Esports Analytics: How do we provide analytical support to players and teams?

Esports is the playing of computer games in a competitive environment, whether at the amateur or professional level. In recent years esports has become a global phenomenon and has gained immense momentum, outcompeting many traditional sports in terms of the number of practitioners, prize pools and even viewership – and is a potential future Olympic discipline.

More than 300 million people worldwide play competitive games, and tournament prices have reached more than a dozen million dollars for first place – with dozens of millions of viewers for world finals. On the company side, considerable resources are being allocated to support the esports environment from the main companies in the domain such as Riot Games, Wargaming, Valve, Ubisoft and Turbine. The number of players active in the gaming community is also increasing, with individual games have dozens of millions of active players. 

Just like regular sports, esports practitioners, whether professionals or amateurs, need to analyse player behaviour in order to facilitate training, commentating and overall development. But analysing player behaviour in a competitive digital game is a far more complex challenge than in e.g. soccer or basketball, because digital games provide a much greater variety of abilities to the players. This not the least because many of these games are set in fantastic environments that leave such mundane considerations as physics and reality and enters the realm of the fantastic. This means that esports analytics has to cover a wide variety of competitive games, from sport-simulation titles such as the FIFA-series of Soccer games to fantastical games such as League of Legends, which sees mythical heroes combating each other using everything from Medieval weapons to magical spells. 

Millions of people worldwide generate their own statistics from matches, and tournaments use numbers heavily when describing games. However, the more sophisticated forms of analytics used in regular sports have yet to make a substantial impact in esports.

DC Labs researchers seek to change this, by applying advanced machine learning and data mining principles to finding patterns in the playstyles of esports players across amateur and professional levels, and developing visualisation tools that allow players to take control of their stats and use them to improve their performance – thus democratising analytical support.