ABSTRACT
In this work, we analyze how the esports transfer market is organized with the help of mixed methods. We assume that a combination of Social Network Analysis and Machine Learning can help to achieve deeper understanding and to find patterns which are hidden from the one-side analysis. For the research, we gathered information about transfers of Dota 2 teams made between The Internationals of 2016 and 2017 and built a network based on this data. For the ERGM, we checked the importance of belonging to one region and organization, difference of skills, and participation in TI, and for association rules, on a par with the regions, we added players roles, and a metric of their personal performance -- fantasy points. Summing up the results, we found out the importance of homophily within regions, detected presence of vertical mobility, and discover the influence of the specific players roles.
- Christopher Carling, Alan McCall, Franck Le Gall, and Gregory Dupont. 2018. Injury risk and patterns in newly transferred football players: a case study of 8 seasons from a professional football club. Science and Medicine in Football 2, 1 (2018), 47--50. arXiv:https://doi.org/10.1080/24733938.2017.1370123Google ScholarCross Ref
- Arpad E Elo. 1978. The rating of chessplayers, past and present. Vol. 3. Batsford London.Google Scholar
- Jürgen Gerhards and Michael Mutz. 2017. Who wins the championship? Market value and team composition as predictors of success in the top European football leagues. European Societies 19, 3 (2017), 223--242.Google ScholarCross Ref
- Oscar Grusky. 1963. The effects of formal structure on managerial recruitment: A study of baseball organization. Sociometry (1963), 345--353.Google Scholar
- Michael Hahsler, Christian Buchta, Bettina Gruen, and Kurt Hornik. 2018. arules: Mining Association Rules and Frequent Itemsets. https://CRAN.R-project.org/package=arules R package version 1.5--5.Google Scholar
- Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. 2011. The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Datasets. Journal of Machine Learning Research 12 (2011), 1977--1981. http://jmlr.csail.mit.edu/papers/v12/hahsler11a.html Google ScholarDigital Library
- Michael Hahsler, Bettina Gruen, and Kurt Hornik. 2005. arules -- A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software 14, 15 (October 2005), 1--25.Google ScholarCross Ref
- Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, Pavel N. Krivitsky, and Martina Morris. 2017. ergm:Fit, Simulate and Diagnose Exponential-Family Models for Networks. The Statnet Project (http://www.statnet.org). https://CRAN.R-project.org/package=ergm Rpackage version3.8.0.Google Scholar
- Ethan Heinzen. 2017. elo: Elo Ratings. https://CRAN.R-project.org/package=elo R package version 1.0.1.Google Scholar
- David R. Hunter, Mark S. Handcock, Carter T. Butts, Steven M. Goodreau, and Martina Morris. 2008. ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software 24, 3 (2008), 1--29.Google ScholarCross Ref
- Xiao Fan Liu, Yu-Liang Liu, Xin-Hang Lu, Qi-Xuan Wang, and Tong-Xing Wang. 2016. The anatomy of the global football player transfer network: Club functionalities versus network properties. PloS one 11, 6 (2016), e0156504.Google Scholar
- Miohk Ryoo, Namjung Kim, and Kyoungju Park. 2018. Visual analysis of soccer players and a team. Multimedia Tools and Applications 77, 12 (01 Jun 2018), 15603--15623. Google ScholarDigital Library
- Thijs A. Velema. 2018. Upward and downward job mobility and player market values in contemporary European professional football. Sport Management Review (2018).Google Scholar
- Ling Zhou and Stephen Yau. 2007. Efficient association rule mining among both frequent and infrequent items. Computers & mathematics with applications 54, 6 (2007), 737--749. Google ScholarDigital Library
Index Terms
- Analysis of Players Transfers in Esports. The Case of Dota 2
Recommendations
Media Metrics in Esports: The Case of Dota 2
CHI PLAY '19 Extended Abstracts: Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play Companion Extended AbstractsEsports organisations and players rely on revenue model based on spectators attention, which makes player and team brands crucial for their success. In this work, we demonstrate our approach to analyse brand value formation based on computational ...
Better Frame Rates or Better Visuals? An Early Report of Esports Player Practice in Dota 2
CHI PLAY '21: Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in PlayEsports athletes often reduce visual quality to improve latency and frame rate, and increase their in-game performance. Little research has examined the effects of this visuo-spatial tradeoff on performance, but we could find no work studying how ...
Why Players use Pings and Annotations in Dota 2
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing SystemsGroupware research has long focused on representing gestures as a means to facilitate collaboration. However, this work has not led to wide support of gesturing in commercial groupware systems. In contrast, Dota 2, a popular MOBA game, provides two ...
Comments