Details

Autor(en) / Beteiligte

• Do, Tiffany D.
• Wang, Seong Ioi
• Yu, Dylan S.
• McMillian, Matthew G.
• McMahan, Ryan P.
• Harteveld, Casper
• Pirker, Johanna
• Fowler, Allan
• Canossa, Alesandro Alessandro
• Arya, Ali Ali

Titel

Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends

Ist Teil von

• Proceedings of the 16th International Conference on the Foundations of Digital Games, 2021, p.1-5

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2021

Quelle

ACM Digital Library

Beschreibungen/Notizen

• 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.