Neural Networks for Standardizing Ratings inLeague of Legends

Sammanfattning: In the game League of Legends (LoL) there are several different regions globally withtheir own rating distribution. The purpose of this thesis is to examine if there are anydifferences in playing strength between the regions, and if so quantify what the offsetsare numerically.Data of matches played online are available publicly. We extracted 8.7 million matchesin total and over 600 different features per match. Each match is also annotated with alocal rating - which represents the rank it was played at. All these matches are betweenteams from similar regions and not across regions - hence the rating is a local one andnot a global one. Absence of a global score prevents us from comparing matches acrossregions. Our goal is to rank the different regions by developing a model that can predicta global score using the data available for local ratings.We first develop a Deep Neural Network (DNN) which is trained on equal amounts ofdata from all the regions to predict a global rating. We then use a Siamese Neural Network (SNN), with the purpose of generating a distribution that would be comparableto the true distribution of ratings. In both the above experiments we hide the regioninformation from the network. We also developed a model that is provided region information in a separate layer while training. The outcome of the DNN model is validatedby using the outcomes of SNN and region-aware models. In order to further improvethe results, we normalize the data with respect to the duration of a match. We performfurther experiments where a model is trained on matches from one specific region andthen use it for predicting ratings of matches from other regions.The results allowed us to rank the different regions based on their performance. Someof the results were surprising - for instance the experiments suggests that Japan andOceania, who has very little presence on the professional e-sports scene, are in the top.