  League of Legends (LoL) is the most widely played multiplayer online battle
arena (MOBA) game in the world. An important aspect of LoL is competitive
ranked play, which utilizes a skill-based matchmaking system to form fair
teams. However, players' skill levels vary widely depending on which champion,
or hero, that they choose to play as. In this paper, we propose a method for
predicting game outcomes in ranked LoL games based on players' experience with
their selected champion. Using a deep neural network, we found that game
outcomes can be predicted with 75.1% accuracy after all players have selected
champions, which occurs before gameplay begins. Our results have important
implications for playing LoL and matchmaking. Firstly, individual champion
skill plays a significant role in the outcome of a match, regardless of team
composition. Secondly, even after the skill-based matchmaking, there is still a
wide variance in team skill before gameplay begins. Finally, players should
only play champions that they have mastered, if they want to win games.
