Abstract:
League of Legends is one of the biggest games in the professional e-sports industry, and it has been a data-driven field for a while. The mechanism of hero selections as to predict which characters will be selected or not in each selection is an important issue. In this study, the mechanism of hero selections in the game has been tried to be examined. Since champion selections are more prominent in competitive and professional matches, the selection rankings of all professional matches in 2020 were obtained by web-scraping methods. After that, the average in-game statistics of all champions were obtained from the Oracle Elixir database, and the two data sets were combined. Machine Learning models were used to analyze drafts. In this context, while the performance of the XGB model was higher for the first three selections, the NB model was ahead of the others for the last two selections. In order to increase the explanatory power of these models and to comment on the characters, Shapley values were used. Models were applied to the World Championship Tournament final in 2020, and the selection probabilities of the champions played in those matches were calculated. Depending on this, these models can be applied for different tournaments and specific teams in the same year. It can help teams and players better analyze their opponents and their own choices.
