Complexity Analysis in Counter Strike 2 Spray Patterns
Resumo
Introduction: The Counter-Strike (CS) franchise has shown benefits for players social development and for the stimulation of motor and cognitive skills, establishing relationships between improvements in physical, mental health and in-game performance. Individual skills of a CS2 player, such as aiming and control over weapon spray patterns, are extremely important for maximizing performance metrics. Objective: this work aims to analyze the complexity of Counter-Strike 2 spray patterns, and then compare it with the cost of each weapon in the game. Additionally, this work also analyzes the length of sprays performed by professional players as a way of showing how difficult is to control the recoil compensation in CS2. Methodology or Steps: The complexity of spray patterns in Counter-Strike 2 was computed using the Lempel-Ziv (LZ) method, adapted to problems in which the data can be represented as time series. In this work, spray patterns are treated as temporal sequences of movement, allowing their complexity to be calculated based on the number of new patterns found throughout the sequence. Results: Within the set of normalized LZ complexities, rifle weapons appear among the highest complexities, but do not represent the most expensive weapon. This indicates that these weapons present greater structural irregularity in the adopted symbolic representation. In contrast, SMGs and machine guns appear as weapons with less complex spray patterns. Lastly, professional players’ spray lengths are considerably smaller than the magazine capacity of the weapons and usually do not empty the full magazine during a spray, even when using weapons with relatively simple spray patterns.
Palavras-chave:
Spray Pattern Complexity, Lempel-Ziv Method, CS 2, Temporal Data
Referências
Durst, D., Xie, F., Sarukkai, V., Shacklett, B., Frosio, I., Tessler, C., Kim, J., Taylor, C., Bernstein, G., Choudhury, S., et al. (2024). Learning to move like professional counter-strike players. In Computer Graphics Forum, volume 43, page e15173. Wiley Online Library.
Kulkarni, A. R. e Kuber, P. M. (2026). Sensor-driven machine learning for cognitive state and performance risk assessment in esports: A systematic review. Electronics, 15(7):1465.
Kulmakorpi, T. (2025). Effects of different physical warm-ups on the aiming performance of counter strike 2 & valorant players. PhD thesis, University of Jyväskylä.
Lempel, A. e Ziv, J. (1976). On the complexity of finite sequences. IEEE Transactions on information theory, 22(1):75–81.
Marshall, S., Mavromoustakos-Blom, P., e Spronck, P. (2022). Enabling real-time prediction of in-game deaths through telemetry in counter-strike: Global offensive. In Proceedings of the 17th international conference on the foundations of digital games, pages 1–10.
Montalvão, J. e Canuto, J. (2014). A lempel-ziv like approach for signal classification. TEMA (São Carlos), 15(2):223–234.
Wright, T., Boria, E., e Breidenbach, P. (2002). Creative player actions in fps online video games: Playing counter-strike. Game studies, 2(2):103–123.
Xia, X., Salinas, A., e Morstatter, F. (2025). Precision under fire: Analysis and predictive modeling in counter-strike: Global offensive. In 2025 IEEE Conference on Games (CoG), pages 1–8. IEEE.
Kulkarni, A. R. e Kuber, P. M. (2026). Sensor-driven machine learning for cognitive state and performance risk assessment in esports: A systematic review. Electronics, 15(7):1465.
Kulmakorpi, T. (2025). Effects of different physical warm-ups on the aiming performance of counter strike 2 & valorant players. PhD thesis, University of Jyväskylä.
Lempel, A. e Ziv, J. (1976). On the complexity of finite sequences. IEEE Transactions on information theory, 22(1):75–81.
Marshall, S., Mavromoustakos-Blom, P., e Spronck, P. (2022). Enabling real-time prediction of in-game deaths through telemetry in counter-strike: Global offensive. In Proceedings of the 17th international conference on the foundations of digital games, pages 1–10.
Montalvão, J. e Canuto, J. (2014). A lempel-ziv like approach for signal classification. TEMA (São Carlos), 15(2):223–234.
Wright, T., Boria, E., e Breidenbach, P. (2002). Creative player actions in fps online video games: Playing counter-strike. Game studies, 2(2):103–123.
Xia, X., Salinas, A., e Morstatter, F. (2025). Precision under fire: Analysis and predictive modeling in counter-strike: Global offensive. In 2025 IEEE Conference on Games (CoG), pages 1–8. IEEE.
Publicado
29/09/2026
Como Citar
SANTOS, Cauan; GARCIA, Rodolfo.
Complexity Analysis in Counter Strike 2 Spray Patterns. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1028-1038.
DOI: https://doi.org/10.5753/sbgames.2026.26379.
