This paper aims to test the validity of the esports ranking system published by HLTV, which is widely recognized and used for ranking teams in Counter-Strike: Global Offensive (CS:GO). The results of the study demonstrate that the HLTV ranking system has a reasonable level of explanatory power in predicting the outcomes of CS:GO matches. Furthermore, the study reveals that the difference in abilities between two consecutively ranked players decreases as their ranking in CS:GO gets lower, similar to tennis rankings, resulting in a pyramid-like structure. As a result, spectators, sponsors, and bettors can rely on the information provided by the ranking system to make informed decisions and enhance their viewing experience during tournaments.
Aoki, R.Y., Assuncao, R.M. & Vaz de Melo, P.O. (2017). Luck is hard to beat: The difficulty of sports prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1367–1376). doi:10.1145/3097983.3098045.
2.
Aparício, D., Ribeiro, P. & Silva, F. (2016). A subgraph-based ranking system for professional tennis players. In Complex Networks VII (pp. 159–171). Springer. doi:10.1007/978-3-319-30569-1_12.
3.
Bozóki, S., Csató, L. & Temesi, J. (2016). An application of incomplete pairwise comparison matrices for ranking top tennis players. European Journal of Operational Research, 248(1), 211–218. doi:10.1016/j.ejor.2015.06.069.
4.
Chiu, S. (2018). Breaking down Criteria CS: GO Edition, Rating KRIMZ and GuardiaN. https://web.archive.org/web/20210213050012/https://www.vpesports.com/csgo/breaking-down-criteria-csgo-edition-rating-krimz-and-guardian/.
5.
Cohen, J. (2021). Liquipedia.net partners with esports entertainment group’sVIE.bet, the world’s most transparent esports betting platform. https://esportsentertainmentgroup.com/liquipedia-net-partners-with-esports-entertainment-groups-vie-bet-the-worlds-most-transparent-esports-betting-platform/#:~:text=Liquipedia%20is%20the%20world’s%20largest,new%20content%20being%20added%20daily.
6.
Hamari, J. & Sjöblom, M. (2017). What is eSports and why do people watch it?Internet Research.
7.
Heasman, J., Dawson, B., Berry, J. & Stewart, G. (2008). Development and validation of a player impact ranking system in Australian football. International Journal of Performance Analysis in Sport, 8(3), 156–171. doi:10.1080/24748668.2008.11868457.
8.
HLTV (2022). CS:GO World ranking on January 3rd, 2022. https://www.hltv.org/ranking/teams/2022/january/3.
9.
Klaassen, F.J. & Magnus, J.R. (2003). Forecasting the winner of a tennis match. European Journal of Operational Research, 148(2), 257–267. doi:10.1016/S0377-2217(02)00682-3.
10.
Lim, E., Lay, B., Dawson, B., Wallman, K. & Aanderson, S. (2009). Development of a player impact ranking matrix in super 14 rugby union. International Journal of Performance Analysis in Sport, 9(3), 354–367. doi:10.1080/24748668.2009.11868492.
Liquipedia (2021c). Team Liquid welcomes you to the esports wiki. https://liquipedia.net/.
14.
Mauboussin, M.J. (2012). The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing. Harvard Business Review Press. https://search-ebscohost-com.zdroje.vse.cz/login.aspx?direct=true&db=nlebk&AN=675193&lang=cs&site=ehost-live&scope=site.
15.
McLeod, C.M., Xue, H. & Newman, J.I. (2021). Opportunity and inequality in the emerging esports labor market. International Review for the Sociology of Sport, 10126902211064092.
16.
Mertz, J., Hoover, L.D., Burke, J.M., Bellar, D., Jones, M.L., Leitzelar, B. & Judge, W.L. (2016). Ranking the greatest NBA players: A sport metrics analysis. International Journal of Performance Analysis in Sport, 16(3), 737–759. doi:10.1080/24748668.2016.11868925.
17.
Reid, M., McMurtrie, D. & Crespo, M. (2010). The relationship between match statistics and top 100 ranking in professional men’s tennis. International Journal of Performance Analysis in Sport, 10(2), 131–138. doi:10.1080/24748668.2010.11868509.
18.
Rogers, R., Farquhar, L. & Mummert, J. (2022). Motivational differences among viewers of traditional sports, esports, and NBA 2K league. Communication & Sport, 10(2), 175–194. doi:10.1177/2167479520942738.
19.
Rosen, S. (1986). Prizes and incentives in elimination tournaments. The American Economic Review, 76(4), 701–715.
20.
Shortell, T. (2001). An Introduction to Data Analysis and Presentation. http://academic.brooklyn.cuny.edu/soc/courses/712/chap18.html.
21.
Spanias, A.D. & Knottenbelt, B.W. (2013). Tennis player ranking using quantitative models. Manuscript. http://www.doc.ic.ac.uk/~wjk/publications/Spanias-Knottenbelt-Mis-2013.pdf.
22.
Torgler, B. (2004). The economics of the FIFA football worldcup. Kyklos, 57(2), 287–300. doi:10.1111/j.0023-5962.2004.00255.x.
23.
Winkler, A. (2016). A history of Liquipedia. https://tl.net/blogs/505910-a-history-of-liquipedia.
24.
Wu, S. (2020). Better collective acquires CS: GO Platform HLTV. https://gamingstreet.com/better-collective-aquires-csgo-platform-hltv/.
25.
Wunderlich, F. & Memmert, D. (2016). Analysis of the predictive qualities of betting odds and FIFA World Ranking: Evidence from the 2006, 2010 and 2014 Football World Cups. Journal of Sports Sciences, 34(24), 2176–2184. doi:10.1080/02640414.2016.1218040.
