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.
翻译：传说联盟(LOL) 是世界上玩得最广的多玩家在线竞技场游戏。 LoLoL的一个重要方面是竞争性排名游戏,它利用基于技能的配对系统组成公平球队。然而,球员的技能水平差异很大,取决于他们选择以哪个冠军或英雄作为球队。在本文中,我们根据球员与其选定冠军的经验,提出了在排名LoL游戏中预测游戏结果的方法。我们利用一个深厚的神经网络发现,在所有球员选出冠军后,可以预测比赛结果的准确率为75.1%,而冠军是在游戏开始前开始的。我们的结果对玩LoLoL和牵线比赛有着重要影响。首先,个人冠军的技能在比赛结果中扮演着重要角色,而不管球队组成如何。第二,即使在以技能为基础的配球比赛之后,球员在游戏开始之前的球队技能方面仍然有很大差异。最后,球员只有在他们想要赢得比赛的情况下,才能打他们已经掌握的冠军。
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