Skip to main content

Applicability of Psychophysiological and Perception Data for Mapping Strategies in League of Legends – An Exploratory Study

  • Conference paper
  • First Online:
HCI in Games (HCII 2023)

Abstract

League of Legends is one of the most popular online games today, with a competitive scenario and a solid and constantly growing player base. This work aims to explore a solution that identifies game events and strategies from the convergence of different psychophysiological metrics from seasoned players in order to create tools to reduce the learning curve of these strategies for beginner and intermediate players. To achieve this, experiments with five participants have been conducted that extract, process, and synthesize data from player profiling, Electrodermal Activity (EDA), Interbeat Interval (IBI), facial expressions, and player perception of the match. The EDA and HR data (derived from the IBI metric) proved to be complementary and correspond with game events and strategies, while facial expression data had inconclusive results in all experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Available at https://leagueoflegends.fandom.com/wiki/League_of_Legends.

  2. 2.

    Available at https://developer.riotgames.com/apis#match-v5/GET_getMatch.

  3. 3.

    Available at https://blitz.gg.

  4. 4.

    Available at https://mobalytics.gg.

  5. 5.

    Available at https://obsproject.com/.

References

  1. Anderson, A.: Comparison of baroreceptor sensitivity with other psychophysiological measures to classify mental workload. Doctor of Philosophy, Iowa State University (2020). https://doi.org/10.31274/etd-20200624-85

  2. Andreassi, J.L.: Psychophysiology: Human Behavior & Physiological Response, vol. 1, 4th edn. Psychology Press (2000)

    Google Scholar 

  3. Aslam, S., Zwart, N., Gouweleeuw, K., Verhoeven, G.: Classification of disappointment and frustration elicited by human-computer interaction: towards affective HCI, August 2019

    Google Scholar 

  4. Braithwaite, D.J.J.: A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments, p. 42 (2013)

    Google Scholar 

  5. Cacioppo, J., Gardner, W., Berntson, G.: The affect system has parallel and integrative processing components. J. Pers. Soc. Psychol. 76, 839–855 (1999). https://doi.org/10.1037/0022-3514.76.5.839

    Article  Google Scholar 

  6. Cacioppo, J.T., Tassinary, L.G., Berntson, G.: Handbook of Psychophysiology, March 2007. https://doi.org/10.1017/CBO9780511546396

  7. Cannon, W.B.: The James-Lange theory of emotions: a critical examination and an alternative theory. Am. J. Psychol. 39(1/4), 106–124 (1927). https://doi.org/10.2307/1415404

    Article  Google Scholar 

  8. Cruz, A.C.S.: League of legends: an application of classification algorithms to verify the prediction importance of main in-game variables, p. 5 (2021)

    Google Scholar 

  9. Dawson, M.E., Schell, A.M., Filion, D.L., Berntson, G.G.: The electrodermal system. In: Cacioppo, J.T., Tassinary, L.G., Berntson, G. (eds.) Handbook of Psychophysiology, 3rd edn., pp. 157–181. Cambridge University Press, Cambridge (2007). https://doi.org/10.1017/CBO9780511546396.007

    Chapter  Google Scholar 

  10. De Rivecourt, M., Kuperus, M.N., Post, W.J., Mulder, L.J.M.: Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight. Ergonomics 51(9), 1295–1319 (2008). https://doi.org/10.1080/00140130802120267

    Article  Google Scholar 

  11. Dengah, H.J.F., Snodgrass, J.G., Else, R.J., Polzer, E.R.: The social networks and distinctive experiences of intensively involved online gamers: a novel mixed methods approach. Comput. Hum. Behav. 80, 229–242 (2018). https://doi.org/10.1016/j.chb.2017.11.004

    Article  Google Scholar 

  12. Fernandes, M.V.: Ajuste dinâmico de dificuldade em jogos digitais : um estudo de caso comparativo entre os modelos afetivo e baseado em desempenho, December 2019. https://bdm.unb.br/handle/10483/29227

  13. Föll, S., et al.: FLIRT: a feature generation toolkit for wearable data. Comput. Methods Prog. Biomed. 212, 106461 (2021). https://doi.org/10.1016/j.cmpb.2021.106461

    Article  Google Scholar 

  14. Kica, A., Paolillo, T.J., O’Donnell, L.R., La Manna, A.J.: Analysis of data gathered from league of legends and the Riot games API, March 2016

    Google Scholar 

  15. Klarkowski, M., Johnson, D., Wyeth, P., Phillips, C., Smith, S.: Psychophysiology of challenge in play: EDA and self-reported arousal. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA 2016, pp. 1930–1936. Association for Computing Machinery, New York, May 2016. https://doi.org/10.1145/2851581.2892485

  16. Kokkinakis, A., et al.: Metagaming and metagames in Esports. Int. J. Esports (2021). https://eprints.whiterose.ac.uk/179913/

  17. Lang, P.J.: The emotion probe: studies of motivation and attention. Am. Psychol. 50(5), 372–385 (1995). https://doi.org/10.1037/0003-066X.50.5.372

