment.

Panos M. Pardalos serves as Distinguished Professor of Industrial and Systems Engineering at the University of Florida. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Dr. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

Victor Zamaraev serves as Research Fellow of the Laboratory of Algorithms and Technologies for Network Analysis at the National Research University, Higher School of Economics. He is also a lecturer at the Department of Applied Mathematics and Informatics of the National Research University Higher School of Economics, Russia, and at the Department of Computational Mathematics and Cybernetics of the N.I. Lobachevsky State University of Nizhni Novgorod. His teaching and research interests are in the area of discrete mathematics with a focus on enumerative and structural graph theory.