Performance analysis in esports: modelling performance at the 2018 League of Legends World Championship. Issue 5 (December 2020)

• Record Type: Journal Article
• Authors: Novak, Andrew R; Bennett, Kyle JM; Pluss, Matthew A; Fransen, Job
• Is Part Of: International journal of sports science & coaching. Volume 15:Issue 5/6(2020)
• Page Start: 809
• Page End: 817
• Publication Date: 2020-12
• Subjects: Electronic sports -- sport analytics -- video games; Coaching (Athletics) -- Periodicals; Sports sciences -- Periodicals; Coaching (Athletics); Sports sciences; Periodicals; 796.077
• DOI: 10.1177/1747954120932853
• Languages: English
• ISSNs: 1747-9541

Abstract

Performance analysis is a well-established discipline in sports science, supported by decades of research. Comparatively, performance analysis in electronic sports (esports) is limited. Therefore, there is an opportunity to accelerate performance outcomes in esports by applying methods grounded in sports science. This study adopted a coach-centred approach to model performance at the 2018 League of Legends World Championship. Three expert coaches rated the proposed relationship between 43 variables and match outcomes in professional League of Legends competition using a Likert scale (1–10). The Likert scale was anchored with 'no relationship' at 1 and 'very strong relationship' at 10. The coaches' median ratings were calculated for each variable. Variables with a median score ≥6 were retained for analyses. A total of 14 variables were collected from the 2018 League of Legends World Championship (n = 119) matches via video annotations and match histories. Generalized Linear Mixed Effects Models with binomial logit link function were implemented with respect to the Blue Side winning or losing the match, and individual teams were specified as random effects. Variables were screened for multicollinearity before using a step-up approach. The best model of performance included Tower Percentage (p = 0.006) and Number of Inhibitors (p = 0.029). This model achieved classification accuracy of 95.8%. While Tower Percentage and Number of Inhibitors contributed to winning or losing, further research is required to determine effective strategies to improve these variables, to understand the relevance of these variables across the complete time-series of the match, and to determine whether performance indicators remain stable across game updates.