I. V. Solntsev’s scientific contributions

This article aims to build a general valuation model that can be applied by investors and current shareholders of professional sport clubs from different countries and leagues. The study is based on panel data on the valuation of soccer clubs published annually by Forbes. Authors analyze all value-drivers that were used previously, expanding the time horizon (number of observations) and incorporating various models including linear and non-linear mixed effect regressions. The best performance is obtained using a mixed-effect model with treebased fi xed part. The following determinants were found signifi cant for the fi xed effect: revenue and number of Google search requests. Analysis of actual deals in 2015–2020 confi rms the model’s predictive ability. It is also shown that since Forbes overestimates the market value of soccer clubs, the proposed model predicts an upper bound on the real value. In this regard, transactions with real value exceeding the estimates are of particular interest. A deeper analysis of such transactions allows to identify additional “non-soccer” factors affecting the deal. Therefore, the proposed model can serve as a tool for the rapid assessment of a soccer club based on open data.

The article investigates the financial resource management f or the development of mass and elite sports at the regional level. The authors used statistical data of the Ministry of Sports that include 28 socio-economic indicators and 39 indicators of sports development in 82 regions for 2012 - 2015. A model of sports development was built using PLS-SEM method. We identified the following latent variables: economic development of the region; funds allocated to sports development; availability of resources; development of mass sports; development level of professional sports; results in elite sports, results in adaptive sports. The level of regional economic development affects the amount of funding allocated to the sports, which in turn determines the availability of resources. Availability of resources affects the success in the development of mass and professional sports. Success in professional sports determines results in great sporting achievements and adaptive sports. Structural modelling allowed us to identify measurable indicators of resources (model inputs) and results of sports development (model outputs). The authors assessed the effectiveness of transformation of inputs into outputs using DEA method. We investigated two models. The first one uses the indicators of mass sports development as outputs, the second one uses the indictors of professional sports development as outputs. The inputs of both models are the indicators of financial resources for sports. The simultaneous review of the effectiveness of two directions allows to emphasize the features of each region and evaluate balance in the development of mass and professional sports. The modelling results allow to identify several groups of regions with similar parameters, which may be due to their similar locations.