    Article  Google Scholar 

  18. Lee, J.S.: Exploring stress in Esports gaming: physiological and data-driven approach on tilt. Ph.D. thesis, UC Irvine (2021). https://escholarship.org/uc/item/61p8c951

  19. Mandryk, R.: Physiological measures for game evaluation. In: Isbister, K., Schaffer, N. (eds.) Game Usability, pp. 207–235. Elsevier, Amsterdam (2008). https://doi.org/10.1016/B978-0-12-374447-0.00014-7

    Chapter  Google Scholar 

  20. McAllister, G., Mirza-Babaei, P., Avent, J.: Improving gameplay with game metrics and player metrics. In: Seif El-Nasr, M., Drachen, A., Canossa, A. (eds.) Game Analytics: Maximizing the Value of Player Data, pp. 621–638. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4769-5_27

    Chapter  Google Scholar 

  21. Nacke, L.E.: An introduction to physiological player metrics for evaluating games. In: Seif El-Nasr, M., Drachen, A., Canossa, A. (eds.) Game Analytics: Maximizing the Value of Player Data, pp. 585–619. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4769-5_26

    Chapter  Google Scholar 

  22. do Nascimento Junior, F.F., Melo, A.S.C., da Costa, I.B., Marinho, L.B.: Profiling successful team behaviors in league of legends. In: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web, WebMedia 2017, pp. 261–268. Association for Computing Machinery, New York, October 2017. https://doi.org/10.1145/3126858.3126886

  23. Oliveira, R.R.A.: Análise de diferentes algoritmos de ajuste dinâmico de dificuldade que utilizam dados de atividade eletrodérmica em jogos digitais, May 2021. https://bdm.unb.br/handle/10483/28952

  24. Ornelas, P.Y.: Injeção de DLL: um estudo de caso aplicado à jogos, October 2019. https://bdm.unb.br/handle/10483/29228

  25. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)

    Article  Google Scholar 

  26. Schachter, S., Singer, J.: Cognitive, social, and physiological determinants of emotional state. Psychol. Rev. 69(5), 379–399 (1962). https://doi.org/10.1037/h0046234

    Article  Google Scholar 

  27. Scherer, K.R.: Emotion as a multicomponent process: a model and some cross-cultural data. Rev. Pers. Soc. Psychol. 5, 37–63 (1984)

    Google Scholar 

  28. Serengil, S.I., Ozpinar, A.: HyperExtended LightFace: a facial attribute analysis framework. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET), Istanbul, Turkey, pp. 1–4. IEEE, October 2021. https://doi.org/10.1109/ICEET53442.2021.9659697

  29. Siqueira, E.S., Santos, T.A.A., Castanho, C.D., Jacobi, R.P.: Estimating player experience from arousal and valence using psychophysiological signals. In: 2018 17th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), Foz do Iguaçu, Brazil, pp. 107–10709. IEEE, October 2018. https://doi.org/10.1109/SBGAMES.2018.00022

  30. Tan, C.T., Bakkes, S., Pisan, Y.: Inferring player experiences using facial expressions analysis. In: Proceedings of the 2014 Conference on Interactive Entertainment, Newcastle, NSW, Australia, pp. 1–8. ACM, December 2014. https://doi.org/10.1145/2677758.2677765

  31. Thayer, R.E.: The Biopsychology of Mood and Arousal. Oxford University Press, New York (1989)

    Google Scholar 

  32. Valenza, G., Lanata, A., Scilingo, E.P.: The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Trans. Affect. Comput. 3(2), 237–249 (2012). https://doi.org/10.1109/T-AFFC.2011.30

    Article  Google Scholar 

  33. Vallverdú, J., Trovato, G.: Emotional affordances for human–robot interaction. Adapt. Behav. 24(5), 320–334 (2016). https://doi.org/10.1177/1059712316668238

    Article  Google Scholar 

  34. Watson, D., Wiese, D., Vaidya, J., Tellegen, A.: The two general activation systems of affect: structural findings, evolutionary considerations, and psychobiological evidence. J. Pers. Soc. Psychol. 76(5), 820–838 (1999). https://doi.org/10.1037/0022-3514.76.5.820

    Article  Google Scholar 

  35. Wu, M., Lee, J.S., Steinkuehler, C.: Understanding tilt in Esports: a study on young league of legends players. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI 2021, pp. 1–9. Association for Computing Machinery, New York, May 2021. https://doi.org/10.1145/3411764.3445143

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tiago B. P. e Silva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bandeira, I.N., Castanho, C.D., e Silva, T.B.P., Sarmet, M.M., Jacobi, R.P. (2023). Applicability of Psychophysiological and Perception Data for Mapping Strategies in League of Legends – An Exploratory Study. In: Fang, X. (eds) HCI in Games. HCII 2023. Lecture Notes in Computer Science, vol 14047. Springer, Cham. https://doi.org/10.1007/978-3-031-35979-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35979-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35978-1

  • Online ISBN: 978-3-031-35979-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics