4055726

An open-sourced optical tracking and advanced eSports analytics platform for league of legends

In this paper, we will use our unique data to present a novel approach and answer a question that is of important general interest to all sports: how much does an individual`s performance contribute to a team`s likelihood of winning? This is a central question of sports analytics for traditional sports as well. Like points scored or defended in traditional team sports, eSports measure kills and deaths. But, again like traditional team sports, these standard metrics do not do a great job of predicting team performance. There is some correlation to be sure, but high scoring players on bad teams are so common as to be a cliché—in all sports. Unfortunately, there are not that many games to analyze in traditional sports. Esports, however, is different; millions of games are played every day. We tracked and analyzed millions of games of LoL using our new methodology for extracting high-frequency and high-fidelity eSports data. Next, we developed and calibrated a live, in-game win probability model based on the conditions of the game at each moment. Finally, we improved the standard metrics to look at worthless deaths and smart kills. Worthless deaths are ones that do not increase your team`s win probability. Smart kills are ones that do increase your team`s win probability. With these two new metrics, suddenly the relation between individual performance and team performance is virtually a straight line. We show the difference between the upward sloping but relatively flat relationship of the standard stats on the left-hand side, and the practically straight one-to-one relationship of our new advanced stats on the right. Not only do these results suggest specific new ways forward for traditional sports, they also provide a method for generating brand-new insights: try things on eSports, where the data is far larger and cleaner, and carry those lessons back to the non-e-sports. Finally, with eSports itself poised to take over traditional sports in the future, these insights will be valuable for their own sake as well, in addition to improving all other sports analytics.
© Copyright 2018 MIT Sloan Sports Analytics Conference 2018. Kaikki oikeudet pidätetään.

Aiheet: E-sport tietokone analyysi
Aihealueet: urheilukilpailut tekniset ja luonnontieteet
Julkaisussa: MIT Sloan Sports Analytics Conference 2018
Julkaistu: 2018
Sivuja: 1-16
Julkaisutyypit: kongressin muistiinpanot
Kieli: englanti (kieli)
Taso: kehittynyt