E-sports organizations with franchised networks: formalization of technological and economic development based on optimal operation and upgrade of the hardware

Economic Annals-ХХI: Volume 187, Issue 1-2, Pages: 146-162

Citation information:
Chyzmar, I., & Hoblyk, V. (2021). E-sports organizations with franchised networks: formalization of technological and economic development based on optimal operation and upgrade of the hardware. Economic Annals-XXI, 187(1-2), 146-162. doi: https://doi.org/10.21003/ea.V187-15

Ivan Chyzmar
PhD Student (Economics),
Department of Economics and Finance,
Mukachevo State University
26 Uzhhorodska Str., Mukachevo, 89600, Ukraine
ORCID ID: https://orcid.org/0000-0002-1715-1310

Volodymyr Hoblyk
D.Sc. (Economics), Professor, First Vice-Rector,
Mukachevo State University
26 Uzhhorodska, Mukachevo, 89600, Ukraine
ORCID ID: https://orcid.org/0000-0003-1830-3491

E-sports organizations with franchised networks: formalization of technological and economic development based on optimal operation and upgrade of the hardware

Abstract. The paper focuses on the priority given to the technological and economic development of e-sports organizations with franchised networks. Attention is paid to the specificity of the process of timely upgrading of e-sports hardware, which involves the upgrading and introduction of new, more sophisticated and advanced gaming and other devices, which determines the number of e-sports disciplines and available e-sports events for the е-sports arena. The success of franchising networks depends on the quantity of the c-sports hardware, which should be similar to the hardware used by the main arena in order to ensure the functionality of a well-structured state-of-the-art training process for e-sport athletes.

The purpose of the study is to present a basis for the technological and economic development of e-sports organizations with a franchising network within the formalized system in terms of finding optimal solutions to the problem of hardware upgrades.

The study presents a model of technological and economic development of all e-sports organizations with franchised networks as а content area, posing the rules of operation, regulating the process of hardware upgrades  and focusing on the sustainability of development.

Dynamic programming methods based on Bellman’s equations and the formalization of the problem of hardware replacement through graphic notations, cloud computing in the AnyLogic environment are used to identify and illustrate the features of such solutions.

The result of the research is a description of technological development of e-sports organizations with a franchise network with the use of a model that approximates the optimal way of operation and upgrade of related hardware. This development illustrates the optimal way of operation and hardware upgrade of Blizzard Arena and Overwatch League, represented by participants from the United States, the United Kingdom, Germany, France, South Korea and Ukraine. The franchised Overwatch League includes the main arena (Blizzard Arena), as well as Florida Mayhem Club (Miami-Orlando, USA), New York Excelsior Club (New York, USA), London Spitfire Club (London, UK), Vault Club 15 (Kyiv, Ukraine), Immortals Club (Los Angeles, USA), NRG E-sports Club (Berlin, Germany), Misfits Club (Seoul, South Korea), PS4 Training Base (Beijing, China) and Xbox One Training Base (Paris, France).

According to the obtained data, the formalization of the technological and economic development of Blizzard Arena suggests a solution to the problem of finding an optimal strategy relevant to optimization of hardware up to the moment of its transfer to the franchised network. Such formalization is highly relevant. They rely on the possible state of the system proposed in our research. That system state identifies the functional Bellman equations. Naturally, emerges a possibility to significantly reduce investment in the e-sports environment of the main arena and the franchise while controlling the quality and functionality of e-sports hardware.

The organizations’ focus on two-stage upgrades will reduce investment in major hardware. The study illustrates the formalization of the techno-economic development of the Blizzard Arena through a two-stage upgrade of the Aerocool Advanced Technologies franchise (primary franchise – from producer, secondary franchise – from franchise Blizzard Arena operator). Based on the specific features of the Bellman equations, the development of the Blizzard Arena must take into account the model which determines the feasibility of hardware transferring to a franchised network during the third period of operation, where it operated as long as franchisees enter the maximum profit area. When using the Bellman equations, we suggest that the arena focus on the moment when the function values will correspond to the replacement state of the hardware and the franchise member on the stability of the environment.

The formalization of the technical development will make it possible to orient the e-sport organizations with franchised networks to search for conditions sufficient for optimal operation and upgrade of their hardware. As a result, there will appear an optimized system with a content area, which will provide a stable environment for e-sports events at the arena and athletes’ training in franchise clubs.

Keywords: E-sport; Franchise; Blizzard Arena; Overwatch League; Hardware; Upgrade; Bellman Equations; Graphic Notations; Dynamic Programming

JEL Classіfіcatіon: C44; C 60; C80

Acknowledgements and Funding: The authors received no direct funding for this research.

