eSport or competitive video gaming brings together millions of players and viewers from across the world. Having witnessed a steep increase in popularity over the last few years, industry estimates forecast the viewer base to increase from 230 million in 2015 to 427 million in 2019.
With the growing fanbase, the viewing arena has also expanded from television sets at homes to gigantic screens in stadiums. Professional teams participate in multiplayer competitions which often have prizes worth millions of dollars.
Following on the success of predicting cinematic awards, the soccer World Cup, and various big-ticket sporting events, Bing is now predicting major eSport tournaments. Bing’s first foray into eSports was in 2016 and has since been expanding to cover major tournaments catering to massively multiplayer online role-playing games (MMORPGs) such as Dota2 and League of Legends.
Currently, Bing is predicting the most followed eSports event, the League of Legends World Championship, also known as ‘The Worlds’. With the finals of the 2018 World Championship being held on November 3, 2018, in South Korea between Fnatic and Invictus Gaming, Bing predicts Invictus Gaming to take home the prize money of $823,250. Last year, the finals of the tournament were watched by 60 million people, making it one of the world’s most-watched matches.
Predictability in eSports
Building prediction models for eSports is different from how we predict outcomes of leading global sporting events. Compared to most sporting events, eSports has richer data. However, the element of unpredictability is much higher for eSports primarily due to two factors. First, frequent roster changes make it difficult to evaluate current team performance. Second, the selection phase plays a vital role in the outcome of the match as each player gets to choose from a myriad of characters, resulting in various team combinations that are difficult to predict beforehand. The actions of a handful of players can affect the outcome of the tournament. The uncertainty makes forecasting sporting events more challenging and fun.
The building blocks of the predictive model
We use a combination of long-term signals (such as matchmaking rating) and short-term spikes to balance between teams that have a historically good record as opposed to teams that are currently performing well. Moreover, we account for team-level and player-level statistics.
To counter the constant change in line-up, Bing Predicts includes a model that tracks team performance as well as the career performance of individual players based on in-game features such as gold per minute (GPM) and experience per minute (XPM) along with external features such as win rates. Further, our model computes changes in key statistics for each player and each team after a roster shuffle to quickly update predictions. However, accommodating the same was difficult for the finals of Dota2 – ‘The International 2018’ as complexities increased because of roster changes in the Evil Geniuses’ team and OG’s new line-up.
Another factor that plays an important role in deciding the outcome of games like Dota 2 is its drafting phase. Further, we computed a relative measure of a team’s strength to its peers. This factor helped in determining the overall win for the model.
These lessons will help us improve the prediction competencies of our models for the upcoming tournaments.
Just as in real-world sport, the human spirit plays an important role in determining the winners of virtual games. At Bing Predicts, our aim is to make our model stronger and better with every game and add to the excitement.