Contribution: The authors contributed equally to this work.

Data Availability Statement: The dataset is available from the authors upon request.

DOI: https://doi.org/10.21003/ea.V187-15


  1. AnyLogic. (2018). Official web site.
    http://www.anylogic.ru (in Russ.)
  2. Arrow, K., Chenery, H., & Solow, R. (1961). Capital-Labor Substitution and Economic Efficiency. The Review of Economics and Statistics, 43(3), 225-250.
  3. Bellman, R. (2010). Dynamic Programming. Princeton. New Jersey: Princeton University Press.
  4. Bellman, R. (2013). Dynamic Programming. Mineola. New York: Dover Publications, Inc.
  5. Bertsekas, D. P. (2012). Dynamic Programming and Optimal Control. Belmont, Massachusetts.
  6. Bhondekar, A. P., Renu, V., Singla, M., & Ghanshyan, C. (2009, March 18-20). Genetic Algorithm Based Node ­Placement Methodology For Wireless Sensor Networks [Paper presentation]. Proceedings of the ­International ­MultiConference of Engineers and Computer Scientists, 1, Hong Kong.
  7. Blizzard Arena. (2020). Official website.
  8. Bohner, M., Stanzhytskyi, A., & Bratochkina, A. (2013). Stochastic dynamic equations on general time scales. Electronic Journal of Differential Equations, 57, 1-15.
  9. Carter, M. W., Price, C. C., & Rabadi, G. (2019). Operations research: a practical approach. Boca Raton: CRC Press.
  10. Chen, B. Y. (2012). Classification of h-homogeneous production functions with constant elasticity of substitution. Tamkang Journal of Mathematics, 43(2), 321-328.
  11. Denardo, E. V. (2012). Dynamic Programming: Models and Applications. New York, Courier Corporation.
  12. Farnham, D. (2018, February 28). Overwatch’s new hero, Brigitte Lindholm, is a tanky support.
  13. Lapkina, I., & Malaksiano, M. (2018). Elaboration of the hardware replacement terms taking into account wear and tear and obsolescence. Eastern-European Journal of Enterprise Technologies, 3(3(93)), 30-39.
  14. Lapkina, I., & Malaksiano, M. (2018). Estimation of fluctuations in the performance indicators of hardware that operates under conditions of unstable loading. Eastern-European Journal of Enterprise Technologies, 1(3(91)), 22-29.
  15. Lutsenko, I. (2016). Definition of efficiency indicator and study of its main function as an optimization criterion. Eastern-European Journal of Enterprise Technologies, 6(2(84)), 24-32.
  16. Mensch, A., & Blondel, M. (2018). Differentiable Dynamic Programming for Structured Prediction and Attention.
  17. Mishra, S. K. (2010). A brief history of production functions. The IUP Journal of Managerial Economics, 8(4), 6-34.
  18. Noghin, V. D. (2018). Reduction of the Pareto set: an axiomatic approach. Studies in Systems, Decision and Control.
  19. Rardin R. L. (2015). Optimization in Operations Research. New York: Pearson.
  20. Renshaw, G. (2016). Maths for Economics (4rd ed.). New York: Oxford University Press.
  21. Richard, E. B. (2003). Dynamic programming. Dover Books on Computer Science. Mineola, NY, United States.
  22. Sokolovska, Z. M., Yatsenko, N. V., & Khortiuk, M. V. (2019). The Simulation Models of Activities of IT Firms on the Basis of AnyLogic Platform. Business Inform, 6, 61-76.
    https://doi.org/10.32983/2222-4459-2019-6-61-76 (in Ukr.)
  23. Strakhov, E. M. (2013). Dynamic Programming in Structural and Parametric Optimization. International Journal of Pure and Applied Mathematics, 82(3), 503-512 (in Ukr.).
  24. Taha, H. (2017). Operations Research: an Introduction (10th ed., rev.). Boston: Princeton.
  25. Winter Simulation Conference. (2018, December 9-12). The premier international forum for disseminating recent advances in the field of system simulation.
  26. Yatsenko, T. O., & Svystun, L. A. (2019). Processes and methods for value engineering in enterprise management task systems. Efficient Economy, 5 (in Ukr.).
  27. Zaidon, Z., Wei, W., & Honglei, X. (2009). Hamilton-Jacobi-Bellman equations on time scales. Mathematical and Computer Modelling, 49(9-10), 2019-2028.
  28. Zerbini, C., Luceri, B., & Vergura, D. (2017). Leveraging consumer’s behaviour to promote generic drugs in Italy. Health Policy, 121(4), 397-406.

Received 18.11.2020
Received in revised form 20.12.2020
Accepted 11.01.2021
Available online 28.02.2021